TigerData logo
TigerData logo
  • Product

    Product

    Tiger Cloud

    Robust elastic cloud platform for startups and enterprises

    TimescaleDB Enterprise

    Self-managed TimescaleDB for on-prem, edge and private cloud

    Open source

    TimescaleDB

    Time-series, real-time analytics and events on Postgres

    Search

    Vector and keyword search on Postgres

  • Industry

    Data Centers

    Energy & Utilities

    Oilfield Services

    Smart Manufacturing

    Crypto

  • Docs
  • Pricing
  • Developer Hub

    Changelog

    Benchmarks

    Blog

    Community

    Customer Stories

    Events

    Support

    Integrations

    Launch Hub

  • Company

    About

    TigerData logo

    Timescale

    Partners

    Security

    Careers

View on GitHub (opens in a new tab)

Contact usStart a free trial
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
Meter 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 WorkData Analytics vs. Real-Time Analytics: How to Pick Your Database (and Why It Should Be PostgreSQL)How to Choose a Real-Time Analytics DatabaseUnderstanding OLAP: What It Is, How It Differs From OLTP, and Running It on PostgreSQLColumnar Databases vs. Row-Oriented Databases: Which to Choose?What Is the Best Database for Real-Time AnalyticsPostgreSQL as a Real-Time Analytics DatabaseHow to Choose an OLAP DatabaseHow 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
Meter 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 WorkData Analytics vs. Real-Time Analytics: How to Pick Your Database (and Why It Should Be PostgreSQL)How to Choose a Real-Time Analytics DatabaseUnderstanding OLAP: What It Is, How It Differs From OLTP, and Running It on PostgreSQLColumnar Databases vs. Row-Oriented Databases: Which to Choose?What Is the Best Database for Real-Time AnalyticsPostgreSQL as a Real-Time Analytics DatabaseHow to Choose an OLAP DatabaseHow 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
Tiger Data

Products

  • TimescaleDB
  • Tiger Cloud
  • TimescaleDB Enterprise
  • Postgres Search Stack

Industry

  • Data Centers
  • Energy & Utilities
  • Oilfield Services
  • Smart Manufacturing
  • Crypto

Support

  • Cloud Status
  • Support
  • Security
  • Terms of Service
  • Code Of Conduct

Learn

  • Documentation
  • Blog
  • Tutorials
  • Changelog
  • Success Stories

Company

  • About
  • Contact Us
  • Careers
  • Newsroom
  • Brand
  • Events

Products

  • TimescaleDB
  • Tiger Cloud
  • TimescaleDB Enterprise
  • Postgres Search Stack

Industry

  • Data Centers
  • Energy & Utilities
  • Oilfield Services
  • Smart Manufacturing
  • Crypto

Support

  • Cloud Status
  • Support
  • Security
  • Terms of Service
  • Code Of Conduct

Learn

  • Documentation
  • Blog
  • Tutorials
  • Changelog
  • Success Stories

Company

  • About
  • Contact Us
  • Careers
  • Newsroom
  • Brand
  • Events
Privacy preferencesLegalPrivacySitemap

Subscribe to the Tiger Data newsletter

Strategic Partner with Inductive Automation — Ignition

2026 (c) Timescale, Inc., d/b/a Tiger Data.
All rights reserved.

Tiger Data

2026 (c) Timescale, Inc., d/b/a Tiger Data.
All rights reserved.

Privacy preferencesLegalPrivacySitemap
Tiger Data avatar

By Tiger Data team

Published at Aug 22, 2024

Table of contents

    Try for free

    Start supercharging your PostgreSQL today.

    Columnar Databases vs. Row-Oriented Databases: Which to Choose?

    Tiger Data avatar

    By Tiger Data team

    Published at Aug 22, 2024

    Written by Haziqa Sajid

    Choosing the right database structure determines the efficiency and performance of your application. An application's speed, scalability, and maintenance depend upon the underlying database structure. Columnar databases and row-oriented databases are two common database structures with distinct characteristics.

    Columnar databases organize data by column/field, making aggregating data and performing calculations easier. On the other hand, row-oriented databases read and write data row by row, making them suitable for transactional workloads and complex queries.

    In this blog post, we compare the key characteristics of columnar and row-oriented databases. We also discuss factors to consider when deciding between the two and how TimescaleDB makes PostgreSQL efficient for analytical operations and high-volume data by cleverly engineering both structures into a single database. 

    What Are Columnar Databases?

