ParkerDB #

If you have large amount of data, ParkerDB can provide ultra-low-latency and high-concurrency point queries. And the ingestion process is only limited by the speed of your network.

Traditional reverse ETL workflows can be complex and resource-intensive, often involving multiple steps:

1. Read data from the data warehouse using a Spark job.
2. Send the data to a Kafka topic to manage update throttling.
3. Consume data from Kafka and store it in a database or Cassandra cluster.
4. Access the data via a caching layer to reduce latency. Each of these steps adds complexity, potential errors, and significant overhead.

ParkerDB simplifies this process by providing fast and scalable point lookup for big data tables, without the need for an extensive ETL pipeline.

Key Benefits #

• Ultra low latency and high concurrency.
  • A single moderate server can handle 20,000 queries per second with P99 latency under 1ms, all without a cache layer.
• Fast and efficient data publishing.
  • Simply download your data warehouse tables in Parquet format for seamless integration.
• Horizontal scalability.
  • Scale effortlessly by adding more servers to accommodate growing query volumes and larger tables.
• Optional Bring Your Own Cloud (BYOC) deployment.
  • Keep your data and credentials secure on your own servers.
  • Reduce network latency by hosting ParkerDB on your own cloud provider or on-premises cloud.

Why ParkerDB is so fast? #

Suppose you have sorted your warehouse table by the primary key and saved it in the parquet format, ParkerDB will build indexes in memory and serve the queries with O(1) disk access.

Contact us #

Email: support at parkerdb.com