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Top 10 Emerging Technologies For Big Data

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. Read More

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