tigerdata.com

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:

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.

Tiger Data (tigerdata.com) extends standard PostgreSQL through a set of storage, partitioning, and query capabilities built for machine-generated data — sensor readings, metrics, and event streams. The core idea is to keep one Postgres interface while changing how data is physically organized underneath, so ingestion stays fast, years of history stay affordable, and live and historical queries can run together. This applies if you already work in Postgres and need to handle high-volume time-series workloads without adding a separate time-series database.

Automatic partitioning by time and key

Standard Postgres tables grow as single objects, which slows reads and writes as volume climbs. Tiger Data adds automatic partitioning that splits data by time and by key. New data lands in recent partitions while older ones stay untouched, so writes don't contend with the full table and time-range queries only scan relevant partitions. This is the mechanism behind the faster reads and writes the platform advertises.

Row storage for writes, columnar storage for analytics

Tiger Data uses a hybrid storage model. Incoming data is written in row format, which suits fast ingestion. For analytics, data is converted to columnar storage with compression. Columnar layout reads only the columns a query needs and compresses well, so aggregations over long histories cost less I/O and less disk. The practical effect: you write like a normal Postgres table but query historical ranges at analytical speed.

Tiered storage across SSD and object storage

Hot, recently written data stays on SSD. Colder data moves to low-cost object storage. Queries can still reach the cold tier, so you keep years of history without paying SSD prices for all of it. This is the main lever for controlling cost as data volume grows into terabytes and beyond.

Time-series functions and Postgres-native access

Tiger Data ships 200+ SQL functions for time-based analytics — things like time bucketing, gap filling, and downsampling that would otherwise require custom SQL. Access stays Postgres-native: SQL, APIs, a CLI, and a UI. There is also hybrid search combining keywords, vectors, filters, and ranking, and lakehouse integration to ingest from Kafka and S3 and replicate to Iceberg.

What this means in practice

A concrete case: a fleet of machines emits readings every second. You ingest in row format, partition by time so each day is isolated, compress older partitions into columnar storage, and push data older than a few months to object storage. A dashboard querying the last hour hits SSD; a quarterly trend report scans compressed columnar data; a year-over-year audit reads from object storage. All of it runs through ordinary SQL.

The platform reports real-world speed-ups of 10x to 40x depending on query, range, and data frequency, and cites a single TimescaleDB instance handling 3 trillion metrics per day and 3 petabytes of stored data. A free trial and a pricing page are available if you want to test against your own workload.

When this fits — and when it doesn't

Choose Tiger Data if you are already on Postgres, your data is time-stamped and high-volume, and you want to avoid operating a second database system. It is less compelling if your workload is mostly transactional with little time-series character, or if you need a non-Postgres engine. The extension model means your existing SQL, drivers, and tooling carry over, which is the main reason teams pick it over a standalone time-series database.

Tiger Data states that its platform is built to meet the security and operational requirements of production systems, covering three areas: protection of data in motion and at rest, operational resilience through high availability and recovery, and formal compliance with SOC 2 Type II and GDPR support. These claims are appropriate to use as a shortlist filter, but for an enterprise adoption decision you should confirm the specifics — scope, regions, and contractual terms — directly through the security documentation or a sales conversation, since the public page summarizes rather than details them.

Security controls

Tiger Data lists the following security capabilities for enterprise deployments:

• Encryption at rest and in transit — data is encrypted both while stored and while moving between clients and the service.
• Private networking — connectivity options that keep traffic off the public internet.
• Access controls — mechanisms to restrict who can reach data and administrative functions.

These are the baseline controls most security review teams ask about first. The public page does not specify encryption algorithms, key management ownership, or which private networking products are supported, so those details need to come from the security documentation or your account team.

Reliability and recovery

For production machine-data workloads, Tiger Data states it provides:

• High availability — designed to keep the service running through component failures.
• Automated backups — regular backup handling without manual scheduling.
• Point-in-time recovery — the ability to restore to a specific moment, which matters when bad writes or ingestion errors need to be rolled back precisely.

The page does not publish recovery time objectives, recovery point objectives, or backup retention windows. If your team has internal RTO/RPO targets, ask for them explicitly rather than assuming they match your requirements.

Compliance posture

Tiger Data states it supports:

• SOC 2 Type II — an audited control report covering security, availability, and related trust criteria over a period of time.
• GDPR support — features and commitments relevant to handling EU personal data.
• Enterprise security standards — a general statement that the platform is built for enterprise requirements.

Two practical notes: SOC 2 Type II reports are usually shared under NDA rather than published, so request the report and check its audit period and scope. For GDPR, confirm where data is stored, what data processing terms apply, and whether a data processing agreement is available.

How to verify before committing

Because the public page is a summary, treat verification as a short, structured process:

1. Request the security documentation — ask for the SOC 2 Type II report, the encryption and key management description, and the list of supported private networking options.
2. Map controls to your own requirements — compare the documented controls against your internal security baseline, especially around access control granularity and audit logging.
3. Confirm recovery commitments in writing — ask for RTO, RPO, backup retention, and point-in-time recovery windows, and check whether they appear in the contract or service terms.
4. Clarify GDPR specifics — data residency, the data processing agreement, and subprocessors.
5. Use a trial to test operationally — the site offers a free trial and a demo; a trial lets your team validate backup/restore behavior and access controls in practice rather than on paper.

