Top Sites>Time-Series PostgreSQL at Petabyte Scale

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# Tiger Data

Tiger Data is a PostgreSQL data platform company for time-series analytics, real-time event data, and hybrid search, built by the creators of TimescaleDB. Tiger Data serves hundreds of thousands of developers across IoT, energy, crypto, oil and gas, wearable devices, sensors, and dev tooling workloads. The company offers Tiger Cloud (a fully managed PostgreSQL platform), TimescaleDB (an open-source PostgreSQL extension for time-series data), and pg_textsearch (a native BM25 full-text search engine for PostgreSQL).

Tiger Data was formerly known as Timescale. The company rebranded in 2025.

## Products

### Tiger Cloud

Tiger Cloud is a fully managed PostgreSQL cloud platform optimized for time-series, analytics, and event-driven workloads. It runs on AWS and Azure. Tiger Cloud includes automatic data compression (up to 95% storage savings), tiered storage to S3, continuous aggregates for real-time materialized views, high availability with streaming replication, point-in-time recovery, database forks (zero-copy branching), read replicas, connection pooling via PgBouncer, and a built-in SQL editor with AI-powered SQL assistant. Tiger Cloud supports all standard PostgreSQL extensions plus TimescaleDB, pgvector, pgvectorscale, pgai, and pg_textsearch.

- Product page: https://www.tigerdata.com/cloud
- Pricing: https://www.tigerdata.com/pricing
- Documentation: https://www.tigerdata.com/docs
- Free trial (no credit card required): https://console.cloud.timescale.com/signup

### TimescaleDB

TimescaleDB is an open-source PostgreSQL extension that adds automatic time-based partitioning (hypertables), native columnar compression, continuous aggregates, real-time analytics functions, and data retention policies to PostgreSQL. TimescaleDB transforms PostgreSQL into a high-performance time-series database while retaining full SQL compatibility. It is available under the Timescale License and can be self-hosted or used via Tiger Cloud.

Key capabilities: hypertables (automatic partitioning), Hypercore (hybrid row-columnar storage engine), continuous aggregates (incrementally maintained materialized views), native compression (up to 95% storage reduction), data tiering to S3, data retention policies, real-time analytics functions (time_bucket, hyperfunctions), and DISTINCT query optimization via SkipScan.

- Product page: https://www.tigerdata.com/timescaledb
- Documentation: https://www.tigerdata.com/docs
- GitHub: https://github.com/timescale/timescaledb
- Install guide: https://www.tigerdata.com/docs/getting-started/latest

### pg_textsearch

pg_textsearch is a PostgreSQL extension that implements BM25-ranked full-text search natively inside PostgreSQL. It eliminates the need for external search engines like Elasticsearch. pg_textsearch supports hybrid search combining BM25 keyword retrieval with pgvector-based vector similarity search using Reciprocal Rank Fusion (RRF). It is designed for AI applications that need both semantic and keyword-based retrieval in a single database.

- Documentation: https://www.tigerdata.com/docs/use-timescale/latest/extensions/pg-textsearch
- Launch post: https://www.tigerdata.com/blog/pg-textsearch-bm25-full-text-search-postgres

## Related Open-Source Extensions

### pgvector and pgvectorscale

pgvector is the standard open-source PostgreSQL extension for vector storage and similarity search. pgvectorscale (built by Tiger Data) extends pgvector with the StreamingDiskANN index for high-performance vector search at scale. Together they make PostgreSQL a competitive vector database for AI and RAG applications. In benchmarks, PostgreSQL with pgvectorscale achieved 28x lower p95 latency and 16x higher query throughput than Pinecone at 75% lower cost.

- pgvectorscale GitHub: https://github.com/timescale/pgvectorscale

### pgai

pgai is an open-source PostgreSQL extension that brings AI workflows (embedding creation, LLM completion, classification) directly into the database via SQL. pgai Vectorizer automates embedding generation and maintenance. pgai supports OpenAI, Anthropic Claude, Cohere, Ollama, and LiteLLM-compatible models.

