Google cloud big data summit master gcp big data summit la - 10-20-2015

Big Data Summit - Google Venice
October 20, 2015

Agenda
2:00 – 2:30
2:30 – 3:30
3:30 - 4:00
4:00 - 4:30
4:30 - 5:00
5:00 - 6:00
Registration & Welcome
GCP Big Data Overview by Rohit Khare, Google PM
Customer Stories - BlueCava & Pixalate
Panel Discussion, Q&A
Partner Story, Magnus Unum
Reception & Networking

3
● Parking behind Chaya Restaurant on Navy Street
● Visitor badges
● Washrooms
● Beverage & food service
● Wireless access “GoogleGuest”
Logistics
3

01 GCP Big Data Overview
Rohit Khare, Google PM
deck

Confidential & ProprietaryGoogle Cloud Platform 5
Build. Store. Analyze.
Google Cloud Platform for Big Data
Focus on insights, not infrastructure
Big Data Summit, Los Angeles — October 20, 2015
Rohit Khare, Google Cloud Product Manager
William Vambenepe, Lead Product Manager for Big Data

Google cloud big data summit master gcp big data summit la - 10-20-2015

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IaaS PaaS SaaS
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Google Cloud Platform
Cloud Computing

Enterprise Cloud Platform market will exceed $43B globally by 2018.
2013

Affordable
Capacity
The decreasing cost of storage
enables virtually unlimited
storage in the cloud. $600 can
buy enough storage for the
world’s music.
(Source: McKinsey Global Institute May 2011)
Computing as a utility is now
available for easy purchase,
provided from massively
efficient data centers.
(Source: Nicholas Carr, The Big Switch, 2008)
The internet allows for a model
of real-time access to new
innovation, information and
applications from a wide range
of devices.
IT Trends
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computing
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access

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Cloud Computing Patterns

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cloud
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powerful, highest quality cloud
infrastructure on the planet.

Cloud Platform is built on the same
infrastructure that powers Google.

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ColossusMapReduce
SpannerBig Table
Dremel
GFS
Google Innovations in Software
2013 2014
Dataflow
Kubernetes

A look inside

NetworkingCompute Big Data Management Storage Mobile
Developer
Tools

ManagementNetworkingCompute Big Data Storage Mobile
Developer
Tools
Compute
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Developer
Tools
Storage
Cloud
Storage
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SQL
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Datastore
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BigTable

NoSQL SQL Blob Block
Easy-to-use storage options

Cloud Storage

Cloud Storage: Value
• Safe: Redundant storage at multiple physical
locations. OAuth and granular access controls form
strong, configurable security
• Ease of Use: Same APIs as other CGS products
• High Performance: We provide, 99.95% SLA and 24x7
phone support
• Pricing: Pay only for what you use with some of the
lowest prices in the industry

Cloud Storage: Features
• 3 storage options
○ Standard: The highest level of durability,
availability and performance
○ DRA: High level of durability, availability and
performance
○ Nearline: High performance data archiving, online
backup, and disaster recovery

Cloud Datastore

Cloud Datastore: Value
• Accessible Anywhere
• Secure Sharing
• Same High Replication Datastore Used By
App Engine Apps Today
• Equally Fast Queries For Any Sized Dataset
• Data is Replicated Across Several Data Centers
• Use From Any Application or Language
• Serving 4.5 Trillion Requests Per Month

Cloud Datastore: Features
• Auto-scale
• Schemaless Access
• SQL-like Capabilities
• Authentication That Just Works
• Fast and Easy Provisioning
• RESTful Endpoints
• ACID Transactions
• Local Development Tools
• Built-in Redundancy

Cloud SQL

Cloud SQL
• Fully managed
• Ease of Use
• Highly Reliable
• Flexible Charging
• Security, Availability, Durability
• EU and US Data Centers
• Easy Migration & Data Portability
• Control

Cloud BigTable

Developer
Tools
Big Data
Big Query
Cloud
Pub/Sub
Cloud
Dataflow

Manage the Entire Lifecycle of Big Data
Store AnalyzeProcessCapture

Manage the Entire Lifecycle of Big Data
Cloud Logs
Google App
Engine
Google Analytics
Premium
Cloud Pub/Sub
BigQuery Storage
(tables)
Cloud Bigtable
(NoSQL)
Cloud Storage
(files)
Cloud Dataflow
BigQuery Analytics
(SQL)
Capture Store Analyze
Batch
Cloud
DataStore
Process
Stream
Cloud
Monitoring
Cloud
Bigtable
Real time analytics
and Alerts
Cloud Dataflow
Cloud Dataproc

