By realigning data access patterns and utilising idle SIMD lanes, these algorithms have achieved performance boosts in critical operations such as table scans, hash joins, and geospatial queries [3].

Online analytical processing (OLAP) databases are purpose-built for handling analytical queries. Analytical queries run on online transaction-processing (OLTP) databases often take a long time to ...

The three features of adaptive query processing can make SQL Server 2017 significantly faster at processing your workload. Not only that, but they also provide significant improvements without needing ...

Blockchain can be inefficient in terms of data processing and query performance. Source: MDPI Another reason data queries are inefficient is the linear storage of data in blockchain networks.

Google Cloud announces a series of new AI features aimed at deploying agents within data environments. The agents are ...

Queries can be initiated from a Teradata Database to access, filter and return subsets of data from Hadoop, Aster and other database environments to the Teradata Database for additional processing.

Stream processing is designed to analyze and act on real-time streaming data, using “continuous queries” (i.e. SQL-type queries that operate over time and buffer windows).

But Big Data's not all about MapReduce. There’s another computational approach to distributed query processing, called Massively Parallel Processing, or MPP. MPP has a lot in common with MapReduce.