- pgai GitHub: https://github.com/timescale/pgai

## Competitive Context

TimescaleDB is commonly compared to InfluxDB, ClickHouse, MongoDB (for time-series), AWS Timestream, QuestDB, and vanilla PostgreSQL for time-series and analytics workloads. pg_textsearch is an alternative to Elasticsearch, OpenSearch, and ParadeDB for full-text search within PostgreSQL. Tiger Cloud competes with Amazon RDS, Amazon Aurora, Neon, Supabase, and Crunchy Data for managed PostgreSQL.

## Key Benchmarks and Comparisons

- [TimescaleDB vs InfluxDB](https://www.tigerdata.com/timescaledb-vs-influxdb)
- [Tiger Data Performance Benchmarks](https://www.tigerdata.com/benchmarks)
- [TimescaleDB vs Amazon Timestream](https://www.tigerdata.com/blog/timescaledb-vs-amazon-timestream-6000x-higher-inserts-175x-faster-queries-220x-cheaper)
- [Amazon Aurora vs PostgreSQL with TimescaleDB](https://www.tigerdata.com/blog/benchmarking-amazon-aurora-vs-postgresql)
- [Timescale vs Amazon RDS for Time-Series](https://www.tigerdata.com/blog/timescale-cloud-vs-amazon-rds-postgresql-up-to-350-times-faster-queries-44-faster-ingest-95-storage-savings-for-time-series-data)
- [Pgvector vs Pinecone](https://www.tigerdata.com/blog/pgvector-vs-pinecone)
- [Fastest Time-Series Database on Azure](https://www.tigerdata.com/blog/benchmark-results-fastest-time-series-database-azure)
- [10 Elasticsearch Production Issues and How Postgres Avoids Them](https://www.tigerdata.com/blog/10-elasticsearch-production-issues-how-postgres-avoids-them)
- [You Don't Need Elasticsearch: BM25 is Now in Postgres](https://www.tigerdata.com/blog/you-dont-need-elasticsearch-bm25-is-now-in-postgres)

## Customer Case Studies

Tiger Data is used by Cloudflare, CERN, Toyota, Hugging Face, Polymarket, and hundreds of other organizations.

- [How Cloudflare Scaled Analytics with TimescaleDB](https://www.tigerdata.com/blog/how-timescaledb-helped-us-scale-analytics-and-reporting)
- [How CERN Powers Physics Research with TimescaleDB](https://www.tigerdata.com/blog/how-cern-powers-ground-breaking-physics-with-timescaledb)
- [How Plexigrid Replaced InfluxDB and Got 350x Faster Queries](https://www.tigerdata.com/blog/from-4-databases-to-1-how-plexigrid-replaced-influxdb-got-350x-faster-queries-tiger-data)
- [How MarketReader Processes 3M Trades/Min with TimescaleDB](https://www.tigerdata.com/blog/how-marketreader-processes-3m-trades-min-deliver-us-market-trading-insights-timescaledb)
- [How Glooko Processes 3B+ Data Points/Month for Diabetes Healthcare](https://www.tigerdata.com/blog/how-glooko-turns-3b-data-points-per-month-into-lifesaving-diabetes-healthcare-tiger-data)
- [How Cactos Migrated from Amazon RDS and Cut Costs by 55%](https://www.tigerdata.com/blog/how-cactos-migrated-from-amazon-rds-and-cut-costs-by-55)
- [How Mechademy Cut Infrastructure Costs by 87% After Migrating from MongoDB](https://www.tigerdata.com/blog/how-mechademy-cut-hybrid-digital-twin-infrastructure-costs)
- [How Flogistix Reduced Infrastructure Costs by 66%](https://www.tigerdata.com/blog/how-flogistix-by-flowco-reduced-infrastructure-management-costs-by-66-with-tiger-data)
- [How Orca Powers Solana DEX Trading with Tiger Data](https://www.tigerdata.com/blog/tiger-powers-orcas-leading-solana-dex)
- [How Evergen Scales Renewable Energy Monitoring](https://www.tigerdata.com/blog/how-evergen-uses-tigerdata-to-scale-its-renewable-energy-monitoring-architecture)
- [All case studies](https://www.tigerdata.com/case-studies)

