A practical use case based session which covers different aspects of big data, how to go about creating analytics and how modern day dynamic cloud computing can be used to deliver big data lake based embedded analytics.

1. By: Milind Zodge
Big Data to
Analytics

2. Agenda
2
Big Data01
Big Data Forecast02
Data Analytics Process03
Embedded Analytics
Use Case
04
Dynamic Cloud
Computing
05
Big Data Lake06
Milind Zodge

3. Small Data vs Big Data
3
Small Data
 Low Volumes
 Batch Velocities
 Structured Varieties
Big Data
 Into Petabyte Volumes
 Real-time Velocities
 Multistructured Varieties
Vs
Milind Zodge

4. What is Big Data
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5. 3 Vs of Big Data
5
3 Vs of
Big Data
Velocity
Variety
Volume
 Terabytes
 Records
 Transactions
 Tables, files
 Batch
 Near time
 Semi structured
 Streams  Structured
 Unstructured
 Semi structured
Milind Zodge

6. Forms/ Type of Big Data
6
Structured
01
Enterprise
systems
Data
warehouses
Databases
Unstructured
02
Audio/ video
streams
Analog data
GPS tracking
information
Semi-Structured
03
Xml
E- Mail
EDI
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7. How Big is Big Data
7
Number of emails
sent every second
2.9 Million
Data consumed by
households each
day
375 Megabytes
Video upload to
YouTube every
minute
20 Hours
Data per day
processed by Google
24 Petabytes
Tweets per day
50 Million
Total minutes spent on
Facebook each month
700 Billion
Data sent and
received by mobile
internet users
1.3 Exabytes
Products ordered
on amazon per
second
72.9 Items
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8. Big Data Market Forecast
8
$58.08 B
$61.16 B
$12.25 B
$48.79 B
$54.05 B
2019
04
2020
05
2012
01
2017
02
2018
03
Milind Zodge

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12. Data Analytics Process
12
Data
Data can be stored in data lake
environment on various different
technologies
Decision
Recommendations will be
generated based on insights
which will help for decision
making
Info
From this harmonized data
analytics can be determined
which will generate information
Insight
Using the information and the
historical outcomes insights can
be formed using machine
learning algorithms
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13. Embedded Analytics
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15. 15Milind Zodge
Data

16. 16Milind Zodge
Info
Data

17. 17Milind Zodge
Info
Data
Insight

18. 18Milind Zodge
Insight
Data
Info
Decision

19. 19Milind Zodge

20. Dynamic Cloud Computing and Big Data Lake
Lambda
Function
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21. Dynamic Cloud Computing and Big Data Lake
Lambda
Function
21
S3 Glue
Crawler
Glue
Catalog
Redshift
Spectrum
Kenesis
Firehose
JS-Tracker
Recorder
Milind Zodge

22. Dynamic Cloud Computing and Big Data Lake
Lambda
Function
22
S3 Glue
Crawler
Glue
Catalog
Redshift
Spectrum
Kenesis
Firehose
JS-Tracker
Recorder
External Data
Lambda
Function Glue ETL S3
Milind Zodge

23. Dynamic Cloud Computing and Big Data Lake
Lambda
Function
23
S3 Glue
Crawler
Glue
Catalog
Redshift
Spectrum
Kenesis
Firehose
JS-Tracker
Recorder
External Data
Lambda
Function Glue ETL S3
Analytics
Milind Zodge

24. 24Milind Zodge

25. THANK YOU
milzod milzod@gmail.com