Episodes – For the Love of Data

E035 – Your Data and an Announcement

Rob Furr — Mon, 03 Dec 2018 03:53:12 +0000

Announcement:

Celebrating FTLOD’s 3 year anniversary this month
Covered a diverse range of topics from BBQ and chocolate to alogorithms and graph databases
Future episodes will be much more ad-hoc and when I come across a topic that is interesting
Please stay subscribed
Please reach out on Twitter or LinkedIn to let me know what your favorite episode has been

The Importance of Your Data

Quotes:
- With great power comes great responsibility -Amazing Spider Man #15

Data Commercialization
- Ford’s CEO recently suggested that the data collected by the company’s financial services arm also represents a valuable, low-overhead asset.¹
- Not just driving data, but also using data from purchase process such as marital status, income, etc.
- However, in desperation to maintain profits, what would some companies do?
- Know how your data is being used.
- Tim Cook recently criticized Google, FB, and others (not by name) of creating a “‘data industrial complex’ in which our personal information ‘is being weaponized against us with military efficiency.’”².
- Talked about the echo chamber that social networks and algorithms can create
- However, this is not all data doomsday
  - Data is helping us achieve better, deeper, faster insights than ever before
  - We are bettering our health, optimizing economies, and identifying connections that we never could have before
  - All this reward comes with some risks that we need to manage and be aware of
Data Breaches
- Marriott disclosed a 500MM record breach. Not the biggest, but it hackers had access since 2014.
- Names, phone numbers, email addresses, passport numbers, date of birth and arrival and departure information. For millions others, their credit card numbers and card expiration dates were potentially compromised.³
What to do to protect yourself if your data is part of a breach:⁴
- Sign up for services like SpyCloud (it is free)
- Change your password – and ideally switch to unique passphrases
- Monitor your accounts for suspicious activity
- Open a separate credit card for online transactions
- Limit the information you share
- Avoid saving credit card information on websites
- Be vigilant

Music:

Deep Sky Blue by Graphiqs Groove via FreeMusicArchive.org

Sources:

E034 – Using Data to Make Perfect Chocolate – Part 2

Rob Furr — Wed, 31 Oct 2018 11:38:36 +0000

In the second part of this two-part episode, we do a data deep dive into a decadent vat of chocolate. We talk about various stats and data with Brian Mikiten, former process engineer and founder of Casa Chocolates in San Antonio, TX. We also cover the types of chocolate and how much of chocolate making is an art vs. a science. See part one for the history of chocolate and an overview of how to make it.

Types
- White
- Dark
- Milk
- Ruby – created in 2017 from Ruby cocoa beans by Barry Callebaut in Switzerland
  
  Photo via bakemag.com

Chocolate data
- World Chocolate Day is July 7th. US National Chocolate Day is October 28.
- The United States accounts for 20% of the world’s chocolate consumption.
- On the average Valentine’s Day, nearly $400 million of chocolate is purchased around the world, accounting for 5% of the industry’s total sales.
- 22% of all chocolate consumed between 8pm and midnight.
- Chocolate significantly reduces theta activity in the brain, which is associated with relaxation, which is why we want to eat chocolate when we’re feeling stressed out.
- Myth: Chocolate is high in caffeine (contains ~6mg/bar, same as decaf coffee)
- More than 70% of Americans prefer milk chocolate
- In 2011, Thorntons created the world’s largest chocolate bar, which weighed in at 12,770 lbs. It measured 13 ft. by 13 ft. by 1 ft.
- Top companies by sales (via https://www.icco.org)
- $ / ton by date
- Top 10 World Cocoa Producers
Science / data driven production of chocolate
- Equipment used
- Variables evaluated / controlled
- What is your test process?
Science vs. Art of chocolate making
Bean profiles
Brian’s background and history of Casa Chocolate
What Casa Chocolate’s approach is to making chocolate
Tips for getting started at home
Where people can find out more about Brian and Casa Chocolate

Music:

Deep Sky Blue by Graphiqs Groove via FreeMusicArchive.org

Sources

E033 – Using Data to Make Perfect Chocolate – Part 1

Rob Furr — Wed, 24 Oct 2018 19:37:23 +0000

In the first part of this two-part episode, we do a data deep dive into a decadent vat of chocolate. We talk about history and how to make chocolate. In part two, we will talk about various stats and data with Brian Mikiten, former process engineer and founder of Casa Chocolates in San Antonio, TX.

History of chocolate
- Evidence dates back as early as 1500 BC
- Fermented beverages date back to 350BC
- Believed to have originated with Mesoamericans
- Made its way to Europe where sugar was added in 16th century
- In 1828, Dutch chemist Coenraad Johannes van Houten used alkaline salts to process into “Dutch cocoa”
- 1847 – J.S. Fry and Sons created the first chocolate bar
- 1876 – Swiss chocolatier Daniel Peter added milk powder to create milk chocolate
- 2/3 of cocoa today is produced in Western Africa
- Fair trade chocolate certifies that chocolate is not gathered with child or slave labor
Overview of chocolate making
- Harvesting – pods contain ~40 cacao beans
  
  Photo via TripAdvisor.com
- Roasting
- Cracking
- Winnowing
- Grinding
- Conching
- Tempering
- Molding

Music:

Deep Sky Blue by Graphiqs Groove via FreeMusicArchive.org

Sources

E027 – The Data Quadrant

Rob Furr — Sat, 28 Apr 2018 19:18:09 +0000

My guest in today’s episode is Ronald Damhof (@ronalddamhof), the creator of the Data Quadrant. This quadrant is a sense-making framework in the complex word of data that enables a common frame of reference between managers, domain experts and engineers. This model is used by many organisations to formulate data strategy and justify investments in the data domain. It is used as the strategic underpinning for a data architecture, it guides the ‘rules of the game’ and it separates the fundamental concerns in data. Furthermore, it explains how an organisation can toggle the need to innovate with data and the need to deploy and use data at scale, repeatedly, safe, lawful, with constant quality and robust.

Topics:

Ronald background as a “data fundamentalist”
His concept of a full scale data architect
The push / pull point, from 1950s Toyota, applied to data
Development styles from systemic to opportunistic
Data Vault’s influence on the quadrant
Where data modelling (Q1) and data lakes (Q3) fit into the quadrants
Where should you start? Q1/Q2 or Q3/Q4
90% of organizations in the Netherlands are using Data Vault

General recommendations on tools by quadrant:

Q1
- Automation – Wherescape or custom
- Federalization – mainly still RDBMS
Q2
- API’ing the data
- Losing faith in datasets and data marts
Q3
- Fast infra
- Doesn’t believe Hadoop is a good fit for most orgs.
- Likes fast analytical DBs like Vertica or MonetDB?
Q4
- Open source
- R, Python, Git, Dataiku
- Abstraction layer away from code is helpful
- Azure Platform

Ronald Damhof’s background:

Primary degree in Economics
Certified Data Vault Grand Master
Data Architect at the Dutch Central Bank in the Netherlands

Music

Deep Sky Blue by Graphiqs Groove via FreeMusicArchive.org

Sources:

E026 – The Four Types of Automation

Rob Furr — Fri, 30 Mar 2018 05:30:14 +0000

Introductory Product Models:

Only partner implementations (BluePrism)
Limited Features (WorkFusion)
Customer Revenue Limited (UI Path)
Single License (Softomotive)

Music

Deep Sky Blue by Graphiqs Groove via FreeMusicArchive.org

Sources:

E025 – The Hype of AI

Rob Furr — Wed, 28 Feb 2018 05:28:14 +0000

Thank you my friend and fellow Capco cohort, Daragh Fitzpatrick, for joining me on this episode of FTLOD where we cut through the Hype of AI to understand some of the key challenges and opportunities facing consumers and businesses alike when working with or alongside AI.

Given that we’re talking about AI, I also have a twist for today’s interview–transcription! Today’s episode is transcribed here using machine learning from webASR, a free service provided through the University of Sheffield’s Machine Intelligence for Natural Interfaces (MINI).

Note: The transcription is wonderful as a starting point and for a free service, but it does diverge from the actual conversation fairly significantly at times. Please listen to the episode as you read along.

Topics:

Definition of AI and the singularity–should we be concerned?
What’s going on in the AI space?
Typical use cases in industry
RPA vs. AI and different use cases
Recommendation systems
Challenges in profiling users or customers
Ethical challenges and consequences of bad AI or black box AI
AI is like fire: it can be highly useful, but it can also be a weapon and burn you.
Perceptions of AI that are overhyped
Not every product or service needs AI to be good
At what point does intelligence begin?
The fourth industrial revolution and its impact on society
How to responsibly introduce life-altering AI
Will AI supplement our lives and give us better quality of live, or will it make us do more, faster, stronger?
Advancements in how AI plays the game, Dover

Some of the items we discuss are available in the following places:

Music

Deep Sky Blue by Graphiqs Groove via FreeMusicArchive.org

E021 – Data Deep Dive – Halloween Spending and Candy

Rob Furr — Tue, 31 Oct 2017 04:29:05 +0000

Just in time for Halloween this year, we take a look at the way people will spend their money on Candy and other goods during this spooky time.

Spending

People in the US are expected to spend $9.1 billion on Halloween this year, according to a study by the National Retail Federation.

Several predictions about this year’s Halloween season include:

U.S. consumers are projected to drop $82.93 on average, up almost 12 percent from $74.34 last year.
More than 171 million consumers are expected take part in Halloween festivities.
Adults ages 18-34 are projected to spend on average $42.39, compared with $31.03 for all adults.

