USE CASE: DYNAMIC ANALYTICS
Seamless high-performance queries across real-time and historical data.
THE CHALLENGE
Balancing cost, speed, and complexity
When real-time and historical data need to be combined on the fly for complex analytics, the cost-performance tradeoff is a constant struggle. Keeping everything in a single transactional system can provide short-term improvements to query performance, but it eventually leads to costly storage bloat. Separating data across different systems can be more cost-efficient, but it introduces latency, adds complexity to data management, and poses security and compliance risks.
OUR SOLUTION
Perform dynamic analytics with Analytics Accelerator
EDB Postgres AI addresses dynamic analytics challenges with the Analytics Accelerator. Tiered tables automatically offload cold data from Postgres to lakehouse tables in cost-efficient object storage—and a vectorized query engine enables seamless SQL queries across storage tiers with 30x faster performance compared to standard Postgres. This streamlined approach simplifies data tier management, boosts performance, and optimizes costs—all on top of a secure, enterprise-hardened Postgres foundation.
Tiered tables
Query hot data and cold data with a single dedicated node, ensuring optimal performance by automatically offloading cold data to columnar tables in cost-efficient object storage, reducing the complexity of managing analytics over multiple data tiers.
Vectorized query engine
Upgrade your standard Postgres query engine with a vectorized query engine that is optimized for columnar data formats, allowing hot and cold data queries to run 30x faster compared to standard Postgres.
Enterprise-hardened Postgres
Organizations can avoid vendor lock-in with open source Postgres while getting enterprise-grade enhancements to security, availability, and support. The solution can be deployed on-premises, in a private cloud, or in a hybrid environment, offering maximum flexibility to meet specific business needs.
DEMO
DEMO
Improve Analytics Insight without Sacrificing Performance
Improve Analytics Insight without Sacrificing Performance
Analytics Accelerator provides optimized storage and faster query performance while ensuring secure, streamlined operations—ultimately lowering costs, accelerating insights, and supporting scalable, future-ready dynamic analytics systems.
Efficient data storage
Columnar tables are faster to query and have a 5x smaller disk footprint than standard Postgres tables—and object storage is 18x as cost-effective as solid state drive storage.
Accelerated performance
The vectorized query engine powers 30x faster queries, on average, compared to standard Postgres, ensuring high performance for both hot and cold data queries.
Streamlined, secure operations
Consolidated data management, built-in security best practices, and 24/7/365 support make it possible to maintain scalable, resilient systems while gaining maximum value from tiered data and reducing operational burden.
EDB Postgres AI enables dynamic analytics
Analytics Accelerator leverages tiered tables to manage hot and cold data—automatically offloading cold data from Postgres tables to lakehouse tables in cost-efficient object storage. A compute-dedicated node uses the vectorized query engine to query lakehouse tables in object storage 30x faster than standard Postgres—scaling up to meet query demands and then scaling all the way down to zero when not in use.
Related Products and Solutions
EDB Postgres AI
A modern Postgres data platform for powering mission-critical workloads from edge to core.
EDB Analytics Accelerator
Unlock rapid analytics in Postgres with the PGAA extension.
EDB Postgres in Public Cloud
Deploy self-hosted EDB Postgres AI in your cloud of choice.
EDB Postgres Distributed
Up to 99.999% uptime and 5x throughput performance versus native logical replication.
Resources
EDB Mission Control: A Postgres Lakehouse Innovation
Transforming Real-Time Analytics and AI Workloads with PostgreSQL
Unleashing AI with PostgreSQL: What is a Lakehouse?
Elevating Postgres Security with the EDB Trust Center
Dynamic analytics—sometimes called tiered analytics—involves analyzing both hot (real-time) and cold (historical) data simultaneously to generate actionable insights. EDB Postgres AI enables this with the Analytics Accelerator solution, leveraging tiered tables that seamlessly integrate hot data with cold data stored in columnar lakehouse tables. It also features a vectorized query engine optimized for these formats, ensuring high-performance queries across both data types, while also optimizing storage costs and simplifying data management within a unified Postgres environment.
EDB addresses these challenges with hybrid tables that integrate hot data with cold data stored in columnar lakehouse tables. This approach leverages a vectorized query engine optimized for columnar formats, delivering 30x faster queries compared to standard Postgres while ensuring high performance across both data tiers.
Key features include:
- Unified transactional, analytical, and AI data management
- Tiered tables and flexible object storage integrations
- Vectorized query engine (optimized for queries over columnar data)
- Enterprise-hardened Postgres (secure, scalable, ACID compliant, expert support)
- Broadly compatible open source foundation
- On-premises, private cloud, or hybrid deployment options
Benefits include:
- Streamlined operations
- Simplified data management
- 30x faster analytical queries compared to standard Postgres
- Seamless scalability
- Greater security and control
EDB optimizes storage costs with a combination of columnar lakehouse tables—which are 5x smaller on disk compared to Postgres tables and indexes—and object storage—which is 18x cheaper than SSD storage. This allows businesses to reduce overall storage expenses without sacrificing query performance.
EDB Postgres AI integrates with any Postgres source, anywhere. It also integrates with flexible storage locations that are up to 18x more cost-efficient than solid state drives—including Amazon S3, MinIO, or the local filesystem—and leverages the standardized Delta Table open table format, making it compatible with other tools and services across the lakehouse ecosystem.