Data Engineering for Data Scientists

Data Engineering for Data Scientists
Jonathan Lacefield – Solution Architect
DataStax

Introduction
• Jonathan Lacefield
– Solutions Architect, DataStax
– Former Dev, DBA, Architect, reformed PM
– Email: jlacefie@gmail.com
– Twitter: @jlacefie
– LinkedIn: www.linkedin.com/in/jlacefield

DataStax Introduction
1. Commercial Provider of Apache Cassandra
2. Provider of Proprietary Software Built on
Apache Cassandra
3. Deliverer a linearly scalable, “always-on” Data
Platform on the foundation of Apache Cassandra
and the integration of:
1. Apache Spark
2. Apache SOLR
3. Apache Hadoop
4. TitanDB

DataStax, What we Do (Use Cases)
• Fraud Detection
• Personalization
• Internet of Things
• Messaging
• Lists of Things (Products, Playlists, etc)
• Smaller set of other things too!
We are all about working with temporal data sets at
large volumes with high transaction counts
(velocity).

“One believes things because one has been
conditioned to believe them.”
― Aldous Huxley, Brave New World

After today, you will have enough knowledge to walk into
any organization and communicate with Data Engineers,
in their terms, to effectively design Analytical solutions
based on modern technologies.

Agenda
• Background and Context
– From 1 Database to Distributed, Polyglot Persistence
Data Stores
• Data Engineering Concepts 101
– The CAP Theorem and it’s Variants
• Data Engineering Concepts 102
– Deeper into CAP
• The Data Stores You Will (Probably) Use
• The Architectures in Which You Will Participate

What’s Happened in the Last 10 Years
OLTP
Web Application Tier
OLAP
Statistical/Analytical Applications
ETL
2005

2015
OLTP
Web Application Tier
OLAP
Statistical/Analytical Applications
ETL

Innovations in Data Engineering
• 2000 – Eric Brewer’s Cap Theorem, proved in 2002
– http://en.wikipedia.org/wiki/CAP_theorem
• 2004 – Google MapReduce
– http://research.google.com/archive/mapreduce.html
• 2006 – Google Big Table
– http://static.googleusercontent.com/media/research.google.com/en/us/archive/
bigtable-osdi06.pdf
• 2007 – Amazon Dynamo
– http://www.allthingsdistributed.com/files/amazon-dynamo-sosp2007.pdf
• 2008 – Polyglot Persistence
– https://www.altamiracorp.com/blog/employee-posts/polyglot-persistence
• 2009 – NoSQL (in modern terms) Introduced
– http://en.wikipedia.org/wiki/NoSQL
• 2012 – Berkley Spark
– https://amplab.cs.berkeley.edu/wp-content/uploads/2012/01/nsdi_spark.pdf
• …

Today
F1 F2 F3
F4 F5 F6
F7 F8 F9
Distributed File
Systems
ETL
• Polyglot Persistence and Services Integration
are the Norm
• Data Stores are Distributed
• Centralize Data via File Systems
• Hadoop, GFS, S3, etc
• Open Source Rules
• Analytical Applications
• Python, R, Scala, Java
• Data Pipelines (not depicted)

To succeed you
must thrive in this
environment!
1 + 1 = 2 Only Sometimes

CAP Theorem (The Foundation)
It is impossible for a distributed computer system to
simultaneously provide all three of the following
guarantees:
• Consistency (all nodes see the same data at the same
time)
• Availability (a guarantee that every request receives
a response about whether it succeeded or failed)
• Partition tolerance (the system continues to operate
despite arbitrary message loss or failure of part of
the system)

Consistency
Add nodes in a system see the same data at the
same time.
V1 V1 V1 V1
V1

Availability
A guarantee that every request receives a
response about whether it succeeded or failed.
V1 V1 V1 V1
Request Response

Partition Tolerance
The system continues to operate despite arbitrary
message loss or failure of part of the system.
Graphic and following example, borrowed from here –
http://www.slideshare.net/YoavFrancis/cap-theorem-theory-implications-and-practices

CAP an Example
V2 V2V2 V1
Partition

CAP an Example
V2 V2 V2 V1
V1
Partition

In a Distributed Environment, one
must trade availability, consistency,
or partition tolerance.

Availability Techniques
Either a system is available in the face of any failure or it is not.
Leader | Follower
Leader
Follower
Follower
Peer – to - Peer
Availability Vulnerability Availability Resilient*

Data Engineering for Data Scientists

I’m biased, but to me…
Truly Available Systems MUST
BE Distributed Across
Geographical Boundaries.

