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Tiger MCP vs. a Generic Postgres MCP Server
Time Series Forecasting with PostgreSQL: Methods, SQL Examples, and When to Use PythonTime-Series Analysis and Forecasting With Python Why Consider Using PostgreSQL for Time-Series Data?Time-Series Analysis in RWhat Is Temporal Data?Understanding Autoregressive Time-Series ModelingAlternatives to TimescaleStationary Time-Series AnalysisWhat Are Open-Source Time-Series Databases—Understanding Your OptionsIs Your Data Time Series? Data Types Supported by PostgreSQL and TimescaleUnderstanding Database Workloads: Variable, Bursty, and Uniform PatternsThe Best Time-Series Databases Compared (2026)Time Series Anomaly Detection: Methods, SQL, and Real-Time ImplementationAWS Timestream Alternatives: Your Migration Options After LiveAnalyticsTime-Series Database: What It Is, How It Works, and When You Need OneWhat Is a Time Series and How Is It Used?How to Work With Time Series in Python?Tools for Working With Time-Series Analysis in PythonGuide to Time-Series Analysis in PythonCreating a Fast Time-Series Graph With Postgres Materialized Views
OpenTelemetry Backend: Choosing Metrics Storage That Doesn't Lock You InGPU Cluster Monitoring: The Database Behind AI Infrastructure TelemetryWhat Is DCIM Software, and What Database Does It Use?Data Center Power Monitoring: The Database Layer Behind PUE, PDU, and Sustainability MetricsPrometheus Long-Term Storage: Thanos, Mimir, VictoriaMetrics, and PostgreSQL Compared
Understanding PostgreSQLUnderstanding SQL Aggregate FunctionsUnderstanding the rank() and dense_rank() Functions in PostgreSQLUnderstanding percentile_cont() and percentile_disc() in PostgreSQLStructured vs. Semi-Structured vs. Unstructured Data in PostgreSQLOptimizing Your Database: A Deep Dive into PostgreSQL Data TypesPostgres Cheat SheetHow to Address ‘Error: Could Not Resize Shared Memory Segment’ Understanding the Postgres extract() FunctionUnderstanding FROM in PostgreSQL (With Examples)How to Install PostgreSQL on LinuxHow to Install PostgreSQL on MacOSUnderstanding FILTER in PostgreSQL (With Examples)Understanding HAVING in PostgreSQL (With Examples)5 Common Connection Errors in PostgreSQL and How to Solve ThemUnderstanding GROUP BY in PostgreSQL (With Examples)How to Fix No Partition of Relation Found for Row in Postgres DatabasesUnderstanding LIMIT in PostgreSQL (With Examples)How to Fix Transaction ID Wraparound ExhaustionUnderstanding ORDER BY in PostgreSQL (With Examples)Understanding PostgreSQL FunctionsUnderstanding WINDOW in PostgreSQL (With Examples)Understanding PostgreSQL WITHIN GROUPPostgreSQL Mathematical Functions: Enhancing Coding EfficiencyUnderstanding DISTINCT in PostgreSQL (With Examples)Understanding WHERE in PostgreSQL (With Examples)Understanding OFFSET in PostgreSQL (With Examples)Understanding the Postgres string_agg FunctionUnderstanding PostgreSQL SELECTWhat Characters Are Allowed in PostgreSQL Strings?What Is Data Transformation, and Why Is It Important?What Is Data Compression and How Does It Work?Using PostgreSQL UPDATE With JOINUnderstanding PostgreSQL User-Defined FunctionsUnderstanding Foreign Keys in PostgreSQLData Processing With PostgreSQL Window FunctionsUnderstanding PostgreSQL Date and Time FunctionsWhat Is a PostgreSQL Full Outer Join?What Is a PostgreSQL Inner Join?What Is a PostgreSQL Left Join? And a Right Join?PostgreSQL Join Type TheoryA Guide to PostgreSQL ViewsStrategies for Improving Postgres JOIN PerformanceUnderstanding PostgreSQL's COALESCE FunctionUnderstanding PostgreSQL Array FunctionsUnderstanding PostgreSQL Conditional FunctionsUsing PostgreSQL String Functions for Improved Data AnalysisPostgreSQL vs. Cassandra: The Decision Framework for Time-Series and Write-Heavy WorkloadsPostgreSQL Joins : A SummaryWhat Is a PostgreSQL Cross Join?Data Partitioning: What It Is and Why It MattersSelf-Hosted or Cloud Database? A Countryside Reflection on Infrastructure ChoicesUnderstanding ACID Compliance
Connect Your AI Coding Agent to Tiger Cloud: Setup for Cursor, Claude Code, VS Code, and MoreHow to Monitor and Optimize PostgreSQL Index PerformanceGuide to PostgreSQL PerformancePostgreSQL Performance Tuning: Designing and Implementing Your Database SchemaPostgreSQL Performance Tuning: Key ParametersPostgreSQL Performance Tuning: Optimizing Database IndexesDetermining the Optimal Postgres Partition SizeWhen to Consider Postgres PartitioningAn Intro to Data Modeling on PostgreSQLDesigning Your Database Schema: Wide vs. Narrow Postgres TablesGuide to PostgreSQL Database OperationsBest Practices for Time-Series Data Modeling: Single or Multiple Partitioned Table(s) a.k.a. Hypertables Best Practices for (Time-)Series Metadata Tables A PostgreSQL Database Replication GuideA Guide to Data Analysis on PostgreSQLHow to Query JSONB in PostgreSQLHow to Compute Standard Deviation With PostgreSQLPostgreSQL Performance Tuning: How to Size Your DatabaseSQL/JSON Data Model and JSON in SQL: A PostgreSQL PerspectiveUnderstanding PostgreSQL TablespacesTop PostgreSQL Drivers for PythonPg_partman vs. Hypertables for Postgres PartitioningExplaining PostgreSQL EXPLAINGuide to PostgreSQL Database DesignHow PostgreSQL Data Aggregation WorksBuilding a Scalable DatabaseA Guide to pg_restore (and pg_restore Example)How to Index JSONB Columns in PostgreSQLWhat Is Audit Logging and How to Enable It in PostgreSQLOptimizing Array Queries With GIN Indexes in PostgreSQLHow to Reduce Bloat in Large PostgreSQL TablesGuide to PostgreSQL SecurityHow to Query JSON Metadata in PostgreSQLRecursive Query in SQL: What It Is, and How to Write OneNavigating Growing PostgreSQL Tables With Partitioning (and More)What Is a PostgreSQL Temporary View?How to Use PostgreSQL for Data TransformationHandling Large Objects in PostgresHow to Use Psycopg2: The PostgreSQL Adapter for PythonA Guide to Scaling PostgreSQLAWS Timestream for InfluxDB Alternative: When You Need to Look FurtherHow to Migrate from AWS Timestream to PostgreSQL: A Technical GuideHow to Choose a Database: A Decision Framework for Modern ApplicationsGuide to Postgres Data Management
Time-Series Database High Availability and Disaster RecoveryBest Practices for Scaling PostgreSQLHow to Store Video in PostgreSQL Using BYTEABest Practices for Postgres PerformanceHow to Design Your PostgreSQL Database: Two Schema ExamplesBest Practices for PostgreSQL Database OperationsHow to Manage Your Data With Data Retention PoliciesBest Practices for PostgreSQL Data AnalysisBest Practices for PostgreSQL AggregationBest Practices for Postgres Database ReplicationBest Practices for Postgres Data ManagementHow to Use a Common Table Expression (CTE) in SQLHow to Use PostgreSQL for Data NormalizationTesting Postgres Ingest: INSERT vs. Batch INSERT vs. COPYBest Practices for Postgres SecurityHow to Handle High-Cardinality Data in PostgreSQLPostgreSQL Compression: Every Option, When To Use Each, and What To Expect