According to the survey, consumers plan to spend:

$3.4 billion on costumes (purchased by 69 percent of Halloween shoppers),
$2.7 billion on candy (95 percent),
another $2.7 billion on decorations (72 percent)
and $410 million on greeting cards (37 percent).

Among Halloween celebrants:

71 percent plan to hand out candy,
49 percent will decorate their home or yard,
48 percent will wear costumes,
46 percent will carve a pumpkin,
35 percent will throw or attend a party,
31 percent will take their children trick-or-treating,
23 percent will visit a haunted house and 16 percent will dress pets in costumes.

Top Costumes

More than 3.7 million children plan to dress as their favorite action character or superhero, 2.9 million as Batman characters and another 2.9 million as their favorite princess while 2.2 million will dress as a cat, dog, monkey or other animal.

Proving that Halloween isn’t just for kids, a record number of adults (48 percent) plan to dress in costume this year. More than 5.8 million adults plan to dress like a witch, 3.2 million as their favorite Batman character, 3 million as an animal (cat, dog, cow, etc.), and 2.8 million as a pirate.

Pets won’t be left behind when it comes to dressing up for Halloween. Ten percent of pet lovers will dress their animal in a pumpkin costume, while 7 percent will dress their cat or dog as a hot dog and 4 percent as a dog, lion or pirate.

Candy

CandyStore.com released data from 10 years of bulk candy online sales that show favorite candies by state.

STATE	TOP CANDY	POUNDS	2ND PLACE	POUNDS	3RD PLACE	POUNDS
TX	Starburst	1952361	Reese’s Cups	1927663	Almond Joy	837525

STATE	TOP CANDY	POUNDS	2ND PLACE	POUNDS	3RD PLACE	POUNDS
AL	Candy Corn	55274	Hershey’s Mini Bars	54369	Tootsie Pops	42533
AK	Twix	4678	Blow Pops	4578	Kit Kat	3892
AZ	Snickers	904633	Hershey Kisses	817463	Hot Tamales	527843
AR	Jolly Ranchers	225990	Butterfinger	215897	Hot Tamales	89027
CA	M&M’s	1548990	Salt Water Taffy	1345782	Skittles	1034527
CO	Milky Way	5620	Twix	5478	Hershey Kisses	4087
CT	Almond Joy	2457	Milky Way	1985	M&M’s	1023
DE	Life Savers	20748	Skittles	18072	Candy Corn	10217
FL	Skittles	630938	Snickers	587385	Reese’s Cups	224637
GA	Swedish Fish	130647	Hershey Kisses	109672	Jolly Ranchers	55049
HI	Skittles	267872	Hershey Kisses	264728	Milky Way	139874
ID	Candy Corn	85903	Starburst	60826	Reese’s Cups	39847
IL	Sour Patch Kids	155782	Kit Kat	151786	Reese’s Cups	95627
IN	Hot Tamales	95092	Starburst	78920	Snickers	34589
IA	Reese’s Cups	58974	M&M’s	53982	Butterfinger	25782
KS	Reese’s Cups	231476	M&M’s	230082	Dubble Bubble Gum	159092
KY	Tootsie Pops	67829	3 Musketeers	60273	Reese’s Cups	30865
LA	Lemonheads	102833	Reese’s Cups	89738	Jolly Ranchers	45092
ME	Sour Patch Kids	58290	M&M’s	45938	Starburst	16782
MD	Milky Way	38782	Reese’s Cups	30748	Blow Pops	12093
MA	Sour Patch Kids	75638	Butterfinger	73892	Salt Water Taffy	45982
MI	Candy Corn	146782	Skittles	135982	Starburst	87740
MN	Tootsie Pops	195783	Skittles	194672	Almond Joy	98726
MS	3 Musketeers	109783	Snickers	103993	Butterfinger	57829
MO	Milky Way	42739	Dubble Bubble Gum	34751	Butterfinger	24780
MT	Dubble Bubble Gum	24675	M&M’s	14673	Twix	13784
NE	Sour Patch Kids	106728	Salt Water Taffy	78624	M&M’s	23674
NV	Hershey Kisses	322884	Candy Corn	203746	Skittles	167837
NH	Snickers	63876	Starburst	62468	Salt Water Taffy	25987
NJ	Skittles	159324	Tootsie Pops	157893	M&M’s	110673
NM	Candy Corn	83562	Milky Way	65682	Jolly Ranchers	45721
NY	Sour Patch Kids	200008	Candy Corn	101292	Reese’s Cups	56776
NC	M&Ms	96110	Reese’s Cups	95763	Candy Corn	62308
ND	Hot Tamales	65782	Jolly Ranchers	61829	Candy Corn	51827
OH	Blow Pops	150324	M&M’s	146782	Starburst	105752
OK	Snickers	20938	Dubble Bubble Gum	10283	Butterfinger	8892
OR	Reese’s Cups	90826	M&M’s	67626	Tootsie Pops	42774
PA	M&M’s	290762	Skittles	281847	Hershey’s Mini Bars	150372
RI	Candy Corn	17862	M&M’s	13894	Twix	9003
SC	Candy Corn	114783	Skittles	98782	Hot Tamales	41892
SD	Starburst	24783	Jolly Ranchers	22983	Candy Corn	7827
TN	Tootsie Pops	59837	Salt Water Taffy	34859	Skittles	20938
TX	Starburst	1952361	Reese’s Cups	1927663	Almond Joy	837525
UT	Jolly Ranchers	475221	Reese’s Cups	29823	Tootsie Pops	198564
VT	Milky Way	29837	M&M’s	27811	Skittles	17662
VA	Snickers	26783	Hot Tamales	26178	Candy Corn	18726
WA	Tootsie Pops	223850	Salt Water Taffy	210981	Hershey Kisses	78662
DC	M&M’s	26092	Tootsie Pops	21364	Blow Pops	14763
WV	Blow Pops	43776	Hershey’s Mini Bars	23554	Milky Way	18911
WI	Starburst	116788	Butterfinger	115982	Jolly Ranchers	42998
WY	Reese’s Cups	32889	Salt Water Taffy	26555	Skittles	20812

FiveThirtyEight took a different approach by analyzing data from 269,000 head-to-head matchups between candies. Their findings:

Reese’s took 4 of the top 10 spots!

They boiled it down into the following elements:

Music

In This Creepy, Sleepy Backward Town by Squire Tuck via Free Music Archive

Sources

E020 – How Crisis Text Line uses data to save lives

Rob Furr — Wed, 27 Sep 2017 02:13:59 +0000

If you’re in crisis, text 741741 if you’re in the US to talk with a counselor now. In this episode we speak with the people behind Crisis Text Line and Crisis Trends, two services that use data to make a difference for those going through a crisis or looking for someone with whom to talk.

Overview

Texters contact the hotline by texting the shortcode 741741. Volunteers are logged onto “the platform”, which is on CTL’s internal site, to receive these messages and access counselor tools.
Their data is collected in real time and is updated in close to real time: https://crisistrends.org/
This is the TED talk where the founder introduced her idea for the organization: https://www.ted.com/talks/nancy_lublin_texting_that_saves_lives
This is the TED talk 3 years later where the founder shared an update on CTL’s success and shared information about how they use data intelligently on their platform: https://www.ted.com/talks/nancy_lublin_the_heartbreaking_text_that_inspired_a_crisis_help_line

Key Stats

Over 1 million messages transmitted per month
75% of texters are under 25
10% under age 13
65% say they have shared something with Crisis Text Line that they haven’t shared with anyone else
Usually at least one active rescue per day
Take people based on severity and have the ability to initiate an active rescue (via 911)
- Words like ibuprofen, aspirin, tylenol are more indicative of active rescue need than the words die, overdose, suicide
- emoji is 4x more of an indicator
Roots of CTL go back to 1906 when Save-A-Life League started via newspaper ads
- The Samaritans was the first phone suicide hotline and started in November 1953
Founded by Nancy Lublin, who is also the CEO of DoSomething.org, in 2011

Introductions – background, how they got their start, how they got involved in CrisisTextLine
- Staci – volunteer
- Scotty – Data Scientist
History of Crisis Text Line and high-level structure (where they operate, # of locations, # of employees / volunteers)
Staci’s experience
- What was training like?
- Where do she take sessions and how often?
- How do she feel after a session?
- Her experience as a counselor and thoughts on the impact, data, etc.
What ways they collect data
- #s of texters
- UI platform for counselors
- Types of data they collect
- Types of technologies used to collect/manage it – both publicly, behind the scenes, for presentations, etc.
What ways they use data
- CrisisTrends.org site
- Anonymity, opt-in/opt-out options and how frequent each occur
Key stats they feel are most important/surprising/alarming, etc.
- How has data made an impact to those in need?
- How has data made an impact to counselors?
- How has data made an impact to the organization?
- How has data made an impact to the crisis advocacy sector as a whole?
What ways can other people can use their data
- Do they encourage that visitors explore to find their own insights?
- Will data be available by zip code at some point?
Data Science
- What tools and techniques do they see being most important in the near term?
- What do they see as becoming less important in the near term?
- What is something they could have told their earlier selves that would have made their path to this point easier?
Organization Info
- How someone can get involved
- What they need most
- What is in store for the future? New technologies, platforms for contact, etc.
- How someone can contact them

Music

Deep Sky Blue by Graphiqs Groove

Sources

https://youtu.be/KOtFDsC8JC0 – TED talk about origin
https://www.crisistextline.org/
https://crisistrends.org/
http://www.newyorker.com/magazine/2015/02/09/r-u

E017 – Tech Spec – Tableau 10.3 New Features

admin — Thu, 29 Jun 2017 11:19:12 +0000

In this episode we cover the new features in Tableau 10.3. This version debuted on May 31st, and a 10.3.1 update was released on 6/21/17.