Where this fits in a buying decision

If your review requires SOC 2 Type II, encryption in transit and at rest, private networking, and point-in-time recovery as table stakes, Tiger Data's stated feature set covers those categories and is worth progressing to documentation review. The decision point is not whether these features exist — it is whether the documented scope and contractual commitments match your specific requirements. Enterprises with strict data residency, custom RTO/RPO, or sector-specific regulatory obligations should resolve those questions in the sales conversation before adoption, since the public page does not address them.

Tiger Data (tigerdata.com) is built for machine and sensor data workloads that need high-volume ingestion, years of cheap historical storage, and combined live-plus-historical queries — all inside one Postgres. It fits teams already working with PostgreSQL who are processing time-series data from factories, wells, grids, or fleets and are hitting the limits of vanilla Postgres on volume or query speed.

Who it's for

Tiger Data targets organizations whose machines generate continuous data: industrial operations, energy, utilities, and fleet management. The site names factories, wells, grids, and fleets as the environments it serves, and describes the platform as "Postgres for sensor and machine data."

If your workload looks like any of these, it's in scope:

• High-frequency metrics ingestion — many devices or sensors writing on short intervals, continuously.
• Long retention requirements — you need to keep years of history without letting storage costs grow linearly with raw volume.
• Mixed query patterns — dashboards and alerts read recent ("hot") data while analytics and reporting scan large historical ranges.
• Existing Postgres investment — you want to stay on Postgres-native access (SQL, APIs, CLI, UI) rather than adopt a separate time-series database.

What the platform provides for these workloads

The capabilities listed on the site map directly to the pain points of machine-data workloads:

Scale signals to compare against your own workload

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 upper-bound reference points rather than guarantees for your data shape — query performance depends on range, frequency, and query type. One customer quote on the site reports speed-ups of 10x to 40x "depending on the query, range, and data frequency," which is a useful reminder that gains vary by access pattern.

When Tiger Data is a good fit — and when it isn't

Good fit if:

• Your data is time-stamped machine or sensor output at high volume.
• You want to keep live and historical data queryable together in one Postgres.
• You need compression and tiering to control storage cost over multi-year retention.
• You already run Postgres and want to avoid a second database system.

Consider alternatives if:

• Your workload is primarily transactional (OLTP) with little time-series character — the partitioning, columnar, and tiering features add little value.
• You need only small-scale time-series storage where vanilla Postgres with an extension is sufficient.
• Your pipeline requires a lakehouse-only architecture with no Postgres serving layer.

Getting started

The site offers a free trial and a sales contact path, plus a pricing page at tigerdata.com/pricing. For a concrete evaluation, load a representative slice of your own data — including your longest retention window and your heaviest analytical query — and compare ingest rate, compression ratio, and query latency against your current setup. That test will tell you more than the headline scale numbers.

Tiger Data is a PostgreSQL platform built by the creators of TimescaleDB, aimed at time-series and machine data at petabyte scale. It is a fit if you need to ingest high-frequency sensor or machine data, keep years of history, and query live and historical data together in one Postgres — and you want to evaluate it through a free trial or a sales demo before committing.

Who it's for

Tiger Data positions itself around "sensor and machine data" — factories, wells, grids, and fleets that produce continuous streams of readings. The site cites a single TimescaleDB instance handling 3 trillion metrics per day, 1 quadrillion data points stored, and 3 petabytes of data volume. That scale claim is the core of the pitch: one Postgres instance rather than a separate time-series database plus a relational store.

If your workload is ordinary transactional data, the time-series features below add little. If your workload is high-volume, append-heavy metrics with long retention and mixed live/historical queries, the feature set maps directly to those needs.

Core capabilities

The design theme is consolidation: ingestion, compression, retention tiering, and analytics all live inside Postgres, so you avoid running a separate time-series system next to your relational one.

What users report

The site quotes Martin Zemko, a software engineer at CERN, describing speed-ups "from 10x to 40x, depending on the query, range, and data frequency," and calling Hypercore "a big win." Treat these as vendor-published customer statements rather than independent benchmarks — useful as a signal of the workload types where gains appear (large time ranges, high-frequency data), not as a guarantee for your queries.

Operational and compliance posture

For production adoption, the site lists:

• Security: encryption at rest and in transit, private networking, access controls
• Reliability: high availability, automated backups, point-in-time recovery
• Compliance: SOC 2 Type II and GDPR support

These are the checkboxes most enterprise reviews ask for; verify specifics against your own requirements during a demo rather than relying on the summary.

How to evaluate it

1. Start a free trial if you want to test ingestion and query performance on your own data shape. The site offers this as the primary self-serve path.
2. Request a demo or contact sales if you need to discuss migration from an existing Postgres or time-series setup, or want the compliance and reliability details confirmed for procurement.
3. Check pricing on the pricing page before assuming cost structure — the site links to it directly, and plan details are not summarized here.
4. Test the migration path if you already run Postgres: the pitch is that TimescaleDB-style capabilities extend standard Postgres, so a proof-of-concept on a representative table is the fastest way to confirm fit.

Common sticking points to plan for

• Data volume assumptions: the petabyte-scale claims describe large deployments; validate performance at your actual ingest rate and retention window during the trial.
• Lakehouse dependencies: Kafka, S3, and Iceberg integration assumes those systems exist in your stack — confirm the connectors match your versions.
• Compression trade-offs: columnar compression favors analytical reads; check that your write pattern and query mix benefit rather than adding complexity.

Bottom line

Tiger Data is worth a trial if you run high-volume time-series or machine data on or near Postgres and want partitioning, compression, tiered storage, and time-series SQL in one system. If your data is small, purely transactional, or already well served by a separate analytics stack, the value is lower. Use the free trial for hands-on validation and sales contact for migration, compliance, and pricing specifics.