Over the past eight or nine years, applying DevOps practices to various areas of technology within business has grown in popularity and produced demonstrable results. These principles are particularly fruitful when applied to a data analytics environment. Bob Eilbacher explains how to implement a strong DevOps practice for data analysis, starting with the necessary cultural changes that must be made at the executive level and ending with an overview of potential DevOps toolchains. Bob also outlines why DevOps and disruption management go hand in hand. Topics include: - The benefits of a DevOps approach, with an emphasis on improving quality and efficiency of data analytics - Why the push for a DevOps practice needs to come from the C-suite and how it can be integrated into all levels of business - An overview of the best tools for developers, data analysts, and everyone in between, based on the business’s existing data ecosystem - The challenges that come with transforming into an analytics-driven company and how to overcome them - Practical use cases from Caserta clients This presentation was originally given by Bob at the 2017 Strata Data Conference in New York City.

1. Strata 2017
Creating a DevOps
Practice for Analytics
Bob Eilbacher
September 28, 2017

2. Agenda
 About Caserta
 DevOps
 DevOps for Analytics
 Organization and Teams
 Questions

3. About Caserta
 Data Intelligence Consulting and Modern Data Engineering
 Award-winning data innovation
 Internationally recognized work force
 Strategy, Architecture, Governance, Implementation

4. About Caserta
 Architecture & Design
 Implementation Services
 Disruption Management
 Strategic Technical Consulting
 Training & Education
 Application Innovation
 Cloud Management

5. What is DevOps for Analytics?
First some terminology…
 DevOps
 Associated with movement primarily in application
development space for last 5-10 years
 Focused on very fast and continuous software product
releases
 Think intra-day Prod releases at Netflix, Amazon, etc.
 Convergence of development and operations
methodologies to minimize TTR
 Tons of resources – devops.com, DZone

6. What is DevOps for Analytics?
Some more terminology…
 DataOps
 Re-emergent term
 Seems to have a broader context
 Applying DevOps to data management or to handling
backend databases
 Also tends to carry real legacy connotation
 Manual operations of database backups and restores,

7. What is DevOps for Analytics?
And finally…
 AnalyticsOps
 This is a term that we see starting to be used more
 Its focused on applying DevOps practices within a data
analytics and data science context
 This is the area we’re interested in for this talk
 We’ll use the terms AnalyticsOps or the more explicit
DevOps for Analytics interchangeably

8. DevOps…
 Speak with anyone and they will tell you first that DevOps
is a culture
 Based primarily on teamwork

9. DevOps…

10. DevOps…
 Speak with anyone and they will tell you first that DevOps is a
“culture”
 Based primarily on teamwork
 Aims to address the underlying conflict between
development and operations objectives
Innovation @ speed vs. Performance @ quality
Change vs. Stability
 Culture is not “implemented”
 It needs to evolve
 Good news is it can be seeded

11. DevOps…
 It works!
 75% of IT and product dev organizations were successfully
using DevOps to some extent
– Source: RightScale 2016 State of the Cloud Report
 It’s flexible
 No two companies’ DevOps approaches will look the same
 Infinite number of ways to create teamwork
 A reflection of the organization itself

12. DevOps…
 DevOps tenets
 Continuous Integration
 Test Automation
 Continuous Delivery
 Continuous Deployment
 End-to-end automation is still aspirational for most
companies
 Justify how much automation you need based on business
requirements.

13. DevOps…
 What DevOps is not is a toolchain implementation
 Tools help the team execute within the culture
 Don’t run out and put an end to end chain in place and then
expect adoption
 Lets talk about tools for a minute …
 Explosion of both open-source and commercial DevOps
tooling
 Serve every discrete need
 requirements management, SCM, test automation, defect
tracking, build, deployment, monitoring and more
 1,500+ tools available

14. DevOps…
 Tooling categories:
 Code : Code development, version control tools, code merging
 Build : Continuous integration tools, build status
 Test : Test and results determine performance
 Package : Artifact repository, application pre-deployment
staging
 Release : Change management, release automation
 Configure : Infrastructure configuration and management,
Infrastructure as Code tools
 Monitor : Applications performance monitoring, end user
experience

15. DevOps…
Source: XebiaLabs

16. Why DevOps for Analytics?
“The fact is that analytic teams are
being compared by their businesses to
Amazon Prime – 2-day delivery of
almost anything”
Source: Unknown