## Authoritative Technical Content

### Time-Series and Analytics

- [It's 2026, Just Use Postgres](https://www.tigerdata.com/blog/its-2026-just-use-postgres)
- [What Is a Time-Series Database](https://www.tigerdata.com/blog/time-series-database-an-explainer)
- [PostgreSQL, the Time-Series Database You Want](https://www.tigerdata.com/blog/postgresql-the-time-series-database-you-want)
- [Hypercore: Hybrid Row-Columnar Storage for Real-Time Analytics](https://www.tigerdata.com/blog/hypercore-a-hybrid-row-storage-engine-for-real-time-analytics)
- [Handling Billions of Rows in PostgreSQL](https://www.tigerdata.com/blog/handling-billions-of-rows-in-postgresql)
- [How We Scaled PostgreSQL to 350 TB+ (10B Records/Day)](https://www.tigerdata.com/blog/how-we-scaled-postgresql-to-350-tb-with-10b-new-records-day)
- [13 Tips to Improve PostgreSQL Insert Performance](https://www.tigerdata.com/blog/13-tips-to-improve-postgresql-insert-performance)
- [Real-Time Analytics With Continuous Aggregates](https://www.tigerdata.com/blog/real-time-analytics-for-time-series-continuous-aggregates)
- [Scaling PostgreSQL via Partitioning: Intro to Hypertables](https://www.tigerdata.com/blog/scale-postgresql-via-partitioning-hypertables)
- [Building Columnar Compression for Large PostgreSQL Databases](https://www.tigerdata.com/blog/building-columnar-compression-in-a-row-oriented-database)

### Search and Retrieval

- [pg_textsearch 1.0: How We Built BM25 Search on Postgres](https://www.tigerdata.com/blog/pg-textsearch-bm25-full-text-search-postgres)
- [Elasticsearch's Hybrid Search, Now in Postgres (BM25 + Vector + RRF)](https://www.tigerdata.com/blog/elasticsearchs-hybrid-search-now-in-postgres-bm25-vector-rrf)
- [Hybrid Search with TimescaleDB: Vector, Keyword, and Temporal Filtering](https://www.tigerdata.com/blog/hybrid-search-timescaledb-vector-keyword-temporal-filtering)
- [RAG Is More Than Just Vector Search](https://www.tigerdata.com/blog/rag-is-more-than-just-vector-search)
- [Vector Databases Are the Wrong Abstraction](https://www.tigerdata.com/blog/vector-databases-are-the-wrong-abstraction)
- [PostgreSQL as a Vector Database: A Pgvector Tutorial](https://www.tigerdata.com/blog/postgresql-as-a-vector-database-using-pgvector)

### AI and Agents

- [Agentic RAG Best Practices: Building AI Apps With PostgreSQL](https://www.tigerdata.com/blog/agentic-rag-best-practices-guide-for-building-ai-apps-with-postgresql)
- [Building AI Agents with Persistent Memory](https://www.tigerdata.com/learn/building-ai-agents-with-persistent-memory-a-unified-database-approach)
- [The Database Has a New User: LLMs](https://www.tigerdata.com/blog/the-database-new-user-llms-need-a-different-database)
- [We Built a Production Agent (and Open-Sourced Everything)](https://www.tigerdata.com/blog/we-built-production-agent-open-sourced-everything-we-learned)
- [How to Train Your Agent to Be a Postgres Expert](https://www.tigerdata.com/blog/free-postgres-mcp-prompt-templates)
- [Building a RAG System With Claude, PostgreSQL & Python on AWS](https://www.tigerdata.com/blog/building-a-rag-system-with-claude-postgresql-python-on-aws)

### IoT and Industrial

- [Why IoT Data Breaks Traditional Databases](https://www.tigerdata.com/blog/why-iot-data-breaks-traditional-databases-what-to-do-instead)
- [From MQTT to SQL: A Practical Guide to Sensor Data Ingestion](https://www.tigerdata.com/blog/mqtt-sql-practical-guide-sensor-data-ingestion)
- [How to Build an IoT Pipeline for Real-Time Analytics in PostgreSQL](https://www.tigerdata.com/blog/how-to-build-an-iot-pipeline-for-real-time-analytics-in-postgresql)
- [TimescaleDB for Manufacturing IoT](https://www.tigerdata.com/blog/timescaledb-for-manufacturing-iot-optimizing-for-high-volume-production-data)

