Today, more than 300 companies are relying on TiDB in production to meet their OLTP/OLAP, database scalability, real-time analytics, and unified storage needs.

There’s no denying that analytic (i.e. OLAP) and transactional (i.e. OLTP) workloads put different demands on the underlying database. They have different I/O patterns, different latency expectations, ...

This results in dramatically faster large-scale analytics and transactions without significantly increasing development and management overhead. To learn more about accelerating MySQL for Demanding ...

With this emphasis on reading only, OLAP systems enjoy a speed advantage over their OLTP cousins. However, a read-only approach to the database architecture is not the only distinction of the OLAP ...

Despite the widespread use of PostgreSQL by app developers to manage transactional data, its use in analytic apps has been quite limited.

Aerospike on Tuesday took the covers off its new Graph database that can support both Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) workloads. The new database ...

And now Spark is becoming the foundation for convergence of transactional (OLTP), analytical (OLAP) and streaming data processing. One data platform to rule them all -- OLTP, OLAP, and streaming.

Combining OLTP and OLAP processing is a difficult problem to solve, as the workload characteristics differ hugely. Most OLTP systems are dealing with high-volume, short transactions over very discrete ...

The time-consuming extract, transform, load (ETL) process between OLTP and OLAP systems before model training can begin eliminates the possibility of real-time continuous learning.

A dual-engine architecture refers to a database that has specialized computation engines and storage representations that facilitate the computation for both OLTP and OLAP at the same time.