Data Driven Alerts
1. Only on Tableau Server
2. Receive an alert when a mark crosses a visual threshold
3. Can use on any viz with a continuous numeric axis
4. Can sign up yourself and others; then each person can self-administer
5. Default check rate is 60 minutes or when an extract is refreshed. Can customize with this command:

tabadmin set dataAlerts.checkIntervalInMinutes

tabadmin restart

Tableau Bridge – Limited Release
1. Connect to live, on-premise data from Tableau Online
2. Replaces the sync client – is basically the sync client + live query functionality. Client is installed and ran behind your firewall and pushes data to Tableau Online.
3. Live connections must be enabled by administrators. Limited to RDBMSs (MySQL, SQL Server, etc.)
4. Oracle cloud hosted DBs must use Tableau Bridge
1. Must run as a service to enable live connections
1. Must embed credentials in Tableau Bridge if you want it to automatically update on a schedule
2. Will restart every hour minimum. You can set this window with this command:

tabonlinesyncclientcmd.exe SetDataSyncRestartInterval –restartInterval=

Best Practices (https://www.tableau.com/about/blog/2017/5/introducing-tableau-bridge-live-queries-premises-data-tableau-online-70767)
1. Split bridges into two machines: one for extract refreshes and another for live queries, unless usage is extremely low
2. Run the bridge continuously (ideally on a VM in a data center)
3. Tune dashboards and queries to leverage extracts for summarized data

Smart Table and Join Recommendations – Machine Learning will recommend tables and joins (even on non-similar names) based on previous usage metrics
PDF Connector
1. Connect to PDFs, identify tables, and pull data out
2. Less copying/pasting/massaging of data to get it ready for Tableau
3. Somewhat limited at this time, but continuing to be developed
More Union support in more connectors
1. DB2
2. Hadoop
3. Teradata
4. Netezza
New connectors
1. Amazon Athena
2. MongoDB BI
3. OneDrive
4. ServiceNow
5. Dropbox
6. JSON – scan entire file, not just a sample
Automatic Query Caching – Tableau server can pre-cache queries in recent workbooks after an extract refresh to speed up performance on initial load.
Miscellaneous
1. More options in Web Authoring (drills, formats, changing displays)
2. Story points navigator – more streamlined
3. Mobile – Android improvements, banner to Tableau Mobile, universal linking that allows you to click and open in Tableau Mobile
4. Tooltip selections – highlight data from tooltip links
5. Latest date filter
6. Distribute evenly
7. Maps – French, Netherlands, Australian, and New Zealand updates
8. Apply table calc filters to totals
9. Custom subscriptions – days/hours, etc.
10. APIs – various REST updates (tags on sources and views, switch sites, get sites list, etc.)

Music is Deep Sky Blue by Graphiqs Groove

Sources

E016 – For the Love of Sunscreen

admin — Wed, 31 May 2017 07:59:59 +0000

In this episode, data sheds some (sun)light on what Rob did wrong on a recent trip to the Caribbean and explains the terrible sunburn he has right now. Just in time for Memorial Day and Summer, we take a look at many recent findings and how they will lead us to a healthier outdoor lifestyle.

A LOT of this content came from the Environmental Working Group (EWG). Please visit their site for more great info and the source of much of this episode.

EWG recently released it’s 2017 EWG Sunscreen Guide with research and guidance on sunscreen efficacy, ingredients, and health risks. It is chock full of great information to keep you safe and dispels many misconceptions that most people hold.

Why are sun rays harmful?

UV radiation penetrates the skin and produces genetic mutations that can cause cancer
UVA
- Less intense than UVB, but 30-50x more prevalent
- Dominant tanning ray
- UVA rays penetrate deeper, suppress the immune system, cause harmful free radicals to form, and are associated with higher risk of melanoma
UVB
- UVB rays are the primary cause of sunburns and non-melanoma skin cancer.
- Most intense from 10AM-4PM April through October
- Most reflected by snow or ice
- The chemicals in sunscreen help combat UVB rays more than UVA

Why the Sun (UV Exposure) is Harmful³

New melanoma cases among American adults has tripled since the 1970s, from 7.9 per 100,000 people in 1975 to 25.2 per 100,000 in 2014 (NCI 2017)
Melanoma death rate for white American men, the highest risk group, has escalated sharply, from 2.6 deaths per 100,000 in 1975 to 4.4 in 2014
Since 2003, the rates of new melanoma cases among both men and women have been climbing by 1.7 and 1.4 percent per year, respectively, according to the federal Centers for Disease Control and Prevention (CDC 2016)
More than 3 million Americans develop skin cancer each year (ACS 2017)
Most cases involve one of two disfiguring but rarely fatal forms of skin cancer – basal and squamous cell carcinomas. Studies suggest that basal and squamous cell cancers are strongly related to UV exposure over years.
- Several researchers have found that regular sunscreen use lowers the risk of squamous cell carcinoma (Gordon 2009, van der Pols 2006) and diminishes the incidence of actinic keratosis – sun-induced skin changes that may advance to squamous cell carcinoma (Naylor 1995, Thompson 1993)
- Researchers have not found strong evidence that sunscreen use prevents basal cell carcinoma (Green 1999, Pandeya 2005, van der Pols 2006, Hunter 1990, Rosenstein 1999, Rubin 2005).
- Both UVA and UVB rays can cause melanoma, as evidenced by laboratory studies on people with extreme sun exposures. In the general population, there is a strong correlation between melanoma risk and a person’s number of sunburns, particularly those during childhood (Dennis 2010).
- The use of artificial tanning beds dramatically increases melanoma risk (Coleho 2010).
People who rely on sunscreens tend to burn, and sunburns are linked to cancer.
- When people use sunscreen properly to prevent sunburn, they often extend their time in the sun. They may prevent burns, but they end up with more cumulative exposure to UVA rays, which inflict subtler damage (Autier 2009, Lautenschlager 2007).

However, research isn’t conclusive how the link between UV exposure and sunscreen.

Scientists don’t know conclusively whether sunscreen can help prevent melanoma. There are studies on both sides that say it helps or it does not.
Several factors suggest that regular sun exposure may not be as harmful as intermittent and high-intensity sunlight. Paradoxically, outdoor workers report lower rates of melanoma than indoor workers (Radespiel-Troger 2009).
- Melanoma rates are higher among people who live in northern American cities with less year-round UV intensity than among residents of sunnier cities (Planta 2011).
- Researchers speculate that higher vitamin D levels for people with regular sun exposure may play a role in reduced melanoma risk (Godar 2011, Newton-Bishop 2011, Field 2011).
  - So DRINK MILK!
- The consensus among researchers is that the most important step people can take to reduce their melanoma risk is to avoid sunburn but not all sun exposure (Planta 2011).

What is SPF?

SPF = Sun Protection Factor
How much longer it will take for sun to redden skin than without it (i.e., SPF 15 = 15x longer for the sun to redden you.

IBISWorld, a market research company, reports that sunscreen product sales grew 2.6 percent a year between 2011 and 2016, and generated $394 million annually (IBISWorld 2016)³

Effects by Age

Baby skin is thinner and absorbs more water
Infant and toddler skin has less melanin, which protects from UV light
The older you are the thicker and more pigmented you get, which is more protective
Very few studies are done on the effects on small children
Adults older than 60 are also more sensitive to sunlight

Tanning beds are BAD!

Emit up to 12x the UVA of the sun
People who use tanning beds are 1.5-2.5x more likely to get cancer.
The risk of melanoma goes up when you use a tanning bed at any age, but the International Agency for Research on Cancer calculates that if you start using tanning beds before age 30, your risk of developing melanoma jumps by 75 percent³.

Vitamin A is a bad ingredient

Vitamin A in the form of retinyl palmitate can harm skin when combined with sunlight. Luckily its usage has been falling.

Sprays are convenient, but not the best option

Inhaling the chemicals in the spray can be bad, most people apply too light of a coat, and people miss spots. Despite this their use is on the rise, increasing 27%.

High SPFs are deceiving²

Correctly applied SPF 50 blocks 98% of UVB rays; SPF 100 blocks 99%
The higher the SPF, the more UVB it blocks, but the less UVA it blocks
The way sunscreens are measured may not reflect real world conditions
- In lab measurements, small changes in light can change an SPF 100 sunscreens rating to SPF 37
People spend more time in the sun when they wear a higher SPF
Higher doses of ingredients may be harmful when absorbed into the skin
If you don’t apply enough, or misapply, an SPF 100 sunscreen’s actual rating could be as low as SPF 3.2. T-Shirts are SPF 5.
Most countries cap advertisements at 50+ (Europe, Japan, Canada, etc.); Australia caps at 30

European Sunscreens > American Sunscreens?

Several European companies have developed chemicals that are better at blocking UVA, but these have not yet been approved by the FDA. Europe also requires that the advertised SPF (which is its UVB rating) be no more than 3x the UVA rating.

