best managed Postgres for time-series / telemetry data

TigerData exists for a very specific reason: plain managed Postgres starts to fall over when you push it with time-series and telemetry workloads. Ingest rates climb, tables bloat, queries mix real-time and months of history, and suddenly your “boring and reliable” Postgres is slow, expensive, and wrapped in fragile streaming glue. This guide explains what “best managed Postgres for time-series / telemetry data” actually means, how TigerData is built, and when it’s the right fit.

Quick Answer: TigerData is a managed Postgres platform purpose-built for live telemetry—time-series, event, and tick data—by extending standard PostgreSQL with TimescaleDB, row-columnar storage, compression, and tiered storage. You get the Postgres you know, plus time-series primitives that keep ingest fast, queries responsive, and storage costs predictable at massive scale.

The Quick Overview

• What It Is: TigerData is a managed PostgreSQL service (Tiger Cloud) with the TimescaleDB extension and additional storage/analytics primitives designed for time-series and telemetry workloads.
• Who It Is For: Engineering teams collecting large volumes of time-stamped data—IoT metrics, observability traces, financial ticks, product analytics, blockchain data—who want to stay on Postgres instead of stitching together Kafka, Flink, and a lakehouse.
• Core Problem Solved: Plain Postgres becomes slow and expensive when you combine high ingest, large historical tables, and analytical queries. TigerData solves this with Postgres-native partitioning, compression, and lakehouse integration so you can handle real-time + historical workloads without brittle multi-system pipelines.

How It Works

Under the hood, TigerData is vanilla PostgreSQL plus extensions and platform features tuned for telemetry. You still use SQL, existing drivers, and Postgres tooling—but behind the scenes TigerData introduces primitives that change the scaling profile of time-series data:

• Hypertables automatically partition large tables by time and key.
• Hypercore row-columnar storage stores recent data in row form (fast writes) and older data in compressed columnar form (fast analytics, much smaller footprint).
• Tiered storage moves cold data to object storage while keeping it queryable via Postgres.
• Lakehouse integration streams data to and from Kafka, S3, and Iceberg without custom glue code.
• Time-series functions give you 200+ SQL tools for gaps, interpolation, downsampling, and more.

You keep the Postgres interface; TigerData handles the time-series mechanics and the managed operations.

1. Ingest: Time-series-optimized tables via hypertables

Instead of manually sharding tables or writing partition management scripts, you define a hypertable: a logical table that TigerData partitions by time and (optionally) by a secondary key such as device_id or tenant.

Conceptually:

1. You define a standard schema:

   CREATE TABLE sensor_readings (
     time        TIMESTAMPTZ       NOT NULL,
     device_id   TEXT              NOT NULL,
     temp_c      DOUBLE PRECISION  NOT NULL,
     humidity    DOUBLE PRECISION  NOT NULL,
     status      TEXT,
     PRIMARY KEY (time, device_id)
   );
   

2. You convert it to a hypertable (syntax varies by version, but conceptually):

   SELECT create_hypertable(
     'sensor_readings',
     'time',
     'device_id',
     number_partitions => 8
   );
   

3. TigerData automatically:

   • Creates time-based chunks.
   • Routes inserts to the correct chunk.
   • Prunes chunks on time-based queries.
   • Applies retention and compression policies.

You keep writing to sensor_readings like any table. Under the hood, it’s partitioned for high ingest and fast time-based filtering.

2. Store: Row-columnar storage with compression and tiering

Once data ages out of the hot path, the requirements change: you care more about cheap, scan-friendly storage than single-row writes.

TigerData uses a hybrid storage model:

• Row store for hot data

  • Recent data is kept in regular Postgres heap pages.
  • Optimized for single-row inserts, small transactions, and low-latency reads.

• Columnar store for warm/cold data

  • Older chunks are converted to a columnar format.
  • Columns compress independently, often yielding up to ~95–98% compression on time-series with repeated keys and numeric values.
  • Columnar scans favor analytical queries (GROUP BY, aggregates, window functions) across large time ranges.