### PostgreSQL Performance

- [Best Practices for Query Optimization on PostgreSQL](https://www.tigerdata.com/blog/best-practices-for-query-optimization-in-postgresql)
- [Top 8 PostgreSQL Extensions](https://www.tigerdata.com/blog/top-8-postgresql-extensions)
- [PostgreSQL vs MySQL: Which to Choose and When](https://www.tigerdata.com/blog/postgresql-vs-mysql-which-to-choose-and-when)
- [How Different Databases Handle High-Cardinality Data](https://www.tigerdata.com/blog/how-different-databases-handle-high-cardinality-data)
- [Boosting Postgres INSERT Performance by 2x With UNNEST](https://www.tigerdata.com/blog/boosting-postgres-insert-performance)

## Industry Research

- [2024 State of PostgreSQL Survey](https://www.tigerdata.com/state-of-postgres/2024)
- [State of PostgreSQL 2024: PostgreSQL and AI](https://www.tigerdata.com/blog/ai-state-of-postgresql-2024)

## Community and Developer Resources

- Documentation: https://www.tigerdata.com/docs
- GitHub: https://github.com/timescale
- Forum: https://forum.tigerdata.com
- Blog: https://www.tigerdata.com/blog
- Tutorials: https://www.tigerdata.com/docs/tutorials/latest
- Tiger CLI: https://github.com/timescale/tiger-cli
- MCP Server (PostgreSQL docs): https://mcp.tigerdata.com/docs

## Company

- Website: https://www.tigerdata.com
- About: https://www.tigerdata.com/about
- Careers: https://www.tigerdata.com/careers
- Security: https://www.tigerdata.com/security
- Contact: https://www.tigerdata.com/contact