The Best PostgreSQL Extensions for Analytics Workloads: A Field GuidePostgreSQL Extensions: amcheckPostgreSQL Extensions: Unlocking Multidimensional Points With Cube PostgreSQL Extensions: hstorePostgreSQL Extensions: ltreePostgreSQL Extensions: Secure Your Time-Series Data With pgcryptoPostgreSQL Extensions: pg_prewarmPostgreSQL Extensions: pgRoutingPostgreSQL Extensions: pg_stat_statementsPostgreSQL Extensions: Database Testing With pgTAPPostgreSQL Extensions: Install pg_trgm for Data MatchingPostgreSQL Extensions: PL/pgSQLPostgreSQL Extensions: Using PostGIS and Timescale for Advanced Geospatial InsightsPostgreSQL Extensions: Intro to uuid-osspTurning PostgreSQL Into a Vector Database With pgvector
What Is ClickHouse and How Does It Compare to PostgreSQL and TimescaleDB for Time Series?Timescale vs. Amazon RDS PostgreSQL: Up to 350x Faster Queries, 44 % Faster Ingest, 95 % Storage Savings for Time-Series DataWhat We Learned From Benchmarking Amazon Aurora PostgreSQL ServerlessTimescaleDB vs. Amazon Timestream: 6,000x Higher Inserts, 5-175x Faster Queries, 150-220x CheaperHow to Store Time-Series Data in MongoDB and Why That’s a Bad IdeaPostgreSQL + TimescaleDB: 1,000x Faster Queries, 90 % Data Compression, and Much MoreEye or the Tiger: Benchmarking Cassandra vs. TimescaleDB for Time-Series Data
MES Data Architecture: Modeling Work Orders, Genealogy, and Historian IntegrationMeter Data Management: Why AMI Interval Data Breaks Legacy MDM SystemsGeofencing and Driver Behavior Analytics: How to Store Both in One DatabaseWhich Database for a Digital Twin? Digital Twin Database Options ComparedGrid Modernization: Why the Data Layer Decides Whether It WorksFleet Telemetry Database: How to Store and Query Vehicle Sensor Data at ScaleUnderstanding IoT (Internet of Things)A Beginner’s Guide to IIoT and Industry 4.0DERMS Database: Solving the Single Source of DER Data ProblemForecasting the Physical World: Foundation Models for Predictive MaintenanceBuilding Energy Management System: The Data Layer Behind Energy Monitoring, Sub-Metering, and M&VSmart Building Analytics: Turning BMS Telemetry Into Occupancy, Energy, and HVAC InsightEV Charging Load ManagementBuilding Management System Database: Architecture for BACnet, Modbus, and Smart Building Sensor Data at ScaleSmart Grid Data Platform: Architecting for SCADA, AMI, PMU, and DERMS Data at ScaleDigital Twin Architecture: The Database and Data Model Behind a Digital TwinCAN Bus Data Logger: Decoding DBC and J1939 Signals into PostgresPredictive Maintenance Database Architecture: Storing and Querying Sensor Data for Failure PredictionPhysical AI Telemetry: Database Architecture for Autonomous-Vehicle FleetsPlant Historian: What It Captures and Where the Analytics Layer BeginsManufacturing Analytics Database: Architecture for Real-Time Production DataRobot Fleet Telemetry: Database Architecture for AMR, Industrial Robot, and Service Robot DataData Center Monitoring: What Database Stores Your Telemetry?EV Charging Management System: Architecture, OCPP Data, and the Right DatabaseIIoT Database Requirements: Six Things Your Database Must DoWater Utilities Database: How to Store and Query SCADA, AMI, and Quality Data at ScaleWhat Is an Edge Database? On-Device Storage, Sync Patterns, and Choosing the Right StackData Historian vs. Time-Series Database: How to Choose and When to SwitchWhat Is a Data Historian?The Best Databases for IoT in 2026: A Practical ComparisonHow Hopthru Powers Real-Time Transit Analytics From a 1 TB TableStoring IoT Data: 8 Reasons Why You Should Use PostgreSQLHow to Simulate a Basic IoT Sensor Dataset on PostgreSQLFrom Ingest to Insights in Milliseconds: Everactive's Tech Transformation With TimescaleHow Ndustrial Is Providing Fast Real-Time Queries and Safely Storing Client Data With 97 % CompressionWhy You Should Use PostgreSQL for Industrial IoT Data Migrating a Low-Code IoT Platform Storing 20M Records/DayHow United Manufacturing Hub Is Introducing Open Source to ManufacturingBuilding IoT Pipelines for Faster Analytics With IoT CoreVisualizing IoT Data at Scale With Hopara and TimescaleDB
A Brief History of AI: How Did We Get Here, and What's Next?A Beginner’s Guide to Vector EmbeddingsPostgreSQL as a Vector Database: A Pgvector TutorialUsing Pgvector With PythonHow to Choose a Vector DatabaseVector Databases Are the Wrong AbstractionUnderstanding DiskANNA Guide to Cosine SimilarityStreaming DiskANN: How We Made PostgreSQL as Fast as Pinecone for Vector DataImplementing Cosine Similarity in PythonVector Database Basics: HNSWVector Database Options for AWSVector Store vs. Vector Database: Understanding the ConnectionPgvector vs. Pinecone: Vector Database Performance and Cost ComparisonHow to Build LLM Applications With Pgvector Vector Store in LangChainHow to Implement RAG With Amazon Bedrock and LangChainRAG Is More Than Just Vector SearchRefining Vector Search Queries With Time Filters in Pgvector: A TutorialUnderstanding Semantic SearchWhat Is Vector Search? Text-to-SQL: A Developer’s Zero-to-Hero GuideWhen Should You Use Full-Text Search vs. Vector Search?HNSW vs. DiskANNVector Search vs Semantic SearchBuilding AI Agents with Persistent Memory: A Unified Database ApproachNearest Neighbor Indexes: What Are IVFFlat Indexes in Pgvector and How Do They WorkPostgreSQL Hybrid Search Using Pgvector and CohereBuilding an AI Image Gallery With OpenAI CLIP, Claude Sonnet 3.5, and Pgvector
PostgreSQL OLTP: Transactions, Scale, and the Architecture That Makes It WorkHow to Choose an OLAP Database: A Practical Decision Guide (2026)Data Analytics vs. Real-Time Analytics: How to Pick Your Database (and Why It Should Be PostgreSQL)How to Choose a Real-Time Analytics DatabaseColumnar Databases vs. Row-Oriented Databases: Which to Choose?What Is the Best Database for Real-Time AnalyticsPostgreSQL as a Real-Time Analytics DatabaseUnderstanding OLAP: What It Is, How It Differs From OLTP, and Running It on PostgreSQLHow to Build an IoT Pipeline for Real-Time Analytics in PostgreSQL
Alternatives to RDSWhy Is RDS so Expensive? Understanding RDS Pricing and CostsEstimating RDS CostsHow to Migrate From AWS RDS for PostgreSQL to TimescaleAmazon Aurora vs. RDS: Understanding the Difference
5 InfluxDB Alternatives for Your Time-Series Data8 Reasons to Choose Timescale as Your InfluxDB Alternative InfluxQL, Flux, and SQL: Which Query Language Is Best? (With Cheatsheet)What InfluxDB Got WrongTimescaleDB vs. InfluxDB: Purpose Built Differently for Time-Series Data
5 Ways to Monitor Your PostgreSQL DatabaseHow to Migrate Your Data to Timescale (3 Ways)Postgres TOAST vs. Timescale CompressionBuilding Python Apps With PostgreSQL: A Developer's GuideData Visualization in PostgreSQL With Apache SupersetMore Time-Series Data Analysis, Fewer Lines of Code: Meet HyperfunctionsIs Postgres Partitioning Really That Hard? An Introduction To HypertablesPostgreSQL Materialized Views and Where to Find ThemTime-Series Downsampling: The Complete Guide to Tiered Data Resolution in SQLContinuous Aggregates: Incremental Materialized Views for Time-Series DataComplete Guide: Migrating from MongoDB to Tiger Data (Step-by-Step)Timescale Tips: Testing Your Chunk Size
Postgres cheat sheet
HomeAlternativesTime-series basicsData Center & Infrastructure TelemetryPostgres basicsPostgres guidesPostgres best practices
Home
