Big Data created an incredible hype in the market by establishing a whole new level of data exploration and analysis applications. It’s not a secret anymore that in order to keep up with the pace business world is moving today, organizations need to more easily and quickly access, interpret and distribute real-time analysis from a growing array of internal and external data to achieve their corporate objectives.
With the development of SQL Server 2012 and Panorama Necto, organizations have gained the ability not only to get a simple and secure access to Big Data, but to make it useful and meaningful for further exploration and advanced analysis. The application of Business Intelligence 3.0 capabilities enables them to master the vast amounts of unstructured data in a matter of minutes.
2. We Live in a Data Explosion Era
Data explosion caused by:
Cloud computing, Rise of mobility, Globalization, Social
media, machine data, web logs, sensor networks, RFID tags
In 2012, the data overload will reach 2720 exabytes
In 2015, the data overload will reach 7910 exabytes
By 2020, the prediction is 35 Zettabytes.
3. Main trends in BI today:
Big data
It is easy to collect data, but difficult to make sense of it using
traditional BI tools.
The useful life of information has decreased, and so has the
utility of BI tools
Consumerization of Enterprise BI
Use to get everything from the internet
Access to analysis on their daily job
Social & Contextual BI
Mobile BI
4. The Challenge: “Decision Window” is
Narrowing, while data is growing..
Lots of data need to be digested quickly, or risk of
being irrelevant
Decision need to be taken faster than competitors
do
The “holy grail” is to shorten the time from Data to
Action Time
Decision
Window
5. BI 3.0 helps business users make sense of Big Data
Consumerization
Big Data BI 3.0 Of Enterprise
BI
Shortening the time from Data to Action…
7. Gartner 10 top trends for 2012
Analytics Social & Contextual Tablets
Cloud In memory Big data
* more: mobile, apps store, internet of thing, low energy servers
9. BIG Data is:
Data sets of extreme volume and variety
The “V”s
Volume – exceed physical limited of
scalability
Velocity - decision window small compared to
data change rate
Variety – many formats makes the integration
expensive.
Variability – many options for analysis
10. Market sizing and growth
In 2011 Big Data was a 9B$
business, which represents only 2%
of 407B$ spent on Enterprise IT.
By 2021 Big Data will become a
huge 86B$ business, which will
represents 11% of spent on
Enterprise IT.
10 years CAGR of 25% for BigData
compared to Enterprise IT CAGR
of only 5%
12. The best „Return on Investment‟ from
Big data is Analytics
Organizations collects huge
amount of data
The most common use case
for such data is to analyze and
find hidden
insights, correlations, etc
13. Storing and processing Big Data with
the Hadoop Framework
Map SQL
BigData HDFS Hbase Hive BI
Reduce Server
Hadoop framework BI Suite
14. +
Enterprise-Ready
• Native connector
• Enhanced Security
• Ease of deployment
• Integration to the Enterprise Data
Warehouse
• Simplified programming
• Seamless connectivity via BI tools
and Excel
15. When you have a World of Data..
You need a better Compass
16.
17. Panorama NectoTM
BI 3.0: Build Your Corporate Intelligence
ADVANCED ANALYTICS
Easy-to-use analytics for
business users and
advanced analytical
SOCIAL BI
capabilities for power users
Engaging platform for
collaborative decision
making
Self
Service
CONTEXTUAL
DISCOVERY
Intelligent BI engine that
automatically pushes relevant
insights by understanding
user’s behavior
18. Contextual discovery helps you focus
on what's relevant
INTERESTS GRAPH
- Tags
- Likes
- Visits
“This is what I like”
SOCIAL GRAPH Example: Amazon, netflix
- Friending
- Discussions
- Annotations
DATA
- Insights
- Exceptions
- Models
“This is who I know”
Example: Facebook, linkedin
19.
20. Social BI, your essential tool for Big Data
Leverage the Power of Many to get better
Insights
Work collaboratively for better and faster
insights and decision making
Create add-hoc teams to discuss subjects at
hand
Add unstructured knowledge layer
Follow other’s work
Enables you to work in an ever evolving
data generation
21. Advanced Analytics on Big Data
Slice and dice
Drill-ups, drill-downs and drill-throughs
Advanced filtering
Simple and bubble up exceptions
Formulas and parameters
Instant calculated members
Sliding filters
Interactive Charting
One click interactive reporting
Large dimension handling
Advanced MDX and DAX tools
And much, much more
22. True Self Service on Big Data
Users can create their own
Workboards or easily find the
WorkBoard they should work on
With Necto and SQL 2012 you
can easily add your own sources
of information and connect them
to the organizational data for
better and deeper insight
24. Azure and Hadoop
Windows Azure is Microsoft computing
and storage Cloud
Azure provides the Hadoop framework as
storage and processing layer and solves
the two issues of the data explosion
Moving data to Azure removes the need of
managing hardware and data maintenance
Dealing with massive data analysis using the
Azure hadoop framework
25. Public data
Analyzing organizational data help
business users understand WHAT has
happened.
Mashing up this data with public Big Data
sources can help business users
understand WHY things happened
27. The Big Data Demonstration Flow
1 2 3 4
Azure Hadoop Publish to Insights
Data Cluster SQL 2012 through
Market Necto
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45. Try it yourself
Download Necto from
http://go.panorama.com/trial-1
Request access to Hadoop on Azure
https://www.hadooponazure.com/
Use those slides to guide you through the
process, connect Necto to Hadoop and
enjoy the experience.
Notes de l'éditeur
Data sets of extreme volume and varietyThe “V”sVolume – exceed physical limited of vertical scalabilityVelocity - decision window small compared to data change rateVariety – many formats makes the integration expensive. Variability – many options for analysis
HDFS (Storage) => FilesMap Reduce (get the data), using native Java or Pig latin (Query Lang) => Map create giant hashtable, and reduce create a BLOB of mapped and reduced data. Hbase (NoSQL DB) => "Table"Hive (Metadata Store, "DW", more similar to SQL, where we plug in SQLSqoopPig is the query lang
As organizations collect more data it becomes inefficient to manage it in houseROI on hardware and maintenance is very highData retrieval and analysis becomes impossibly slow and requires a new approach to handle big data sizeMoving data to the cloud solves the 1st problemUsing Hadoop or similar framework solves the 2ndData can be spread across thousands of machinesQueries are distributed concurrently across machines, spreading the CPU load