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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
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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
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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 Sep 18, 2026

Table of contents

    Grid Modernization: Why the Data Layer Decides Whether It Works

    Grid Modernization: The Data Layer Problem

    By Tiger Data Team

    Updated at Sep 18, 2026

    Utilities are on pace to spend roughly $5.8 trillion on grid modernization globally between 2026 and 2035. Most of that money is going toward hardware: smart meters, sensors, switches, substation upgrades. Less is going toward the systems that turn what those devices report into something a utility can actually query and act on. That gap is the subject of this guide.

    What does grid modernization actually mean?

    Grid modernization is the process of applying digital sensing, communication, control, computing, and data-management systems to the electric grid, unifying data from SCADA, AMI, PMU, and DERMS into infrastructure that utilities can query, analyze, and act on in real time. The U.S. Department of Energy says the term itself "has no strict definition."

    That's the thesis of this piece: the harder, less-discussed part of grid modernization isn't the hardware, it's the data layer underneath it. Utilities can deploy every smart meter and sensor on their roadmap and still lack a unified, queryable view of the grid, because the systems generating that data were never built to talk to each other.

    Tiger Data is a Postgres-based database company, and this piece makes the case for a data-platform-first approach. When choosing your approach, weigh it against your own team's requirements and existing investments.

    Grid modernization vs. smart grid: are they the same thing?

    Not quite. Grid modernization is the broader initiative: the overall program of applying digital technology to grid operations. A smart grid, one instrumented with two-way digital communication and automated control, is one outcome of it.

    "Smart grid" is the term most consumers and industry press use day to day. Grid modernization describes the same underlying data problem, at the program level rather than the single-outcome level, and it's the term this guide uses throughout.

    The four systems behind a modern grid

    A modernized grid depends on four telemetry systems, each built for a different job: SCADA (Supervisory Control and Data Acquisition) monitors and controls grid operations, AMI (Advanced Metering Infrastructure) captures customer-side metering, PMU/WAMS (Phasor Measurement Units / Wide-Area Monitoring Systems) capture high-frequency phase and frequency data to detect instability before it cascades, and DERMS (Distributed Energy Resource Management System) coordinates rooftop solar, batteries, EV chargers, and other grid-edge devices.

    System

    What it monitors

    Typical data shape/frequency

    Where this cluster covers it

    SCADA

    Substations, breakers, transformers, grid operations

    Discrete events and periodic polling, seconds to minutes

    Smart Grid Data Platform and the SCADA data management guide

    AMI

    Customer meters, consumption

    Interval reads, every 15-60 minutes per meter

    Meter Data Management and AMI database architecture

    PMU/WAMS

    Phase angle, frequency, grid stability

    Continuous high-frequency streams, up to 60+ samples/second

    Smart Grid Data Platform

    DERMS

    Solar, batteries, EV chargers, grid-edge devices

    Dispatch commands and telemetry, seconds to minutes

    DERMS Database

    Here's the practical, everyday form the "grid modernization is a data problem" thesis takes: most utilities run all four as separate systems on separate databases, maintained by separate teams. SCADA lives in a historian, AMI in a meter data management (MDM) system, PMU data wherever the transmission operator's monitoring vendor put it, DERMS in whatever platform its aggregation vendor shipped. None were built to answer a question spanning more than one of them.

    The mechanics behind each system, historian and Ignition-based OT integration on the SCADA side, exception queues and VEE on the AMI side, dispatch and aggregation on the DERMS side, are covered in the linked spokes above. This guide stays at the category level.

    This fragmentation isn't unique to electric utilities, either. Water utilities run a similar mix of SCADA, AMI, and quality data across separate systems, covered in the Water Utilities Database guide.

    Why data infrastructure, not hardware, is the real constraint

    Most utility modernization plans read like hardware plans. Deploy smart meters. Install PMUs. Upgrade switches. That's necessary, but it's not the constraint that actually determines whether a modernization program pays off.

    The AMI spoke in this cluster documents a pattern utilities are now calling "AMI 2.0": data volume from newly deployed meters and sensors has been outpacing the governance and architecture planning needed to make sense of it. Hardware gets funded and installed. The schema standards, query layer, and ownership model underneath it usually don't get the same budget.

    The practical consequence: a utility can deploy every smart meter and PMU on its roadmap and still be unable to answer a basic cross-system question, like correlating a voltage event with nearby DER output at the same timestamp, if that data lives in four incompatible silos.

    This isn't true of every utility, but industry coverage consistently points to it as a recurring pattern: hardware investment outpacing the data layer that would let a utility use what the hardware produces.

    Why now: the investment driving grid modernization

    The scale of investment now underway makes this an urgent architecture question, not a theoretical one. Global grid modernization investment is forecast at roughly $5.8 trillion between 2026 and 2035, with about $700 billion allocated specifically to digital grid technology. In the U.S. alone, utilities are expected to invest on the order of $1 trillion over the coming decade.