BigQuery

BigQuery: Value
● Performance: Ingest data at 100K rows/second
and process real-time queries
● Ease of use: No administration for performance
and scale
● Scale: No need to worry about growing data.
Unlimited storage with pay as you go pricing
model
● Non-technical analysts can drive queries on
massive datasets using BI tools

BigQuery: Features
● Interactive query performance: Query multi-
terabyte datasets in an ad hoc manner
● SQL: Familiar SQL-like query syntax and intuitive
user interface
● Data mashup: Query across diverse datasets
● Highly Available: Data replication in multiple
geographies. Data is available and durable even
in the case of extreme failure modes
● Secure: Access to data is controlled using
customer-owned ACLs

Cloud Pub/Sub

Cloud Pub/Sub: Value
● Scalable, flexible, and reliable enterprise
message-oriented middleware to the cloud
● Provides asynchronous messaging, allowing
secure and highly available communication
between independently written applications
● Delivers low-latency, durable messaging that
helps developers quickly integrate systems
hosted on the Google Cloud Platform and
externally

Cloud Pub/Sub: Features
• Unified messaging: Durability and low-latency
delivery in a single product
• Global presence: Connect services located
anywhere in the world
• Flexible delivery options: Both push- and pull-
style subscriptions supported
• Data reliability: Replicated storage and
guaranteed at-least-once message delivery
• Data security and protection: Encryption of data
on the wire and at rest

Cloud Dataflow

Cloud Dataflow: Value
• Reduce cost of processing large datasets
• Save time: Automatically optimizes data-centric
pipeline code by collapsing multiple logical passes
into a single execution pass
• Increase efficiencies: Fully manages the lifecycle of
required compute resources
• Simple: Dataflow makes it easy to write data-
processing pipelines that incorporate both batch
and stream-processing capabilities and is
language-agnostic

Cloud Dataflow: Features
• Unified programming model for both batch and
stream-based data analysis
• Managed scaling: Manages the lifecycle of
required compute resources
• Reliable & consistent processing: Built-in support
for fault-tolerant execution
• Monitoring: Provides lifecycle statistics including
in flight information like real time pipeline
throughput, real time step lag and real time
worker log inspection

Cloud Dataproc

Programming
Resource
provisioning
Performance
tuning
Monitoring
Reliability
Deployment &
configuration
Handling
growing scale
Utilization
improvements
Typical Big Data Processing
Focus on Insight,
Not infrastructure
Programming
Big Data with Google
Reduce Time to Understanding

Continuously accommodating greater data
volumes and new data sources
Capture and store all data for all business
functions
Complexity of building and maintaining a Big
Data system with consistent ease of use
Reducing the time from data collection to action
Managing the cost of the data platform
1
2
3
4
Hurdles to innovate and iterate with Big Data
5
Keep system reliables/running
Keep your data secure
Collaboration within or across organizations7
8
9
6
Traditional Big Data = Big Problems

Google BigQuery
Google Compute and APP Engine Scalable VMs
TBs of Data
Process in seconds
Data Collection
ETL
Raw Data Storage
Aggregation
Analytics Storage
Visualization
Google Cloud Storage
1
2
3
4
5
6
Interactive
Dashboards + apps
BI tools
Google
Spreadsheets
1
2
Collection
Transformation
Data processing
Cleansing4
Serve Analytics
Raw Data Storage
BigQuery Staging
3 BigQuery Aggregate
Staging
Raw Data Storage AdHoc Queries
REST API
5
6
Google Confidential

Google confidential │ Do not distribute
Overview:
Data to process: Data in the Consolidated Audit Trail (CAT).
A data repository of all equities and options orders, quotes,
and events
Challenges:
How to process the CAT and organize 100 billion market
events into an “order lifecycle” in a 4 hour window
Store 6 years (~30PB) of data
Cloud Bigtable to process and run queries
and tolerate volume increases
6 BILLION
MARKET EVENTS
WRITTEN PER HOUR
1.7 GIGs
PER SECOND
PER HOUR
6 TBs
10 BN
WRITTEN
PER HOUR BURSTS
1.7 GIGABYTES
PER SECOND
10 TERABYTES
PER HOUR