Tips to Stay Safe in the Sun

Know how intense the sun is

Check a site like http://sunburnmap.com/

Know your ingredients and pick the right SPF

Know what protects you best. Check if a sunscreen’s claims are accurate, and check how harmful the ingredients may be at http://wsw.ewg.org/sunscreen/

FDA-Approved Sunscreens		Side Effects
Active Ingredient/UV Filter Name	Range Covered
	UVA1: 340-400 nm
	UVA2: 320-340 nm
	UVB: 290-320 nm
Chemical Absorbers:
Aminobenzoic acid (PABA)	UVB
Avobenzone	UVA1	Relatively high skin allergen
Cinoxate	UVB
Dioxybenzone	UVB, UVA2
Ecamsule (Mexoryl SX)	UVA2
Ensulizole (Phenylbenzimiazole Sulfonic Acid)	UVB
Homosalate	UVB	Slight skin penetration; disrupts some hormones
Meradimate (Menthyl Anthranilate)	UVA2
Octocrylene	UVB	Relatively high allergen
Octinoxate (Octyl Methoxycinnamate)	UVB	Slight skin penetration; acts like hormone in body; moderate allergen
Octisalate ( Octyl Salicylate)	UVB
Oxybenzone	UVB, UVA2	Penetrates skin significantly; acts like estrogen in the body; relatively high allergen
Padimate O	UVB
Sulisobenzone	UVB, UVA2
Trolamine Salicylate	UVB
Physical Filters:
Titanium Dioxide	UVB, UVA2	Inhalation concerns
Zinc Oxide	UVB,UVA2, UVA1	Inhalation concerns

Table From http://www.skincancer.org/prevention/uva-and-uvb

Follow these tips

Seek the shade, especially between 10 AM and 4 PM.
Do not burn.
Avoid tanning and UV tanning booths.
Cover up with clothing, including a broad-brimmed hat and UV-blocking sunglasses.
Use a broad spectrum (UVA/UVB) sunscreen with an SPF of 15 or higher every day. For extended outdoor activity, use a water-resistant, broad spectrum (UVA/UVB) sunscreen with an SPF of 30 or higher.
Apply 1 ounce (2 tablespoons) of sunscreen to your entire body 30 minutes before going outside. Reapply every two hours, or immediately after swimming or excessive sweating.
Keep newborns out of the sun. Sunscreens should be used on babies over the age of six months.
Examine your skin head-to-toe every month.
See your physician every year for a professional skin exam.
Don’t forget to sunscreen your lips

Most tips From http://www.skincancer.org/prevention/uva-and-uvb

At a glance, do these things:

Other places to protect yourself

Car windows block a lot of UVB, but not UVA
- Two studies found significantly more melanoma on the left side of the body/face, suggesting long exposure in cars puts you at more risk
- Car windshields block a lot of UVB and UVA because of the plastic in the middle (around SPF 50); side windows do not do so well (around SPF 16)
- Transparent window films block out almost 100% of both UVA and UVB
Skip the sunroof and convertible
Check office windows and skylights to see if they are glass or plastic and if they are treated with a UV film

Tips if you get a Sunburn¹⁷

Take frequent cool baths or showers to help relieve the pain. As soon as you get out of the bathtub or shower, gently pat yourself dry, but leave a little water on your skin. Then, apply a moisturizer to help trap the water in your skin. This can help ease the dryness.
Use a moisturizer that contains aloe vera or soy to help soothe sunburned skin. If a particular area feels especially uncomfortable, you may want to apply a hydrocortisone cream that you can buy without a prescription. Do not treat sunburn with “-caine” products (such as benzocaine), as these may irritate the skin or cause an allergic reaction.
Consider taking aspirin or ibuprofen to help reduce any swelling, redness and discomfort.
Drink extra water. A sunburn draws fluid to the skin’s surface and away from the rest of the body. Drinking extra water when you are sunburned helps prevent dehydration.
If your skin blisters, allow the blisters to heal. Blistering skin means you have a second-degree sunburn. You should not pop the blisters, as blisters form to help your skin heal and protect you from infection.
Take extra care to protect sunburned skin while it heals. Wear clothing that covers your skin when outdoors. Tightly-woven fabrics work best. When you hold the fabric up to a bright light, you shouldn’t see any light coming through.

Tips from https://www.aad.org/public/skin-hair-nails/injured-skin/treating-sunburn

Music

“Wear Sunscreen Commencement Speech” by Mike Harper, KNVE

Sources

E015 – BBQ Showdown (Pellet Grill vs Big Green Egg)

admin — Sun, 30 Apr 2017 02:48:04 +0000

Join me and my special guest, Colby “meat whore” Pritchett (@colbypritchett) on this BBQ showdown where we pit the Big Green Egg against the Green Mountain Grill Pellet Smoker. We also cover the history, styles, stats, and health facets of different types of BBQ.

History

Bbq evolves from the spanish word ‘barbacoa’, but where the word actually originated is still debated.
BBQ dates back to the colonial era. George Washington even attended bbq’s
Woods commonly selected for their flavor include mesquite, hickory, maple, guava, kiawe, cherry, pecan, apple and oak. Woods to avoid include conifers. These contain resins and tars, which impart undesirable resinous and chemical flavors.
The most popular foods for cooking on the grill are, in order: burgers (85 percent), steak (80 percent), hot dogs (79 percent) and chicken (73 percent).
MAY is national BBQ month
Only 10% of grill owners have a backyard kitchen, equipped with premium furniture and lighting?
The longest barbecue measured 8,000 m (20,246 ft) and was created by the people of Bayambang, (Philippines), in Bayambang, Pangasinan, Philippines on 4 April 2014. The record attempt took place during the Malangsi Fish-tival in order to celebrate the 400th anniversary of the city Bayambang. The barbecue was made up of 8,000 grills connected to each other, each measuring 1m in length, 58 cm in height and 21 cm in width. 50,000 kg of fish, 2,000 kg of salt, 480 blocks of ice and 6,000 bags of charcoal were used. 8,000 people were involved.

Styles

There are different regional barbecue styles all across the country. Although they all cook their meat low and slow, that’s where the similarities stop. Some cook pig, some smoke different cuts of beef, some lamb, and some chicken. Sauces are also varied: some are vinegar and pepper-based; others utilize brown sugar and molasses; in some, mustard is the predominant flavor; and tomato is the primary flavor in others. While there are plenty of nuances and micro-regional styles, there are four styles that anyone who claims to be a barbecue lover should know about.

In North Carolina, barbecue revolves around the pig: the “whole hog” in the east and the shoulder in the west. The pork is chopped up and usually mixed with a vinegar-based sauce that’s heavy on the spices and contains only a small amount of tomato sauce, if any.

In Memphis, it’s all about the ribs. Wet ribs are slathered with barbecue sauce before and after cooking, and dry ribs are seasoned with a dry rub. You’ll also find lots of barbecue sandwiches in Memphis: chopped pork on a bun topped with barbecue sauce, pickles, and coleslaw.

Kansas City barbecue uses a wide variety of meat (but especially beef) and here it’s all about the sauce, which is thick and sweet. Kansas City is a barbecue melting pot, so expect to find plenty of ribs, brisket, chicken, and pulled pork there, all served with plenty of sauce. Brisket burnt ends are also a specialty here.

And there are a few different styles native to Texas, but the most famous variety is the Central Texas Hill Country “meat market” style: heavy on the beef brisket, which has been given a black pepper-heavy rub. Sauce and side dishes usually play second fiddle, because in Texas it’s all about the meat, be it ginormous beef ribs, pork ribs, chicken, brisket, or sausage.

– http://lehighvalleymarketplace.com/get-sauced-the-nations-top-bbq-regions/

Brisket Cuts

USDA Utility, Cutter, Canner Beef. These are the lowest grades of beef and used primarily by processors for soups, canned chili, sloppy Joe’s, etc. You will not likely see them in a grocery.
USDA Standard or Commercial Beef. Practically devoid of marbling. If it does not have a grade on the label it is probably standard or commercial. These grades are fine for stewed or ground meat, but they are a bad choice for the grill. About 2% fat.
USDA Select Beef. Slight marbling. If you know what you are doing you can make this stuff tender. Otherwise, get a higher grade. About 2 to 4% fat.
USDA Choice Beef. Noticeable marbling, but not a lot. This is a good option for backyard cooks. About half of all beef is marked USDA Choice. There are actually three numbered sublevels of USDA Choice. Certified Angus Beef (CAB) is limited to only the top two levels. Reliable sources tell me that Walmart “Choice Premium” is USDA Choice. The word “premium” is all about marketing and not to be confused with USDA Prime. 4-10% fat. A 12 ounce ribeye typically sell for about $8 to 10 retail at the time of this writing in 2010, and prices fluctuate depending on supply and demand as well as weather which impacts the cost of feed.
USDA Prime Beef. Significant “starry night” marbling. Often from younger cattle. Prime is definitely better tasting and more tender than Choice. Only about 3% of the beef is prime and it is usually reserved for the restaurant trade. About 10 to 13% fat, about $20-30 for a 12 ounce ribeye at retail. A dry aged steak can be15-18% fat and $30-35 or more for a 12 ounce ribeye.
Black Angus. Black Angus cattle are considered by many to be an especially flavorful breed. Alas, it is almost impossible to know if what you are buying really is Angus.
Certified Angus Beef. The Certified Angus Beef (CAB) brand is a trademarked brand designed to market quality beef. To wear the CAB logo, the carcass is supposed to pass 10 quality control standards and CAB must be either USDA Prime or one of the two upper sublevels of USDA Choice. Most of it is USDA Choice. CAB costs a bit more because the American Angus Association charges a fee to “certify” the cattle and higher markups take place on down the line.
Interestingly, CAB does not actually certify that the beef labeled Certified Angus Beef is from the highly regarded Angus breed. Their major control is that the cattle must have a black hide, which is a genetic indicator that there are Angus genes in the cattle, but not a guarantee.
Wagyu Beef. Wagyu cattle have Japanese blood lines and are now raised in the US and other countries. Their genetic heritage can be any of a number of Japanese cattle breeds. American Wagyu does not have to adhere to the standards as Kobe beef (below), and many of the Wagyu are cross bred with local breeds to make them better adapted to the local climates and diseases. Wagyu and Angus crosses are frequent, and they make mighty fine meat. Wagyu is usually extremely marbled, usually 4 to 10 BMS, more than USDA Prime, but not as much as Kobe, and the flavor and texture is distinctive. It is also about twice the price of USDA Prime. One can only wonder how long before the cross breeding and lack of enforceable standards dilute the quality.

