TigerData logo
TigerData logo
  • Product

    Tiger Cloud

    Robust elastic cloud platform for startups and enterprises

    Agentic Postgres

    Postgres for Agents

    TimescaleDB

    Postgres for time-series, real-time analytics and events

  • Docs
  • Pricing

    Pricing

    Enterprise Tier

  • Developer Hub

    Changelog

    Benchmarks

    Blog

    Community

    Customer Stories

    Events

    Support

    Integrations

    Launch Hub

  • Company

    Contact us

    About

    Timescale

    Partners

    Security

    Careers

Log InTry for free
Home
Stationary Time-Series AnalysisThe Best Time-Series Databases ComparedTime-Series Analysis and Forecasting With Python Alternatives to TimescaleWhat Are Open-Source Time-Series Databases—Understanding Your OptionsWhy Consider Using PostgreSQL for Time-Series Data?Time-Series Analysis in RAWS Time-Series Database: Understanding Your OptionsWhat Is Temporal Data?What Is a Time Series and How Is It Used?Is Your Data Time Series? Data Types Supported by PostgreSQL and TimescaleUnderstanding Database Workloads: Variable, Bursty, and Uniform PatternsHow to Work With Time Series in Python?Tools for Working With Time-Series Analysis in PythonGuide to Time-Series Analysis in PythonUnderstanding Autoregressive Time-Series ModelingCreating a Fast Time-Series Graph With Postgres Materialized Views
PostgreSQL Join Type TheoryStructured vs. Semi-Structured vs. Unstructured Data in PostgreSQLUnderstanding PostgreSQLUnderstanding FILTER in PostgreSQL (With Examples)Understanding Foreign Keys in PostgreSQLUnderstanding GROUP BY in PostgreSQL (With Examples)Understanding PostgreSQL User-Defined FunctionsUnderstanding PostgreSQL's COALESCE FunctionUnderstanding SQL Aggregate FunctionsUsing PostgreSQL UPDATE With JOINOptimizing Your Database: A Deep Dive into PostgreSQL Data TypesHow to Install PostgreSQL on LinuxUnderstanding FROM in PostgreSQL (With Examples)How to Address ‘Error: Could Not Resize Shared Memory Segment’ Understanding HAVING in PostgreSQL (With Examples)How to Fix No Partition of Relation Found for Row in Postgres DatabasesHow to Fix Transaction ID Wraparound ExhaustionUnderstanding LIMIT in PostgreSQL (With Examples)Understanding ORDER BY in PostgreSQL (With Examples)Understanding WINDOW in PostgreSQL (With Examples)Understanding PostgreSQL WITHIN GROUPPostgreSQL Mathematical Functions: Enhancing Coding EfficiencyUnderstanding DISTINCT in PostgreSQL (With Examples)Using PostgreSQL String Functions for Improved Data AnalysisData Processing With PostgreSQL Window FunctionsUnderstanding WHERE in PostgreSQL (With Examples)PostgreSQL Joins : A SummaryWhat Is Data Compression and How Does It Work?What Is Data Transformation, and Why Is It Important?How to Install PostgreSQL on MacOS5 Common Connection Errors in PostgreSQL and How to Solve ThemUnderstanding PostgreSQL FunctionsUnderstanding OFFSET in PostgreSQL (With Examples)Understanding PostgreSQL Date and Time FunctionsUnderstanding the Postgres string_agg FunctionWhat Is a PostgreSQL Inner Join?What Is a PostgreSQL Left Join? And a Right Join?A Guide to PostgreSQL ViewsData Partitioning: What It Is and Why It MattersUnderstanding ACID Compliance Understanding percentile_cont() and percentile_disc() in PostgreSQLUnderstanding PostgreSQL Conditional FunctionsUnderstanding PostgreSQL Array FunctionsWhat Characters Are Allowed in PostgreSQL Strings?What Is a PostgreSQL Full Outer Join?What Is a PostgreSQL Cross Join?Understanding PostgreSQL SELECTSelf-Hosted or Cloud Database? A Countryside Reflection on Infrastructure ChoicesStrategies for Improving Postgres JOIN PerformanceUnderstanding the Postgres extract() FunctionUnderstanding the rank() and dense_rank() Functions in PostgreSQL
Pg_partman vs. Hypertables for Postgres PartitioningPostgreSQL Performance Tuning: Designing and Implementing Your Database SchemaPostgreSQL Performance Tuning: Key ParametersPostgreSQL Performance Tuning: Optimizing Database IndexesNavigating Growing PostgreSQL Tables With Partitioning (and More)Top PostgreSQL Drivers for PythonWhen to Consider Postgres PartitioningUnderstanding PostgreSQL TablespacesWhat Is Audit Logging and How to Enable It in PostgreSQLHow to Reduce Bloat in Large PostgreSQL TablesDetermining the Optimal Postgres Partition SizeGuide to PostgreSQL Database OperationsDesigning Your Database Schema: Wide vs. Narrow Postgres TablesBest Practices for (Time-)Series Metadata Tables Guide to Postgres Data ManagementHow to Query JSONB in PostgreSQLHow to Index JSONB Columns in PostgreSQLHow to Monitor and Optimize