Tiger Data

Founded by Ajay Kulkarni in 2017

Store and query massive time-series data using PostgreSQL at petabyte scale

Cost

Free Trial

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use Tiger Data to run time-series workloads on PostgreSQL at massive scale — handling trillions of metrics per day. It supports automatic time-based partitioning, hybrid row-columnar storage with compression, and tiered storage across SSD and object storage. You can ingest data from Kafka and S3, query using 200+ time-series SQL functions, and run hybrid search with vectors and filters. It's built for sensor data, machine data, industrial systems, and AI workloads without managing your own database infrastructure.

What Tiger Data does

Partition tables automatically by time range without manual configurationCompress and convert row data to columnar format for analytics queriesTier data automatically between SSD and object storage based on ageWrite SQL queries using built-in time-series functions like downsampling and gap-fillingIngest streaming data from Kafka topics into time-series tablesRun vector similarity searches alongside SQL filters in a single querySet up high availability with automated backups and point-in-time recoveryConnect to the database using standard PostgreSQL drivers and toolsAutomatically partitions data by time and key for fast reads and writesStores rows for writes and converts to columnar format for analytics with compressionKeeps hot data on SSD and moves older data to cheap object storage automaticallyIngests data from Kafka and S3, and replicates to Apache IcebergIncludes 200+ SQL functions specifically for time-based analyticsSupports hybrid search combining keyword, vector, filter, and ranking queriesHandles 3 trillion metrics per day in real production deploymentsFully accessible via SQL, APIs, CLI, and a web UI