Nutrition Facts

Brisket Sales

Beef Brisket unit sales (in millions of pounds)

2014 Brisket Sales by Holiday in US (millions of pounds)

538 – Where’s the Beef
- US Cattle Herds are shrinking (97mm in ’07 –> 88.5mm in ’14)
  - Fertilizer, fuel, and feed rose
  - Droughts hit
- Prices are rising

2016 Sales by Restaurant	Pecan Lodge	Ten 50 BBQ	Franklin’s Austin
Brisket	6,700	2,100	10,662
Sausage	1,525	2,000	1,200
Ribs			1,823
Mac & Cheese	4,000
Potato Salad			75
Beans	1,600		600
Peach Cobbler	340
Sides		600
Torpedos		6,500
Rolls/Bread	4,200	4,000
Notes	Brisket is their single largest expense – more than rent, electricity, etc.

Dickey’s – uses BigData and near real-time analytics of store data (synced every 20 min.) to analyze sales trends, inventory, etc.
- If ribs aren’t selling well, they can send a text message coupon out to affect sales
- Tools: iOLAP vendor, implemented Yellowfin BI and Syncsort DMX ETL on Amazon Redshift

Other Stats

75% of U.S. adults own a grill or smoker.
The majority of grill owners (63%) use their grill or smoker year-round and 43% cook at least once a month through winter.
Nearly a third of current owners plan to grill with greater frequency this year.
Barbecuing isn’t just an evening activity: 11% of grill owners prepared breakfast in the past year.
The five most popular days to barbecue, in order are: July Fourth; Labor Day & Memorial Day (tied); Father’s Day; Mother’s Day.
The top three reasons for cooking outdoors, in order are: to improve flavor; for personal enjoyment; for entertaining family and friends.
Gas grills are easily the most popular style, the choice of 62% of households that own a grill.

Pellet Grills

Traeger patent granted in 1986 and expired in 2006
Continuous fuel source like gas; indirect heating like a traditional smoker, so no flame ups, precise temperature control
For people who approach cooking as a science rather than an art (but there’s still art to it)
Induction fan makes grill like a convection oven
Hopper -> Auger -> firebox -> induction fan
Pro Tips:
- MAKE SURE YOU DON’T RUN OUT OF FUEL
- Have your vent open almost all the way
- Turn off in proper way to prevent clogs and lock-ups
  - Taking it apart to clean it is fraught with peril
- It may still have hot spots like any other grill or oven
- Use food grade pellets, not cheap ones for heaters (these can be scrap wood, shredded pallets, etc.)
- Wifi sounds cool and is, but sometimes it is temperamental and easier just to use w/o it, particularly if in a hurry
- Use your own remote thermometers to watch different parts of grill and multiple pieces of meat at once
- I still use a gas grill to do direct heat or searing
- Can use a thermal blanket to insulate during winter or in cold locations – will use less pellets when you do this
- Get the smallest grill you can stand. The bigger the grill the more pellets required to cook, so you may just be paying to heat air
What to look for:
- Variable temperature setting (not three positions)
- Hopper capacity
- Meat probes
- Shelves and hooks
- Wifi / smart phone connectivity – verify whether it is only on local Wi-fi or internet capable
- Larger temperature range offers more options for cold smoking, steaks, etc.
- Some have options for pizza stones, sear plates, etc.
Cool infographic about pellet grills

– Infographic from Grilling with Rich

Big Green Egg

The design is based on ancient clay cooking vessels up to 3,000 years old.
Kamado style clay pot grills with removable lids originated in Japan. Kamado means “cooking range” or “stove” in Japanese.
Very fuel efficient as they hold heat extremely well regardless of the weather. The fact that is holds heat and traps in moisture causes the meat to stay juicy and not dry out.
US Air Force servicemen started bringing Kamado style grills back to the US after World War II.
In the 1960s people started manufacturing them in the US.
Ed Fisher discovered these grills overseas and returned to the US to start the Big Green Egg company in 1974.

Health Tips

Grilling Danger #1: Char
- While char marks in grilled meat look appealing and give a tasty flavor, the char is laden with cancer-causing compounds called heterocyclic amines (HCAs) that form when meat and high heat are combined to create a blackened crust. The more char that’s created, the more carcinogens result that coat your food. High levels of HCAs can cause cancer in laboratory animals exposed to them, and epidemiological studies show that eating charred meats may be associated with an increased risk of colorectal, pancreatic and prostate cancer.
Grilling Danger #2: Smoke
- Barbecue smoke contains polycyclic aromatic hydrocarbons (PAHs), toxic chemicals that can damage your lungs. As meat cooks, drippings of fat hit the coals and create PAHs, which waft into the air. If you are a grill chef who loves to stand over the barbeque, you are inhaling these toxins. The smoky smell on your clothes and in your hair is also coating the inside of your lungs. The more your grill smokes, the more PAH is generated. The toxins are absorbed along with that delicious smoky flavor right into your food.
Grilling Danger #3: Harmful byproducts
- When food is cooked at very high temperatures, a chemical chain reaction can occur that creates inflammatory products called advanced glycation end products (AGEs) that are harmful to your cells and associated with cellular stress and aging. As suggested by the name ‘end product,’ your body cannot digest them or get rid of them easily. Over time, AGEs accumulate in your organs and cause damage. Where do you find AGEs in the barbeque? In the char.
How to avoid the dangers
- Use marinades and rubs – Coating the meat in herbs with a rub containing rosemary, thyme, pepper or smothering with thick marinades not only adds delicious flavor but can also help reduce the creation of carcinogens by grilling by up to 96%. A tasty marinade also reduces dripping fat and smoke and helps prevent char, thereby lowering the amount of all 3 threats – HCAs, PAH, and AGEs – in your food. Take home message: Boosting flavor can reduce risk.
- Pre-cook your meat – As easy way to decrease toxins created by the barbecuing is to pre-cook your meat halfway over low heat in a skillet or the oven before putting them on the grill. Precooking removes some of the fat that can drip and smoke, and it greatly reduces the amount of time your meat sits on the grill being exposed to toxins. Less time at high heat also means fewer AGEs are created in your meat. Extra bonus: with precooking, you can barbeque the food much faster to feed the hungry troops.
  - Marinate the food in alcohol before barbecuing it. According to research published by the Journal of Agricultural and Food Chemistry, soaking meat in a marinade of beer – especially stout or black beer – reduces the creation of PAHs (cancer-causing carcinogens) when it’s grilled by around 50%
- Reduce drippings – Using a simple piece of aluminum foil as a protective barrier under the meat helps prevent drippings from smoking, thereby reducing the amount of PAH blowing into your food and your lungs. Keeping drippings in the foil can also help to keep your food moist. Another great way to reduce drippings is to choose leaner cuts of meat and trim off any excess fat before you put them on the grill.
- Grill veggies – Grilled vegetables do not contain the HCA carcinogens even when charred. Vegetable kabobs made with peppers, cherry tomatoes and red onions are great on the grill, and offer many healthy nutrients and cancer fighting substances you can’t get from a steak or chicken breast.

Music: Good BBQ by the Riptones via FreeMusicArchive.org

Sources:

013 – For the Love of Graph Databases

admin — Tue, 21 Feb 2017 17:30:31 +0000

Where did graphs come from? (Graph Theory History)

In its simplest form, Graph Theory defines a graph as a construct made up of vertices, nodes, or points which are connected by edges, arcs, or lines.1 The connections may be directed, indicating a direction from one node to another, or undirected. Properties are attributes associated with nodes that describe the node in some detail.

Graph theory is applied in many disciplines from linguistics to computer science, physics, and chemistry. Popular uses will be discussed below. Leonhard Euler published “Seven Bridges of Königsberg” in 1736; this is commonly attributed as the first paper about graph theory. James Joseph Sylvester published a paper in 1878 where the term “graph” was first introduced. The first textbook was later published in 1936.1

There are various algorithms that define how to best traverse through a graph from one node to another based on the edges between them.

So what…I’ve never used a Graph Database.

Have you ever used Google? If so, then you’ve used the most well-known implementation of a graph database in recent times.
Google, Faceook, and LinkedIn all use proprietary forms of graph databases to underpin parts of their websites.

How Google uses Graphs

In the original 1998 academic paper that Sergey Brin and Lawrence Page wrote, they described PageRank, the graph portion of their first implementation of Google.

Basically, all webpages are treated as nodes. The hyperlinks between the pages are edges, and an algorithm assigns a weight to the credibility of each page. The more links a page has to credible sources, the higher that page’s credibility becomes. A search is a) broken down into a series of words, b) used to find pages that most closely correlate to those words, and c) page results are ranked according to their credibility, or PageRank.

As of mid-2016, the size of Google’s index as 130 trillion. Google has a nice infographic site on how search works here.

What’s so good about a graph database?

For use cases involving complex relationships and traversal of these, graphs make great choices. They can provide10:

Flexible and agile – a graph database should closely match the structure of the data it uses. This allows developers to start work sooner without the added complexity of mapping data across tables. Neo4J call this ‘whiteboard friendliness’ – meaning what you draw as the design on your whiteboard is how the data is stored in your database.

