Peaka

Peaka positions itself as a serverless connector infrastructure that aims to eliminate the traditional ETL bottleneck by enabling real-time, cross-sourced querying across more than 300 relational databases, NoSQL stores, SaaS tools, and APIs—without copying or syncing data. This zero-ETL approach is the platform’s core differentiator: instead of moving data into a central warehouse or lake, Peaka presents each source as a virtual table that can be queried in place using standard SQL. For data engineers tired of maintaining brittle pipelines and analysts frustrated by stale snapshots, this promises a fundamentally leaner architecture. The platform’s standout strengths revolve around three capabilities: federated queries that can join data from, say, a PostgreSQL database, a MongoDB collection, and a REST API in a single SQL statement; streaming ingestion from Kafka or Segment into Peaka’s BI Table for real-time analytics; and instant replication via change data capture (CDC) to keep virtual tables nearly current without batch jobs. Additionally, Peaka converts APIs and NoSQL databases into SQL-queryable tables, which lowers the barrier for business users and developers who prefer SQL over custom scripting. The platform also includes a semantic layer for defining business logic and offers deployment options: Peaka Cloud (SaaS) or Embedded Peaka (for embedding into other products). Security is addressed with SOC 2 Type 1 & Type 2 reports, which is a baseline requirement for enterprise adoption. However, there are important caveats. Pricing is not publicly listed—prospective users must contact sales, which can be a friction point for smaller teams or individual evaluators. The platform is relatively new, with a smaller community than established tools like dbt or Fivetran, so troubleshooting and third-party resources are limited. Performance at scale, especially for complex federated queries across many sources, is not well documented; users with heavy workloads should test thoroughly. The workflow that Peaka fits best is one where data freshness matters more than historical depth, and where the cost and complexity of maintaining ETL pipelines outweigh the benefits of a centralized copy. Data engineers can use it to reduce pipeline maintenance, analysts can query live data without waiting for pulls, and BI professionals can build dashboards that reflect current state. For software developers, the ability to treat APIs as SQL tables simplifies backend integration logic. A practical buyer should start with a small proof of concept that mixes two or three diverse sources (e.g., a SQL database, a NoSQL store, and a REST API) to validate query performance and latency. Teams that require heavy transformation, complex aggregations, or long historical windows may still need a warehouse; Peaka is best suited for operational analytics and real-time access use cases. Ultimately, Peaka is a promising tool for organizations looking to reduce data duplication and accelerate time-to-insight, but it requires careful evaluation of scale and support needs.