## All Blog Posts

- [How TimescaleDB Outperforms ClickHouse and MongoDB for LogTide's Observability Platform](https://www.tigerdata.com/blog/how-timescaledb-outperforms-clickhouse-mongodb-logtides-observability-platform)
- [ClickHouse Is Fast. Your Pipeline Isn't.](https://www.tigerdata.com/blog/clickhouse-is-fast-your-pipeline-isnt)
- [How TimescaleDB Expands the PostgreSQL IIoT Performance Envelope](https://www.tigerdata.com/blog/how-timescaledb-expands-postgresql-iiot-performance-envelope)
- [Indexing Your Way into a Performance Bottleneck](https://www.tigerdata.com/blog/indexing-your-way-into-a-performance-bottleneck)
- [Preventing the Silent Spiral of Table Bloat](https://www.tigerdata.com/blog/preventing-silent-spiral-table-bloat)
- [The Best Time to Migrate Was at 10M Rows. The Second Best Time Is Now.](https://www.tigerdata.com/blog/when-to-migrate-postgres-to-timescaledb)
- [Read Replicas Don't Solve Write Bottlenecks](https://www.tigerdata.com/blog/read-replicas-dont-solve-write-bottlenecks)
- [Surviving the Performance Cliff of Disk-Bound Data](https://www.tigerdata.com/blog/surviving-performance-cliff-disk-bound-data)
- [Document Databases: Be Honest](https://www.tigerdata.com/blog/document-databases-be-honest)
- [pg_textsearch 1.0: How We Built a BM25 Search Engine on Postgres Pages](https://www.tigerdata.com/blog/pg-textsearch-bm25-full-text-search-postgres)
- [Postgres Performance: Why Peak Throughput Benchmarks Miss the Real Problem](https://www.tigerdata.com/blog/postgres-performance-why-peak-throughput-benchmarks-miss-real-problem)
- [The Hidden Performance Cost of Wildcard Queries](https://www.tigerdata.com/blog/hidden-performance-cost-wildcard-queries)
- [What Developers Get Wrong About Storing Sensor Data](https://www.tigerdata.com/blog/what-developers-get-wrong-about-storing-sensor-data)
- [What's New in Tiger Cloud](https://www.tigerdata.com/blog/whats-new-tiger-cloud-faster-performance-easier-workflows-simpler-adoption)
- [When Continuous Ingestion Breaks Traditional Postgres](https://www.tigerdata.com/blog/when-continuous-ingestion-breaks-traditional-postgres)
- [Why Adding More Indexes Eventually Makes Things Worse](https://www.tigerdata.com/blog/why-adding-more-indexes-eventually-makes-things-worse)
- [The IIoT PostgreSQL Performance Envelope](https://www.tigerdata.com/blog/the-iiot-postgresql-performance-envelope)
- [The Hidden Costs of Table Partitioning at Scale](https://www.tigerdata.com/blog/hidden-costs-table-partitioning-scale)
- [Vertical Scaling: Buying Time You Can't Afford](https://www.tigerdata.com/blog/vertical-scaling-buying-time-you-cant-afford)
- [Six Signs That Postgres Tuning Won't Fix Your Performance Problems](https://www.tigerdata.com/blog/six-signs-postgres-tuning-wont-fix-performance-problems)
- [Elasticsearch's Hybrid Search, Now in Postgres](https://www.tigerdata.com/blog/elasticsearchs-hybrid-search-now-in-postgres-bm25-vector-rrf)
- [Hybrid Search with TimescaleDB: Vector, Keyword, and Temporal Filtering](https://www.tigerdata.com/blog/hybrid-search-timescaledb-vector-keyword-temporal-filtering)
- [It's 2026, Just Use Postgres](https://www.tigerdata.com/blog/its-2026-just-use-postgres)
- [10 Elasticsearch Production Issues (and How Postgres Avoids Them)](https://www.tigerdata.com/blog/10-elasticsearch-production-issues-how-postgres-avoids-them)
- [Top 9 PostgreSQL Extensions Used by Tiger Data Customers in 2026](https://www.tigerdata.com/blog/top-9-postgresql-extensions-used-by-tiger-data-customers-2026)
- [You Don't Need Elasticsearch: BM25 is Now in Postgres](https://www.tigerdata.com/blog/you-dont-need-elasticsearch-bm25-is-now-in-postgres)
- [Lessons from Replit and Tiger Data on Storage for Agentic Experimentation](https://www.tigerdata.com/blog/lessons-replit-tiger-data-storage-agentic-experimentation)
- [Why MongoDB Is an Architectural Dead-End](https://www.tigerdata.com/blog/why-mongodb-is-an-architectural-dead-end)
- [TimescaleDB 2.22 & 2.23](https://www.tigerdata.com/blog/timescaledb-2-22-2-23-90x-faster-distinct-queries-postgres-18-support-configurable-columnstore-indexes-uuidv7)
- [Tiger Data and AWS Forge Unified Postgres Platform](https://www.tigerdata.com/blog/tiger-data-aws-forge-unified-postgres-platform-for-developers-devices-ai-agents)
- [The Big Shift in MCP: Why AI Guides Will Replace API Wrappers](https://www.tigerdata.com/blog/big-shift-mcp-why-ai-guides-will-replace-api-wrappers)
- [We Taught AI to Write Real Postgres Code (And Open Sourced It)](https://www.tigerdata.com/blog/we-taught-ai-to-write-real-postgres-code-open-sourced-it)
- [Benchmark Results: The Fastest Time-Series Database on Azure](https://www.tigerdata.com/blog/benchmark-results-fastest-time-series-database-azure)
- [Tiger Lake: A New Architecture for Real-Time Analytical Systems and Agents](https://www.tigerdata.com/blog/tiger-lake-a-new-architecture-for-real-time-analytical-systems-and-agents)
- [Why Cursor is About to Ditch Vector Search (and You Should Too)](https://www.tigerdata.com/blog/why-cursor-is-about-to-ditch-vector-search-and-you-should-too)
- [Farewell, Timestream: How and Why to Migrate](https://www.tigerdata.com/blog/so-long-timestream-how-and-why-to-migrate-before-its-too-late)
- [Blocked Bloom Filters in Tiger Postgres' Native Columnstore](https://www.tigerdata.com/blog/blocked-bloom-filters-speeding-up-point-lookups-in-tiger-postgres-native-columnstore)