Greater performance – compared to NoSQL stores or relational databases, graph databases offer much faster access to complex connected data, mainly as they lack expensive ‘join’ operations. In one example, a graph database was 1000x faster than a relational database when working with a query depth of four.

[Caveat: I did not perform this comparison, but I imagine a properly indexed instance of an Oracle database could complete this query in a decent amount of time, perhaps not as fast as Neo4j, but I bet it would at least finish the query.]

Lower latency – users of graph databases experience lower levels of latency. As the nodes and links ‘point’ to one another, millions of related records can be traversed per second and query response time remains constant irrespective of the overall database size.

– Sample graph query

Good for semi-structured data – graph databases are schema free, meaning patchy data, or data with exceptional attributes, don’t pose a structural problem.

(All of these bullets above are from https://cambridge-intelligence.com/keylines/graph-databases-data-visualization/)

When should you use a graph database?

The most popular and hottest use cases of graph DBs at the moment are:

Social network connections
Credit card fraud analysis
Recommendation engines
Master Data Management (MDM) – i.e., 360-degree view of customer
Logistics planning for transportation, traffic, shipping, etc.
Computer/telecom network planning and analysis

These boil down to the following uses10:

Path finding: Their traversal efficiency make graph databases an effective path-finding mechanism. Links can be weighted, or assigned relative distances or times, to ascertain the shortest and most efficient routes between two nodes in a network.
Mapping dependencies: networks of computers and hardware can be modeled as graphs to find components with many dependents that may be potential weak points or vulnerabilities. Other dependency networks, for example corporate or investment structures can be mapped in a similar manner.
Communications: Communications between people can be stored as graphs. Applying network analysis measures can help find influential individuals.

All of these bullets above are from https://cambridge-intelligence.com/keylines/graph-databases-data-visualization/

The Panama Papers13,14

In 2016 11.5 million documents comprising 2.6TB of information were leaked from a Panama law firm (Mossack Fonseca). These documents were scanned and processed into the Neo4j graph database where investigative journalist used graph visualizations to uncover hidden insights and relationships that would have otherwise been missed.

See the articles at Neo4J for more information on how this information was analyzed.

What graph databases should I use9?

Neo4j is far and away the most popular graph database. Neo4j and several of the other top graph DBs are all open source. Below is the trend of popularity for these databases from DB-engines.com. Neo4j is first with a score of 36.27, followed by OrientDB (5.87) and Titan (5.08).

Rank			DBMS	Database Model	Score
Feb 2017	Jan 2017	Feb 2016			Feb 2017	Jan 2017	Feb 2016
1.	1.	1.	Neo4j	Graph DBMS	36.27	+0.00	+3.98
2.	2.	2.	OrientDB	Multi-model	5.87	+0.06	-0.55
3.	3.	3.	Titan	Graph DBMS	5.08	-0.42	-0.27

Tips for converting from a RDBMS to Graph (from Neo4j)12:

Each entity table is represented by a label on nodes
Each row in a entity table is a node
Columns on those tables become node properties.
Remove technical primary keys, keep business primary keys
Add unique constraints for business primary keys, add indexes for frequent lookup attributes
Replace foreign keys with relationships to the other table, remove them afterwards
Remove data with default values, no need to store those
Data in tables that is denormalized and duplicated might have to be pulled out into separate nodes to get a cleaner model.
Indexed column names, might indicate an array property (like email1, email2, email3)
Join tables are transformed into relationships, columns on those tables become relationship properties

Music:

Music for today’s podcast is Cyanos by Graphiqs Groove via FreeMusicArchive.org.

Sources:

012 -For the Love of Guns

admin — Fri, 27 Jan 2017 10:15:09 +0000

Sheer Quantity

3% of gun owners own almost 50% of all civilian guns. These 7.7mm “super owners” own between 8-140 guns (on average 17)15
In 2013, U.S. gun manufacturers built 10,844,792 guns, and we imported an additional 5,539,539; the number dropped slightly in 2014.16
There are over 300 million guns owned by civilians (legal and illegal)11
The government holds approximately 2.7mm guns

NPR.org stacked bar chart showing firearms by type and year

History

National Firearms Act of 1934 (NFA)4

In 1934 Congress passed a law taxing the makers and distributors of firearms as a way to curtail the usage of weapons commonly used in gang activity at the time. It also required firearms to be registered with the Secretary of Treasury and compelled holders of unregistered firearms to register them and be subject to prosecution for having an unregistered firearm. This provision was ruled to have violated the 5th Amendment to the Constitution (against self-incrimination) in 1968. At this point the NFA was unenforceable.

Gun Control Act of 1968 (GCA)1

The assassination of JFK prompted this law because Oswald’s weapon was purchased from a mail-order catalog. The NRA supported this measure, and its passage in October 1968 came after recent assassinations of MLK and Robert Kennedy. The bill banned mail-order sales and prevented felons, drug users and mentally ill citizens from owning guns. The bill required firearms sellers to be licensed and prevented various interstate transactions unless they took place under a federally licensed dealer.

The bill established that persons over 18 could purchase rifles and shotguns, and one must have been over 21 to purchase a handgun. People would have to fill out Form 4473, the Firearms Transaction Record, when purchasing a gun from a dealer to certify that they are none of these prohibited parties6. The bill also required that all guns made or imported into the US bear a serial number and removal of that identifier became a felony offense. Furthermore, this bill closed the loophole in the NFA by preventing the registration of a firearm from being used as evidence in any crime occurring before the time of registration.

President Johnson, who asked for provisions of the bill, wanted it to also license individuals and said it fell short of protecting Americans at a time when 160 million guns existed in the US2. Johnson stated that the gun lobby defeated this measure. In 1993, the Brady Bill enhanced this by requiring more stringent background checks before selling a gun to a purchaser.

Firearm Owners’ Protection Act of 1986 (FOPA)3

In 1982 a Senate subcommittee report found that 75% of ATF prosecutions regarding firearms targeted ordinary, law-abiding citizens on technicalities or entrapment. This report and lobbying prompted the passage of FOPA in 1986. The law loosened restrictions on interstate gun sales and mailed ammunition, banned machine guns made after the bill passed from being sold to the general public, and limited ATF inspections to once a year, generally.

Registry Prohibition

A key part of FOPA was the restriction that the government cannot require firearms, their owners, or transactions involving firearms to be reported to any government entity.
The ATF is barred from consolidating or centralizing dealer records
- The bureau consolidated 252 million records of active shop owners from 2000-2016, but had to delete them after the GAO found they did not comply with FOPA
According to Pew Research Center, most Americans favor a federal database to track gun sales (70% overall, 55% Republicans)13

Traces:

1,500 / day or 373,000 / year in 2015 by 50 Bureau of Alcohol, Tobacco, and Firearms (ATF) agents
Urgent traces done in 24 hours; average trace takes 5 business days
These records are stored in 15,000 boxes
Required to be “unsearchable” – no keyword searches, sorting by date or anything else.
Some records are on toliet paper or napkins (a snub by shop owners who dislike the reporting requirement)
As of 2013, 70% of traces ID the buyer of a gun14
285 million records from closed up shops saved in 25 “data systems”7 known as the Firearms Tracing System (FTS)
- All bullets below are from https://en.wikipedia.org/wiki/Firearm_Owners_Protection_Act
- Multiple Sale Reports. Over 460,000 (2003) Multiple Sales reports (ATF F 3310.4 – a registration record with specific firearms and owner name and address – increasing by about 140,000 per year). Reported as 4.2 million records in 2010.
- Suspect Guns. All guns suspected of being used for criminal purposes but not recovered by law enforcement. This database includes (ATF’s own examples[citation needed]), individuals purchasing large quantities of firearms, and dealers with improper record keeping. May include guns observed by law enforcement in an estate, or at a gun show, or elsewhere.[citation needed] Reported as 34,807 in 2010.
- Traced Guns. Over 4 million detail records from all traces since inception. This is a registration record which includes the personal information of the first retail purchaser, along with the identity of the selling dealer.
- Out of Business Records. Data is manually collected from paper Out-of-Business records (or input from computer records) and entered into the trace system by ATF. These are registration records which include name and address, make, model, serial and caliber of the firearm(s), as well as data from the 4473 form – in digital or image format. In March, 2010, ATF reported receiving several hundred million records since 1968.
- Theft Guns. Firearms reported as stolen to ATF. Contained 330,000 records in 2010. Contains only thefts from licensed dealers and interstate carriers (optional). Does not have an interface to the FBI’s National Crime Information Center (NCIC) theft data base, where the majority of stolen, lost and missing firearms are reported. See eTrace below.

Hawaii & the “Rap Back” FBI database12

In 2016, Hawaii became the first state to require gun owners names to be posed to the FBI “Rap Back” database. This allows them to be notified if a gun owner from their state is arrested for a crime anywhere in the US.
Visitors to Hawaii packing heat must register and be placed on the list, but they request to be removed from the database after departure.

eTrace

When a gun is recovered at a crime scene, it can and usually is ran through a firearms trace with the ATF. This is done through a system called eTrace.
eTrace is a digital system that tracks submissions and trace results
It is more dynamic and usable because once a gun is in this system, it may be searched by owner name, serial number, etc.
However, non-crime scene guns are not in this system

ATF 2014 Firearms Trace Data10

The top 10 states with the most recoveries and traces are:

California
Florida
Texas
Illinois
Georgia
North Carolina
Ohio
Pennsylvania
Maryland
New York

In 2014, the number of firearms recovered and traced = 246,087

Top Categories of Recovered Firearms

Pistol – 131,562
Revolver – 43,799
Rifle – 38,854
Shotgun – 29,970
Derringer – 2,197
Receiver/Frame – 1,301
Machinegun – 717
The national possessor age is 36 years old.
In 2014, pistols and revolvers accounted for the majority of traced firearms.
National time-to-crime average is 10.88 years.

