DataOps requires a cultural shift that brings the principles of lean manufacturing and DevOps to data analytics. It breaks down silos between developers, data scientists, and operators, resulting in rapid cycle times and low error rates. At Spotify in 2013, the concept of DataOps did not exist but the Swedish company needed a way to align the people, processes, and technologies of the data organization to accelerate the development of high-quality analytics. The result was a Swedish-style DataOps, influenced by Scandinavian culture and agile principles, that enabled the company to become a true data-driven leader.

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DataOps in practice -
Swedish style
Lars Albertsson (@lalleal)
Scling
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Who’s talking?
...
Google - video conference, engineering productivity
...
Spotify - data engineering
...
Independent data engineering consultant
Banks, media, startups, heavy industry, telco
Founder @ Scling - data-value-as-a-service
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Contents
Journey to DataOps
Experiences that shaped my data engineering
IMHO principles of successful DataOps
Toolbox
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● Spotify information is old history
● Previously published
● Today is very different

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Spotify data 2007-2013
● Hadoop installed 2007
● Use cases: reporting, insights, recommendations
● Cultural aspects:
○ Autonomous teams
○ Eliminate waste
○ Learn and adapt
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Traditional systems
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Mutation
Early Hadoop:
● Weak indexing
● No transactions
● Weak security
● Batch transformations

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Data lake
Transformation
Cold
store
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Mutation
Immutable,
shareable
Early Hadoop:
● Weak indexing
● No transactions
● Weak security
● Batch transformations
DataOps workflows:
● Immutable, shared data
● Resilient to failure
● Quick error recovery
● Low-risk experiments
Data factories

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What conclusion from this graph?
COVID-19 fatalities / day in Sweden
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Wrong conclusion, every day
● Downward trend every day!
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Normalise data collection to compare
9Graph by Adam Altmejd, @adamaltmejd

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Normalise data collection to compare
10Graph by Adam Altmejd, @adamaltmejd

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Forecast for analytics with fresh data
11Graph by Adam Altmejd, @adamaltmejd

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From craft to process
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From craft to process
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Multiple time windows

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From craft to process
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Multiple time windows
Assess ingress data quality

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From craft to process
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Multiple time windows
Assess ingress data quality
Assess outcome data quality

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From craft to process
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Multiple time windows
Assess ingress data quality
Assess outcome data quality
Repair broken data
Intermediate datasets, reusable between pipelines

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From craft to process
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Multiple time windows
Assess ingress data quality
Repair broken data from
complementary source
Assess outcome data quality

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From craft to process
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Multiple time windows
Assess ingress data quality
Repair broken data from
complementary source
Forecast based on history
Assess outcome data quality

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From craft to process
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Multiple time windows
Assess ingress data quality
Repair broken data from
complementary source
Forecast based on history,
multiple parameter settings
Assess outcome data quality

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From craft to process
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Multiple time windows
Assess ingress data quality
Repair broken data from
complementary source
Forecast based on history,
multiple parameter settings
Assess outcome data quality
Assess forecast success,
adapt parameters

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Towards sustainable production ML
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Multiple models,
parameters, features
Assess ingress data quality
Repair broken data from
complementary source
Choose model and parameters based
on performance and input data
Benchmark models
Try multiple models,
measure, A/B test

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Risky operations
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How to I test the pipeline?
You temporarily change the
output path and run manually.
Don’t do that.
What if I forget to change path?

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2013
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● Teams: Analytics computation (AC), data collection (DC), recommend, reporting (1)
● Folklore development cycle & operations
● Unsatisfied needs in other teams

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luigid
Redundant "edge nodes" with Luigi workers, scheduled with cron. Compute + data in Hadoop.
On-prem Hadoop production
Worker
10 * * * * luigi --module mymodule MyDaily
23 * * * * luigi --module other OtherDaily
Master
Executor
Worker
HDFS metadata
Data
Control
(+data)
Submit job
10 * ...
23 * ...

