What is the Present State of the Art Of In-Memory Analytics? презентация

What is the Present State of the Art Of In-Memory Analytics? Timo Elliott, Innovation Evangelist timoelliott.com

Disclaimer

A Bit of History

LEO: Lyon’s Electronic Office, 1951

1980s – first in-memory BI tools

1995: Windows 95 & 32-bit Architectures

Complex Event Processing

Complex Event Processing

In-Memory and The Internet of Things

“Traditional” Business Intelligence Slow Painful Expensive

It’s Like An Onion…

What Was The Problem?

Why Talk About In-Memory?

Analysts Recommend In-Memory

Companies Like Yours Are Implementing In-Memory

Database vendors are investing in in-memory

All Analytics Vendors Now Support In-Memory To Some Extent

What Is In-Memory? And why now?

What Is In-Memory?

In-Memory Databases vs. Caching

In-Memory Computing Costs have Plummeted

In-Memory Computing Costs have Plummeted

Prices Continue to Slide

In-Memory Computing

Row vs. Column Databases

Column Databases

Massively Parallel Systems

Column Stores, Compression, and Parallel Processing

“In-Chip” Processing E.g. SiSense Vector-based instructions Cache-optimized Decompression Close collaboration between in-memory software vendors and chip developers (e.g. SAP & Intel Haswell)

Massively Parallel Hardware

In-Database Processing

Move Processing to the Data

In-Database Analytics

Real-Time Data

Transactions ACID (Atomicity, Consistency, Isolation, Durability) is a set of properties that guarantee that database transactions are processed reliably.

In-Memory Enterprise Applications E.g. Microsoft SQL Server In-Memory OLTP

In-Memory Enterprise Applications E.g. SAP S/4 HANA

Hybrid Transactional Analytical Processing

Virtuous Circle of Technology

Apache Spark

Lots of Support for Spark

Spark Extensions

Persistence & Failover

Next-Generation Chips Are On Their Way NVM non-volatile memory

Scale Up

What About Scale?

Balancing Data Temperature and Costs

What Type of In-Memory Is The Right One?  

Fast-Moving Market

Hybrid vs. Pure In-Memory Tradeoffs

Top Benefits

Speed

Agility (Speed of Change)

Simplification = Lower Costs “In-memory changes the cost equation through simplification. It can help save costs on hardware and software, as well as reduce labor required for administration and development needs. Based on a composite cost model, an in-memory platform can save an organization 37% across hardware, software, and labor costs, depending on various factors.”

Lower Costs “Don’t let somebody say to you we can’t go in-memory because it’s so much more money. Acquisition costs may be higher. If you calculate out a TCO, it’s going to be less.” Donald Feinberg, Gartner

New, Simpler Infrastructures and Business Models

Conclusion

Myths & Facts

Business Impact of In-Memory Computing Reducing applications running cost via data base/legacy applications offloading Improving transactional applications performance Enabling horizontal, elastic scalability (scale up/down) Boosting response time in analytical applications Low latency (<1 microsecond) application messaging Dramatically shortening batch processes execution time Enabling real-time, "self-service" business intelligence and unconstrained data exploration Detecting correlations/patterns across million of events in "a blink of an eye" Supporting "big data" (big data needs big memory) Running transactional and analytical applications on the same physical dataset

In-Memory Changes Everything

Thank you!