Until recently, this ACID compliancy wasn’t fulfilled by Hadoop transactions, but the advent of Hive 2 (June 2016) and others has changed this. Now you can have an equally valid acidity on Hadoop as you would expect on DB2.
This brings up once more the question on the relevance of DB2.
IDAA, the Analytics Accelerator for DB2 z/OS, enables a new architecture that combines transaction and analytical processing in a single, hybrid system – enabling the next level of real-time analytics resulting in the simplification of the information management infrastructure.
If you haven’t heard about Spark, it is about time you read an article on it. But do we actually know what Apache Spark is exactly? Why should(n’t) we use it? Is it fit for purpose or not? Many things are still unclear to a broad audience; I hope to reveal a little more on this subject.
Apache Spark has become a hot topic in the world of Analytics. It is seen as a better performing alternative to Map/Reduce , and as a means to do analytics combining data across a wide set of data sources. This video is a excellent introduction to what Spark is, and how it works with DB2 for z/OS.
The Logical Data Warehouse is a data management architecture for analytics. It is a collection of data repositories and associated data services for management and access to data. IBM Fluid Query is a new software component that extends the concept of a federated query to Big Data.