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Ghost in the cluster
● Jobs were deployed with Debian packages + Puppet on pet machines.
○ Multiple pets for redundancy. Race to run job.
● "This monitor daemon is at 100%. Since 6 months. I'll kill it."
● "Data is wrong. But we fixed this bug 6 months ago?!?"
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Start of a DataOps journey
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Stateful Stateless
Pets Cattle
Folklore
Golden pathTest in prod
Local test
CI/CD
Weeks to learn
New pipeline
< 1 day
Days to mend
Bug fix
< 1 hour

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On-prem pipeline deployment pipeline
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source
repo Luigi DSL, jars, config
my-pipe-7.tar.gz
Luigi
daemon
> pip install my-pipe-7.tar.gz
Worker
Worker
Worker
Worker
Worker
Worker
Worker
Worker
Redundant cron schedule,
higher frequency
All that a pipeline needs, installed atomically
10 * * * * luigi --module mymodule MyDaily
Standard deployment artifact Standard artifact store

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Principle: Functional pipelines
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● Raw source of truth + data refinement factory
● Immutable datasets & artifacts
● Deterministic, idempotent, reproducible deployment & processing
● Key success factor: workflow orchestration
○ Oozie, Rambo, Builder, Builder2, Luigi
○ Key properties:
1. Pure Python
2. Simplicity
3. All the features it lacks

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Big data - a collaboration paradigm
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Stream storage
Data lake
Data
democratised

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● Technically
○ Data available
○ Reusable QA
● Operationally
○ Continuous deployment
○ Hands off operations
○ Monitoring, debugging
● Bottom-up innovation
Enabling teams
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"The actual work that went into
Discover Weekly was very little,
because we're reusing things we
already had."
https://youtu.be/A259Yo8hBRs
https://youtu.be/ZcmJxli8WS8

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Principle: Small scope components
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● Do one thing well. Less is more.
● Complex systems from replaceable bricks
○ Cloud/OSS over enterprise vendors
○ Simplicity over features
Solvable
challenge
~2000 lines of code
Perpetual
complexity

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Cloud native deployment
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source
repo Luigi DSL, jars, config
my-pipe:7
Luigi
daemon
Worker
Worker
Worker
Worker
Worker
Worker
Worker
Worker
Redundant cron schedule,
higher frequency
kind: CronJob
spec:
schedule: "10 * * * *"
command: "luigi --module mymodule MyDaily"
Docker image Docker registry
S3 / GCS
Dataproc /
EMR

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Data platform gravitation
● Hadoop all the things.
● Data is there. Simple test, simple deploy, simple ops.
● Autonomous teams - no mandate. Natural gravity.
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3434
Nearline
● Stream storage
● Asynchronous event
processing
● 10 ms - 1 hour
Data integration timescales
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Job
Stream
Offline
● File storage
● Asynchronous batch
processing
● 1 minute -
Online
● SOA / microservices
● Synchronous RPC
● 1-100 ms
Stream
Job
Stream

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3535
Upgrade
● Careful rollout
● Risk of user impact
● Proactive QA
Operational manoeuvres - online
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3636
Upgrade
● Careful rollout
● Risk of user impact
● Proactive QA
Operational manoeuvres - online
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Service failure
● User impact
● Data loss
● Cascading outage

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3737
Upgrade
● Careful rollout
● Risk of user impact
● Proactive QA
Operational manoeuvres - online
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Service failure
● User impact
● Data loss
● Cascading outage
Bug
● User impact
● Data corruption
● Cascading corruption

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Operational manoeuvres - offline
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Upgrade
● Instant rollout
● No user impact
● Reactive QA
Service failure
● Pipeline delay
● No data loss
● No downstream impact
Bug
● Temporary data
corruption
● Downstream impact

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Life of an error, batch pipelines
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● Faulty job, emits bad data
1. Revert serving datasets to old
2. Fix bug
3. Remove faulty datasets
4. Backfill is automatic (Luigi)
Done!
● Low cost of error
○ Reactive QA
○ Production environment sufficient

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Production critical upgrade
● Dual datasets during transition
● Run downstream parallel pipelines
○ Cheap
○ Low risk
○ Easy rollback
● Testable end-to-end
No dev & staging environment needed!
∆?

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Operational manoeuvres - nearline
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Upgrade
● Swift rollout
● Parallel pipelines
● User impact, QA?
Service failure
● Pipeline delay
● No data loss
● Downstream impact?
Bug
● Data corruption
● Downstream impact
Job
Stream
Stream
Job
Stream
Job
Stream
Stream
Job
Stream
Job
Stream
Stream
Job
Stream

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Life of an error, streaming
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● Works for a single job, not pipeline. :-(
Job
StreamStream Stream
Stream Stream Stream
Job
Job
Stream Stream Stream
Job
Job Job
Reprocessing in Kafka Streams

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Data speed Innovation speed
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Nearline
Data processing tradeoff
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Job
Stream
OfflineOnline
Stream
Job
Stream

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Separating online & offline
● Daily user DB dump. Cassandra can handle the load.
○ Load spike became 25 h long…
● New recommendation model! Cassandra can replicate to all regions.
○ Who saturated the Atlantic link?
● Batch jobs saturate one resource.
○ Bad neighbours.