Google confidential │ Do not distribute
Overview:
Data to process: standard game KPIs, marketing data, custom game insight
Several dozen gigabytes of raw logs per day
Challenges:
Struggled to process large volume of data
Long delays between triggering logs and querying data; problematic for games
running live events
Issues controlling permissions
Long-running queries, clunky analysis
Overview:
Data to process: Standard game KPIs, marketing data,
custom game insight
Several dozen gigabytes of raw logs per day
Challenges:
Struggled to process large volume of data
Long delays between triggering logs and querying data; problematic
for games running live events
Issues controlling permissions
Long-running queries, clunky analysis
“BigQuery has helped us focus on actually using data instead
of exhausting ourselves just trying to get to the data.”
CRUNCH
150GIGS OF DATA IN
15 SECONDS
INSTANT
LOG INGESTION
SCALE
WITHOUT
CLOGGING
THE SYSTEM
F L E X I B I L I T Y
ON PERMISSION
CONTROLS

700million
“App Engine enabled us to focus on developing the
application. We wouldn’t have gotten here without the
ease of development that App Engine gave us.”
Bobby Murphy, CTO
Snapchat sends
photos and videos each day Google App Engine
scaled seamlessly
during growth to
millions of users
Small team is able
to innovate quickly
and expand
globally

Big Data Partner Ecosystem
Chartio

02 Customer Story - BlueCava
Reza Qorbani, CTO
deck

BLUECAVA, INC. / 2015BLUECAVA, INC. / 2015 PAGE 59
CROSS SCREEN STARTS HERE

BLUECAVA, INC. / 2015
BLUECAVA
Business / Product / Challenges
PAGE 60

INTRODUCTION
PAGE 61
Reza Qorbani
CTO @ BlueCava
• Work with Google Big Data Team in past 1.5 years
• Move from 100% Private Cloud to Hybrid Environment
• Deep Integration with Big Query
Email
reza.qorbani@bluecava.
com
Twitter
@qorban
i

DISPLAYMOBILEVIDEOEXCHANGESOCIAL
Real-time
Intelligence
ABOUT – BlueCava
PAGE 62
VALIDATION
DEMOGRAP
H
LOCATIONEXCHANGECOVERAGE
Association
Graph
DataTech
Platforms
AdTech
PlatformsOpen Network that Optimizes
Cross-Screen Marketing
MARTECH PLATFORMS & SERVICES

ABOUT – Association Graph
PAGE 63
House
Hold
Consumer B Consumer A Consumer C
IDFA APN BCID

ABOUT – Coverage
PAGE 64
100M / House Holds
240M / Consumers
600M / Devices

ABOUT – Volume
PAGE 65
5 TB Daily
Daily RAW Logs
250k req/sec
From Partners and Exchanges
1.3 Petabyte
Total Storage
25 Billion IDs
Including our Partner IDs

ABOUT – Challenge
PAGE 66
− Generate data for customers
− Multiple extraction at time
− Keep data for months
− Highly Available
− Easily run Ad-Hoc queries
− Handle lots of POCs
− Flexible to Change
− Unified Data Store
− Bandwidth Cost
− Storage Cost
− Infrastructure Cost
− Operation Cost
Cost Flexibility Delivery

ARCHITECTURE
BlueCava Platform Overview / Before / Now / Future!
PAGE 67

ARCHITECTURE – BlueCava Platform Overview
PAGE 68
CORE INTERNAL CUSTOMER
PLATFORM
EDGEX BIDDER OPERATIONS QUALITY API PORTAL
METADATA PREPARE
LOGGING AGGREGATE
FILTER
DETECTOR
TRANSFER / PREPARE PROCESS / ASSOCIATION ANALYZE / REPORT
AG AE DB

ARCHITECTURE – Before
PAGE 69
WEST (IRVINE) EAST (ASHBURN)
CORECORECUSTOMERINTERNAL
PLATFORM
BACKUP / DR
Geographic Load Balancing
XDC NET

ARCHITECTURE – Before / Challenges
PAGE 70
Cost
Estimate of $1.5M upfront to scale up
High Monthly Bandwidth cost
Need to Extend Operation team
Scalability
Performance
Storage
Complexity
Resource Limitations
Datacenter Issue with Traffic spikes
Need to scale down after POC finishes
Some processes took more than a day
Customer delivery takes 5-10 hours
Ad-Hoc queries taking hours
Need more historical data to increase quality
Need to keep customer data for months
Deliver large amount of data to customers
Simple Tasks Require Data Engineering Expertise
Customizing Data Output was hard
Data Scientists need meaningful data set
QA/Dev Environment Separation
Ad-Hoc queries create issue for production

ARCHITECTURE – Before / Solution
PAGE 71
Big Query
▪ Big Data as a Service
▪ Extremely cost effective for our use-case
▪ Support Hierarchical Data Model
▪ Extremely fast
▪ Query using SQL
▪ Solve most of our Big Data challenges
▪ Fraction of cost (It was Unbelievable)
▪ Customer Delivery in Seconds!!!
▪ We dropped Delivery Spark Cluster (10 nodes)
▪ We dropped Ad-Hoc Hadoop Cluster (100x nodes)
▪ Offload ALL Customer Facing Jobs
▪ Only 2 Sprints Development (6 Weeks)