Tiger MCP vs. a Generic Postgres MCP Server
Time Series Forecasting with PostgreSQL: Methods, SQL Examples, and When to Use PythonTime-Series Analysis and Forecasting With Python Why Consider Using PostgreSQL for Time-Series Data?Time-Series Analysis in RWhat Is Temporal Data?Understanding Autoregressive Time-Series ModelingAlternatives to TimescaleStationary Time-Series AnalysisWhat Are Open-Source Time-Series Databases—Understanding Your OptionsIs Your Data Time Series? Data Types Supported by PostgreSQL and TimescaleUnderstanding Database Workloads: Variable, Bursty, and Uniform PatternsThe Best Time-Series Databases Compared (2026)Time Series Anomaly Detection: Methods, SQL, and Real-Time ImplementationAWS Timestream Alternatives: Your Migration Options After LiveAnalyticsTime-Series Database: What It Is, How It Works, and When You Need OneWhat Is a Time Series and How Is It Used?How to Work With Time Series in Python?Tools for Working With Time-Series Analysis in PythonGuide to Time-Series Analysis in PythonCreating a Fast Time-Series Graph With Postgres Materialized Views
OpenTelemetry Backend: Choosing Metrics Storage That Doesn't Lock You InGPU Cluster Monitoring: The Database Behind AI Infrastructure TelemetryWhat Is DCIM Software, and What Database Does It Use?Data Center Power Monitoring: The Database Layer Behind PUE, PDU, and Sustainability MetricsPrometheus Long-Term Storage: Thanos, Mimir, VictoriaMetrics, and PostgreSQL Compared
Understanding PostgreSQLUnderstanding SQL Aggregate FunctionsUnderstanding the rank() and dense_rank() Functions in PostgreSQLUnderstanding percentile_cont() and percentile_disc() in PostgreSQLStructured vs. Semi-Structured vs. Unstructured Data in PostgreSQLOptimizing Your Database: A Deep Dive into PostgreSQL Data TypesPostgres Cheat SheetHow to Address ‘Error: Could Not Resize Shared Memory Segment’ Understanding the Postgres extract() FunctionUnderstanding FROM in PostgreSQL (With Examples)How to Install PostgreSQL on LinuxHow to Install PostgreSQL on MacOSUnderstanding FILTER in PostgreSQL (With Examples)Understanding HAVING in PostgreSQL (With Examples)5 Common Connection Errors in PostgreSQL and How to Solve ThemUnderstanding GROUP BY in PostgreSQL (With Examples)How to Fix No Partition of Relation Found for Row in Postgres DatabasesUnderstanding LIMIT in PostgreSQL (With Examples)How to Fix Transaction ID Wraparound ExhaustionUnderstanding ORDER BY in PostgreSQL (With Examples)Understanding PostgreSQL FunctionsUnderstanding WINDOW in PostgreSQL (With Examples)Understanding PostgreSQL WITHIN GROUPPostgreSQL Mathematical Functions: Enhancing Coding EfficiencyUnderstanding DISTINCT in PostgreSQL (With Examples)Understanding WHERE in PostgreSQL (With Examples)Understanding OFFSET in PostgreSQL (With Examples)Understanding the Postgres string_agg FunctionUnderstanding PostgreSQL SELECTWhat Characters Are Allowed in PostgreSQL Strings?What Is Data Transformation, and Why Is It Important?What Is Data Compression and How Does It Work?Using PostgreSQL UPDATE With JOINUnderstanding PostgreSQL User-Defined FunctionsUnderstanding Foreign Keys in PostgreSQLData Processing With PostgreSQL Window FunctionsUnderstanding PostgreSQL Date and Time FunctionsWhat Is a PostgreSQL Full Outer Join?What Is a PostgreSQL Inner Join?What Is a PostgreSQL Left Join? And a Right Join?PostgreSQL Join Type TheoryA Guide to PostgreSQL ViewsStrategies for Improving Postgres JOIN PerformanceUnderstanding PostgreSQL's COALESCE FunctionUnderstanding PostgreSQL Array FunctionsUnderstanding PostgreSQL Conditional FunctionsUsing PostgreSQL String Functions for Improved Data AnalysisPostgreSQL vs. Cassandra: The Decision Framework for Time-Series and Write-Heavy WorkloadsPostgreSQL Joins : A SummaryWhat Is a PostgreSQL Cross Join?Data Partitioning: What It Is and Why It MattersSelf-Hosted or Cloud Database? A Countryside Reflection on Infrastructure ChoicesUnderstanding ACID Compliance
Connect Your AI Coding Agent to Tiger Cloud: Setup for Cursor, Claude Code, VS Code, and MoreHow to Monitor and Optimize PostgreSQL Index PerformanceGuide to PostgreSQL PerformancePostgreSQL Performance Tuning: Designing and Implementing Your Database SchemaPostgreSQL Performance Tuning: Key ParametersPostgreSQL Performance Tuning: Optimizing Database IndexesDetermining the Optimal Postgres Partition SizeWhen to Consider Postgres PartitioningAn Intro to Data Modeling on PostgreSQLDesigning Your Database Schema: Wide vs. Narrow Postgres TablesGuide to PostgreSQL Database OperationsBest Practices for Time-Series Data Modeling: Single or Multiple Partitioned Table(s) a.k.a. Hypertables Best Practices for (Time-)Series Metadata Tables A PostgreSQL Database Replication GuideA Guide to Data Analysis on PostgreSQLHow to Query JSONB in PostgreSQLHow to Compute Standard Deviation With PostgreSQLPostgreSQL Performance Tuning: How to Size Your DatabaseSQL/JSON Data Model and JSON in SQL: A PostgreSQL PerspectiveUnderstanding PostgreSQL TablespacesTop PostgreSQL Drivers for PythonPg_partman vs. Hypertables for Postgres PartitioningExplaining PostgreSQL EXPLAINGuide to PostgreSQL Database DesignHow PostgreSQL Data Aggregation WorksBuilding a Scalable DatabaseA Guide to pg_restore (and pg_restore Example)How to Index JSONB Columns in PostgreSQLWhat Is Audit Logging and How to Enable It in PostgreSQLOptimizing Array Queries With GIN Indexes in PostgreSQLHow to Reduce Bloat in Large PostgreSQL TablesGuide to PostgreSQL SecurityHow to Query JSON Metadata in PostgreSQLRecursive Query in SQL: What It Is, and How to Write OneNavigating Growing PostgreSQL Tables With Partitioning (and More)What Is a PostgreSQL Temporary View?How to Use PostgreSQL for Data TransformationHandling Large Objects in PostgresHow to Use Psycopg2: The PostgreSQL Adapter for PythonA Guide to Scaling PostgreSQLAWS Timestream for InfluxDB Alternative: When You Need to Look FurtherHow to Migrate from AWS Timestream to PostgreSQL: A Technical GuideHow to Choose a Database: A Decision Framework for Modern ApplicationsGuide to Postgres Data Management
Time-Series Database High Availability and Disaster RecoveryBest Practices for Scaling PostgreSQLHow to Store Video in PostgreSQL Using BYTEABest Practices for Postgres PerformanceHow to Design Your PostgreSQL Database: Two Schema ExamplesBest Practices for PostgreSQL Database OperationsHow to Manage Your Data With Data Retention PoliciesBest Practices for PostgreSQL Data AnalysisBest Practices for PostgreSQL AggregationBest Practices for Postgres Database ReplicationBest Practices for Postgres Data ManagementHow to Use a Common Table Expression (CTE) in SQLHow to Use PostgreSQL for Data NormalizationTesting Postgres Ingest: INSERT vs. Batch INSERT vs. COPYBest Practices for Postgres SecurityHow to Handle High-Cardinality Data in PostgreSQLPostgreSQL Compression: Every Option, When To Use Each, and What To Expect