Sources:

Music

Gunslinger by The Long Ryders via FreeMusicArchive.org

006 For the Love of Cheesecake – For the Love of Data

admin — Sat, 30 Jul 2016 03:32:07 +0000

National Cheesecake Day!

June 30, 2016 is National Cheesecake Day, a likely commercially driven holiday to which I, for one, am happy to fall victim. Adam’s PB Cup Fudge Ripple is one of my favorites and also one of the worst (go figure).

History (#4)

Believed to have originated around 2,000 BC in Greece
Was served to athletes in first Olympic games as a source of energy
Original recipe, documented in 230 AD was: mashed cheese, honey, flour heated into a mass
Around 18th century, more modern-like recipe emerged

Facts

Sonya Thomas holds the record for eating 11 pounds of cheesecake in 9 min. (9/26/2004). (#5)
Largest Cheesecake weight 6,900 pounds and was formed in Lowville, NY on 9/21/2013.

The cake measured 2.292 m (7 ft 6.25 in) in diameter, and .787 m (2 ft 7 in) tall. (#6)

Cheesecake Factory

Cheesecake Factory, back in 2013, began using IBM big data analytics to analyze consumption and ingredients on products across all their locations (#1). They also had 2.1b in revenue in 2015 (#3).

Cheesecake Factory Nutrition Info

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Cheesecake Factory Nutrition Info (#2)

Sources:

005 For the Love of Fireworks – For the Love of Data

admin — Tue, 28 Jun 2016 13:11:31 +0000

News:

Big Data falls off the hype cycle: http://www.datasciencecentral.com/profiles/blogs/big-data-falls-off-the-hype-cycle
Tableau 10 in beta:
- http://www.tableau.com/about/blog/2016/4/10-reasons-join-tableau-10-beta-53165
- http://www.tableau.com/coming-soon

Fireworks!

NOTE: Overall, statistics are hard to follow across sites and even different reports from the same groups.

2015 Consumption Statistics:

Consumption: 260.7 Million lbs. (Consumer), 24.6 million lbs. (Display) (#5 APA)
- *** The consumer weight of fireworks used is roughly equivalent to the weight of the entire population of Hawaii! ***
Revenue: $755 million (Consumer), $340 million (Display) (#5 APA). Focusing on the consumer spending:
- This is over 100x more than the revenue Katy Perry would have generated with the 7 million U.S. sales of her song “Firework” on iTunes.
- This is more than the all the money we spent at In ‘N Out Burger in 2015.
- If one person spent the same amount on Roman Candles, it would take them over 1,000 years to use the amount of fireworks we purchase in a year.

Are fireworks Dangerous?

67% of fireworks injuries occur around July 4th

Injuries by Age: This graph makes it seem like young adults are most commonly injured, but when you look at 20 year bands, it breaks down differently:
- 0-19 = 47%
- 5-24 = 49%
- 10-24 (smaller than 20yr band) = 32%
- 25-44 = 34%

12,000 fireworks injuries (CPSC) out of 31 million injuries = .04% of injuries are fireworks (#7 APA)
11 Deaths (#9 CPSC)

– #8 APA

Usage is growing but injuries are falling according to the American Pyrotechnics Association
- Injuries are falling while consumption goes up. Injuries are also falling as our population has increased. However, in absolute terms injuries are relatively constant

APA also contends that fireworks injuries are a small minority of total injuries to kids

– #10 APA

Only three states (DE, MA, NJ) ban fireworks (#8 APA)

Links:

Consumer Products Safety Commission (CPSC) Fireworks Infographic – http://www.cpsc.gov/PageFiles/150398/Fireworks-Infographic-2015-web.pdf?epslanguage=en
National Fire Protection Agency – http://www.nfpa.org/public-education/by-topic/outdoors-and-seasonal/fireworks/reports-and-statistics-about-fireworks
Washington State Patrol – http://www.wsp.wa.gov/fire/statistics.htm
Statistics Brain (Various Sources) – http://www.statisticbrain.com/firework-statistics/
American Pyrotechincs Association – http://www.americanpyro.com/industry-facts-figures
2015 US Population – http://www.usnews.com/opinion/blogs/robert-schlesinger/2014/12/31/us-population-2015-320-million-and-world-population-72-billion
Fireworks injuries in perspective – http://www.americanpyro.com/assets/docs/FactsandFigures/fireworks%20injuries%20perspecitive.2016.pdf
Fireworks liberalization- http://www.americanpyro.com/assets/docs/FactsandFigures/consumpvinjuriesliberalizationgraph%201980-2010.pdf
CPSC 2014 Fireworks Report – http://www.cpsc.gov/en/Media/Documents/Research–Statistics/Injury-Statistics/Fuel-Lighters-and-Fireworks/2014-Fireworks-Annual-Report/?utm_source=rss&utm_medium=rss&utm_campaign=Fuel%2c+Lighters+and+Fireworks+Injury+Statistics
APA Injuries to Children – http://www.americanpyro.com/assets/docs/FactsandFigures/injuries%20to%20children%20ages%205-18%202016.pdf
US Income – http://www.deptofnumbers.com/income/us/
NFPA Fireworks Info Sheet – http://www.nfpa.org/~/media/files/research/fact-sheets/fireworksfactsheet.pdf?la=en
APA Fireworks Injures vs. Consumption – http://www.americanpyro.com/assets/docs/FactsandFigures/fireworks%20related%20injuries%20rtable%201976%20-2015.pdf
Katy Perry Firework Wikipedia – https://en.wikipedia.org/wiki/Firework_(song)
In ‘N Out Burger Sales – http://nrn.com/top-100/2015-top-100-restaurant-chain-countdown#slide-43-field_images-136081

002 What Hot Models Look Like – For the Love of Data

admin — Mon, 29 Feb 2016 07:04:07 +0000

Summary:

Hot models…data models that is. A survey of many of the most popular data modeling approaches in the news today. Third Normal Form, Anchor Modeling, Data Vault, Data Lakes, Data Swamps. What do they do well, what do they do badly, and which is the one true data model to rule them all? (Hint: it depends, as usual.)

Third Normal Form (3NF) (a.k.a. Naomi Sims)

History: E.F. Codd defined 3NF in 1971 while working at IBM.

Basic Concept:

“The Key, the Whole Key, and Nothing but the Key” -Bill Kent

The gold standard for purist relational database design. If a table has the following characteristics:

1NF – a) Values in a particular field must be atomic and b) a single row cannot have repeating groups of attributes
2NF – in addition to being in 1NF, all non-key attributes of the table depend on the primary key
There is no transitive functional dependency

Pros:

A battle-tested, well-understood modeling approach that is extremely useful for transactional (OLTP) applications
Easy to insert, update, delete data because of referential integrity
Avoids redundancy, requiring less space and less points of contact for data changes
Many software tools exist to automatically create, reverse engineer, and analyze databases according to 3NF
Writing to a 3NF DB is very efficient

Cons:

Reading from a DB in 3NF is not as efficient
Not as easily accessed by end-users because of the increased number of joins
More difficult to produce analytics (trends, period-to-date aggregations, etc.)
Many times even transactional systems are slightly de-normalized from 3NF for performance or audit-ability
Some people feel that 3NF is no longer as appropriate in an era of cheap storage, incredibly fast computing, and APIs

Source: ewebarchitecture.com

(Sources:https://en.wikipedia.org/wiki/Third_normal_form; http://www.1keydata.com/database-normalization/third-normal-form-3nf.php; http://www.island-data.com/downloads/papers/normalization.html; http://www.johndcook.com/blog/2012/11/21/rise-and-fall-of-the-third-normal-form/)

Anchor Modeling (incorporates Sixth Normal Form [6NF]) (a.k.a. Gisele Bundchen)

History: Created in 2004 in Sweden

Basic Concepts: Mimics a temporal database

anchors – entities or events
- Example: A person
attributes – properties of anchors
- Example: A person’s name; can be historical, such as favorite color)
ties – relationships between anchors
- Example: Siblings
knots – shared properties, such as states or reference tables – combination of an anchor and a single attribute (no history)
- Example: Gender – only male/female

Pros:

Incremental change approach – previous versions of a schema are always encompassed in new changes, so backwards compatibility is always preserved
Reduced storage requirements by using knots

Cons:

Many entities are created in the database
Joins become very complex; hard for end user to understand model
Daunting for new technical resources to come up to speed initially

Source: bifuture.blogspot.com

(Sources: Wikipedia,http://en.wikipedia.org/w/index.php?title=Anchor_Modeling& oldid =570899764; http://www.anchormodeling.com/)

Data Vault (DV) (a.k.a. Heidi Klum)

History: Dan Linstedt developed the started implementing data vaults in 1990 and published the first version of the methodology (DV1.0) in 2000. He published an updated version (DV2.0) in 2013. The methodology is proprietary and Dan restricts who can train others by maintaining a copyright on the methodology and requiring people who train others to be Data Vault certified. You can still implement data vaults; you just cannot train others on it without being certified.

Basic Concept:

“A single version of the facts (not a single version of the truth)”

“All the data, all the time” – Dan Linstedt

The data fault consists of three primary structures and supporting structures such as reference tables and point-in-time bridge tables. The three main structures are:

Hubs – a list of unique business keys that change infrequently with no other descriptive attributes (except for meta data about load times and data sources). A good example of this is a car or a driver.
Links – relationships or transactions between hubs. These only define the link between entities and can easily support many-to-many relationships; again no descriptive attributes on these tables other than a few meta-attributes. An example of this would be a link between cars and their drivers.
Satellites – Satellites may attach to hubs or links and are descriptive attributes about the entity to which they connect. A satellite for a car hub could describe the year, make, model, current value, etc. These often have some sort of effective dating.

