There's no lack of database choices when it comes to building your next big data analytics platform. Relational data stores, column-oriented databases, in-memory data grids, graph databases, scale-out ...

In the most recent releases, SQL Server went beyond relational data and enabled support for graph data, R, and Python machine learning, while making SQL Server available on Linux and containers in ...

Big Data, that is data which pushes the limits of conventional data management technology, is difficult or impossible to manage with relational databases. In fact, that’s pretty much the ...

The second pressure has been created by the rise of AI, ML, and the various analytics systems. These are nothing more than incredibly intricate, powerful machines which run on data as their essential ...

As SQL, MapReduce, in-memory, stream processing, graph analytics and other types of workloads are able to run on Hadoop with adequate performance, more businesses will use Hadoop as an enterprise ...

You will see how businesses are leveraging analytics and machine learning to solve these problems and how you can apply in your own use cases. Knowledge of Excel and/or SQL is highly recommended. If ...

With growth in unstructured big data, RDBMS is inadequate for big data analytics. Know how to use SQL and MapReduce for big data analytics, instead.

Available starting today, Neo4j Aura Graph Analytics is said to work with any kind of data source, including Oracle, Microsoft SQL, Databricks, Google BigQuery, Snowflake and Microsoft OneLake.

Big Data adoption in enterprises soared from 17% in 2015 to 59% in 2018, reaching a Compound Annual Growth Rate (CAGR) of 36%. Telecommunications, advertising, and insurance are the three ...