The Best PostgreSQL Extensions for Analytics Workloads: A Field GuidePostgreSQL Extensions: amcheckPostgreSQL Extensions: Unlocking Multidimensional Points With Cube PostgreSQL Extensions: hstorePostgreSQL Extensions: ltreePostgreSQL Extensions: Secure Your Time-Series Data With pgcryptoPostgreSQL Extensions: pg_prewarmPostgreSQL Extensions: pgRoutingPostgreSQL Extensions: pg_stat_statementsPostgreSQL Extensions: Database Testing With pgTAPPostgreSQL Extensions: Install pg_trgm for Data MatchingPostgreSQL Extensions: PL/pgSQLPostgreSQL Extensions: Using PostGIS and Timescale for Advanced Geospatial InsightsPostgreSQL Extensions: Intro to uuid-osspTurning PostgreSQL Into a Vector Database With pgvector
What Is ClickHouse and How Does It Compare to PostgreSQL and TimescaleDB for Time Series?Timescale vs. Amazon RDS PostgreSQL: Up to 350x Faster Queries, 44 % Faster Ingest, 95 % Storage Savings for Time-Series DataWhat We Learned From Benchmarking Amazon Aurora PostgreSQL ServerlessTimescaleDB vs. Amazon Timestream: 6,000x Higher Inserts, 5-175x Faster Queries, 150-220x CheaperHow to Store Time-Series Data in MongoDB and Why That’s a Bad IdeaPostgreSQL + TimescaleDB: 1,000x Faster Queries, 90 % Data Compression, and Much MoreEye or the Tiger: Benchmarking Cassandra vs. TimescaleDB for Time-Series Data
MES Data Architecture: Modeling Work Orders, Genealogy, and Historian IntegrationMeter Data Management: Why AMI Interval Data Breaks Legacy MDM SystemsGeofencing and Driver Behavior Analytics: How to Store Both in One DatabaseWhich Database for a Digital Twin? Digital Twin Database Options ComparedGrid Modernization: Why the Data Layer Decides Whether It WorksFleet Telemetry Database: How to Store and Query Vehicle Sensor Data at ScaleUnderstanding IoT (Internet of Things)A Beginner’s Guide to IIoT and Industry 4.0DERMS Database: Solving the Single Source of DER Data ProblemForecasting the Physical World: Foundation Models for Predictive MaintenanceBuilding Energy Management System: The Data Layer Behind Energy Monitoring, Sub-Metering, and M&VSmart Building Analytics: Turning BMS Telemetry Into Occupancy, Energy, and HVAC InsightEV Charging Load ManagementBuilding Management System Database: Architecture for BACnet, Modbus, and Smart Building Sensor Data at ScaleSmart Grid Data Platform: Architecting for SCADA, AMI, PMU, and DERMS Data at ScaleDigital Twin Architecture: The Database and Data Model Behind a Digital TwinCAN Bus Data Logger: Decoding DBC and J1939 Signals into PostgresPredictive Maintenance Database Architecture: Storing and Querying Sensor Data for Failure PredictionPhysical AI Telemetry: Database Architecture for Autonomous-Vehicle FleetsPlant Historian: What It Captures and Where the Analytics Layer BeginsManufacturing Analytics Database: Architecture for Real-Time Production DataRobot Fleet Telemetry: Database Architecture for AMR, Industrial Robot, and Service Robot DataData Center Monitoring: What Database Stores Your Telemetry?EV Charging Management System: Architecture, OCPP Data, and the Right DatabaseIIoT Database Requirements: Six Things Your Database Must DoWater Utilities Database: How to Store and Query SCADA, AMI, and Quality Data at ScaleWhat Is an Edge Database? On-Device Storage, Sync Patterns, and Choosing the Right StackData Historian vs. Time-Series Database: How to Choose and When to SwitchWhat Is a Data Historian?The Best Databases for IoT in 2026: A Practical ComparisonHow Hopthru Powers Real-Time Transit Analytics From a 1 TB TableStoring IoT Data: 8 Reasons Why You Should Use PostgreSQLHow to Simulate a Basic IoT Sensor Dataset on PostgreSQLFrom Ingest to Insights in Milliseconds: Everactive's Tech Transformation With TimescaleHow Ndustrial Is Providing Fast Real-Time Queries and Safely Storing Client Data With 97 % CompressionWhy You Should Use PostgreSQL for Industrial IoT Data Migrating a Low-Code IoT Platform Storing 20M Records/DayHow United Manufacturing Hub Is Introducing Open Source to ManufacturingBuilding IoT Pipelines for Faster Analytics With IoT CoreVisualizing IoT Data at Scale With Hopara and TimescaleDB
A Brief History of AI: How Did We Get Here, and What's Next?A Beginner’s Guide to Vector EmbeddingsPostgreSQL as a Vector Database: A Pgvector TutorialUsing Pgvector With PythonHow to Choose a Vector DatabaseVector Databases Are the Wrong AbstractionUnderstanding DiskANNA Guide to Cosine SimilarityStreaming DiskANN: How We Made PostgreSQL as Fast as Pinecone for Vector DataImplementing Cosine Similarity in PythonVector Database Basics: HNSWVector Database Options for AWSVector Store vs. Vector Database: Understanding the ConnectionPgvector vs. Pinecone: Vector Database Performance and Cost ComparisonHow to Build LLM Applications With Pgvector Vector Store in LangChainHow to Implement RAG With Amazon Bedrock and LangChainRAG Is More Than Just Vector SearchRefining Vector Search Queries With Time Filters in Pgvector: A TutorialUnderstanding Semantic SearchWhat Is Vector Search? Text-to-SQL: A Developer’s Zero-to-Hero GuideWhen Should You Use Full-Text Search vs. Vector Search?HNSW vs. DiskANNVector Search vs Semantic SearchBuilding AI Agents with Persistent Memory: A Unified Database ApproachNearest Neighbor Indexes: What Are IVFFlat Indexes in Pgvector and How Do They WorkPostgreSQL Hybrid Search Using Pgvector and CohereBuilding an AI Image Gallery With OpenAI CLIP, Claude Sonnet 3.5, and Pgvector
PostgreSQL OLTP: Transactions, Scale, and the Architecture That Makes It WorkHow to Choose an OLAP Database: A Practical Decision Guide (2026)Data Analytics vs. Real-Time Analytics: How to Pick Your Database (and Why It Should Be PostgreSQL)How to Choose a Real-Time Analytics DatabaseColumnar Databases vs. Row-Oriented Databases: Which to Choose?What Is the Best Database for Real-Time AnalyticsPostgreSQL as a Real-Time Analytics DatabaseUnderstanding OLAP: What It Is, How It Differs From OLTP, and Running It on PostgreSQLHow to Build an IoT Pipeline for Real-Time Analytics in PostgreSQL
Alternatives to RDSWhy Is RDS so Expensive? Understanding RDS Pricing and CostsEstimating RDS CostsHow to Migrate From AWS RDS for PostgreSQL to TimescaleAmazon Aurora vs. RDS: Understanding the Difference
5 InfluxDB Alternatives for Your Time-Series Data8 Reasons to Choose Timescale as Your InfluxDB Alternative InfluxQL, Flux, and SQL: Which Query Language Is Best? (With Cheatsheet)What InfluxDB Got WrongTimescaleDB vs. InfluxDB: Purpose Built Differently for Time-Series Data
5 Ways to Monitor Your PostgreSQL DatabaseHow to Migrate Your Data to Timescale (3 Ways)Postgres TOAST vs. Timescale CompressionBuilding Python Apps With PostgreSQL: A Developer's GuideData Visualization in PostgreSQL With Apache SupersetMore Time-Series Data Analysis, Fewer Lines of Code: Meet HyperfunctionsIs Postgres Partitioning Really That Hard? An Introduction To HypertablesPostgreSQL Materialized Views and Where to Find ThemTime-Series Downsampling: The Complete Guide to Tiered Data Resolution in SQLContinuous Aggregates: Incremental Materialized Views for Time-Series DataComplete Guide: Migrating from MongoDB to Tiger Data (Step-by-Step)Timescale Tips: Testing Your Chunk Size
Postgres cheat sheet
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2026 (c) Timescale, Inc., d/b/a Tiger Data.
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2026 (c) Timescale, Inc., d/b/a Tiger Data.
All rights reserved.

