operations then you can lower your costs, make your team more productive, scale more efficiently, and lower the risk of failure. Demandbase, creator of a targeting and personalization platform for business-to-business (B2B) companies, uses Qubole and a data lake on AWS to reduce the management complexities and costs of processing and analyzing their data. Hear how Qubole empowers Demandbase to analyze trillions of rows of structured and unstructured data in real time, making their data scientists and data engineers productive since day one.

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November 1, 2017 | 11:00 AM PT
Automating Big Data
Technologies for Faster Time-
to-Value
© 2017, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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Today’s Presenters
David Potes, Solutions Architect, Amazon Web Services
Minesh Patel, Technical Director, Qubole
Seth Myers, Senior Data Scientist, Demandbase

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Today’s Agenda
1. An overview of AWS and AWS Marketplace, with an emphasis on
AWS data lake solutions and Qubole
2. Overview of the Qubole solutions featured in our story
3. Challenges faced by Demandbase
4. The Demandbase success story with AWS and Qubole
5. Q&A/Discussion

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Learning Objectives
1. How to dramatically reduce management complexities for analytics
operations
2. How to reduce the costs of processing and analyzing data in a data
lake on AWS
3. How to operate at the scale and efficiency of a large enterprise,
with a small data team

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Introduction to Data Lake
Concepts

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Unlocking Data
Most companies and organizations are embarking on
ambitious innovation initiatives to unlock their data.
The data already exists but goes unused or is locked away
from complimentary data sets in isolated data silos.

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Enter Data Lake Architectures
Data Lake is a new and increasingly
popular architecture to store and analyze
massive volumes and heterogeneous
types of data.

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Benefits of a Data Lake – All Data in One Place
Store and analyze all of your data,
from all of your sources, in one
centralized location.
“Why is the data distributed in
many locations? Where is the
single source of truth ?”

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Benefits of a Data Lake – Quick Ingest
Quickly ingest data
without needing to force it into a
pre-defined schema.
“How can I collect data quickly
from various sources and store
it efficiently?”

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Benefits of a Data Lake – Storage vs Compute
Separating your storage and compute
allows you to scale each component as
required
“How can I scale up with the
volume of data being generated?”

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Benefits of a Data Lake – Schema on Read
“Is there a way I can apply multiple
analytics and processing frameworks
to the same data?”
A Data Lake enables ad-hoc
analysis by applying schemas
on read, not write.

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AWS Approach to Data Lake

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Amazon S3 is the Data Lake

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Designed Benefits of an Amazon S3 Data Lake
Fixed Cluster Data Lake Amazon S3 Data Lake
• Limited to only the single tool contained
on the cluster (i.e. Hadoop or data
warehouse or Cassandra, etc.). Use
cases & ecosystem tools change
rapidly
• Expensive to add nodes to add storage
capacity
• Expensive to replicate data against
node loss
• Complexity in scaling local storage
capacity
• Long refresh cycles to add additional
storage equipment
• Decouple storage and compute by
making Amazon S3 object based
storage, not a fixed tool cluster the data
lake
• Flexibility to use any and all tools in the
ecosystem. The right tool for the job
• Future proof your architecture. As new
use cases and new tools emerge you
can plug and play current best of breed.

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Why Amazon S3 for Data Lake?
Designed for 11 9s
of durability
Designed for
99.99% availability
Durable Available High performance
 Multiple upload
 Range GET
 Store as much as you need
 Scale storage and compute
independently
 No minimum usage commitments
Scalable
 Amazon EMR
 Amazon Redshift
 Amazon DynamoDB
Integrated
 Simple REST API
 AWS SDKs
 Read-after-create consistency
 Event notification
 Lifecycle policies
Easy to use

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Automating Complex Tasks
Qubole makes Big Data technologies swift and simple

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About Qubole
One of the largest cloud-
agnostic Big Data as a Service
companies
Founded by the pioneers of “big
data” @ Facebook and the
creators of Apache Hive

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Poll Question #1
What is the status of your big data initiative?

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The Vision

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Qubole Data Service
Amazon

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Autonomous Data Management

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Qubole Cloud Agents

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Total Cost Savings Among Qubole Customers in 2016
and 2017
Cluster Life
Cycle
Management
$150M
Workload-
aware
Autoscaling
$121M
Spot
Shopper
$40M
 Cluster Life Cycle Management
Savings
– Amount saved by automatically
terminating a cluster when inactive
 Workload-aware Auto-scaling Saving
– Amount saved by predictively adjusting
the number of nodes to meet demand
 Spot Shopper savings
– Amount saved by utilizing SPOT
instances

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Architectural Diagram

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Poll Question #2
What big data technology are you using or evaluating?

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Why Qubole?

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Demandbase Automates With
Qubole
Demandbase provides more value for their B2B marketing customers
by automating Big Data and Machine Learning operations.

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Who is Demandbase?
Demandbase is a B2B marketing automation company that leverages
artificial intelligence to automate all aspects of the advertising, selling,
and marketing process.

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The Challenge
• Many factors determine which accounts a business should target
• Do they have a need/budget for the product?
• Are they currently in-market for the product?
• Do they have decision makers ready to buy?
• These insights must come from many different types of big datasets
• Demandbase’s previous account identification tool took multiple days to
run
• Our clients could not iterate or modify their strategies with such slow
turn-around

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The Data Used to Identify Accounts
• To determine an account’s need for the product
• We have firmographic information on 14 Million accounts
• We’ve built a knowledge graph of all accounts using NLP
technology that crawls 350 TB of web pages a month
• To determine if an account is in-market
• We track 700 Billion web interactions a year, each one mapped
to employees across all accounts
• To identify decision makers
• We are currently tracking over a 100 Million employees across
all accounts

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All 14M accounts are scored,
top 5K available to user
Keywords extracts from 700B
web interactions
Buyers at each account
identified from 100M+ contacts
Company 2
Company 3

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The Solution
• The user requests a new list of accounts with a button-
press
• 60 EC2 servers are spun up
• A machine learning algorithm is built using Spark and MLLIB
• For each of 14 Million accounts
• Information about relevant web interactions, buyers, online content, etc. fed into
machine learning model
• The model scores each account
• Top 5K accounts are pushed to web app, along with
relevant info
• From button-press to new account list – 20 minutes

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Qubole Makes This Possible
• Qubole manages all of our EC2 instances
• So far, we’ve tested 20 different concurrent models (20 X 60
EC2 servers) successfully
• Qubole keeps our costs down through dynamic bidding and
heterogeneous server clusters
• Our web app calls Qubole’s easy-to-implement Play API, which
spins up the EC2 instances and deploys our Spark job
• With Qubole taking care of the infrastructure, we could focus on
developing the machine learning
• Qubole allowed us to build a self-serve machine-learning-as-service
solution

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Next Steps and Further Information
• Try a pre-configured production-ready Qubole deployment on AWS Data Lake:
• https://aws.amazon.com/quickstart/architecture/qubole-on-data-lake-foundation/
• Buy on AWS Marketplace:
• https://aws.amazon.com/marketplace/pp/B06XX76R24
• Learn more about Qubole:
• https://www.qubole.com/products/qds-for-aws/
• Learn more about Demandbase:
• https://www.demandbase.com/technology/
• Try AWS:
• https://aws.amazon.com/

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Q & A

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Thank you!