    Since columnar databases store data in fields rather than rows, they efficiently retrieve and analyze data. Storing data in columns allows accessing specific records without dealing with irrelevant data. All values in a column are grouped on the disk, and the data is stored in record order. Here is an example of how data values are stored in columnar databases.

    image

    Columnar dataset storage

    This columnar structure allows access to data elements from multiple columns that belong to the same record despite the column-based structure. 

    Key characteristics 

    Columnar databases have a number of features suitable for analytical operations:

    1. Columnar databases store all the values for a specific column together.

    2. Data for empty cells is omitted in the columnar database, and similar data types are stored together. Therefore, it supports advanced compression like run length compression and token stores.

    3. Columnar databases excel at analytical queries that filter and aggregate data across large datasets.

    Use cases 

    Columnar databases are extensively used in modern applications due to their compression and analytical capabilities. Let's look at some of its use cases:

    Data warehousing

    Columnar databases suit data warehousing environments due to their compression capabilities, efficient storage, and fast query times. These features help data warehouses handle complex datasets with growing volumes. 

    Big data analytics and business intelligence

    Columnar databases are widely used in big data analytics and business intelligence platforms. This is because storing data in fields allows selective access to relevant columns and apply compression techniques.

    IoT processing

    IoT devices collect data with multiple attributes that need to be transmitted over networks. Therefore, columnar databases reduce storage requirements by storing data in record order and enable faster operations by minimizing I/O operations.

    Examples of Columnar Databases

    Columnar databases are widely used in big data storage and manipulation. Some of the most popular columnar databases are: 

    Google BigQuery

    Google BigQuery is a serverless multi-cloud data warehouse with a columnar database structure. It offers AI assistance, manages all data types and open formats, and provides built-in capabilities to run machine learning modes and data governance.

    Amazon Redshift

    Amazon Redshift is a fully managed cloud data warehouse that uses a columnar structure. It is designed for fast and scalable analysis of massive datasets, automatic scaling, and built-in security.

    SAP HANA

    SAP HANA is an in-memory, columnar database management system specifically designed for business operations. It also offers a platform for application development using various tools and languages.

    Snowflake

    Snowflake is a cloud-based data warehouse built for fast and scalable data analysis. It uses columnar storage, zero-copy cloning, near-zero management, and automatic scaling.

    What Are Row-Oriented Databases?

    Row-oriented databases store data row by row. Storing data in rows allows horizontal access to data from a table. Accessing a value in row databases outputs all columns of the queried row at once. Here is an example of how data is stored in row-oriented databases.

    image

    Row-oriented dataset storage

    Key characteristics

    Row-oriented databases have features that make them efficient for transactional workloads:

    1. Each row in a row database represents a single record, and each column represents an attribute of that record. 

    2. Due to the easy access of individual records, row-oriented databases are optimized for CRUD (create, read, update, delete) operations.

    3. CRUD operations capability makes a row-oriented database ideal for transactional systems that involve frequent inserts, updates, and retrievals of specific data.

    Use cases

    Row-oriented databases have been used in multiple applications for several decades. Here are some common use cases:

    Transactional systems

    Transactional systems store a company's daily transactions. They ensure data integrity with ACID (atomicity, consistency, isolation, durability) properties. For example, a POS (point-of-sale) system reads data through barcode scans and stores it in a row database.

    OLTP (Online Transaction Processing)

    OLTP systems manage multiple transactions concurrently over the Internet. These systems ensure data integrity through atomicity and concurrency properties. For example, airline reservation systems simultaneously manage flight bookings, seat availability, and passenger information for multiple users.

    General-purpose applications

    Most low-scale applications rely on storing and managing individual records in row-oriented databases. They can handle various data types and complex queries involving joins across multiple tables. For example, library management systems use row-oriented databases to store books and customer data.

    Examples of Row Databases

    Multiple companies offer relational database management systems with various features, performance levels, and pricing options. A few common examples are:

    PostgreSQL

    PostgreSQL is an advanced relational database management system. It supports various data types, allows complex data manipulation, and advanced indexing techniques. PostgreSQL prioritizes data security with features like role-based access control, encryption, and auditing.

    MySQL

    MySQL is a relational database management system, meaning it stores data row by row in tables. Due to its ease of use, reliability, and scalability, MySQL is one of the most widely used databases.

    Microsoft SQL Server

    Microsoft SQL server is a database management system developed by Microsoft. It offers integration with the Microsoft ecosystem and uses transact-SQL (T-SQL) as the query language for SQL Server. 

