RDS/Aurora alternatives for high-ingest telemetry with long retention

Most Postgres teams hit the same wall on Amazon RDS and Aurora: ingest is fine at first, but as telemetry volume and retention grow, performance degrades and costs spike. High-ingest workloads with multi-year retention push RDS/Aurora into constant vacuuming, bloated indexes, and oversized clusters—exactly when you need predictable performance and costs the most.

This guide walks through why RDS and Aurora struggle with high-ingest telemetry, what to look for in an alternative, and how a Postgres-native platform like TigerData changes the calculus without forcing you off SQL.

Quick Answer: TigerData is a Postgres + TimescaleDB platform purpose-built for high-ingest, long-retention telemetry. It keeps a standard Postgres interface but adds hypertables, row-columnar storage, and tiered storage so you can handle billions of events per day with years of history—without RDS/Aurora-style cost blowups.

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The Quick Overview

• What It Is: A Postgres-native platform (Tiger Cloud plus TimescaleDB) optimized for time-series, event, and tick data at very large scale.
• Who It Is For: Teams ingesting massive telemetry streams—IoT, observability, trading, Web3, product analytics—who need both real-time and long-term historical queries.
• Core Problem Solved: Plain RDS/Aurora Postgres becomes slow and expensive for time-based telemetry as tables grow, indexes bloat, and you bolt on Kafka/S3/lakehouse plumbing to cope. TigerData gives you the same Postgres you know, but with time-series primitives and managed operations tuned for this workload.

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Why RDS and Aurora struggle with high-ingest telemetry

RDS and Aurora are solid for transactional workloads and modest time-series volumes, but they are general-purpose Postgres services. High-ingest telemetry with long retention stresses three things they don’t handle well by default:

1. Insert-heavy, append-only patterns

   • Millions of INSERTs per second into a few hot tables.
   • Mostly time-ordered data with an ever-growing “tail.”

2. Mixed real-time + historical access

   • Dashboards hitting “last 5 minutes” plus “last 30 days” on every refresh.
   • Backfills, replays, and long-range analytics queries.

3. Long retention on a single cluster

   • Months or years of data in the same logical dataset.
   • Storage growth forces you into larger node classes just to keep IOPS and cache headroom.

On RDS/Aurora, that shows up as:

• Growing tables and bloated indexes
  • Large heap + multiple B-tree indexes on a single table.
  • Increasing cost for VACUUM / AUTOVACUUM and index maintenance.
• Query latency spikes
  • Sequential scans across huge tables for time-based queries.
  • Inefficient use of I/O for analytical aggregations.
• Scale-through-instances, not architecture
  • Your only real lever is “bigger instance / more replicas.”
  • Moving to Aurora doesn’t change the underlying access patterns; it just changes storage behavior.

When you’re ingesting telemetry at scale, you need architectural primitives tailored for time-based data, not just bigger hardware.

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What to look for in an RDS/Aurora alternative for telemetry

If you’re evaluating alternatives for high-ingest telemetry with long retention, focus on three categories:

1. Storage and partitioning primitives

   • Time- and key-based partitioning that’s automatic.
   • Efficient compression for “cold” historical data (ideally 90%+).
   • Ability to keep hot data on fast disks and cold data on object storage.

2. Query and analytics primitives

   • Native support for fast aggregations over time ranges.
   • Columnar or row-columnar storage for analytic queries.
   • Rich time-series functions (downsampling, gaps, rates, time buckets).

3. Operational and cost model

   • No complex, fragile pipelines just to keep systems in sync.
   • Transparent pricing without per-query or per-scan surcharges.
   • Managed HA, backups, and point-in-time recovery suitable for production/regulatory needs.

That’s where TigerData fits: Postgres plus time-series primitives, with managed cloud operations that understand telemetry workloads.

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How TigerData works as a Postgres-native alternative

TigerData doesn’t ask you to abandon Postgres. It extends it with targeted primitives that solve the RDS/Aurora pain points for high-ingest telemetry.

Under the hood, there are three core building blocks:

1. Hypertables (automatic partitioning)
2. Hypercore row-columnar storage
3. Tiered storage to low-cost object storage

1. Hypertables: automatic partitioning for time + key

You define your telemetry table as a hypertable instead of a plain table:

CREATE TABLE readings (
  time         TIMESTAMPTZ NOT NULL,
  device_id    TEXT        NOT NULL,
  metric       TEXT        NOT NULL,
  value        DOUBLE PRECISION,
  tags         JSONB
);

SELECT create_hypertable(
  'readings',
  by_range('time'),
  by_hash('device_id'),
  chunk_time_interval => INTERVAL '1 day'
);

TigerData (via TimescaleDB) automatically:

• Splits the table into time-based chunks, optionally with a secondary key (e.g., device_id).
• Routes INSERTs into the right chunk with almost zero overhead.
• Manages chunk lifecycle (indexing, compression, movement to cheaper storage) via policies.

