In this slidecast, Dev Patel and Poulomi Damany from BitYota describe the company's Data Warehouse Service. "Our vision is to make data and analytics accessible to all. There's a revolution underway and we're taking sides. We want to create a data platform that enables everyone -- from data scientists and engineers to SQL savvy analysts, business and product users, to understand their data, to build better products/services, and create new avenues of growth and productivity." Watch the video presentation: http://wp.me/p3RLEV-1SS

1. BitYota – data warehouse service !
An overview!
Dev Patel, CEO!
© 2014

2. BitYota: Who we are!
Problem
Today’s
big
data
analy/cs
is
either
a
‘Big
Cost’
or
a
‘Big
Headache’
or
both
for
companies
of
all
sizes.
Users
have
to
learn
new
skills
and
CEOs
need
to
buy
uniquely
engineered,
prohibi/vely
expensive
systems.
Solu+on
BitYota
offers
a
be-er
alterna0ve:
A
Data
Warehouse
Service
for
Big
Data
analy0cs.
This
PaaS
offering
takes
away
both
big
cost
and
big
headache,
making
analy0cs
accessible
to
everyone
at
scale,
with
no
compromise
on
func0onality
or
service
levels.
Customers
Mobile
apps,
E/commerce,
Adver/sing/Marke/ng,
Games
Founded
Sep
2011
by
data
experts:
Dev
Patel,
Harmeek
Bedi
and
Soren
Riise.
Background
Opportunity
Team
Company
has
raised
$12M
through
Seed
&
Series
A
from
Globespan
Capital,
Social+Capital
Partnership,
Dawn
Capital,
Andreessen
Horowitz,
Crosslink
Capital,
Morado
Ventures,
&
individual
investors
Maynard
Webb,
Graham
Summers,
Jerry
Yang
and
Sharmila
Mulligan.
Companies
are
increasingly
looking
to
gain
insights
from
their
data
via
analy/cs.
Analy/cs
for
big
data
in
the
cloud
is
BitYota’s
opportunity.
Management:
Dev
Patel,
CEO;
Harmeek
Singh
Bedi,
CTO;
Soren
Riise,
Chief
Cloud
Service;
Poulomi
Damany,
VP
Product.
Its
core
team
has
35+
years
of
big
data
experience
at
Yahoo!,
Oracle,
Veritas/
Symantec,
Informix,
BMC,
Kabira/Tibco,
and
Microso_.

3. Does This Sound Like You?!!
OR
You’re a company
that just launched
.. And you need some
critical insights for what’s
next
OR
Your data infrastructure can’t scale
.. And you can’t spare
any more engineers or
money to maintain it
You’re a
company that
just launched
…
You have lots of data
in multiple silos
.. And it takes too long for
your analysts to get answers
Today’s Big Data = Big Cost/Big Headache!
3

4. What questions do you want answered?!
How can I combine social profiles, in-app
purchases, and event stream data?
Who are my
best users?
How do I
increase
engagement?
Why is the
new app
version
crashing?
Access
patterns by
OS/ device?
What’s my ROI on my marketing spend?
Where should I be spending more/less $$$?
4

5. BitYota: Data warehouse for next gen data!
Variety, semistructured data
Velocity, analytics on
fresh data
Velocity, “fast”
analytics
No translation to
structure & No
data modeling
Continuous extract of
changing data from
MongoDB
MPP architecture
– scale with
Compute
Cloud, easy set up
with Burst capacity
Agility & Time-toMarket
No CAPEX & low
OPEX
Elastic scale up/
down
Integration into
SQL/BI ecosystem
Managed Service &
Pay per Use
5

6. BitYota is focused on use cases where …!
Customers want:!
1. Analytics over data from multiple sources!
2. Migrate analytics from on-premise to Cloud !
3. Analytics on data from single source NoSQL or
relational transactional systems !
4. Analytics on “fresh data” !
6

7. Markets for BitYota!
Companies in!
• Advertising/Marketing!
• Social Media!
• SaaS!
• Games & Entertainment!
• E-commerce!
• Communication & Productivity !
7

8. BitYota focused in new Big Data Analytics!
• Data from Multiple sources
in Multiple formats !
User profiles
Social data
Server Logs
!
!
Volume
Variety
Velocity
!
Semi-structured data types (JSON,
XML), Data types for new applications –
timestamp, IP, location, etc!
Table Layout – row and column, on disk,
memory, external tables !
• Fast time to analytics !
!
Load and explore directly, not dependent
on slow & fragile ETL!
• Interactive analytics !
!
Inventory
Deploy in a heterogeneous environment;
scale out; scale storage & compute
independently!
• Flexible Storage !
Sales Orders/
Returns
Website Views
& Clicks
• Cost effective, elastic capacity!
Use ANSI SQL directly on new data
types. Leverage existing BI tools!
8

9. Business Analytics on data from MongoDB!
Mobile/Web
Apps
Primary
shard
SQL over JSON, and
access from BI tools
BitYota
Cluster
Secondary
shards
Compute
nodes
Mongo
dump
Oplog
Tail
Load
BitYota
Extract
Tool
BSON,
JSON
Extract
Load
Data
nodes
Schedule incremental extract and load
MongoDB extract format (BSON)
Transform
&
Analyze
Joins across collections
SQL over JSON, UDFs
Transforms into Cols for performance
Views for BI tool
9

10. Process to Load Data into BitYota!
SOURCE
JSON
DATA
LOAD
DATA
"session":[{
"u":"8927ABBCD2873CCD",
"v":"1.0",
"uid","TheTestUser1",
"dv":"Apple
iPhone
3GS",
"t":200
}
CREATE
TABLE
session(
jdoc
JSON
)
1-­‐+me
setup
• Scheduled
Load
• Schema
auto-­‐discovered
• Table
auto-­‐created
ANALYZE
DATA
OPTIMIZE
DESIGN
SELECT
jdoc-­‐>'u’,
jdoc-­‐>’t’
FROM
session;
CREATE
TABLE
session_cols
(
u
TEXT,
t
INT,
origjdoc
JSON
)
PARTITION
BY
RANGE
(t)
(PARTITION
VALUES
('0'),
PARTITION
VALUES
('50'),
PARTITION
VALUES
('200')
)
COLUMNSTORE
STORAGE
(SEGMENTSIZE
13102
TABLESIZE
200000);
INSERT
INTO
session_cols
SELECT
jdoc-­‐>'u',
( jdoc-­‐>'t')::int8,
jdoc
FROM
session;;
Change
MongoDB
JSON
doc
structure
any/me
=
NO
extra
downstream
effort
needed
10

11. As a Service!
• Launch cluster in minutes !
• Removes the ‘headache’ of database management!
• No hardware, no software installation & upgrades; no licenses !
• Available on AWS & Rackspace!
11

12. Recap!
• BitYota is a Cloud based Data Warehouse Service for
Big Data Analytics.!
• Its core attributes are:!
• 100% Service oriented!
• Analytics on data from multiple sources/formats!
• Analytics on “fresh” data !
• Customers are gaining deep insights on their business
operations!
• Customers in Games, Mobile apps, advertising/
marketing, e/commerce!
12