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By Tiger Data Team

Updated at Oct 1, 2026

Table of contents

    OpenTelemetry Backend: Choosing Metrics Storage That Doesn't Lock You In

    OpenTelemetry Backend Without Lock-In

    By Tiger Data Team

    Updated at Oct 1, 2026

    OpenTelemetry defines how telemetry gets collected and exported: the SDKs instrument your application code, the Collector receives and processes that data, and OTLP (the OpenTelemetry Protocol) is the wire format it all travels over. None of that is a database. OpenTelemetry deliberately doesn't ship a storage engine, which is why choosing an OpenTelemetry backend, the system that actually receives your exported metrics and makes them queryable over time, is a decision every team adopting OTel eventually has to make on its own.

    That decision matters more than it looks. The backend you pick determines your query language, your operational footprint, and, more than either of those, how easily you can leave later.

    The field breaks into a few camps. Purpose-built OLAP engines like ClickHouse, and ClickHouse-backed platforms like SigNoz, lead on raw throughput at high cardinality. SaaS-only platforms like Last9 trade infrastructure ownership for a proprietary data store. The Prometheus-ecosystem long-term storage layers, Grafana Mimir, Thanos, and VictoriaMetrics, extend an existing scraping and alerting stack. And general-purpose PostgreSQL, extended with Tiger Data's time-series capabilities, treats metrics as just another table you can join against the rest of your data.

    One disclosure upfront: Tiger Data sells a Postgres-based database, and Postgres is one of the seven options evaluated below. The bar is the same for every option here, including the sections where Tiger Data is not the right fit.

    Every backend below locks you into something: a query language, a proprietary storage format, a SaaS pricing model, or an operational stack you now have to run. None of them are lock-in-free. The real question isn't which one is fastest. It's which lock-in you can actually live with.

    What is an OpenTelemetry backend, and why doesn't OTel ship one?

    An OpenTelemetry backend is whatever system receives your exported metrics and makes them queryable over time, whether that's a columnar OLAP database, a Prometheus-compatible store, a SaaS platform, or Postgres. OpenTelemetry itself ships none of that: the SDKs instrument application code, the Collector batches and routes the data, and OTLP is the wire format everything speaks. None of those three pieces is a database, and OpenTelemetry has no opinion about what that system should be.

    That's a deliberate design choice. OpenTelemetry is vendor-neutral by design: the project's job ends at "here's your data, in a consistent format," and the surrounding ecosystem's job is to offer many valid places to send it. That's why the "which backend" question exists at all, and why the honest answer is: it depends.

    The mechanical reason backend choice stays modular is the Collector's exporter model. Because the Collector supports multiple exporters, OTLP-native, Prometheus remote-write-compatible, and a long list of vendor-specific options, a team can point the same instrumented application at a different backend without touching a line of application code. Swap the exporter configuration, not the instrumentation.

    One scoping note before going further: OpenTelemetry covers three stable signal types, metrics, traces, and logs, and this page is scoped to metrics storage specifically. The backend calculus for metrics, cardinality, retention windows, downsampling strategy, query pattern, differs meaningfully from how you'd think about trace or log storage, which tend to be dominated by different access patterns and retention economics.

    If you need the fundamentals first, temporality, gauges versus counters versus histograms, how the SDK actually builds a metric, what are OpenTelemetry metrics covers that ground in depth.

    Since OTel won't make this decision for you, the next section lays out the axes that separate one backend from another, before getting into a full comparison and a decision framework.

    The decision axes: what actually separates OpenTelemetry metrics backends

    Every backend in this comparison gets evaluated against the same six axes, in the same order, throughout the rest of this page:

    • Storage model. Columnar OLAP, relational, or a proprietary time-series format. This determines compression characteristics and how well a system handles high-cardinality label sets.

    • Query language. PromQL or MetricsQL (Prometheus-ecosystem native), SQL, or a proprietary query interface. This is the single biggest portability factor on this list: SQL skills transfer everywhere, while a proprietary query language is a skill investment that only pays off inside that one system.

    • Deployment model. Self-hosted, SaaS-only, or both. SaaS-only options trade operational burden for platform dependency.

    • Operational complexity. How many components need to be run, tuned, and kept healthy, a single binary versus a distributed system with half a dozen moving parts.

    • Lock-in risk. How hard it is to get your data, and your query logic, back out if you switch later. This is the axis most vendor comparison pages skip entirely.

    • Best-fit use case. The honest answer to "who is this actually right for," not "who should buy this."

    Here's the point that matters more than any individual axis: every backend below locks a team into something on one or more of these six dimensions. A query language. A storage format. A pricing model. An operational stack you now have to run. None of the seven options are lock-in-free. The goal isn't finding the one that avoids lock-in. It's picking the lock-in you can live with, deliberately, instead of discovering it eighteen months in.

    This is a framework, not a preview of conclusions. Rankings and recommendations come later, in the decision framework further down this page.

    Comparing the major OpenTelemetry metrics backends

    Each backend below accepts OpenTelemetry-exported metrics, either natively over OTLP or through the Collector's Prometheus remote-write-compatible exporter, but the seven differ fundamentally across the six axes above. The full comparison table comes first, then a profile of each.

