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Webinar to discuss scaling Postgres for AI applications with TimescaleDB

🔄 Updated 1h ago
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Key points

  • Webinar on October 21 about scaling Postgres.
  • Matty Stratton from Tiger Data will present.
  • Focus on extending Postgres with TimescaleDB.
  • Addresses challenges with high data volume and AI applications.

Webinar Announcement

A webinar is scheduled for October 21 to discuss scaling PostgreSQL for demanding applications. The event will feature Matty Stratton, Head of Developer Advocacy and Docs at Tiger Data.

Addressing Postgres Limitations

The discussion will focus on how vanilla Postgres can reach its limits when handling continuous streams of live data, particularly with increased database activity from AI applications. This can lead to slower queries, more maintenance, and increased upkeep.

As tables and indexes grow, maintaining query speed requires more attention. The webinar will explore how these pain points emerge as data volume and velocity increase.

Extending Postgres with TimescaleDB

The webinar will demonstrate how TimescaleDB can extend Postgres capabilities to address these scaling challenges. This approach aims to provide solutions without requiring a complete migration to a new database system.

Strategies for scaling Postgres typically involve optimizing queries, partitioning data, tuning autovacuum, or upgrading hardware. However, these methods can demand significant engineering effort.

Avoiding Database Migration

The session will highlight the difficulties and costs associated with abandoning Postgres for a specialized system. Such migrations often involve learning new query languages or APIs and maintaining duplicate data during validation.

The webinar will present TimescaleDB as an alternative to avoid these migration complexities while still addressing performance and scalability needs.

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Reporting from

A webinar on October 21 will feature Matty Stratton from Tiger Data discussing how to extend PostgreSQL with TimescaleDB. The session will address challenges faced when scaling vanilla Postgres for high-volume data and AI applications.