How to Start Using Tiger Data and Which Integrations Are Available

Updated

Tiger Data (tigerdata.com) is the company behind TimescaleDB, offering a PostgreSQL platform built for time-series and machine data at petabyte scale. To start, you have three entry points: start a free trial, contact sales for a demo or migration help, or connect through existing integrations with your cloud provider and Postgres ecosystem. Which path fits depends on whether you want to evaluate the platform hands-on, plan an enterprise rollout, or plug it into an existing data stack.

Start with a free trial if you want to evaluate hands-on

The site offers a free trial as the self-serve entry point. Use it when you want to test ingestion speed, compression, and query performance against your own machine data before committing.

A practical evaluation sequence:

  1. Load a representative slice of data — for example, a few weeks of sensor or metrics data rather than a synthetic sample, so partitioning and compression behave realistically.
  2. Test the write path — confirm ingestion keeps up with your expected metrics-per-second rate.
  3. Test the read path — run your heaviest time-range and aggregation queries against both recent (hot) and older (compressed) data.
  4. Check tiered storage behavior — verify that moving colder data to object storage still meets your query latency needs.

The site cites a single TimescaleDB instance handling 3 trillion metrics per day, 1 quadrillion data points stored, and 3 petabytes of data volume. Treat these as reference scale points for sizing conversations, not as guarantees for your workload.

Contact sales for demos, migration, and enterprise requirements

Choose the sales path if you are migrating an existing PostgreSQL or time-series deployment, need a demo tailored to your architecture, or have enterprise security and compliance requirements to satisfy.

Bring these questions to that conversation:

  • Migration plan — how existing schemas, hypertables, and continuous aggregates map onto the platform.
  • Security posture — the site lists encryption at rest and in transit, private networking, and access controls.
  • Reliability terms — high availability, automated backups, and point-in-time recovery are listed as platform capabilities; confirm the specifics for your tier.
  • Compliance evidence — SOC 2 Type II and GDPR support are stated; request the reports your procurement process needs.

Integrations: where your data already lives

Tiger Data positions itself as working "where your data already lives," spanning cloud infrastructure, industrial automation, and the wider Postgres ecosystem. The site points to an integrations page for the full list; the concrete capabilities described are:

Integration area What it covers
Cloud providers Run Tiger Data with your preferred cloud provider
Postgres ecosystem Standard Postgres-native access via SQL, APIs, CLI, and UI
Kafka and S3 Ingest data from Kafka and S3
Iceberg Replicate data out to Iceberg for lakehouse workflows

If your pipeline already lands raw machine data in Kafka or S3, the ingest path is the first thing to validate. If your analytics team works from a lakehouse, the Iceberg replication path matters more.

What you get once connected

The platform's core primitives shape how you should design your first schema:

  • Automatic partitioning — time- and key-based partitioning for fast reads and writes; you get this without hand-managing partitions.
  • Row-columnar storage — row storage for writes, columnar storage with compression for analytics, so live and historical data can be queried together.
  • Tiered storage — hot data on SSD, colder data on low-cost object storage.
  • Time-series functions — 200+ SQL functions for time-based analytics.
  • Search — hybrid retrieval combining keywords, vectors, filters, and ranking.

A customer quoted on the site, Martin Zemko, a software engineer at CERN, reports speed-ups "from 10x to 40x, depending on the query, range, and data frequency." That range is a useful expectation-setter: gains vary by query pattern, so benchmark your own.

Choosing your next step

  • Evaluating the technology → start the free trial and run the four-step test above.
  • Planning a production migration or needing enterprise terms → contact sales with your migration and compliance questions.
  • Already have a data platform → check the integrations page first, then validate the Kafka/S3 ingest or Iceberg replication path that matches your stack.

Pricing details are not specified in the available material; use the pricing page or the sales conversation to get figures for your scale.