    Comparison table

    Backend

    Storage model

    Query language

    Deployment model

    Operational complexity

    Lock-in risk

    Best-fit use case

    ClickHouse

    Columnar OLAP

    SQL (ClickHouse dialect)

    Self-hosted or ClickHouse Cloud

    Medium-high (cluster sizing and tuning at scale)

    Medium (SQL-like, but ClickHouse-specific dialect and storage format)

    Teams that need raw high-cardinality scale and are willing to run and tune a dedicated analytics cluster

    SigNoz

    Columnar OLAP (ClickHouse under the hood)

    Query Builder, PromQL, and ClickHouse SQL 

    Self-hosted or SigNoz Cloud

    Medium-high (inherits ClickHouse's operational model)

    Medium-high (tied to ClickHouse's storage format; SigNoz doesn't own its own storage layer)

    Teams that want an OTel-native UI and platform and are comfortable owning a ClickHouse cluster underneath it

    Last9

    Proprietary (events and time-series store)

    PromQL (Prometheus-compatible), plus Last9's own UI 

    SaaS-only (managed BYOC in your own cloud account available)

    Low (fully managed)

    High (no self-serve self-hosted option; data lives in a proprietary SaaS store)

    Teams that want a fully managed platform and are comfortable without a self-hosted or exportable-by-default option

    Grafana Mimir

    Object storage plus Kafka between the write and read paths  (3.0+)

    PromQL only

    Self-hosted or Grafana Cloud

    Very high (ingester, querier, compactor, store-gateway, distributor, Kafka)

    Medium (open storage format, but PromQL-only query surface)

    Large, multi-tenant Prometheus deployments already invested in the Grafana ecosystem

    Thanos

    Object storage (S3, GCS, Azure)

    PromQL only

    Self-hosted only

    High (Sidecar, Store Gateway, Querier, Compactor as separate processes)

    Medium (open storage format, but PromQL-only query surface)

    Existing Prometheus fleets that want long-term storage without changing scrape configuration

    VictoriaMetrics

    Custom columnar (open source) 

    MetricsQL (PromQL superset)

    Self-hosted (single binary or cluster) or VictoriaMetrics Cloud

    Low (single-node) or high (cluster mode)

    Medium (open-ish format, but MetricsQL is not pure PromQL)

    Cost-conscious teams that want the simplest possible single-binary deployment and don't need SQL

    PostgreSQL + Tiger Data

    Relational (hypertables) with columnstore compression

    SQL (native); Prometheus remote-write data ingested via Telegraf 

    Self-hosted (TimescaleDB Community or TimescaleDB Enterprise) or Tiger Cloud (managed)

    Medium (standard Postgres operations; no separate analytics cluster)

    Low (data lives in standard Postgres tables, exportable with pg_dump; TimescaleDB features need a host that supports the extension)

    Teams already running Postgres who want SQL joins between metrics and application or business data in one system

    ClickHouse

    ClickHouse is an open-source columnar OLAP database built for high-throughput analytical queries. ClickHouse Cloud and the ClickStack observability bundle package it as a full backend for OpenTelemetry-exported telemetry, including metrics.

    The open-source core is free and self-hostable; ClickHouse Cloud is priced on usage (compute plus storage). Query language is SQL, in ClickHouse's own dialect: familiar syntax, but with ClickHouse-specific functions and table engines that don't transfer directly to a standard SQL database.

    Directionally, ClickHouse tends to lead this category on raw compression ratio and query throughput at very high cardinality, and its adoption momentum in observability is real and still growing. The tradeoff shows up operationally: running it well at scale means owning cluster sizing, sharding, and replication as a dedicated surface, a second specialized system alongside whatever you already run for application data.

    Best fit: teams whose primary constraint is raw scale and who are willing, or already staffed, to run a dedicated analytics cluster for it. Deployment options: self-hosted or ClickHouse Cloud.

    SigNoz

    SigNoz is an open-source, OpenTelemetry-native observability platform covering metrics, traces, logs, and dashboards, with ClickHouse as its underlying storage engine. The open-source core is free and self-hostable; SigNoz Cloud is priced on usage.

    Queries run through SigNoz's Query Builder, PromQL, or ClickHouse SQL against the ClickHouse store underneath. The strength is purpose-built OTel-native UX: SigNoz is built around OpenTelemetry's data model from the ground up, not retrofitted onto it, and adoption here is growing fast.

    The limitation is that SigNoz doesn't own its own storage story. Choosing it means inheriting ClickHouse's cluster-sizing and tuning burden underneath the platform layer, not avoiding it.

    Best fit: teams that want an OTel-native UI out of the box and are comfortable operating, or paying SigNoz Cloud to operate, a ClickHouse cluster underneath it. Deployment options: self-hosted or SigNoz Cloud.

    Last9

    Last9 is a SaaS observability platform for OpenTelemetry and Prometheus-format metrics, priced on events and cardinality rather than raw storage volume. It's SaaS-only on standard plans; an Enterprise BYOC option lets the workload run inside a customer's own cloud account, but that requires a custom contract rather than a self-serve deployment.

    Querying is Prometheus-compatible, so existing PromQL dashboards and alert rules carry over from Prometheus and Grafana, and Grafana can stay as the UI. 

    The limitation is data portability. There's no self-hosted option (BYOC is still Last9-managed), so leaving means an export and migration project, not a config change.

    Best fit: teams that want a fully managed platform and prioritize not running infrastructure over data portability. Deployment options: SaaS-only, with managed BYOC available. 

    Grafana Mimir

    Grafana Mimir is a horizontally scalable, multi-tenant, Prometheus-compatible long-term storage backend, and the successor to Cortex. Mimir 3.0 introduced a Kafka-based architecture that decouples the read and write paths, so heavy query load doesn't slow ingestion. 

    It's open-source and self-hostable, with Grafana Cloud offering it as a managed, usage-priced service. Query language is PromQL only. The strength is genuine multi-tenant, SaaS-scale Prometheus deployments with first-class Grafana Cloud integration.

    The limitation is real: ingester, querier, compactor, store-gateway, distributor, and now Kafka in 3.0 and later are all separate components to deploy and keep healthy, substantial overhead for a small-to-mid deployment that doesn't need it.

    Best fit: large organizations running Prometheus-format metrics at genuine multi-tenant scale, or teams already committed to Grafana Cloud. Deployment options: self-hosted or Grafana Cloud.

    Thanos

    Thanos is a set of open-source CNCF components, Sidecar, Store Gateway, Querier, Compactor, and Ruler, that add long-term storage and a global query view on top of an existing Prometheus fleet.

    It's fully open-source with no vendor SaaS tier; cost is object storage plus the operational headcount to run it. Query language is PromQL only. The strength: no change to existing Prometheus scrape configuration and broad support for any S3-compatible object storage.

    The limitation is four-plus separate processes to manage, query fanout latency that grows with data volume, and no SaaS option if you'd rather offload operations.

    Best fit: teams with an existing Prometheus fleet who want long-term storage without disrupting how Prometheus already scrapes and writes. Deployment options: self-hosted only.

    VictoriaMetrics

    VictoriaMetrics is a ground-up, Go-based time-series database, not built on Prometheus's own TSDB, that accepts Prometheus remote_write and OTLP metrics natively. It's available as a single binary or a horizontally scalable cluster.

    It's open-source and self-hostable (the single binary is free), with VictoriaMetrics Cloud and Enterprise licensing available for managed or advanced features. Query language is MetricsQL, a PromQL superset; most queries behave identically, though some PromQL edge cases run differently under its extensions.

    The strength is real: community benchmarks point to meaningfully better compression than Prometheus's own TSDB, and single-node deployment is simple by design, one binary, no external dependencies. The limitation is no SQL support, and cluster mode, needed for horizontal scale, reintroduces real operational complexity.

    Best fit: cost-conscious teams that want the simplest possible operational footprint for a standalone metrics store and don't need SQL joins to other data. Deployment options: self-hosted (single-node or cluster) or VictoriaMetrics Cloud.

    PostgreSQL + Tiger Data

    PostgreSQL, extended with Tiger Data's time-series capabilities, hypertables, columnstore compression, continuous aggregates, receiving OpenTelemetry-exported metrics through the OTel Collector's Prometheus remote-write-compatible exporter and a Telegraf relay that writes them into hypertables. Covered in depth two sections below.