    The defining trend shaping how that money gets spent is IT/OT/IIoT convergence: utilities moving toward connected sensors, asset-health monitoring, and real-time analytics for one unified view of operations, asset performance, and customer demand, rather than the siloed systems that have historically defined utility IT. Current trade press and industry research document this broadly.

    As that spending mix shifts from pure hardware toward digital systems, the data infrastructure question becomes the deciding factor in whether the investment pays off. A utility that spends heavily on sensors and metering without a plan for the data layer underneath ends up with more raw telemetry and the same fragmented visibility it started with.

    The common architecture pattern behind modern grid data platforms

    A consistent technical pattern is emerging for how utilities converge grid data. SCADA-side systems typically connect through OPC-UA, the industrial automation protocol most historians already speak. DERMS and IoT-side systems, including grid-edge devices and DER controllers, typically converge through MQTT, the lightweight protocol built for high device counts and intermittent connectivity.

    Both paths feed into a common schema shape, regardless of which system originated the data:

    Field

    Description

    device_id

    Unique identifier for the asset or meter generating the reading

    timestamp

    When the reading occurred, at source-system precision

    signal_type

    What's being measured (voltage, frequency, kWh, state of charge, breaker status)

    value

    The reading itself

    source_system

    Which system produced this record (SCADA, AMI, PMU, DERMS)

    That shape is deliberately simple, a common denominator rather than a full data model; each source system still carries additional fields specific to its domain. What matters is that a query layer built around this shape can join across all four systems without four separate integration projects.

    This pattern matches what the Smart Grid Data Platform guide lays out at the platform level. Treat that guide as this cluster's primary architectural reference point if you're starting from scratch.

    The governance gap: where grid modernization projects stall

    The AMI spoke documents a governance gap specific to metering data: as interval reads outpace what legacy MDM systems can validate, the absence of a clear ownership model becomes the actual bottleneck, not any limitation in the meters themselves. That finding generalizes across the whole grid modernization picture: as more systems come online, the absence of a single ownership model, shared schema standard, or common query layer becomes the real blocker. A few concrete symptoms are worth checking yourself against:

    • Analysts maintaining parallel exports from SCADA, AMI, and DERMS by hand, because no system can natively query across the others.

    • No single source of truth for a given grid asset. The same transformer might carry three different IDs across three systems, with no reliable way to reconcile them.

    • Dashboards that take days, not minutes, to update after a grid event, because someone has to manually pull and reconcile data before an analyst can start looking at what happened.

    This isn't an argument that more systems are inherently bad; each earns its place for a real operational reason. But the coordination cost between them compounds as more come online, and eventually that cost, not any single system's limitations, becomes what stands between a utility and the cross-system visibility its investment was supposed to buy.

    Choosing a data platform for grid modernization

    Once a utility recognizes the data layer as the actual constraint, the next question is what to build it on. The answer depends on what's already deployed and what compliance requires.

    The case for Postgres and Tiger Data: Hypertables automatically partition time-series data so queries stay fast as telemetry accumulates. Hypercore's columnstore compression keeps fast-accumulating PMU and AMI data affordable to store online instead of archived out of reach. Continuous aggregates pre-compute rollups so dashboards stay fast without nightly batch jobs. Because it's still Postgres, you can join time-series telemetry with relational asset and customer data in standard SQL, a genuine differentiator against purpose-built historians and time-series databases, which typically require a separate relational store for that same context.

    The case for purpose-built historians and time-series databases: These systems carry real strengths: products like the AVEVA PI System have deep native OT and SCADA integration built over decades, vendor certifications procurement may require, and years of hardening in pure OT environments. Teams already standardized on a historian for a single, well-scoped deployment often find switching costs outweigh consolidation's benefit, at least until a second system comes online.

    Where Tiger Data isn't the best fit: a utility running a single, tightly scoped OT deployment with a hard vendor-certification requirement and no near-term plan to unify data across systems may not need a general-purpose platform. The case for consolidation gets stronger as more systems come online.

    If AWS services come up in this comparison: Timestream for LiveAnalytics stopped accepting new customers in June 2025 and isn't an available option. Timestream for InfluxDB is the product AWS continues to develop in that family.

    Decision framework: where should a utility start?

    Utilities considering a modernization program often ask whether to start with hardware, software, or data infrastructure. The answer is that it depends on what's already deployed.

    Start with data infrastructure consolidation if: two or more of SCADA, AMI, PMU, or DERMS are already deployed and generating data with no shared way to query across them, and cross-system analysis takes manual exports and days rather than minutes.

    Prioritize sensor and hardware deployment first if: the data these systems would generate doesn't exist yet at meaningful scale. A data platform has nothing to unify until telemetry is being collected.

    Choose a Postgres-based platform like Tiger Data’s Tiger Cloud or TimescaleDB Enterprise if: your team already works in SQL and Postgres, you need to combine time-series telemetry with relational asset or customer data, or you want one platform serving SCADA, AMI, and DERMS rather than a separate tool per system.