Source: http://www.slideshare.net/kgraziano/introduction-to-data-vault-modeling

General best practices:

Separate attributes from different source systems into their own satellites, at least in a raw data vault. Using this approach it may be common to have a raw data vault that contains source system specific information with all history and attributes maintained and a second downstream business data vault. The business data vault will contain only the relevant attributes, history, or merged data sets that have meaning to the users of that vault.
- Having a raw mart allows you to preserve all historical data and rebuild the business vault if needs change without having to go back to source systems and without losing data if it is no longer available the source system.
Track all changes to all elements so that your data vault contains a complete history of all changes.
Start small with a few sources and grow over time. You don’t have to adopt a big bang approach and you can derive value quickly.
It is acceptable to add new satellites when changes occur in the source system. This allows you to iteratively develop your ETL without breaking previous ETL routines already created and tested.

DV2.0 – DV1.0 was merely the model. DV2.0 is:

An updated modeling approach. Key changes include:
- Numeric IDs are replaced with hash values, created in the staging area, that support better integration with NoSQL repositories
- Because hashes are used, you can parallelize data loads even further because you do not have to lookup a surrogate ID if you have the business key to hash from when you’re bringing in data. This means you can load hubs, links, and satellites at the same time in some cases
- Referential integrity is disabled during loading
Recommended architectures around staging areas, marts, virtualization, and NoSQL
Additional methodology recommendations around Agile, Sixth Sigma, CMMI, TQM, etc.

Pros:

Preserves all data, all the time – this provides the capability for tremendous analysis and responding to changing business needs. The approach allows you to obtain data from multiple sources iteratively and rapidly, preserving backwards compatibility
Works extremely well with massively parallel processing (MPP) databases and hardware
Can be loaded extremely rapidly, particularly using the DV2.0 modeling approach
Lends itself very well to ETL and DW automation/virtualization
DV2.0 covers a wide spectrum of modeling needs from staging and marts to methodology

Cons:

The data model can spawn a lot of tables and make queries very complicated very quickly.
The raw data mart is really not meant for end users to query/explore directly
Iterative additions make the data model more complicated
Although storage may be cheap, keeping all changes for all data in all sources can lead to data sprawl. This also makes a pared down information mart almost a necessity.
Raw DV data is not cleansed and data from multiple sources are not blended when being stored

(Sources: https://en.wikipedia.org/wiki/Data_Vault_Modeling;http://roelantvos.com/blog/?p=1063; https://kentgraziano.files.wordpress.com/2012/02/introduction-to-data-vault-modeling.pdf)

Data Lake (DL) (a.k.a. Brooklyn Decker)

History: Term was coined by Pentaho CTO James Dixon in a blog post in 2010 referring to Pentaho’s data architecture approach to storing data in Hadoop.

Basic Concept: A massive, big data repository, typically on Hadoop or HDFS, at least. Key points are that it is:

Schema-less – data is written to the lake in its raw form without cleansing
Ingests different types of data (relational, event-based, documents, etc.) in batch and/or real-time streaming
Automated meta data management – a best practice is to use tools to automatically catalog meta data to track available attributes, last access times, data lineage, and data quality
Typically multiple products are used to load data into and read data from the lake
Rapid ability to ingest new data sources
Typically only a destination; it is usually not a source from which operational systems will source data

Pros:

Useful when you do not know what attributes will be needed or used.
Schema on Read – can ingest any type of data and allow different users to assess value during analysis
Extremely large scale at low to moderate cost
Can and will use a variety of tools/technologies to analyze/visualize/massage data into a useful form

Cons:

Can me seen as a vast wasteland of disorganized data, particularly without good meta data
Consumers must understand raw data in various systems to know how to integrate and cleanse it in order to derive meaningful information
High likelihood that different consumers will perform very similar operations to retrieve data (i.e., overlap and duplication of efforts). Slight differences between groups can lead to reconciling differences
Uncleansed data and multiple versions of the same data may possibly lead to duplication if not handled/filtered carefully
It isn’t SQL – Some users will have to use more than just SQL to derive useful information from data
- Offloading ETL can require significant rework of existing processes to move to something like Hive
Using multiple tool sets can lead to training and supportability challenges if not governed properly
Data curation can by very challenging

– Source: http://www.slideshare.net/ImpetusInfo/enterprise-big-data-lake-challenges-strategies-maximizing-benefits-impetus-webinar-44720024

(Sources: http://jamesdixon.wordpress.com/2010/10/14/pentaho-hadoop-and-data-lakes/; http://knowledgent.com/whitepaper/design-successful-data-lake/; http://martinfowler.com/bliki/DataLake.html; https://hortonworks.com/wp-content/uploads/2014/05/TeradataHortonworks_ Datalake_White-Paper_ 20140410.pdf; http://cacm.acm.org/blogs/blog-cacm/181547-why-the-data-lake-is-really-a-data-swamp/fulltext)

Data Swamp (DS) (a.k.a. Tyra Banks)

History: I’m not including a lot of history here, because this is really an extension of a Data Lake (gone bad).

Basic Concept: A data swamp is a data lake that has been poorly maintained or documented, lacks meta data, or has so much raw data that you don’t know where to start for insights. Or, it could be a combination of several of those points. When you start tracking tons of data from all different sources, but you don’t know who is using what, how to merge data sets, or how to use most of the data in your “data lake”, you’ve really got a data swamp.

Pros:

Hey, you must’ve done something right to get all that data into the repository…?
At least you haven’t lost data that you can’t go back and get.
If it were easy, everyone would be doing it

Cons:

You’ve likely spent a lot of time and effort putting in a data lake/HDFS/Hadoop/Hive/etc. and you’re struggling to operate it at scale or to answer the questions you set out to answer.
You need meta data to clue users into what is most useful, relevant, or recent
You probably need to look into key use cases (low hanging fruit) and start from that point as a place to begin using/resuscitating your repository.

*** The assignment of model names to each data model was an incredibly (un)scientific process of googling various terms like “most famous supermodel ”, “ top supermodel”, etc. and teasing out the most likely #1. Feel free to disagree and let me know your vote and how you obtained it.

001 The Data of Church – For the Love of Data

admin — Fri, 11 Dec 2015 22:04:48 +0000

Churches have a wealth of data that other organizations could only dream about–a weekly stream of attendees and donors who also participate in a wide variety of activities around the organization. In this episode, I sit down with Glen Brechner, the Executive Director of Chase Oaks Church in Plano. Chase Oaks is one of the top twenty churches in the DFW metroplex and is in the top 20% of megachurches in the US.

We discuss how they track member participation and donation information, how they consolidate and align data across multiple campuses, and challenges and opportunities they see with data.

Some of the tools they use include:

Excel (doesn’t everybody!)
Mortarstone
Shelby
Arena

Please leave a comment about the episode and let me know if you have any questions.

000 Introducing “For the Love of Data with Robert Furr” (and what it means for you)

admin — Mon, 19 Oct 2015 04:53:23 +0000

Data, Analytics, Business Intelligence… how do I keep track of what is going on in this ever-expanding technology realm? “For the Love of Data” is a monthly podcast covering data, big data, huge data, tiny data, analytics, and business intelligence trends across the industry. Join the discussion, write a review, or give us your feedback on our site.

This introductory episode covers the podcast’s format, why I want to do it (because I love data!), and who may benefit from listening.

Episodes – For the Love of Data

E035 – Your Data and an Announcement

Announcement:

The Importance of Your Data

Music:

Sources:

E034 – Using Data to Make Perfect Chocolate – Part 2

Music:

Sources

E033 – Using Data to Make Perfect Chocolate – Part 1

Music:

Sources

E027 – The Data Quadrant

Topics:

Music

Sources:

E026 – The Four Types of Automation

Introductory Product Models:

Music

Sources:

E025 – The Hype of AI

Music

E021 – Data Deep Dive – Halloween Spending and Candy

Spending

Top Costumes

Candy

Music

Sources

E020 – How Crisis Text Line uses data to save lives

Overview

Key Stats

Music

Sources

E017 – Tech Spec – Tableau 10.3 New Features

Sources

E016 – For the Love of Sunscreen

Tips to Stay Safe in the Sun

Tips if you get a Sunburn17

Music

Sources

E015 – BBQ Showdown (Pellet Grill vs Big Green Egg)

History

Styles

Brisket Cuts

Nutrition Facts

Brisket Sales

Other Stats

Pellet Grills

Big Green Egg

Health Tips

Sources:

013 – For the Love of Graph Databases

Where did graphs come from? (Graph Theory History)

So what…I’ve never used a Graph Database.

How Google uses Graphs

What’s so good about a graph database?

When should you use a graph database?

The Panama Papers13,14

What graph databases should I use9?

Tips for converting from a RDBMS to Graph (from Neo4j)12:

Music:

Sources:

012 -For the Love of Guns

Sheer Quantity

History

Registry Prohibition

Music

006 For the Love of Cheesecake – For the Love of Data

National Cheesecake Day!

History (#4)

Facts

Cheesecake Factory

Sources:

005 For the Love of Fireworks – For the Love of Data

002 What Hot Models Look Like – For the Love of Data

001 The Data of Church – For the Love of Data

000 Introducing “For the Love of Data with Robert Furr” (and what it means for you)

Tips if you get a Sunburn¹⁷