    It's free to self-host with TimescaleDB Community Edition; Tiger Cloud is the managed option, priced on compute plus storage, and TimescaleDB Enterprise is the self-managed option for on-prem, edge, and private cloud, built to run reliably across sites, fleets, and infrastructure you control. Query language is standard SQL. Prometheus remote-write data can land in it through Telegraf, so existing Prometheus pipelines can keep feeding it. 

    The strength is consolidation: SQL joins between metrics and application or business data in the same database, standard Postgres tooling (backups, roles, extensions, whatever ORM you already run) that a team likely already operates, and no separate analytics cluster to stand up.

    The limitation, stated plainly: teams that need ClickHouse-tier raw OLAP throughput at very large cardinality may be better served by ClickHouse or a ClickHouse-backed platform. This page isn't arguing scale parity that doesn't exist.

    Best fit: teams already running Postgres who want metrics queryable alongside deployment records, customer or tenant data, or other relational business data, without adding a second database system. Deployment options: self-hosted or Tiger Cloud.

    For a closer look at how this plays out specifically for facilities and infrastructure telemetry, see Postgres for data center telemetry.

    ClickHouse vs. Postgres for infrastructure observability: the real tradeoffs

    This is the single most-asked question in this space, so it deserves a direct answer, not just a table cell.

    ClickHouse's strength is raw columnar OLAP throughput and compression at very high cardinality and volume. For teams whose primary bottleneck is ingest or query speed at scale, ClickHouse, or a ClickHouse-backed platform like SigNoz, tends to be the stronger fit, and that gap is real.

    Where the tradeoff flips is operational simplicity and data consolidation. Running ClickHouse well means sizing, sharding, and tuning a dedicated analytics cluster as its own operational surface, a second system with its own on-call rotation and upgrade cadence. Running Postgres with Tiger Data means extending a database most teams already operate. No second cluster, no second set of backup, access, or monitoring tooling to maintain.

    The data-consolidation angle is concrete: SQL joins let a team query OTel metrics directly against deployment events, customer or tenant records, or other application data in the same query. PromQL-only backends can't join at all, and ClickHouse-based backends can join only against data that's been copied or federated into them first. 

    Choose ClickHouse, or SigNoz, when raw scale is the binding constraint and a dedicated analytics cluster is an acceptable, or already-staffed, cost. Choose Postgres with Tiger Data when the workload fits comfortably in a well-tuned relational database and the priority is one operational surface plus SQL joins against the rest of the business. Neither one is worse. They're two different bets on what you're optimizing for.

    Using Postgres as an OpenTelemetry metrics backend

    Here's the actual data path. Prometheus, or the OTel Collector's own Prometheus receiver, continues scraping targets as it does today. The Collector's Prometheus remote-write-compatible exporter, a widely used component in the Collector's core and contrib distributions, ships those samples onward. On the Postgres side, Telegraf receives that stream (its http_listener_v2 input with the prometheusremotewrite parser), and its PostgreSQL output plugin turns it  into inserts against a hypertable, a time-partitioned PostgreSQL table purpose-built for append-heavy, time-ordered writes. Telegraf is an open-source InfluxData project, not a Tiger Data component; Tiger Data's docs cover the Telegraf-to-hypertable setup. Tiger Data's own first-party adapter for this path, Promscale, was discontinued in 2023; see the section below. 

    A more direct route is also emerging: a dedicated Postgres exporter for the Collector, one that would write OTLP metrics straight into a Postgres-compatible database without a remote-write hop, was proposed as a component donation to the Collector's contrib repository in February 2026. It's still working through review rather than shipping as a maintained, supported component, so treat it as a direction worth watching, not something to build a production pipeline on today.

    Columnstore compression, Tiger Data calls the engine Hypercore, keeps that pipeline affordable over time. Infrastructure metrics compress well because adjacent readings from the same host or service are highly repetitive, and Hypercore exploits that pattern automatically, so storage grows at a fraction of the raw rate as you extend retention. 

    Continuous aggregates handle downsampling and rollups. A dashboard asking for average CPU utilization per host per hour over 90 days shouldn't rescan raw per-second metrics on every page load, so continuous aggregates precompute those rollups and refresh them incrementally on a policy you set once, instead of a hand-rolled batch job. 

    The SQL-joins point is where this gets concrete: joining a metrics table against a deployments or hosts table answers "show me error-rate metrics for every host running build X" in one query, against the deployment data where it already lives. PromQL-only backends can't do that, and ClickHouse-based ones need that data copied or federated in first. 

    Deployment comes down to two options: self-hosted PostgreSQL with Tiger Data for teams that want to run it themselves, or Tiger Cloud for teams that want the managed version without owning backups, upgrades, and scaling.

    One misconception worth rebutting directly: Postgres, or TimescaleDB specifically, is not just for IoT. The same hypertable, compression, and continuous-aggregate pattern that works for sensor data works identically for infrastructure and observability metrics. The underlying workload shape, timestamped, high-cardinality, queried over time ranges, is the same whether the source is a factory floor or a Kubernetes cluster.

    For more on why SQL specifically holds up well against the alternatives here, see why SQL is a strong query interface for OTel data. And for teams running this at data-center scale rather than a handful of services, Postgres for scaling AI data center operations covers what changes at that volume.

    SQL and PromQL together: Postgres as a Prometheus remote_write backend

    This isn't an either/or choice between SQL and PromQL, a common point of confusion worth clearing up. Prometheus, or the OTel Collector, keeps handling scraping and short-term buffering exactly as before. PostgreSQL with Tiger Data receives the same data via remote_write, relayed through Telegraf, for durable, SQL-queryable long-term retention. Both run at once.

    The practical benefit over PromQL-only long-term stores, Grafana Mimir, Thanos, VictoriaMetrics, is standard SQL as the primary query surface for long-term analysis, while still running short-term PromQL dashboards against Prometheus itself during the local retention window.

    Mimir and Thanos require several dedicated components purely for long-term metrics storage: ingester, querier, compactor, and store-gateway, plus Kafka for Mimir 3.0 and later, or Sidecar and Store Gateway for Thanos. Postgres with Tiger Data needs one lightweight relay (Telegraf) in front of a database most teams already run, rather than adding a new distributed system.

    This is the OpenTelemetry-native version of a question covered in more depth in the Prometheus long-term storage options guide. If you're evaluating Thanos, Mimir, or VictoriaMetrics specifically as Prometheus remote_write targets, the fuller comparison lives there.

    And if the question isn't just where metrics land long-term but whether to replace Prometheus's scraping layer entirely, Prometheus alternatives compared covers that broader decision.

    What happened to Promscale?

    Readers researching TimescaleDB's history with Prometheus will run into Promscale, so it's worth addressing directly.

    Promscale was TimescaleDB's earlier unified Prometheus and OpenTelemetry backend, built on top of the open-source TimescaleDB extension. It was discontinued in 2023 (maintenance ended April 30, 2023) and is not a current option. The article Promscale was deprecated in 2023 covers the full announcement.

    The capability Promscale explored, TimescaleDB as a metrics store, didn't disappear with the project. Today the path is the OTel Collector's Prometheus remote-write-compatible exporter plus Telegraf, described in the sections above, without Promscale itself in the picture.

    Decision framework: choosing your OpenTelemetry metrics backend

    This is a practical guide, not a ranking. Every backend below is the right call in its own context.

    Choose ClickHouse if:

    • Raw ingest or query throughput at very high cardinality is your binding constraint.

    • You're willing to run and tune a dedicated OLAP cluster as its own operational surface.

    • You want SQL, but don't need it to join against non-telemetry application data.

    Choose SigNoz if:

    • You want an OTel-native UI and dashboarding experience out of the box.

    • You're comfortable operating, or paying for, a ClickHouse cluster underneath that UI.