Impact vs. RDS/Aurora:

• You get the operational simplicity of a single logical table.
• Underneath, the data is effectively partitioned by time (and possibly by key), avoiding giant monolithic tables and index bloat.
• Hot chunks stay small and efficient for real-time query patterns.

2. Hypercore: row-columnar storage for writes and analytics

Telemetry data has two conflicting needs:

• Fast writes for incoming events.
• Fast scans and aggregations for analytics.

Hypercore, TigerData’s storage engine, resolves this by combining:

• Row-oriented storage for hot, recently written chunks.
• Columnar storage for cold, analytical chunks.

Typical workflow:

1. New data lands in row-store format for efficient ingest and OLTP-style access.
2. When a chunk ages out (e.g., older than 7 days), a background policy:
   • Converts it to columnstore.
   • Applies compression (often up to 98%, depending on data).
3. Queries automatically use the right format:
   • Real-time queries hit row-store.
   • Historical analytics scan compressed columnstore chunks.

You control behavior with SQL-level policies, e.g.:

SELECT add_compression_policy(
  'readings',
  compress_after => INTERVAL '7 days'
);

ALTER TABLE readings
SET (
  timescaledb.compress,
  timescaledb.compress_orderby = 'time DESC',
  timescaledb.compress_segmentby = 'device_id'
);

Impact vs. RDS/Aurora:

• You keep ingest performance high without sacrificing analytical speed.
• Compression dramatically reduces storage footprint and I/O, which is where RDS/Aurora costs balloon for long-retention telemetry.

3. Tiered storage: long retention without massive instances

With long retention (months/years), the big cost driver on RDS/Aurora becomes:

• Large GP2/GP3 volumes for historical data.
• Larger instance classes just to maintain IOPS and buffer cache.

TigerData supports tiered storage, moving cold, compressed chunks to low-cost object storage while keeping hot data on local disk:

• Hot/working set: SSD-backed storage.
• Warm/cold history: Object storage that’s much cheaper per TB.

Queries remain Postgres-native; the engine knows where each chunk lives and fetches data as needed. Critical aggregates can be served from continuous aggregates so most queries never touch deep cold storage directly.

Impact vs. RDS/Aurora:

• You can keep years of telemetry online without linearly scaling instance size and disk.
• Storage costs scale with retention, not with the brute-force overprovisioning you’d need on RDS/Aurora.

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How it works: end-to-end telemetry pipeline on TigerData

At a pipeline level, a TigerData-based alternative to RDS/Aurora looks like this:

1. Ingest

   • Stream from Kafka, MQTT, or your apps directly into Postgres via SQL, COPY, or ingest APIs.
   • Optionally ingest from object storage (e.g., S3) when you have batch dumps.

2. Store & optimize

   • Land data in hypertables with time + key partitioning.
   • Apply compression and columnstore conversion via policies.
   • Use tiered storage for long retention.

3. Query & analyze

   • Use SQL (the same as you would on RDS/Aurora) but with 200+ time-series functions and continuous aggregates.
   • Build dashboards and analytics directly on TigerData, or replicate to Iceberg for lakehouse workflows.

Operationally, Tiger Cloud (TigerData’s managed service) covers:

• HA, automatic failover, and multi-AZ deployments (depending on plan).
• Automated backups and point-in-time recovery.
• Usage and cost visibility via Tiger Console.
• No per-query or per-scan fees; you pay for provisioned resources, not “surprise” workloads.

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Features & benefits breakdown

Core Feature	What It Does	Primary Benefit
Hypertables	Automatically partitions tables by time and key into chunks.	Sustains high ingest and avoids table/index bloat that crushes RDS/Aurora at long retention.
Hypercore row-columnar storage	Uses row storage for fresh data and columnar for cold data, with compression.	Fast real-time writes plus fast, cheap historical analytics—without managing two separate systems.
Tiered storage	Moves compressed cold chunks to low-cost object storage.	Keeps years of telemetry online at a fraction of the cost of large RDS/Aurora volumes.

