1) Column-oriented databases
Column oriented database stores data based on columns rather than rows. This allows huge data compression and very fast query times. This allows massive job execution scalability against thousands of servers or clusters of servers.
2) Schema-less databases, or NoSQL databases
Schema less database have some good examples like Key-value stores and document which target on the storage and try to get large number of unstructured, semi-structured, or even structured data.
3) MapReduce
By this programming paradigm once can do massive job execution scalability against thousands of servers or clusters of servers.
4) Hadoop
Hadoop is most popular implementation of MapReduce. It is an open-source framework which allows user to store and process big data in a distributed environment across clusters of computers using simple programming models.
5) Hive
This allows conventional BI applications to run queries against a Hadoop cluster. It is a higher-level model of the Hadoop framework that allows anyone to make queries against data stored in a Hadoop cluster just as if they were manipulating a conventional data store.
6) PIG
PIG is another bridge which tries to bring Hadoop closer to the realities of developers and business users, similar to Hive. Pig is a high-level platform for creating MapReduce programs used with Hadoop. The language for this platform is called Pig Latin.
7) WibiData
WibiData provides big data applications for enterprises to deliver personalized experiences across channels. It’s a platform which built on open-source technologies Apache Hadoop, Apache Cassandra, Apache HBase, Apache Avro and the Kiji Project. It allows web sites to better explore and work with their user data, enabling real-time responses to user behaviour, such as serving personalized content, recommendations and decisions.
8) PLATFORA
By this platform user can turn their queries into Hadoop jobs automatically, thus creating an abstraction layer that anyone can exploit to simplify and organize datasets stored in Hadoop.
9) Storage Technologies
The data volume is growing day by day and that’s the reason we need efficient and effective storage technique. The main evolutions in this space are related to data compression and storage virtualization.
10) SkyTree
SkyTree gives organizations the power to discover deep analytic insights, predict future trends, make recommendations and reveal untapped markets and customers which is a need of Big Data.