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Batch offline vs online
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Raw
Fraud
serviceFraud
model
Orders Orders
Replication /
Backup
Standard procedures Standard proceduresLightweight procedures
● QA driven by internal efficiency
● Continuous deployment
● New pipeline < 1 day
● Upgrade < 1 hour
● Bug recovery < 1 hour
Careful handover Careful handover

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Data quality dimensions
● Timeliness
○ E.g. the customer engagement report was produced at the expected time
● Correctness
○ The numbers in the reports were calculated correctly
● Completeness
○ The report includes information on all customers, using all information from the whole time period
● Consistency
○ The customer summaries are all based on the same time period
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Testing single batch job
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Job
Standard Scalatest harness
file://test_input/ file://test_output/
1. Generate input 2. Run in local mode 3. Verify output
f() p()
Runs well in
CI / from IDE

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Testing batch pipelines - two options
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Standard Scalatest harness
file://test_input/ file://test_output/
1. Generate input 2. Run custom multi-job
Test job with sequence of jobs
3. Verify output
f() p()
A:
Customised workflow manager setup
p()f()
B:

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Monitoring timeliness, examples
● Datamon - Spotify internal
● Twitter Ambrose (dead?)
● Airflow
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Measuring correctness: counters
● Processing tool (Spark/Hadoop) counters
○ Odd code path => bump counter
○ System metrics
Hadoop / Spark counters DB
Standard graphing tools
Standard
alerting
service

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Measuring correctness: counters
● User-defined
● Technical from framework
○ Execution time
○ Memory consumption
○ Data volumes
○ ...
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case class Order(item: ItemId, userId: UserId)
case class User(id: UserId, country: String)
val orders = read(orderPath)
val users = read(userPath)
val orderNoUserCounter = longAccumulator("order-no-user")
val joined: C[(Order, Option[User])] = orders
.groupBy(_.userId)
.leftJoin(users.groupBy(_.id))
.values
val orderWithUser: C[(Order, User)] = joined
.flatMap( orderUser match
case (order, Some(user)) => Some((order, user))
case (order, None) => {
orderNoUserCounter.add(1)
None
})
SQL: Nope

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Data quality - high code vs low code
● 2013: Python MapReduce outdated
● Hive/SQL?
○ Not expressive enough
○ Data quality challenging
● Technical platform + multi-skilled teams!
○ Strong development processes
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Low code / no code platform? Technical platform?

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Measuring consistency: pipelines
● Processing tool (Spark/Hadoop) counters
○ Odd code path => bump counter
○ System metrics
● Dedicated quality assessment pipelines
DB
Quality assessment job
Quality metadataset (tiny)
Standard graphing tools
Standard
alerting
service

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Machine learning operations, simplified
● Multiple trained models
○ Select at run time
● Measure user behaviour
○ E.g. session length, engagement, funnel
● Ready to revert to
○ old models
○ simpler models
Measure interactionsRendez-
vous
DB
Standard
alerting
service
Stream Job
"The required surrounding
infrastructure is vast and
complex."
- Google

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Not all things went well
● Autonomy → excessive heterogeneity
○ 25 ways to store a timestamp?
● Pipeline end-to-end tests
○ Culturally challenging
○ → difficult to change & retire pipelines
● Trial and error to learn

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Data engineering in Scandinavia
● Stockholm region ranks 2nd in unicorns / capita
○ Media, games, fintech
● Critical mass of world class data engineering
○ Limited to a few companies
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Mission: Spread data & AI superpowers
● There are companies to help
● Data & AI capabilities require culture & process change
○ Slow, very slow
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Scandinavian minimalist design
● Lean, simple technology - focus on flow and business value
● Bonnier News data platform, 4-5 persons:
○ Zero to happy customer in 3 weeks.
○ Dozens of ROI pipelines in 8 months.
● Scling retail client, 1-3 persons, after 1 year:
○ 40 sources, 70 pipelines, 200 egress points
○ 3,400 datasets / day
● Typical enterprise numbers
○ Big data project: 6-24 months
○ Analytics department: 100-1000 datasets / day
○ Spotify: 100,000+ datasets / day
○ Google: 1.6B datasets / day (2016)
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Scling - data-value-as-a-service
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Data value through collaboration
Customer
Data factory
Data platform & lake
data
domain
expertise
Value from data!
www.scling.com/reading-list
www.scling.com/presentations
www.scling.com/courses