PostgreSQL Cross-Database Accelerator

Problem Context

Data teams using PostgreSQL for analytics face slow query performance, especially with DBT pipelines. They need to maintain separate databases for production and staging due to PostgreSQL's lack of cross-database queries, which adds complexity and delays. As the team grows, these inefficiencies become unsustainable, forcing costly migrations to dedicated data warehouses.

Pain Points

DBT models take 30+ minutes to run, forcing teams to wait for changes. Cross-database queries are impossible, requiring manual CDC pipelines. Developing new tables is slow, and testing requires waiting for full pipeline runs. The team knows they’ll need to migrate soon, but the cost and effort grow with each passing month.

Impact

Teams waste hours weekly waiting for queries, delaying dashboards and insights. Manual CDC pipelines introduce errors and maintenance overhead. The risk of migration grows as data volume and team size increase, leading to higher long-term costs. Frustration and inefficiency slow down the entire analytics workflow.

Urgency

The problem is already slowing the team down now, and it will only get worse as the data team grows. Waiting for migrations means missing opportunities to scale analytics faster. The cost of fixing this later—both in time and money—will be far higher than addressing it now.

Target Audience

Data analysts, DBT engineers, and analytics teams at mid-sized companies using PostgreSQL for analytics. Startups and scale-ups with growing data teams face this issue as they outgrow their current setup. Companies using DBT or similar tools for transformation pipelines also struggle with these limitations.

Solution Approach

A lightweight, PostgreSQL-compatible layer that accelerates query performance without requiring a full migration. It sits between the database and the analytics tools, optimizing queries and enabling cross-database access natively. The solution focuses on speeding up existing workflows while reducing the need for manual workarounds.

Key Features

Query caching: Automatically caches frequent DBT model runs to reduce wait times. Cross-database queries: Enables direct access to production data from staging without CDC pipelines. Incremental refresh: Only reprocesses changed data, cutting run times by 70%+. Compatibility layer: Works with existing PostgreSQL tools and SQL dialects without changes.

User Experience

Teams connect the tool to their PostgreSQL instance in minutes. DBT models run faster, and new tables are built in seconds instead of minutes. Analysts can query production data directly from staging, eliminating manual CDC setups. The tool integrates seamlessly with existing tools, requiring no code changes.

Differentiation

Unlike full data warehouse migrations, this solution works with existing PostgreSQL setups. It avoids vendor lock-in and costly transitions. The focus on DBT and analytics-specific optimizations makes it more effective than generic database accelerators. No need to rewrite SQL or retrain teams.

Scalability

The tool scales with data growth, automatically optimizing queries as the database expands. Additional features like advanced caching and parallel processing can be added as teams grow. Pricing scales with usage, ensuring cost remains predictable as needs increase.

Expected Impact

Teams save 5+ hours per week waiting for queries, accelerating dashboard delivery. Cross-database access eliminates manual CDC pipelines, reducing errors. Faster iterations mean analytics teams can respond to business needs quicker. The solution buys time before a full migration becomes necessary, lowering long-term costs.