PostgreSQL Index PerformanceOptimizing Array Queries With GIN Indexes in PostgreSQLSQL/JSON Data Model and JSON in SQL: A PostgreSQL PerspectiveHow to Query JSON Metadata in PostgreSQLA Guide to pg_restore (and pg_restore Example)Handling Large Objects in PostgresPostgreSQL Performance Tuning: How to Size Your DatabaseGuide to PostgreSQL PerformanceAn Intro to Data Modeling on PostgreSQLBest Practices for Time-Series Data Modeling: Single or Multiple Partitioned Table(s) a.k.a. Hypertables What Is a PostgreSQL Temporary View?A PostgreSQL Database Replication GuideHow to Compute Standard Deviation With PostgreSQLHow PostgreSQL Data Aggregation WorksBuilding a Scalable DatabaseA Guide to Scaling PostgreSQLHow to Use PostgreSQL for Data TransformationRecursive Query in SQL: What It Is, and How to Write OneGuide to PostgreSQL Database DesignExplaining PostgreSQL EXPLAINA Guide to Data Analysis on PostgreSQLGuide to PostgreSQL SecurityHow to Use Psycopg2: The PostgreSQL Adapter for Python
Best Practices for Postgres Data ManagementBest Practices for Postgres PerformanceBest Practices for Postgres SecurityHow to Design Your PostgreSQL Database: Two Schema ExamplesBest Practices for PostgreSQL Database OperationsBest Practices for PostgreSQL Data AnalysisTesting Postgres Ingest: INSERT vs. Batch INSERT vs. COPYHow to Manage Your Data With Data Retention PoliciesBest Practices for Scaling PostgreSQLHow to Store Video in PostgreSQL Using BYTEAHow to Handle High-Cardinality Data in PostgreSQLHow to Use PostgreSQL for Data NormalizationBest Practices for PostgreSQL AggregationBest Practices for Postgres Database ReplicationHow to Use a Common Table Expression (CTE) in SQL
PostgreSQL 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: Install pg_trgm for Data MatchingPostgreSQL Extensions: Turning PostgreSQL Into a Vector Database With pgvectorPostgreSQL Extensions: Database Testing With pgTAPPostgreSQL Extensions: PL/pgSQLPostgreSQL Extensions: Using PostGIS and Timescale for Advanced Geospatial InsightsPostgreSQL Extensions: Intro to uuid-ossp
PostgreSQL as a Real-Time Analytics DatabaseUnderstanding OLTPWhat Is the Best Database for Real-Time AnalyticsHow to Build an IoT Pipeline for Real-Time Analytics in PostgreSQLHow to Choose a Real-Time Analytics DatabaseOLAP Workloads on PostgreSQL: A GuideHow to Choose an OLAP DatabaseData Analytics vs. Real-Time Analytics: How to Pick Your Database (and Why It Should Be PostgreSQL)Columnar Databases vs. Row-Oriented Databases: Which to Choose?
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 LangChainRetrieval-Augmented Generation With Claude Sonnet 3.5 and PgvectorUnderstanding Semantic SearchWhat Is Vector Search? Vector Search vs Semantic SearchText-to-SQL: A Developer’s Zero-to-Hero GuideNearest Neighbor Indexes: What Are IVFFlat Indexes in Pgvector and How Do They WorkRAG Is More Than Just Vector SearchPostgreSQL Hybrid Search Using Pgvector and CohereImplementing Filtered Semantic Search Using Pgvector and JavaScriptRefining Vector Search Queries With Time Filters in Pgvector: A TutorialBuilding an AI Image Gallery With OpenAI CLIP, Claude Sonnet 3.5, and PgvectorWhen Should You Use Full-Text Search vs. Vector Search?HNSW vs. DiskANN
Understanding IoT (Internet of Things)Storing IoT Data: 8 Reasons Why You Should Use PostgreSQLHow to Choose an IoT DatabaseHow 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 % CompressionA Beginner’s Guide to IIoT and Industry 4.0Why You Should Use PostgreSQL for Industrial IoT DataHow Hopthru Powers Real-Time Transit Analytics From a 1 TB Table Migrating a Low-Code IoT Platform Storing 20M Records/DayMoving Past Legacy Systems: Data Historian vs. Time-Series DatabaseHow 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
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
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
8 Reasons to Choose Timescale as Your InfluxDB Alternative What InfluxDB Got Wrong5 InfluxDB Alternatives for Your Time-Series Data InfluxQL, Flux, and SQL: Which Query Language Is Best? (With Cheatsheet)TimescaleDB vs. InfluxDB: Purpose Built Differently for Time-Series Data
How 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 HyperfunctionsPostgreSQL Materialized Views and Where to Find ThemTimescale Tips: Testing Your Chunk SizeIs Postgres Partitioning Really That Hard? An Introduction To Hypertables5 Ways to Monitor Your PostgreSQL Database
Postgres cheat sheet
HomeTime series basicsPostgres basicsPostgres guidesPostgres best practicesPostgres extensionsPostgres for real-time analytics
Sections