17. Why DevOps for Analytics?

18. Why DevOps for Analytics?
 A couple of recent real world examples…
Data Science Rock Star Process Overengineering

19. Why DevOps for Analytics?
 Analytics and data science projects, what used to take
months to achieve is happening in days or hours
 Businesses typically like that and want more…
 Enabled by the strong trend toward cloud analytic
platforms/services
 Infrastructure as code (IaC) allows extension of software
development practices to servers and infrastructure
 We can automate the build of complex analytic pipelines -
storage, processing engines, etc. with relative ease

20. DevOps for Analytics
 DevOps for Analytics combines the development and
operations teams and establishes best practices that
improve coordination between data science and operations
 BUT… Data Science and Analytics are different from
application development
 Especially in a Big Data environments - need big data to test big
data applications
 Much more diverse mix of tools and technologies – not just java
 Some differences in approach are needed

21. DevOps for Analytics
 AnalyticsOps this is still in its early days
 There aren’t any real solid industry success stories published
 People are still trying to figure out what works and aren’t’ open
kimono and sharing experiences just yet
 Not a lot of experienced practitioners
 But there are some early themes and guidelines emerging

22. DevOps for Analytics
 Environments
 Separate DEV and PROD environments
 Should you reuse any of the PROD data assets?
 Separate landing area, destination area (Data Lake), etc.
 Trickier with increasing data volumes – do it smart to avoid
double costs
 Sharing compute cluster resources is OK
 Make all job inputs and outputs configuration driven (PROD
and DEV code doesn’t change) – for CI

23. DevOps for Analytics
 Automated Testing
 It’s almost impossible to get full code coverage
 How do you unit test SPARK SQL scripts? Regression tests?
Data validation?
 Test data is a complex problem – handle as a cross-functional
initiative.
 Analytic results are often buried in complex outputs, QA
becomes forensic data analysis
 Automate what you can, supplement with community based
real-world data testing in a parallel Dev/Test environment
 The role of the Test/QA Engineer is still really important
 Test/QA Engineers need Data Engineering experience

24. DevOps for Analytics
 Monitoring
 Tracking and analyzing intra-day demand and longer term trends
in infrastructure performance (standard DevOps)
 But then…
 By their nature analytics processes require monitoring and
tuning over time with real-world inputs
 Data drifts; Predictive models have a finite lifetime
 Silent failures
 Feedback to developers so they can see how their code is
performing and affecting the Prod environment
 Continuous improvement
 The next wave is analytics on analytics…

25. DevOps for Analytics
 Emerging DevOps for Analytics environment usually contain
 SCM
 CI
 Repo to store analytics app
 Repo to store configuration
 An API to deploy to the cluster
 Mechanism to monitor behavior and performance

26. DevOps for Analytics Organization
 Building a DevOps for Analytics culture is not an easy
undertaking
 Should fall under the purview of a dedicated data organization
 These organizations are typically lead by the Chief Data
Officer
 More recently by Chief Data Scientist a Chief Analytics Officer
 Key responsibilities include
 Fostering adoption
 Clarifying and aligning to the business' vision
 Securing reasonable funding

27. DevOps for Analytics Organization
 The goal over time is to create lean, highly performant, cross-
functional, extremely effective teams
 Business Stakeholders
 Data Engineers
 Data Analysts & Data Scientists
 QA
 Operations
 All of these skills are important - but when in doubt get more Data
Engineers!
 Everyone on team has an equal voice
 Everyone codes & Everyone needs to know what Prod looks like

28. DevOps for Analytics Organization
 Start-up Condition: Bring in an experienced set of DevOps for
Analytics Engineers
 Help define the culture, lead by example
 Identify the Innovators and get them involved and leading
 The DevOps Engineers job is to ultimately engineer themselves out
of the equation
Source: Matthew Skelton, DevOps Patterns - Team Topologies

29. Final Thoughts
“We aim to engineer systems and processes
to better integrate development and
operations, resulting in decreased time to
market and an application infrastructure
that is instrumented, scalable and fault
tolerant… and immortal!”
- Will Liu, Equinox Data Team

30. Final Thoughts
 There are plenty of benefits in establishing a DevOps
for Analytics culture for your organization
 For the business: Speed to insight
 For the teams: Professional and personal satisfaction
 Be Fearless –
go build your own DevOps for Analytics culture!

31. Questions

32. Happy Birthday Joe Caserta!

33. Thank You
 Bob Eilbacher
 Vice President Operations, Caserta
 bob@casertaconcepts.com
Upcoming Training Opportunity:
Caserta is hosting 3 Days of Training Courses October 18-20th in NYC,
taught by Joe Caserta, co-author of The Data Warehouse ETL Toolkit:
Day 1: Agile Data Warehouse Design & Dimensional Modeling
Day 2: ETL Architecture & Design
Day 3: Big Data for Data Warehouse Practitioners
More info at casertaconcepts.com/event/