    Oracle Database

    Oracle database is developed by Oracle and is well-known for its reliability and scalability. It is integrated with Oracle Cloud Infrastructure (OCI) and uses procedural language integration (PL/SQL) for complex data manipulation and application logic within the database.

    Key Differences Between Columnar and Row-Oriented Databases

    Columnar and row-oriented databases are two fundamental approaches to storing and organizing data, each with distinct characteristics. The following table highlights the key differences between the two database structures:

    Columnar Database

    Row-Oriented Database

    Data Storage

    Data is stored by columns, with all the values for a particular column grouped across different rows. 

    Data is stored in rows, where each row represents a complete record, and each column stores a specific attribute of that record.

    Query Performance

    Performs well for read-heavy workloads but slower for write-heavy workloads. 

    Can be slower for read-heavy workloads as the entire row might need to be scanned for access. However, it is faster for write-heavy workloads as updates are limited to a single row.

    Compression

    Supports efficient compression techniques like run-length encoding for repetitive values, reducing storage requirements.

    The presence of multiple data types in a row makes compression less efficient compared to columnar databases.

    Flexibility

    Less flexible for schema changes.

    Flexible for schema changes.

    Pros and Cons of Columnar Databases

    Columnar databases excel at various functions but have a few limitations. When choosing the most suitable database for your applications, it is vital to consider the advantages and disadvantages. 

    Advantages of columnar databases

    1. Columnar databases excel at retrieving specific columns for analysis, significantly speeding up queries for data warehousing, business intelligence (BI), and other analytical tasks.

    2. Since data within a column is typically homogenous (all the same data type), compression algorithms work much more efficiently, reducing storage requirements significantly.

    3. Columnar databases are optimized for read-heavy workloads where you primarily retrieve data for analysis.

    Disadvantages of columnar databases

    1. Updating data in a columnar database can be slower because changes often affect multiple columns stored in separate locations.

    2. Frequent inserts, updates, and deletes, common in transactional systems, can be less efficient with columnar databases.

    Pros and Cons of Row-Oriented Databases

    Like columnar databases, row-oriented databases have distinct pros and cons that suit certain use cases. 

    Advantages of row-oriented databases

    1. Row-oriented databases are optimized for write-heavy workloads. Storing all data for a record together enables faster inserts, updates, and deletes.

    2. Row-oriented databases excel in Online Transaction Processing (OLTP) systems like e-commerce platforms or banking applications where frequent short transactions are crucial.

    3. Row-oriented databases handle various data types and complex queries involving joins across tables, making them versatile for general-purpose applications.

    Disadvantages of row-oriented databases

    1. Row-oriented databases need to scan entire rows while performing complex queries. This can result in longer wait times.

    2. Row-oriented databases struggle to handle large datasets with high write performance priority.

    How to Choose Between Columnar and Row-Oriented Databases

    Choosing the right database structure requires a thorough consideration of several factors. This begins with assessing your project requirements and ends with making a detailed comparison of the choices available. 

    Assess your needs

    Choosing between a columnar and row-oriented database significantly impacts your system's performance and efficiency. Since both structures support different requirements, evaluating your needs beforehand is crucial. For example, a row-oriented database is suitable for handling online transactions, but columnar works well for data analysis. 

    The key factors to consider when choosing the right database are:

    1. Understanding your data: data volume, data growth rates, update frequency, and access requirements

    2. Application requirements: scalability, performance, consistency, security, and uptime requirements of your application

    3. Workload characteristics: data read/write ratio, query complexity, and latency requirements

    4. Team expertise: your team's existing database skills and training requirements

    Assessing your needs will guide you to the right database choice for your use case, considering the following requirements both database structures follow:

    Columnar Database

    Row-Oriented Database

    Read-heavy operations.

    Write-heavy operations.

    Massive datasets and complex analysis.

    Small datasets with fewer aggregation needs.

    Data volume increases over time.

    Data volume doesn’t grow significantly.

    Workload characteristics

    Columnar databases work well for read-heavy workloads due to their columnar storage format. On the other hand, row-oriented databases support write-heavy workloads as they efficiently store and retrieve entire records. Therefore, your workload characteristics will determine the suitable database structure for your applications. Here’s how to differentiate between read-heavy and write-heavy workloads:

    Read-Heavy Workload

    Write-Heavy Workload

    Most operations involve retrieving data from the database.

    Most operations involve inserting, updating, or deleting data.

    Few operations involve inserting, updating, or deleting data.

    Few operations involve retrieving data.