Time series analysis

Stationary Time-Series Analysis

Python

How to Work With Time Series in Python?Tools for Working With Time-Series Analysis in PythonTime-Series Analysis and Forecasting With Python Guide to Time-Series Analysis in Python

R

Time-Series Analysis in R
Understanding Autoregressive Time-Series ModelingCreating a Fast Time-Series Graph With Postgres Materialized Views

Time series databases

The Best Time-Series Databases ComparedAlternatives to TimescaleWhat Are Open-Source Time-Series Databases—Understanding Your OptionsAWS Time-Series Database: Understanding Your Options

Time series data

What Is Temporal Data?What Is a Time Series and How Is It Used?Is Your Data Time Series? Data Types Supported by PostgreSQL and TimescaleUnderstanding Database Workloads: Variable, Bursty, and Uniform PatternsWhy Consider Using PostgreSQL for Time-Series Data?

Products

Time Series and Analytics AI and Vector Enterprise Plan Cloud Status Support Security Cloud Terms of Service

Learn

Documentation Blog Forum Tutorials Changelog Success Stories Time Series Database

Company

Contact Us Careers About Brand Community Code Of Conduct Events

Subscribe to the Tiger Data Newsletter

By submitting, you acknowledge Tiger Data's Privacy Policy

2025 (c) Timescale, Inc., d/b/a Tiger Data. All rights reserved.

Privacy preferences
LegalPrivacySitemap

Published at Jul 3, 2024

AWS Time-Series Database: Understanding Your Options

Try for free

Start supercharging your PostgreSQL today.

The AWS acronym inside a watch (representing time series) in neon colors.

Written by Carlo Mencarelli

Over the years, Amazon Web Services (AWS) has become a leading provider of cloud database hosting services. They offer everything from general relational databases to specialized ones like ledger and graph databases.