    Choose Last9 if:

    • You want a fully managed platform and would rather not run storage infrastructure at all.

    • Proprietary data storage is an acceptable tradeoff for close to zero operational burden.

    Choose Grafana Mimir if:

    • You're running Prometheus-format metrics at genuine multi-tenant, SaaS scale.

    • You're already invested in the Grafana ecosystem or Grafana Cloud.

    • You have a dedicated platform engineering team to own the operational surface.

    Choose Thanos if:

    • You have an existing Prometheus fleet and want long-term storage without changing scrape configuration.

    • You already have S3-compatible object storage in place.

    Choose VictoriaMetrics if:

    • Operational simplicity is the top priority and a single-binary deployment fits your scale.

    • Pure PromQL or MetricsQL is sufficient and you don't need SQL joins.

    Choose PostgreSQL with Tiger Data if:

    • You already run Postgres in production and want to extend it rather than add a new database system.

    • You need SQL joins between metrics and application, deployment, or customer data.

    • You want one operational surface, backups, roles, monitoring, instead of a separate analytics cluster.

    • Tiger Cloud removes the self-hosting burden if you want the managed version of the same engine.

    Tiger Data isn't the best fit if:

    Your primary requirement is ClickHouse-tier raw OLAP throughput at very large cardinality and you have no existing Postgres investment to build on.

    • You need a pure PromQL-only query surface with no SQL requirement. VictoriaMetrics or Mimir and Thanos are the more direct fit.

    If you're making this decision specifically for data center or facilities infrastructure, what database DCIM software uses and PUE/WUE monitoring at scale cover the adjacent storage questions for that environment.

    Migrating to a Postgres-based OpenTelemetry backend

    A few common paths lead into this architecture:

    From SigNoz (self-hosted ClickHouse). Teams that adopted SigNoz for its OTel-native UI but want out of ClickHouse cluster operations can redirect the OTel Collector's metrics export to Postgres with Tiger Data (via the remote-write and Telegraf path above), keeping OTel instrumentation unchanged.

    From Last9 (SaaS). Moving off a SaaS-only platform means exporting historical data and repointing the Collector's export target to a self-hosted or Tiger Cloud Postgres instance. Expect a data-export project rather than a config change, since Last9 has no self-serve self-hosted or bulk-export-by-default path.

    From Prometheus with Thanos, Mimir, or VictoriaMetrics as the long-term store. The Collector's Prometheus remote-write-compatible exporter can point at a Telegraf listener that writes to Postgres with Tiger Data, alongside or instead of the existing remote_write target. See the Prometheus long-term storage options guide for the fuller migration detail on this specific path.

    From legacy Promscale. Teams still running the deprecated Promscale integration should migrate to the OTel Collector's remote-write exporter plus Telegraf path described above. Promscale itself receives no further updates.

    This is a map of paths, not a step-by-step tutorial. Teams evaluating whether Postgres with Tiger Data fits a data-center-scale deployment specifically can start with Postgres for scaling AI data center operations.

    FAQ

    What's the best backend for storing OpenTelemetry metrics at scale?

    There's no single best backend, it depends on what you're optimizing for. ClickHouse and ClickHouse-backed platforms (SigNoz) tend to lead on raw OLAP throughput at very high cardinality. Prometheus-ecosystem backends (Grafana Mimir, Thanos, VictoriaMetrics) lead on PromQL-native tooling for teams already standardized on that query language. PostgreSQL with Tiger Data is the strongest fit when metrics need to live alongside relational application data in one system with SQL joins.

    What is an OpenTelemetry backend, exactly?

    OpenTelemetry's SDKs and Collector handle instrumentation, collection, and export, OTel deliberately doesn't ship its own storage engine. A backend is whatever database or platform receives exported metrics (via OTLP or a remote-write-compatible exporter) and makes them queryable over time.

    Can I use PostgreSQL as a long-term storage backend for Prometheus remote_write?

    Yes, with a relay. Prometheus or the OTel Collector sends remote_write to Telegraf, whose PostgreSQL output plugin writes the samples into Tiger Data hypertables. Tiger Data doesn't ship its own remote-write endpoint. Metrics become fully SQL-queryable, and columnstore compression plus continuous aggregates handle the retention economics and downsampling that a plain, unextended Postgres table can't manage on its own.

    What are the tradeoffs between ClickHouse and TimescaleDB for infrastructure observability data?

    ClickHouse tends to lead on raw columnar OLAP throughput and compression at very large scale, and that lead is real. TimescaleDB, via Tiger Data, tends to be the better fit when a team wants one operational surface, SQL joins between metrics and application or business data, standard Postgres tooling, and no separate analytics cluster to run and tune. Neither is a universal answer; it's a tradeoff between raw scale-and-speed and operational consolidation-and-simplicity.

    Can an OpenTelemetry backend replace Prometheus entirely?

    In many architectures, yes for the storage and query layer, teams route metrics through the OTel Collector directly to a backend like ClickHouse, VictoriaMetrics, or Postgres instead of relying on Prometheus's own server for long-term storage. Prometheus itself, however, still commonly functions as a scraper/exporter even when its local TSDB isn't the long-term store.

    What's the difference between OpenTelemetry metrics and Prometheus metrics?

    OpenTelemetry defines a vendor-neutral data model and protocol (OTLP) covering metrics, traces, and logs together, with richer semantic conventions and resource attributes. Prometheus metrics are a narrower, metrics-only model tied to its own exposition format and PromQL. The OTel Collector bridges the two, converting between Prometheus-format and OTLP as needed.

    How long can Prometheus store data locally before I need a remote backend?

    Prometheus's local TSDB defaults to 15 days retention and is built for fast recent-data queries, not durable long-term storage. Teams can push that to 30–90 days on local disk, but a single Prometheus server still has no built-in high availability or horizontal query scaling.

    Is ClickHouse overkill for smaller OpenTelemetry deployments?

    Often, yes. For small-to-mid volume deployments, sizing and tuning a ClickHouse cluster can add more operational overhead than the workload actually requires. Teams in that range frequently get comparable retention economics from a database they already run (Postgres) or a simpler single-binary option (VictoriaMetrics) without taking on a second specialized system.

    What happened to Promscale, TimescaleDB's earlier Prometheus integration?

    Promscale, TimescaleDB's earlier unified Prometheus and OpenTelemetry backend, was discontinued in 2023 (maintenance ended April 30, 2023) and is no longer maintained. It is not a current option. The capability it explored, TimescaleDB as a metrics store, is now addressed through the OTel Collector's Prometheus remote-write exporter plus Telegraf. A dedicated Postgres exporter for the Collector has been proposed but isn't part of the contrib distribution yet.

    Do I have to give up PromQL if I store OpenTelemetry metrics in Postgres?

    No, it isn't an either/or choice. Prometheus or the OTel Collector can keep handling scraping and short-term buffering while the same metrics are written to Postgres for SQL-queryable long-term retention. Teams that still want PromQL-style queries can keep a short local retention window in Prometheus alongside the SQL-queryable long-term layer.

    Is SigNoz's backend the same thing as an OpenTelemetry backend?

    SigNoz is an OpenTelemetry-native observability platform, but its underlying storage engine is ClickHouse, SigNoz doesn't own its own storage layer. Choosing SigNoz means inheriting ClickHouse's operational model underneath SigNoz's UI, not avoiding it.

    What's the OpenTelemetry Collector's role in choosing a backend?

    The Collector receives, batches, processes, and exports telemetry, it's what makes backend choice modular in the first place. Because it supports multiple exporters (OTLP, Prometheus remote-write-compatible, vendor-specific), teams can point the same instrumented application at different storage backends without changing application code.