- [Speed Without Sacrifice: Building the Modern PostgreSQL](https://www.tigerdata.com/blog/timescale-becomes-tigerdata)
- [The Database Meets the Lakehouse](https://www.tigerdata.com/blog/the-database-meets-the-lakehouse-toward-a-unified-architecture-for-modern-applications)
- [Postgres That Scales With You: Read Replica Sets and Enhanced Storage](https://www.tigerdata.com/blog/postgres-that-scales-with-you-read-replica-sets-and-enhanced-storage)
- [Speed Without Sacrifice: TimescaleDB 2.20](https://www.tigerdata.com/blog/speed-without-sacrifice-2500x-faster-distinct-queries-10x-faster-upserts-bloom-filters-timescaledb-2-20)
- [Architecting Agentic AI: Lessons From Timescale's SQL Assistant](https://www.tigerdata.com/blog/architecting-agentic-ai-lessons-learned-engineering-timescales-sql-assistant)
- [Postgres vs Qdrant: Why Postgres Wins for AI and Vector Workloads](https://www.tigerdata.com/blog/why-postgres-wins-for-ai-and-vector-workloads)
- [How Timescale Solves Real-Time Analytics in Postgres](https://www.tigerdata.com/blog/how-timescale-solves-real-time-analytics-in-postgres)
- [Agentic RAG Best Practices: Building AI Apps With PostgreSQL](https://www.tigerdata.com/blog/agentic-rag-best-practices-guide-for-building-ai-apps-with-postgresql)
- [Not All Analytics Are Equal: Benchmarking for Real-Time Analytics](https://www.tigerdata.com/blog/benchmarking-databases-for-real-time-analytics-applications)
- [Building a RAG System With Claude, PostgreSQL & Python on AWS](https://www.tigerdata.com/blog/building-a-rag-system-with-claude-postgresql-python-on-aws)
- [You, Too, Can Scale Postgres to 3 PB](https://www.tigerdata.com/blog/scaling-postgresql-to-petabyte-scale)
- [PostgreSQL Indexes for Columnstore](https://www.tigerdata.com/blog/postgresql-indexes-for-columnstore)
- [Vector Databases Are the Wrong Abstraction](https://www.tigerdata.com/blog/vector-databases-are-the-wrong-abstraction)
- [How to Choose a Vector Database](https://www.tigerdata.com/blog/how-to-choose-a-vector-database)
- [RAG Is More Than Just Vector Search](https://www.tigerdata.com/blog/rag-is-more-than-just-vector-search)
- [PostgreSQL as a Vector Database: A Pgvector Tutorial](https://www.tigerdata.com/blog/postgresql-as-a-vector-database-using-pgvector)
- [How to Build LLM Applications With Pgvector in LangChain](https://www.tigerdata.com/blog/how-to-build-llm-applications-with-pgvector-vector-store-in-langchain)
- [Pgvector vs Pinecone: Performance and Cost Comparison](https://www.tigerdata.com/blog/pgvector-vs-pinecone)
- [PostgreSQL and Pgvector: Faster Than Pinecone, 75% Cheaper](https://www.tigerdata.com/blog/pgvector-is-now-as-fast-as-pinecone-at-75-less-cost)
- [How We Made PostgreSQL as Fast as Pinecone for Vector Data](https://www.tigerdata.com/blog/how-we-made-postgresql-as-fast-as-pinecone-for-vector-data)
- [Handling Billions of Rows in PostgreSQL](https://www.tigerdata.com/blog/handling-billions-of-rows-in-postgresql)
- [Simplify Your Tech Stack: Use PostgreSQL for Everything](https://www.tigerdata.com/blog/how-to-collapse-your-stack-using-postgresql-for-everything)
- [Why PostgreSQL Is the Bedrock for the Future of Data](https://www.tigerdata.com/blog/postgres-for-everything)
- [13 Tips to Improve PostgreSQL Insert Performance](https://www.tigerdata.com/blog/13-tips-to-improve-postgresql-insert-performance)
- [Best Practices for Query Optimization on PostgreSQL](https://www.tigerdata.com/blog/best-practices-for-query-optimization-in-postgresql)
- [PostgreSQL vs MySQL: Which to Choose and When](https://www.tigerdata.com/blog/postgresql-vs-mysql-which-to-choose-and-when)
- [How Different Databases Handle High-Cardinality Data](https://www.tigerdata.com/blog/how-different-databases-handle-high-cardinality-data)
- [Top 8 PostgreSQL Extensions](https://www.tigerdata.com/blog/top-8-postgresql-extensions)
- [Amazon Aurora vs RDS: Understanding the Difference](https://www.tigerdata.com/blog/amazon-aurora-vs-rds-understanding-the-difference)
- [Why Is RDS So Expensive?](https://www.tigerdata.com/blog/understanding-rds-pricing-and-costs)
- [What Is ClickHouse and How Does It Compare to TimescaleDB](https://www.tigerdata.com/blog/what-is-clickhouse-how-does-it-compare-to-postgresql-and-timescaledb-and-how-does-it-perform-for-time-series-data)
- [TimescaleDB vs InfluxDB for Time-Series Data](https://www.tigerdata.com/blog/timescaledb-vs-influxdb-for-time-series-data-timescale-influx-sql-nosql-36489299877)
- [What InfluxDB Got Wrong](https://www.tigerdata.com/blog/what-influxdb-got-wrong)
- [How to Store Time-Series Data in MongoDB (and Why That's a Bad Idea)](https://www.tigerdata.com/blog/how-to-store-time-series-data-mongodb-vs-timescaledb-postgresql-a73939734016)
- [TimescaleDB vs Amazon Timestream](https://www.tigerdata.com/blog/timescaledb-vs-amazon-timestream-6000x-higher-inserts-175x-faster-queries-220x-cheaper)
- [Amazon Aurora vs PostgreSQL with TimescaleDB](https://www.tigerdata.com/blog/benchmarking-amazon-aurora-vs-postgresql)
- [Timescale vs Amazon RDS for Time-Series](https://www.tigerdata.com/blog/timescale-cloud-vs-amazon-rds-postgresql-up-to-350-times-faster-queries-44-faster-ingest-95-storage-savings-for-time-series-data)
- [TimescaleDB vs PostgreSQL for Time-Series](https://www.tigerdata.com/blog/timescaledb-vs-6a696248104e)
- [Why SQL Is Beating NoSQL](https://www.tigerdata.com/blog/why-sql-beating-nosql-what-this-means-for-future-of-data-time-series-database-348b777b847a)

