This document provides an overview and strategy for big and fast data initiatives in 2017. It discusses the data landscape including volume, velocity, variety and validity. It evaluates different data platform technologies and outlines requirements. The vision is described as "Business Insights at the Speed of Light". The strategy focuses on speed and leveraging key technologies like Spark. A roadmap with initiatives around insights, infrastructure, ingestion and big BI is presented. High level architectures for streaming and data flow are shown. Finally, data preparation vendors are compared.

1. Big and Fast Data Strategy 2017
Jonathan Raspaud
AVP - Big Data Architecture
February, 2017

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Executive Summary
2017 Data Landscape
Vision
Strategy
Roadmap
Key Initiatives
High Level Architecture
High Level Data Flow
Data Validity Vendor Comparison

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About Jonathan Raspaud:
1998 2000
2006
2011
2012
2017
AVP-Big Data Architecture
Senior Principal Data Architect
Mobility Practice Lead
Manager Business Intelligence
Datawarehouse EngineerSoftware Engineer
Software Engineer
Teamlog
1999
IAE Grenoble
Master of Science in Management
of Information Systems
1997

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2017 Data Landscape (1): The Four V’s
Data Volume:
Billions of Rows
Data Validity:
Format
Process
Data Velocity:
Real time
Streaming
Weblogs
Clickstreams
IoT
Text
Call Center
Chat
Social
Sensors
Markets
Networks
Transportation
IoT
Social
Data Variety:
Structured
Semi-
Structured
Unstructured

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2017 Data Landscape (2): Legacy RDBMS Databases are
poor at:
• Scalability,
• Fast Streaming Data,
• Unstructured Data,
• Schema Flexibility,
• Search,

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2017 Data Landscape (3): MPP/Column-Store Databases:
The Good: The Bad:
SQL based, wide capability with
BI tools
Need to move the data from
operational systems
Good Performance Data loses Freshness
Full support for aggregation and
ad hoc filtering
Ultimate scale limitations
Hard to adapt schema
Can be expensive

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2017 Data Landscape (4): Hadoop:
The Good: The Bad:
Distributed storage and
processing of massive data sets
SQL interfaces are improving but
still not speed-of-thought
Low-cost clusters built from
commodity
hardware

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2017 Data Landscape (5): NoSQL Databases:
The Good: The Bad:
Storage and retrieval of data
which is modeled in means other
than the tabular relations used in
RDBMS
Traditional BI tools lack native
compatibility
More and more application
developers choose NoSQL
Databases as operational
databases
Not optimized for analytic queries
Scalability; schema-less
flexibility, and fast response time
for short-request queries
Some don’t support aggregation
or ad hoc filtering on arbitrary
field

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2017 Data Landscape (6): Search Databases:
The Good: The Bad:
Using a search index technology
is a great way to enable access to
big data in the enterprise
Lacks SQL interface – traditional
BI tools incompatibility
Deliver fast access to
unstructured or semi-structured
information: blog posts and
comments, customer product
reviews, machine logs, JSON
scripts…
Native APIs required to access
data
Very effective with structured
data too

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2017 Data Landscape (7): Cloud Big Data Stores:
The Good: The Bad:
Storing massive amounts of data
in the cloud
Traditional BI tools lack
performance optimized native
integration
Low cost
Easy to manage
Range of storage options: file
system, SQL database, Hadoop,
Spark…

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2017 Data Landscape (8): Fast Data:
The Good: The Bad:
Fast inserts/updates Traditional BI tools lack
integration
Fast analytics Traditional BI tools are not
architected for streaming data
Limited or Lacks SQL interface

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2017 Data Landscape (9): Conclusion
• Legacy BI not designed for Modern Data:
• Hard to use: designed in an age of specialized skills
– Focus on the power user
– Complicated workbench interfaces
– Require SQL coding quickly
• Cannot Scale: deployed on desktops or monolithic servers
– Limited user scalability
– Poor performance
– Not built for embedding in other applications
• Performance Problems: designed for relational data only
– Loss of functionality
– Poor performance
– Limited data scalability

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Modern Big and Fast Data Platform Requirements: 5 V’s
Data Requirement
Volume 1. Immediate visualization & interaction regardless of
size of data
2. Don’t move or copy data
Variety 1. Support a broad range of modern sources without
lock-in
2. Blend multi-source data on-the-fly
3. Extensible data connectors for different types of data
Velocity 1. Support fast data (streaming)
2. Integrate streaming & historical data in a single view
Veracity 1. Master Data Management
2. Definitions
Value 1. Business Insight, Monetization, Optimization, New
Customers

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Vision (Example):
“Business Insights at the Speed of Light”.