Availability Technique Examples
Leader | Follower Peer Based
RDBMS (particularly sharded) Cassandra
MongoDB Riak*
Hadoop (and Ecosystem) DynamoDB
Spark S3
Most Analytical-Oriented Data Stores Favor the Leader |
Follower Approach of Availability.

Consistency Techniques
• Systems that are Leader | Follower based are
typically consistent
• Peer based, or other non Leader | Follower
based systems are vulnerable to consistency.
– These types of systems are typically called
Eventually Consistent because they do tend to
become consistent over a period of time.

Highlighted Consistency Types
Consistency Type Definition Example
Strict A shared-memory system is said to support the strict
consistency model if the value returned by a read
operation on a memory address is always the same as the
value written by the most recent write operation to that
address, irrespective of the locations of the processes
performing the read and write operations. That is, all
writes instantaneously become visible to all processes.
Sequential
(all nodes appear to see
the same order)
The result of any execution is the same as if the (read and
write) operations by all processes on the data store were
executed in some sequential order and the operations of
each individual process appear in this sequence in the
order specified by its program.
Linearizable
(also known as atomic
consistency)
An execution is linearizable if each operation taking place
in linearizable order by placing a point between its begin
time and its end time and guarantees sequential
consistency.
Casual
(order may not be
observed)
Writes that are potentially causally related must be seen
by all processes in the same order. Concurrent writes may
be seen in a different order on different machines.
For more, go here - http://en.wikipedia.org/wiki/Consistency_model
And here - http://en.wikipedia.org/wiki/Linearizability

Highlighted Consistency Protocols
for Eventually Consistent Systems
Protocol Definition
CRDT
(Convergent Replicated Data
Types)
Used to enable abstract functionality in EC Systems. sets, lists,
counters that require additional functionality to ensure they are
accurate in eventually consistent distributed system.
https://vimeo.com/43903960
CRDT – Last Write Win Implementation of CRDT where timestamps are stored in cell
values and the system only returns the replica with the latest
timestamp.
CRDT – Vector Clocks Implementation of CRDT where the system stores and returns a
merged set of all writes. Typically requires a read-before-write
style operation.
Paxos
(2 Phase Commits)
Used to provide strong consistency in an EC system at the cost of
performance for the transaction. The coordinator gets agreement
from participants that the coordinator’s message will be the only
accepted mutation during the operation. Typically require 4 RTT’s
RAMP New Theoretical protocol to provide strong consistency, like Paxos,
at half or better the cost. Writes typically take 2 RTTs and reads
typically take 1-2 RTTs.

Partition Tolerance
• Technically, Partition Tolerance relates to
networking, but it is vague.
• Technically, if the System can withstand a
network partition, then it is tolerant to
Partition.
Note: My interpretation of Partition Tolerance is
controversial as the CAP Theorem is very vague on the
meaning of “Working” when defining Partition
Tolerance.

Trade Offs
In practicality, each “service” chooses to trade
Availability for Consistency.
F1 F2 F3
F4 F5 F6
F7 F8 F9
Lets Say F1, F3, F5, F6, F9 are Leader |
Follower based
Lets Say F2, F4, F7, F8 are Peer based
What does this mean?

Systems by CAP Classification
AP CP AC
Cassandra Hadoop and EcoSystem RDBMS
Riak Spark Vertica
Dynamo Mongo
CouchDB Couchbase

Can your Analytical solution tolerate data sourced from
an non always available system, i.e. holes in data?
Can your Analytical solution tolerate data sourced from
an eventually consistent system, i.e. different results at
different times?
What if your data comes from both types of systems?
What if you are processing your data on one or the other
system?
Practical CAP

Reference Architectures
Here are some views of “standard” architectures
• Lambda
• Kappa
• “Data Lake”

Lambda
http://lambda-architecture.net/

Kappa
Simplified Lambda, where all data is streamed
http://www.kappa-architecture.com/
http://radar.oreilly.com/2014/07/questioning-the-lambda-architecture.html

Data Lake
My view – Data Lake is Marketicture
• Pivotal - http://www.informationweek.com/big-
data/software-platforms/pivotal-subscription-points-to-real-
value-in-big-data/d/d-id/1174110
• Hortonworks -
http://www.slideshare.net/hortonworks/modern-data-
architecture-for-a-data-lake-with-informatica-and-
hortonworks-data-platform
• Cloudera - http://vision.cloudera.com/the-enterprise-data-
hub/
http://www.gartner.com/newsroom/id/2809117

Summary
• Data Scientists will require working knowledge
of Data Engineering
• CAP
– Consistency
– Availability
– Partition Tolerance
• Architectures in the New World

Data Engineering for Data Scientists

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