    Extensive use of caching, indexing, and replication to optimize operations.

    Use of techniques like data partitioning and concurrency to provide isolated environments to users.

    For example, content delivery networks (CDNs) and stock market data retrieval.

    For example, logging systems and IoT data collection.

    Future scalability

    While both columnar and row-oriented databases offer scalability, they have different strengths. Here’s how they differ from each other:

    Columnar Databases

    Row-Oriented Databases

    Columnar databases use horizontal scaling by adding more nodes to the cluster as data size grows. They can handle growing workloads by adding more servers to distribute the load and reducing response time.

    Row-oriented databases use vertical scaling by increasing database resources such as RAM, CPU, etc. They are best for applications with limited parallelization.

    Columnar databases use advanced compression techniques to manage data growth and reduce storage costs.

    Row-oriented databases excel in scenarios where data is frequently updated, and individual records are accessed often. This makes them ideal for applications with uniform data sizes and frequent write operations.

    Bridging the Gap Between Columnar and Row-Oriented Databases

    Both columnar and row-oriented databases have distinct strengths, but each has limitations in certain use cases. Hybrid databases bridge these gaps by combining the advantages of both architectures. TimescaleDB is a prime example, enhancing PostgreSQL to efficiently support both transactional and analytical workloads.

    While PostgreSQL is highly capable, particularly in transactional environments, its performance can suffer as data volumes grow, leading to inefficient storage. TimescaleDB solves this by introducing advanced compression techniques that leverage both columnar and row-oriented storage.

    TimescaleDB uses a hybrid row-columnar mechanism called hypercore. It stores incoming data in row format for efficient updates. As data ages, it consolidates multiple rows into a single row with an array-like structure, reducing storage overhead. This hybrid approach makes TimescaleDB ideal for applications that experience a mix of write-heavy and read-heavy operations, particularly those with large-scale data growth. 

    Rather than forcing developers to choose between a transactional (OLTP) database and an analytics (OLAP) database, hypercore combines the best of both worlds. It blends row-oriented and column-oriented storage formats into one system, creating a hybrid storage engine that seamlessly and automatically shifts data between the two based on how it’s used. 

    By keeping recent data in row format and compressing older data into a columnar structure, TimescaleDB achieves exceptional compression rates—up to 95 %—far beyond what other relational databases can offer. This approach optimizes storage and keeps databases performant even under heavy load. And the cherry on top of the cake is that you can continuously adapt your compression settings to your data’s needs, thanks to customizable compression.

    TimescaleDB's row-columnar compression provides two key benefits:

    1. Enhanced performance for time-series data or time-series-like data: time-series data, which grows continuously, benefits from TimescaleDB’s ability to handle large write volumes and compress older data, making it ideal for analytics, event processing, and other high-demand workloads. Most PostgreSQL use cases are also time-series-like—this means they’re append-heavy (versus update-heavy) with a loosely sequential main key, such as a timestamp or serial event ID.

    2. Efficient storage and retrieval of large datasets: with its hybrid compression, TimescaleDB maintains write performance while significantly reducing storage needs. This allows for rapid retrieval of specific time-based data, accelerating real-time analysis and visualization.

    Additionally, TimescaleDB inherits PostgreSQL’s ease of use and developer-friendliness. Built as an extension, it allows developers to continue using familiar PostgreSQL tools and libraries without the need to learn new skills. However, it enhances PostgreSQL with features like automatic partitioning, continuous aggregation, and query planner improvements, all while maintaining the core PostgreSQL features developers rely on. 

    If you need to scale even further, Timescale Cloud, Timescale’s mature cloud platform, has a multi-tiered storage architecture that enables infinite, low-cost scalability. You can store your older, infrequently accessed data in a low-cost storage tier while still being able to access it—without ever sacrificing performance for your frequently accessed data.

    image

    Conclusion

    Choosing the right database is the key to any project's success. Columnar and row-oriented databases are two common choices for data storage in applications. Workload characteristics, scalability needs, storage costs, and data volume are vital factors to consider when choosing the right database structure. While both database structures have their distinct characteristics, they’re unsuitable for certain application requirements. 

    Columnar databases struggle with write-heavy workloads, whereas row-oriented databases struggle with read-heavy workloads. To address these limitations and cater to both relational, time series, and real-time analytics workloads, TimescaleDB offers hypercore. This approach combines the best of both worlds while maintaining ease of use and a prime developer experience by offering PostgreSQL compatibility. Learn more about hypercore and how it's been expertly engineered over the years.