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About
Tiger Data is the managed cloud platform built by the creators of TimescaleDB — the world's leading time-series extension for PostgreSQL. Designed to power mission-critical IoT, energy, telecommunications, and financial workloads, it enables organizations to store and query trillions of metrics per day across petabytes of data on a single service. The platform combines automatic time- and key-based partitioning, a hybrid row-columnar storage engine, and tiered storage that keeps hot data on SSD while moving colder data to low-cost object storage. Native lakehouse integration lets teams ingest from Kafka and S3 and replicate data to Apache Iceberg — eliminating fragile custom pipelines. Over 200 built-in SQL time-series functions support advanced analytics such as downsampling, gap-filling, and moving averages. Tiger Data also supports hybrid vector and keyword search, making it suitable for AI applications that require retrieval-augmented generation (RAG) alongside operational time-series data. The platform is fully Postgres-native, accessible via SQL, REST APIs, CLI, and a web UI. Enterprise-ready features include SOC 2 Type II compliance, GDPR support, high availability, automated backups, point-in-time recovery, encryption at rest and in transit, and private networking. A free trial is available for developers and startups, with enterprise tiers for large-scale deployments.
Key Features
- Automatic Time-Series Partitioning: Automatically partitions data by time and key for ultra-fast reads and writes at any scale, supporting up to petabytes of data on a single service.
- Hybrid Row-Columnar Storage: Uses row storage for high-throughput writes and columnar storage for analytics, optimizing both ingestion speed and query performance simultaneously.
- Tiered & Lakehouse Storage: Keeps hot data on SSD and automatically moves colder data to low-cost object storage. Natively replicates to Apache Iceberg for Lakehouse analytics via Kafka and S3.
- 200+ Time-Series SQL Functions: Built-in SQL functions for downsampling, gap-filling, moving averages, and other time-based analytics — all accessible via standard PostgreSQL syntax.
- Hybrid AI Vector & Keyword Search: Supports vector embeddings alongside keyword search and filters, enabling RAG pipelines and AI applications directly within the time-series database.
Use Cases
- Ingesting and querying millions of IoT sensor readings per second for industrial monitoring and predictive maintenance dashboards.
- Storing and analyzing financial tick data and trading metrics at petabyte scale with sub-second query response times.
- Replacing fragile Kafka + Flink + custom ETL pipelines with native Lakehouse integration that streams time-series data directly into Apache Iceberg on S3.
- Powering AI applications that combine time-series telemetry with vector search for anomaly detection and retrieval-augmented generation (RAG) workflows.
- Unifying real-time operational data and historical analytics for energy, utilities, and telecommunications companies on a single PostgreSQL-compatible platform.
Pros
- Proven Petabyte-Scale Performance: Trusted by enterprises processing over 3 trillion metrics per day and 3 petabytes of data, with real-world case studies from oil & gas, manufacturing, and telecom.
- PostgreSQL-Native — No New Paradigms: Fully compatible with the Postgres ecosystem, so existing tools, drivers, ORMs, and SQL skills work out of the box without re-learning a proprietary query language.
- Eliminates Complex Data Pipelines: Native Kafka ingestion, S3 integration, and Iceberg replication replace fragile custom ETL pipelines, reducing infrastructure maintenance and engineering overhead.
- Enterprise Security & Compliance: SOC 2 Type II certified with GDPR support, encryption, private networking, and high-availability guarantees suitable for regulated industries.
Cons
- PostgreSQL Lock-In: The platform is tightly coupled to PostgreSQL, which may limit teams that require multi-model databases or non-relational data structures natively.
- Pricing Opacity at Scale: Enterprise and large-scale pricing requires contacting sales, making it difficult for teams to estimate costs before committing to evaluation.
- Steep Learning Curve for Advanced Features: Features like tiered storage policies, continuous aggregates, and Iceberg replication require familiarity with TimescaleDB-specific concepts beyond standard SQL.
Frequently Asked Questions
What is Tiger Data and how does it relate to TimescaleDB?
Tiger Data is the managed cloud platform built by the creators of TimescaleDB. It combines the open-source TimescaleDB time-series engine with enterprise cloud infrastructure, offering automatic scaling, tiered storage, and a fully managed experience without self-hosting.
Can I use Tiger Data for IoT sensor data ingestion?
Yes. Tiger Data is purpose-built for IoT and telemetry use cases. It supports high-throughput ingestion of sensor data, event streams, and tick data, with native Kafka integration and time-based partitioning optimized for continuous data streams.
Does Tiger Data support AI and vector search?
Yes. The platform includes hybrid retrieval capabilities combining vector embeddings, keyword search, filters, and ranking — enabling RAG (Retrieval-Augmented Generation) pipelines and AI applications alongside operational time-series data.
Is there a free plan or trial available?
Yes. Tiger Data offers a free trial for developers and startups to get started without upfront commitment. Enterprise tiers with dedicated infrastructure and SLA guarantees are available by contacting the sales team.
What compliance certifications does Tiger Data hold?
Tiger Data is SOC 2 Type II certified and provides GDPR support. It also offers encryption at rest and in transit, private networking, access controls, high availability, and automated point-in-time recovery.