PAGE 72
Cloud Storage
▪ Nice integration with Big Query
▪ No file size limit like S3
▪ HDFS Integration using Hadoop Connector
▪ Seamless Cost Saving: DRA and Nearline
▪ Solved most of our Storage challenges
▪ Simplified our file delivery
▪ Extremely competitive pricing
▪ No need for Backup ☺

PAGE 73
Compute Engine
▪ Great Sustained Pricing
▪ No need for long-term contract
▪ Simple CLI for Automation
▪ BDUtil Library for Hadoop
▪ Elastic Environment which saved us on Cost
▪ 100+ nodes Hadoop under 6 minutes
▪ Use as On-Demand Resource as needed
▪ Stop purchasing more hardware!

ARCHITECTURE – Now
PAGE 74
WEST (IRVINE) Google Cloud Platform
CORE CUSTOMERINTERNAL
PLATFORM
Cloud Storage
Simple DNS
Interconnect Big Query

ARCHITECTURE – Future!
PAGE 75
Cost
Move all in Cloud
Scalability
World-wide Coverage
Performance
Real-time Association
Simplify
Data Science Lab
Container Engine Dataproc Dataflow Datalab

ARCHITECTURE – Future!
PAGE 76
CORE REALTIME PROCESS
ASSOCIATION GRAPH
QUERY
LAB
STORAGE
INTERNAL
CUSTOMER
BATCH PROCESS

BLUECAVA, INC. / 2015 PAGE 77
THANK YOU

02 Customer Story - Pixalate
Amin Bandeali, CoFounder & CTO
deck

@
Amin Bandeali, Founder & CTO
Pixalate, Inc.

Agenda
● What is Pixalate?
● My Role @ Pixalate
● Pixalate Breadth and Depth
● What is Ad Fraud and why is it important to solve?
● Challenges
● Ad Fraud
● Real World BigQuery Use Cases
● Conclusion

Our Mission
To Rate the Whole Internet…
...and YES we also see what Google doesn’t see!

What is Pixalate?
Pixalate is a defacto Ratings Standard for Programmatic Advertising.
SellerTrustIndex.com

My Role @ Pixalate
● Co-Founder, CTO and Solution Architect
● Real-Time Data Junkie - Contributed to Apache Hadoop Project
● Largest AWS DynamoDB user upon launch - not using it anymore!
● Largest AWS SQS user - not using it anymore!
● Pixalate backend runs Java, NodeJS, Redis, Solr, S3 and BigQuery
● Denied using 25000 free hours of AWS Redshift!
● 70% of Pixalate technology runs on AWS -- 30% on BigQuery
● We move 2TB of data from AWS to Google Storage just for BigQuery

Challenges
Process 1+ Trillion Ad Transactions Data/month
Processing Upto 3 PB/month
Analyze Massive amounts of Data to detect fraud
Create customized reports with NO engineering support!
Close to 1 Trillion rows of data in BigQuery

Ad Fraud against
AdMob and
MacDonlds

Our Realtime Fraud Map
http://www.pixalate.com/map

What’s wrong with this data?

A day in the life of Data Science Team
● An Account Manager requests the data science team for customized report for
a client that measures some specific metrics for the last 6 months of their data.
● Solution 1: AWS EMR - Boring and takes Hours!
○ The Big data engineers will execute an EMR (Hive) job that extracts the data and creates the
report
● Solution 2: BigQuery - Fun and takes Seconds!
○ The data science team implements a usually complex query that calculates all the metrics in
SQL
○ BigQuery will process a couple of TB of data and create the report in few seconds.

Bypassing the Engineers!
● We need to expand a list of 500,000 network addresses in CIDR format (e.g.
128.0.0.1/24) to regular IP format and use them in client reports
● Solution 1: Java
○ provide the Java engineers with the requirements
○ wait for implementation completion
○ wait for UAT and Production push
○ store the data in a database
■ total time ~3 workdays (in Startup Timezone)
● Solution 2: BigQuery
○ the data science team writes a query with 25+ table JOINs and UNIONS that takes care of the
expansion in a clean, easy to test way, and runs it in BigQuery
■ total time ~3 hours