    Choose a purpose-built historian or time-series database if: you operate a single, narrowly scoped OT environment with a hard vendor-certification or compliance requirement a general-purpose database doesn't meet.

    Common migration paths into a unified grid data platform

    Consolidating multiple point solutions. A utility running separate databases per system (a SCADA historian, an AMI/MDM store, a DERMS vendor's own database) migrates onto one platform to eliminate duplicate infrastructure and enable cross-system queries. Plexigrid followed this path, consolidating four databases into one and cutting query times by up to 350x. The full story is also told on the Smart Grid Data Platform guide and revisited in the DERMS Database guide.

    Migrating off a legacy SCADA historian while OT systems stay in place. Some utilities need to replace an aging historian without touching the OT layer feeding it. The SCADA data management guide covers this migration pattern in depth.

    Extending an existing AMI/MDM deployment to absorb DERMS or PMU data. Rather than standing up another isolated database per new system, utilities with a working AMI or MDM deployment can extend it to take on DERMS or PMU data as those systems come online.

    FAQ

    What does grid modernization actually mean?

    The U.S. Department of Energy states that grid modernization has no strict definition, but is generally understood as applying digital sensing, communication, control, computing, and data and information management systems to the electric grid. Tiger Data's working definition narrows that: it's the process of unifying data from SCADA, AMI, PMU, and DERMS into infrastructure that can be queried and acted on in real time.

    Is grid modernization primarily a hardware problem or a data problem?

    Hardware deployment, meters, sensors, and switches, is necessary but not sufficient. Current industry coverage argues that the data layer underneath that hardware, the schema standards, ownership model, and query infrastructure, is the harder, more overlooked constraint. Utilities can deploy every planned sensor and still lack a unified view of the grid if that data stays siloed.

    What data does a modernized grid need to unify?

    Four systems: SCADA (grid operations), AMI (customer metering), PMU/WAMS (high-frequency phase and frequency monitoring), and DERMS (distributed energy resource coordination). The common schema shape tying them together includes a device ID, timestamp, signal type, value, and source system.

    How much is being invested in grid modernization?

    Global grid modernization investment is forecast at roughly $5.8 trillion between 2026 and 2035, with about $700 billion of that specifically allocated to digital grid technology. In the U.S. alone, utilities are expected to invest roughly $1 trillion over the coming decade.

    Where should a utility start with grid modernization: hardware, software, or data infrastructure?

    It depends on what's already deployed. If two or more of SCADA, AMI, PMU, or DERMS already exist and generate data with no shared way to query across them, start with data infrastructure consolidation. If the underlying telemetry doesn't exist yet at meaningful scale, deploy sensors first.

    Is grid modernization the same thing as a smart grid?

    Not exactly. Grid modernization is the broader initiative; a smart grid, a grid instrumented with two-way digital communication and automated control, is one outcome of that initiative.

    What protocols connect grid modernization systems together?

    SCADA-side systems typically converge through OPC-UA, the industrial automation protocol most historians and control systems already use. DERMS and IoT-side systems typically converge through MQTT. Both feed into a common schema built around device ID, timestamp, signal type, value, and source system.

    What's the biggest technical obstacle to a grid modernization data project?

    The governance gap: as data volume and system count grow, the absence of a single ownership model, schema standard, or query layer becomes the actual blocker, not any individual system's limitations. Symptoms include manual data exports, no single source of truth per asset, and dashboards that take days to update after a grid event.

    Can Postgres handle grid-scale telemetry data?

    Directionally, yes, with the right extensions. Tiger Data adds hypertables for automatic time-based partitioning, columnstore compression for affordable long-retention storage, and continuous aggregates for pre-computed rollups, mechanisms specifically built to keep Postgres performant as time-series data volume grows. Whether it's the right fit for a given deployment still depends on scale, existing tooling, and compliance requirements.

    What's the difference between a historian and a grid modernization data platform?

    A historian is typically single-purpose and OT-native, often built specifically for SCADA data. A grid modernization data platform is built to unify telemetry across multiple systems, SCADA, AMI, PMU, and DERMS, in one queryable layer rather than one system at a time.

    How does AMI data fit into grid modernization plans?

    AMI generates customer-side interval metering data, and it's one of the four systems this guide argues needs to sit in a unified, queryable data layer rather than an isolated MDM silo. See Tiger Data's Meter Data Management and AMI database architecture guide for the full argument on the AMR-to-AMI data shift and the governance gap it creates.

    How does AMI data fit into grid modernization plans?

    AMI generates customer-side interval metering data, and it's one of the four systems this guide argues needs to sit in a unified, queryable data layer rather than an isolated MDM silo. See Tiger Data's Meter Data Management and AMI database architecture guide for the full argument on the AMR-to-AMI data shift and the governance gap it creates.