Recommended
Recommended
More Related Content
What's hot
What's hot (16)
Viewers also liked
Viewers also liked (20)
Similar to Trends in Big Data and the Future of Data Analytics
Similar to Trends in Big Data and the Future of Data Analytics (20)
Recently uploaded
Recently uploaded (20)
Trends in Big Data and the Future of Data Analytics
- 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
- 8. Big data ≠ Hadoop
- 9. Main players
- 10. MapR Converged Data Platform
- 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
- 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
Editor's Notes
- With the volume and velocity of IoT data an important advantage is the ability to explore the data directly, without requiring IT to set up the data This is where Apache Drill comes in. This is a SQL query engine that supports self-service data exploration without the need to predefine a schema. Drill is ANSI SQL compliant and plugs right into all of those BI tools you are accustomed to using. With Drill you simply query your data in place; there is no need to perform ETL or to move your data. After all, if you currently use business intelligence tools, you should be enabled to still use them. Data exploration…before and after…. No IT step required…
- With the volume and velocity of IoT data an important advantage is the ability to explore the data directly, without requiring IT to set up the data This is where Apache Drill comes in. This is a SQL query engine that supports self-service data exploration without the need to predefine a schema. Drill is ANSI SQL compliant and plugs right into all of those BI tools you are accustomed to using. With Drill you simply query your data in place; there is no need to perform ETL or to move your data. After all, if you currently use business intelligence tools, you should be enabled to still use them. Data exploration…before and after…. No IT step required…
- With the volume and velocity of IoT data an important advantage is the ability to explore the data directly, without requiring IT to set up the data This is where Apache Drill comes in. This is a SQL query engine that supports self-service data exploration without the need to predefine a schema. Drill is ANSI SQL compliant and plugs right into all of those BI tools you are accustomed to using. With Drill you simply query your data in place; there is no need to perform ETL or to move your data. After all, if you currently use business intelligence tools, you should be enabled to still use them. Data exploration…before and after…. No IT step required…