You control this with policies, e.g.:

SELECT add_compression_policy(
  'sensor_readings',
  INTERVAL '7 days' -- compress chunks older than 7 days
);

On Tiger Cloud, tiered storage can then move those compressed chunks to low-cost object storage while keeping them addressable in SQL. You still SELECT * FROM sensor_readings WHERE time >= now() - interval '365 days';—TigerData handles the placement.

3. Analyze & Serve: Continuous aggregates and hybrid retrieval

To serve dashboards, SLOs, or anomaly detection on top of billions of rows, TigerData leans on:

• Continuous aggregates

  • Materialized rollups that are incrementally updated.

  • Defined via SQL like any view, but maintained by a background job:

    CREATE MATERIALIZED VIEW daily_device_stats
    WITH (timescaledb.continuous) AS
    SELECT
      time_bucket('1 day', time) AS day,
      device_id,
      avg(temp_c) AS avg_temp,
      max(temp_c) AS max_temp
    FROM sensor_readings
    GROUP BY day, device_id;
    

  • A policy keeps them up to date:

    SELECT add_continuous_aggregate_policy(
      'daily_device_stats',
      start_offset => INTERVAL '30 days',
      end_offset   => INTERVAL '1 hour',
      schedule_interval => INTERVAL '15 minutes'
    );
    

  • Dashboards hit the view; TigerData handles incremental refresh.

• Hybrid retrieval for AI/search use cases

  • Combine filters, full-text search, and vectors (via pgvector/pgai) in Postgres.
  • Useful when telemetry feeds models, search, or AI agents alongside structured queries.

The result: you can serve real-time and historical analytics workloads directly from managed Postgres, without a separate time-series database or OLAP warehouse.

Features & Benefits Breakdown

Core Feature	What It Does	Primary Benefit
Hypertables & auto-partitioning	Partitions tables by time and key, manages chunks and indexes automatically.	Keeps write throughput high and query latencies low as tables grow into billions of rows.
Hypercore row-columnar storage	Stores recent data in row form and older data in compressed columnar format.	Fast ingest plus up to ~95–98% compression and analytics-friendly scans on historical data.
Tiered storage & lakehouse integration	Moves cold data to object storage; streams to/from Kafka, S3, Iceberg.	Reduces storage cost, replaces fragile streaming pipelines with native infrastructure.

Additional managed-platform features on Tiger Cloud:

• HA & resilience: Multi-AZ high availability, automated failover, and read replicas.
• Backups & recovery: Automated backups with point-in-time recovery (PITR).
• Security: TLS 1.2+ everywhere, mutual TLS for critical internal traffic, encryption at rest with per-service keys.
• Compliance: SOC 2 Type II availability by plan, GDPR alignment, HIPAA available on Enterprise.
• Transparent billing: No per-query or backup fees; itemized usage, billed monthly in arrears.

Ideal Use Cases

• Best for IoT and telemetry platforms: Because you’re ingesting high-volume sensor/device data with strict latency targets and long retention windows. Hypertables keep ingestion and queries predictable, while compression and tiering prevent storage costs from exploding.
• Best for observability and product analytics: Because you need to join metrics, logs, events, and business data in one Postgres schema. TigerData lets you keep everything in SQL, with continuous aggregates and columnar storage making dashboard queries and SLO calculations fast even over months of history.

Other strong fits:

• Blockchain/Web3 indexing (high write, deep history).
• Financial tick and market data (time-series with strict latency requirements).
• Manufacturing/industrial telemetry (edge-to-cloud pipelines feeding analytics).

Limitations & Considerations

• Not a replacement for all-purpose OLAP warehouses: TigerData makes Postgres perform like a time-series and analytics engine for telemetry-first workloads, but it’s still Postgres. If you want cross-domain BI over dozens of transactional systems, a dedicated warehouse may still make sense alongside TigerData.
• Time-series semantics require some schema planning: To get the full benefits of hypertables and compression, you need to model time as a first-class column, pick appropriate partition keys, and understand when to use continuous aggregates. Important: poorly chosen partitioning (e.g., too many small chunks) can hurt performance; TigerData’s defaults are safe, but high-scale workloads still benefit from deliberate design.

