Hybrid Transactional/Analytical Processing (HTAP) on Hadoop
NOTE - please register for this meetup(webinar) at https://esgyn.com/htap-on-hadoop-webinar
As businesses become more agile, the need for real-time and near real-time analysis on transactional data has become more important than ever. For database veterans, transactions and analytics have always been on two different systems. Such silo-ed approach resulted in expensive ETL processes, specialized data marts, SLA issues, and most importantly analytics on old data.
The latest architectural trends are increasingly leading the way to enable both transactions and analytics on the same data store. Gartner has called this capability of delivering transactions and analytics on the same data store and mixed workloads as Hybrid Transactional/Analytical Processing (HTAP).
Per Gartner, there are two types of HTAP – in-process HTAP and point-of-decision HTAP. The demand for HTAP has always existed from the business, but the technology limitations have forced IT not to deliver thus far.
How can we change this scenario and help businesses become real-time? As most businesses have adopted Hadoop and Big data, is there a way to leverage that infrastructure to achieve this database nirvana? Is it necessary to go all the way to in-memory computing (IMC) or can we leverage intermediate steps such as caching?
You will learn:
What is Hybrid Transactional/Analytics Processing?
How does HTAP differ from existing data platforms/architectures?
How to differentiate between in-process HTAP and point-of-decision HTAP?
Why Hadoop opens new opportunities for HTAP?
What business benefits one can expect from HTAP?
What approaches are required to implement HTAP?
What workloads are pertinent for HTAP?
How to extend HTAP to structured, semi-structured and unstructured data?