From Waste Picking to Innovation
● The amount of digital data in the universe is growing at
an exponential rate, doubling every two years, and
changing how we live in the world.
○ YET only .5% of that data is analyzed!
● If you can’t mine these data easily and extract semantics,
○ then how is data collection different than waste-picking???
● BigQuery enables Innovation
○ It breaks the dependency between data scientists and big-data
engineers
○ Now data scientists can write complex queries and analyse massive
amounts of data without the need of any backend coding (e.g. Java),
or some other big data framework
○ It enables the deep understanding of complex data and their

Cost reduction using BigQuery
● Complex data processing pipelines impose a new cost optimization
challenge
● Main questions to be answered:
○ Where do I store the data I collect?
○ Where/How do I aggregate the data I collect?
○ How do I enhance the data I collect with other metadata?
○ How do I process the data collected?
■ such that the overall cost is minimized??
● BigQuery can HELP!

BigQuery Health Monitoring Using
BigQuery

But Wait!
Here’s the real benefit...

Zero Cost Queries Over Petabytes!
● How can you query PETABYTES of historical data and create time series to
detect traffic anomalies (e.g. network failures, etc)?
● BigQuery Zero Cost queries (a.k.a. table metadata)
○ can give you the big picture regarding table’s data health
■ within seconds
■ without having to run any costly queries
suspicious activity

Big Query Success is
all about the
Architecture
Spend a LOT of time on Table
Schemas (hint: keep them flat)

Learnings
● BigQuery has its gotchas!
○ The wrong Sharding strategy can slow you down
○ Know your Quotas well -- they will haunt you!
○ Balance the table JOINs appropriately
○ Don’t use ORDER BY unless it’s mandatory
○ Avoid “SELECT *” queries on “fat” tables over long time ranges
● Secret recipe
○ push as much complexity as possible to BigQuery using advanced queries
■ usually > 100 lines of SQL code
○ use backend languages (e.g. Java) to simply orchestrate the data pipeline
○ don’t be scared of data duplication -- storage cost is much cheaper than analysis cost!

Q&A
Amin Bandeali
p: 888.749.2528
m: 714.757.9544
e: mab@pixalate.com
t: http://twitter.com/aminbandeali

Panel Q&A
Rohit Khare, GCP Big Data PM
Reza Qorbani, BlueCava CTO
Amin Bandeali, Pixalate CoFounder & CTO

04 Partner Story - Magnus Unum
Rajesh Babu, BI, Big Data & Analytics solutions Architect
Subash D'Souza, Big Data Evangelist
deck

Magnus Unum
Raj Babu & Subash D’Souza
Modern BI & Big Data platform
with Google Cloud

Magnus Unum…what we do
We are a
LA based Big Data, Data Science & Analytics
Consulting Services firm
specialized in advising our clients on Strategy, Road
Map/Blue Print, Implementation, Deployment,
Maintenance/Support/Operations for their Big Data,

• Raj Babu
• Co – Founder, Magnus Unum
• Founder, Agile iSS
• 20 years of experience in the BI & Analytics field
• Worked on numerous, very large BI migration and Integration
projects
• Subash D’Souza
• Over 10 years of experience in building scalable solutions for
various enterprise companies
• Organizer for several LA User Groups including Big Data,
Apache Spark & Apache HBase
Magnus Unum…Leadership

Magnus Unum - Key Services
• Architect, Design & Build Big Data Solutions
• Cloud Migration services for Big Data,
Analytics & BI
• Big Data Engineering & Staffing
• Big Data managed & support services
• Data Science Solutions & Services

Magnus Unum – Expertise
• On-Prem
Cloudera, Hortonworks, IBM, Pivotal & MapR
• Cloud
Google Cloud, Amazon AWS & Microsoft Azure
• Analytics/ Reporting
Tableau, MicroStrategy, SAP BO, Qlik &
Pentaho

Use Case 1 – Migrating your Data
Warehouse and BI to Google Cloud
• Capture / Migrate or Capture
• Storage / Data Management
• Data Processing
• Query/Analytics
• Data Integration
• Access Control

Use Case 2 – Google Analytics detailed
analysis
• Limitation in Google Analytics daily export
• More detailed analysis available as part of
Google Cloud Platform( Must have premium
access)
• Can analyze granular level details of User
Interaction on websites and aggregate the

Please reach out to us for a free
Consultation & Assessment of
your BI, Big Data & Analytics
needs
& additional $500 in GCP credits!

Q&A
Contact
Raj@MagnusUnum.com
Subash@MagnusUnum.com
@sawjd22
https://www.linkedin.com/in/sawjd

cloud.google.com/free-trial
Questions? google-cloud-sw-sales@google.com
Get $300 in credit to use for 60 days.

Google cloud big data summit master gcp big data summit la - 10-20-2015

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