This document discusses trends in big data over the next 5 years. It begins with an overview of how big data has evolved from mainly transactional databases and small social media to today's landscape. It then contrasts old and new data paradigms, discussing distributed storage and main players like MapR. The document outlines the big data ecosystem and tools for ingestion, storage, analysis and presentation. It predicts that by 2025, deep learning will be empowered by big data, decision making will use AI libraries, and bots will provide more human services. The document recommends analyzing only relevant data bytes for decisions, and cautions that creativity is still more important than just big data.

1. Trends in Big Data
Selvaraaju Murugesan
https://www.linkedin.com/in/selvaraaju/

2. Overview
• Big data evolution and role of data
• Big data ecosystem and tools
• Demo of MapR cluster
• Trends in next 5 years
• My personal recommendations
• Conclusion

3. When big data was small ?
• Most of the transactional data was/(is) stored in
databases
• Social media was getting some traction
• Mobile phone penetration was very low
• Managers made decision on reports that is based on
static data (there is no live feed of data that influences
their decision at right time)

4. Big data landscape has changed !

6. Old Vs New Paradigm

7. Distributed Storage

8. Big data ≠ Hadoop

9. Main players

10. MapR Converged Data Platform

11. David vs Goliath
https://www.dezyre.com/article/cloudera-vs-hortonworks-vs-mapr-hadoop-distribution-comparison-/190

12. Ecosystem tools

13. Big data process
Ingestion Storage Analysis Presentation

14. Data Ingestion : Flume / Streamsets / Impala

15. Hive / Hue

16. Self-Service Data Exploration
Data Agility with Less IT Required
Single SQL Interface for Structured and
Semi-Structured Data
Data Exploration

17. Data Analytics – R / Spark R

18. Operationalise – Spark

20. Trends in next 5 years
• Every home will not have super computers but powerful nodes that
can do distributed computing and storage
• Analysing data and decision making will be performed by 5 year old
using standard AI libraries and cheap hardware
• Big data will empower deep learning
• Bots will try to mimic human services

21. What is after big data ?

22. Recommendation
• Big data platform can be implemented in many ways ; Hadoop is not
the only option !
• Analyse important data bytes that is relevant to make business
decisions
• Beware of cloud providers and their traps
• Data driven decision making but hunch is very important
Creativity > big data