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Ideal use cases

• Best for IoT and device telemetry: Because hypertables plus compression handle billions of sensor readings with years of history, while time_bucket() and continuous aggregates power dashboards and alerting without query storms.
• Best for observability and metrics: Because you can ingest high-cardinality metrics, retain raw data at fine granularity, and downsample in-place—removing the need for a separate metrics store and complex ETL to S3/lakehouse.

Other strong fits:

• Financial tick data and market feeds.
• Web and product analytics (events, sessions).
• Web3 and blockchain indexing.
• Any “append-only events + long retention” scenario currently straining RDS/Aurora.

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Limitations & considerations

• Not a drop-in RDS/Aurora control plane: You’ll manage clusters via Tiger Cloud UI/API/CLI, not the RDS/Aurora console. Existing AWS tooling tied specifically to RDS (e.g., RDS Performance Insights) won’t apply; you’ll use TigerData’s observability plus Postgres-native tooling (pg_stat_*, etc.).
• Still Postgres, with its constraints: You keep all the strengths and limits of Postgres (e.g., single-node transaction semantics, extension ecosystem, version lifecycles). For cross-region active-active or extreme multi-tenant fanout patterns, you still need a deliberate architecture—TigerData doesn’t “magic away” all distributed systems trade-offs.

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Pricing & plans (conceptual)

TigerData’s pricing is built around transparent resource usage, not per-query or metered-scan pricing:

• You provision compute and storage for a Tiger Cloud service.
• You get automated backups, HA options, and network egress without surprise surcharges like “per query” or “per million rows scanned.”
• Itemized usage and costs are visible in Tiger Console; you’re billed monthly in arrears.

Example plan alignment (names illustrative; check the site for current SKUs):

• Performance Plan: Best for teams running a single-region, production telemetry workload that needs high ingest and real-time analytics but can tolerate standard support SLAs.
• Scale/Enterprise Plan: Best for organizations with multiple critical workloads, strict uptime/compliance requirements (SOC 2, GDPR, HIPAA), and the need for HA, multi-AZ, advanced security (VPC peering, IP allow lists, mutual TLS for internal traffic), and 24/7 operations support.

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Frequently asked questions

Is TigerData a replacement for RDS/Aurora or an add-on?

Short Answer: It’s a replacement for running Postgres on RDS/Aurora for telemetry workloads.

Details:
TigerData runs Postgres with the TimescaleDB extension as a managed service (Tiger Cloud). Instead of provisioning RDS/Aurora clusters, you provision a TigerData service. Your applications still speak Postgres—same drivers, same SQL—but now you have hypertables, row-columnar storage, and tiered storage baked in.

For teams that still use RDS/Aurora for transactional workloads, a common pattern is:

• Keep OLTP/operational databases on RDS/Aurora.
• Offload high-ingest, long-retention telemetry to TigerData.
• Use Postgres-native replication/integration or lakehouse replication for cross-system workflows.

How does TigerData compare to non-Postgres time-series databases?

Short Answer: You get time-series performance similar to purpose-built systems, but with Postgres and SQL instead of a new query language and a separate stack.

Details:
Specialized time-series databases (InfluxDB, ClickHouse variants, etc.) solve ingest and retention via their own storage engines and query languages. They work, but often at the cost of:

• A separate system to deploy and operate.
• A new query language (Flux, InfluxQL, etc.) and client libraries.
• ETL pipelines between your operational Postgres and the time-series store.

TigerData’s philosophy is different:

• Keep Postgres as the foundation (boring, reliable, extensible).
• Extend it with time-series primitives (hypertables, compression, continuous aggregates).
• Provide managed operations tuned to telemetry (scale, HA, backups, security).

That means your teams reuse existing SQL skills and Postgres tooling, and you avoid the “stitched together Kafka, Flink, and custom glue” pattern just to keep systems in sync.

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Summary

If RDS or Aurora is straining under high-ingest telemetry with long retention—slow queries, vacuum pressure, spiraling storage and instance costs—the problem isn’t Postgres itself. It’s running Postgres without the right primitives for time-based data and without an operations model tuned for this use case.

TigerData offers a Postgres-native alternative:

• Hypertables to partition telemetry by time and key.
• Hypercore row-columnar storage to keep both ingest and analytics fast.
• Tiered storage to retain years of data without RDS/Aurora-style cost blowups.
• Managed operations (HA, backups, PITR, security) with transparent, non-per-query pricing.

You keep SQL, you keep Postgres, and you replace the fragile, high-maintenance telemetry stack built on top of RDS/Aurora with native infrastructure designed for this exact workload.

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