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Strategy (Example):
• Speed is our main strategic asset,
• Spark is the engine that powers all our data initiatives,
• Set the context and get out of the way,
• Build Proof of Concepts ready for Production,
• Public Cloud only,
• Leverage Key Vendors as needed: Paxata, Cloudera, ZoomData, Google,
Amazon…

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Roadmap (Example):
Insights
Infrastructure
Ingestion
Big BI
Strategy
Procurement
Q2 Q3
2017
Q1
Lambda
Architecture
Deskside
People
WorkDay
Oracle
FinancialServiceNow
Human
Resource
Q4
2018
Telecom
TEM
From BI
To Big Data
IOT
Real Time
Data Science
Training
EDL
Mobile BI
Q1
Data ScienceReal Time Self Healing AI Aware
Transportation
Real Time ML
ZoomData PrestoDB Paxata IBM
DS Platform

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Enterprise Data Lake – Ingestion (Example):
Q1 Q2 Q3
Data Ingestion
• Snapchat
Other Source Systems
• Billz
• Workday
“Near Real Time”
Update (Spark batch)
• Instagram
More than once per
day update
• Pinterest
Data Ingestion
• Facebook ✅
• Twitter ✅
• Pinterest ✅
• Youtube ✅
• Instagram ✅
• DCM ✅
Other Source Systems
• Adobe Analytics
• Salesforce Marketing
Near Real Time Update
(Spark Batch)
• Facebook
Data Ingestion
• LinkedIn ✅
• Google Maps ✅
• Waze
Other Source Systems
• GSA
• Salesforce✅
“Near Real Time” Update
(Spark batch)
• Youtube ✅
Data Ingestion
• Wikipedia
• STAT
Real Time Update
(Spark Streaming)
• Twitter
Q4

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Enterprise Data Lake – Infrastructure (Example):
Q1 Q2 Q3
Scalable Database for
Data Marts
• RedShift vs. BigQuery
Security
• Kerberos authentication
• Configure External Authentication for
Cloudera Manager using AD.
Cluster Scaling
DB migration for Hive
Metastore.
Configure high
availability for Hive.
Scalable Database for
Big BI Data Marts
• RedShift vs. BigQuery
Configuration Data
Base
Kafka Cluster
Cloudera Upgrade ✅
Disaster Recovery ✅
Configuration Data Base ✅
Kafka Cluster
• (Test Cluster complete Sprint 190 ✅)
Subnet Migration
Cluster resource upgrade –
scaled out ✅
Q4
Security
• Configure Sentry in Production cluster
Configure external
database for Cloudera
Manager
Hue DB migration to
External Database

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Key Initiatives (Example):
Focus on high impact/high dollar,
Machine Learning/Deep Learning,
Big BI,
Big MDM,

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High Level Streaming Architecture (Example):
Grid Data Visualization
& Reporting
Big and Fast Data Stream and Data Store
PivotReal Time Pipeline
Batch Pipeline
Device Events

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Data Sources Data Driven
Decision
Data Visualization
and Exploration
Ingestion Big Data Store Big BI
The Enterprise Data Lake is the one source of truth for all reports
SQL
Interactive
Reporting
High Level Data Flow (Example):
Relational
Data
(CSV)
Schema Free
Nested
Data
(JSON)
Tableau, PowerBI, Looker
ODBC
JDBC

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Vendor Alteryx Paxata Trifacta
Primary
user
Technical data developer Non-technical business analyst Technical data scientist
Strengths Data integration
Data mapping
Advanced analytics
Data integration and quality
Comprehensive governance model
Centralized collaboration workbench
No coding, scripting required
Visualization
Batch processing
Weaknesses Data cleansing
Data manipulation
Ease of use
Limited enrichment today Only works with information loaded into
Hadoop
Only works with samples of data
Feedback is not in real time
Minimal data quality capabilities
Analysis Alteryx is a full stack BI
tool, and it includes a layer
of data integration
capabilities. Introducing
another BI tool (in addition
to Tableau, Qlik, Excel) is
not ideal, particularly since
it would only be able to
address data migration use
cases. It overlaps with
Snaplogic which Yahoo!
already owns.
Paxata has the most robust
capabilities to address the broadest
set of data preparation use cases.
Their model for data governance is
far above anything else on the
market. They appear to also ingest
the widest range of data sources
and have the ability to scale to a
billion rows. True enterprise
capabilities for security and scale.
Trifacta is not a good fit for our users
since they are all business analysts
and it is very complex to make
changes. Also, the information for
these use cases are coming from
multiple data sources, many of which
are not Hadoop. Trifacta does not have
the data quality capabilities needed for
the broadest number of use cases.
Big and Fast Data Validity: Vendor Comparison