1.
Column-oriented databases- Column-oriented databases store data with a
focus on columns, instead of rows, allowing for huge data compression
and very fast query times.
2. Schema-less
databases, or NoSQL databases- Key-value stores and document stores are
some examples of schema-less databases which focus on the storage and
retrieval of large volumes of unstructured, semi-structured, or even
structured data.
3. MapReduce- This is a
programming paradigm that allows for massive job execution scalability
against thousands of servers or clusters of servers.
4.
Hadoop- Hadoop is by far the most popular implementation of MapReduce,
being an entirely open source platform for handling Big Data.
5.
Hive- Hive- is a "SQL-like" bridge that allows conventional BI
applications to run queries against a Hadoop cluster. It is a
higher-level abstraction 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 that tries to bring Hadoop closer to the
realities of developers and business users, similar to Hive.
7.
WibiData- WibiData is a combination of web analytics with Hadoop. It
allows web sites to better explore and work with their user data,
enabling real-time responses to user behavior, such as serving
personalized content, recommendations and decisions.
8.
PLATFORA- PLATFORA is a platform that turns user's 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- As the data volumes grow, so does the need for
efficient and effective storage techniques. The main evolutions in this
space are related to data compression and storage virtualization.
10.
SkyTree- SkyTree is a high-performance machine learning and data
analytics platform focused specifically on handling Big Data.
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