Pricing & Plans

TigerData’s pricing is built around transparent, resource-based billing on Tiger Cloud—no per-query, per-dashboard, or “gotcha” backup fees. You pay for:

• The compute tier (vCPU/RAM) of your service.
• The volume of primary and tiered storage.
• Optional HA, read replicas, and enterprise features.

At a high level:

• Performance: Best for teams starting with serious telemetry workloads that need solid ingest and query performance plus compression, but don’t (yet) need enterprise SLAs. Ideal for POCs, new products, and early production where you still want managed operations, automated backups, and clear growth paths.
• Scale & Enterprise: Best for teams running mission-critical telemetry platforms requiring multi-AZ HA, fine-grained security controls (VPC peering, IP allowlists), SOC 2 reports, and 24/7 support with SLA-based response. Designed for organizations with strict uptime, compliance, or cost-management requirements.

Note: Exact SKUs, regions, and pricing evolve over time; the Tiger Console and pricing pages show current configurations and costs with no hidden metering.

Frequently Asked Questions

How is TigerData different from “plain” managed Postgres for time-series workloads?

Short Answer: It’s standard Postgres plus time-series primitives (hypertables, compression, tiered storage, lakehouse integration) and a platform tuned for telemetry scale.

Details: A vanilla managed Postgres service (RDS, Cloud SQL, etc.) gives you a well-run single Postgres instance. You can store time-series data, but:

• Tables grow monolithic; partitioning is manual.
• Indexes bloat over time; VACUUM/maintenance gets tricky.
• Long-range analytics queries contend with hot OLTP traffic.
• You often bolt on Kafka, Flink, and a warehouse to cope.

TigerData keeps Postgres as the interface but changes the mechanics:

• Hypertables make partitioning automatic and time-aware.
• Row-columnar storage and compression shrink historical data by up to ~95–98%.
• Continuous aggregates provide incremental rollups.
• Tiered storage and lakehouse integration replace custom pipelines.

For teams asking, “What’s the best managed Postgres for time-series / telemetry data?”, the answer is usually: keep Postgres, but use a platform that’s actually built around time as a first-class dimension. That’s precisely what TigerData does.

Do I need to change my application code to use TigerData?

Short Answer: In most cases, minimal changes—your app still talks to Postgres via standard drivers and SQL.

Details: Because TigerData is Postgres-native:

• Existing drivers (psycopg, pgx, JDBC, etc.) continue to work.
• SQL remains the query language; ORMs and migration tools behave as expected.
• The main changes are:
  • Using hypertables instead of plain tables for telemetry-heavy schemas.
  • Adding policies (add_compression_policy, add_continuous_aggregate_policy) and occasionally tuning chunk intervals and retention windows.
  • Leveraging time-series functions and continuous aggregates in queries and dashboards.

Important: when you introduce continuous aggregates, you’ll want to understand their refresh behavior and watermarks. They are “always fresh enough” for most dashboard/SLO use cases but are not a per-row real-time index; instead, they update on a schedule you control.

Summary

If you’re searching for the best managed Postgres for time-series and telemetry data, the key is not a different query language or a black-box “time-series service.” It’s Postgres with the right primitives:

• Automatic time-based partitioning (hypertables).
• Hybrid row-columnar storage with aggressive compression.
• Tiered storage that moves cold data to cheap object storage.
• Continuous aggregates and time-series SQL functions.
• Lakehouse integration that replaces fragile streaming glue.

TigerData delivers this as a managed Postgres platform—Tiger Cloud—so you can keep your stack simple: Postgres for live telemetry, with the scale to handle quadrillions of data points, and operations (HA, backups, security, compliance) handled for you.

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