Using a Distributed, Columnar Database to Improve Analytics  

The increased requirements of modern analytical workloads, querying billions of rows on demand, are a challenge for relational databases because they're optimized for transactional workloads.

While transactional workload queries tend to be row-oriented (e.g., return every column in a single row), analytical workload queries tend to be column-oriented (e.g., return the aggregate of a single column in every row). By storing columns of data rather than rows of data, columnar databases optimize for analytical workloads without sacrificing the relational model and SQL.

DBTA recently held a webinar featuring Shane Johnson, Senior Director of Product Marketing, MariaDB, who explained how column-based storage improves query performance and storage efficiency and more.

MariaDB AX is a database platform for modern analytics and data warehousing, Johnson explained. It offers the following capabilities:

  • Distributed data
  • Columnar storage
  • Parallel processing
  • Data adapters
  • Connectors (Spark & Kafka)
  • Open source
  • Standard SQL

The platform can help with a variety of use cases and sectors such as the financial services industry. Drivers include becoming customer-centric, facilitating regulatory compliance, and creating competitive advantages.

Within that space MariaDB AX can help with fraud detection, compliance archiving, and investment forecasting.

For other fields such as healthcare, telecommunications, digital advertising, and more, MariaDB can assist in a variety of use cases. Some of these include:

  • Audience segmentation
  • Ad placement
  • Real-time bidding
  • Churn prevention
  • Cross-selling
  • Network optimization
  • Population health management
  • Evidence-based medicine
  • Precision medicine

An archived on-demand replay of this webinar is available here.