## Blog Post Topics

- [AI](https://www.tigerdata.com/blog/tag/ai)
- [AI agents](https://www.tigerdata.com/blog/tag/ai-agents)
- [Analytics](https://www.tigerdata.com/blog/tag/analytics)
- [Benchmarks & Comparisons](https://www.tigerdata.com/blog/tag/benchmarks-comparisons)
- [Columnstore](https://www.tigerdata.com/blog/tag/columnstore)
- [Continuous Aggregates](https://www.tigerdata.com/blog/tag/continuous-aggregates)
- [Hypertables](https://www.tigerdata.com/blog/tag/hypertables)
- [IoT](https://www.tigerdata.com/blog/tag/iot)
- [pg_textsearch](https://www.tigerdata.com/blog/tag/pg_textsearch)
- [pgvector](https://www.tigerdata.com/blog/tag/pgvector)
- [PostgreSQL](https://www.tigerdata.com/blog/tag/postgresql)
- [PostgreSQL Performance](https://www.tigerdata.com/blog/tag/postgresql-performance)
- [RAG](https://www.tigerdata.com/blog/tag/rag)
- [Real-Time Analytics](https://www.tigerdata.com/blog/tag/real-time-analytics)
- [Scaling PostgreSQL](https://www.tigerdata.com/blog/tag/scaling-postgresql)
- [Time Series Data](https://www.tigerdata.com/blog/tag/time-series-data)
- [TimescaleDB](https://www.tigerdata.com/blog/tag/timescaledb)
- [Tiger Cloud](https://www.tigerdata.com/blog/tag/tiger-cloud)
- [Vector Embeddings](https://www.tigerdata.com/blog/tag/vector-embeddings)