One type of specialized database that has been gaining significant popularity over the years is the time-series database (TSDB). DB-Engines, an initiative by Solid-IT to collect and present information about database systems, ranks time-series databases as the second highest change in popularity for the last several years.

image

With the growing importance of time-series data and options available, there are plenty of options available for users of AWS. However, choosing the right solution for your specific needs can be challenging. I hope to clear up some of the confusion and make the choice easier by the end of this article by:

  • Providing a clear understanding of time-series databases

  • Exploring the various time-series database options available on AWS

  • Comparing these possibilities to help you decide what's right for your organization

What Is a Time-Series Database

As mentioned, time-series databases are specialized database systems built to handle time-stamped or time-series data efficiently. Some examples of this data would be server metrics, sensor data, stock market information, or weather data. For more information about time-series data, we have a whole blog you can read: Time-Series Data: What It Is, and How to Use It.

As you may have learned from our blog above, time-series data is time-centric and normally append-only. A TSDB leverages these facts to optimize storage and compute operations and can efficiently handle a large number of transactions in a very short time.

Queries on time-series databases typically involve date ranges, which means the query process needs to be optimized to handle these types of requests. TSDB systems include these optimizations as part of the engine. Some systems can even downsample or archive the data as it becomes older.

If you want to know more about TSDB systems, our recent blog, Time-Series Database: An Explainer, provides more detail.

What you need from a time-series database

When choosing a time-series database, there are several key properties to consider:

  • Scalability: A good TSDB should be able to handle increasing volumes of data and query loads without significant performance degradation.

  • Maintainability: The database should be easy to manage, update, and troubleshoot. It should include features like automated backups and archiving.

  • Reliability: Many users of TSDBs are building new systems from scratch, so having the data when needed is crucial for success.

  • Usability: The database should offer a powerful and intuitive query language that allows users to easily retrieve and analyze time-series data without adding a new language that the developers have to learn and become proficient with.

AWS Time-Series Database Options

Several options are available for time-series database solutions on AWS. These can be broadly categorized into AWS-native solutions and third-party options that can be deployed on AWS infrastructure. Let's explore some of the key options.

Amazon Timestream is AWS's purpose-built time-series database service. It comes in several flavors, depending on your needs, which we’ll cover below.

You can also use Amazon RDS or Amazon Aurora, although they were not built specifically for time-series data. RDS offers typical relational databases, while Aurora offers Amazon’s proprietary versions of MySQL and PostgreSQL with a serverless option available.

Timescale, built on AWS infrastructure, offers another option. It is a full time-series database solution based on PostgreSQL, so you get TSDB performance with the familiarity of PostgreSQL.

AWS solutions

AWS Timestream

Amazon Timestream is AWS's purpose-built time-series database service. It offers two variants: Timestream for LiveAnalytics and Timestream for InfluxDB.

Timestream for LiveAnalytics is Amazon’s proprietary TSDB. The database falls under their serverless offerings, which means it will automatically scale up and down based on performance requirements. It also offers the standard 99.99 percent uptime guarantee that many AWS services do. 

However, the service has some significant limitations, including limited SQL support, specifically the inability to do joins. Additionally, it is a proprietary solution, which means the service is only available on AWS and has no community development.

Timestream for InfluxDB is based on the open-source implementation of InfluxDB, currently compatible with the 2.7 InfluxDB service. The service is fully managed by AWS, meaning you don’t need to worry about installation and some administration tasks. Timestream for InfluxDB is also beholden to the same problems that running InfluxDB on your own is. We’ve written about some of what InfluxDB has gotten wrong over the years, but to name a few: two backend rewrites, two major API changes, and a multiplying product catalog of similarly named services causing confusion.

RDS

RDS is Amazon’s basic database service. It offers several different engines, such as MySQL, PostgreSQL, and Oracle. It does not offer specific TSDB features, but you can leverage open-source options such as the pg_partman PostgreSQL extension to assist with partitioning the time-series data.

This level of flexibility is nice but also adds management and development overhead. You can find more open-source TSDB options in an article we recently published.

Amazon Aurora

Aurora is similar to RDS in that it provides MySQL and PostgreSQL-compatible databases. The difference is that Aurora offers more cloud-specific features, such as storage scaling, point-in-time recovery, and more.

While Aurora does provide a better administration experience, like RDS, it doesn’t offer any TSDB-specific features or capabilities. The added cost and capabilities may not exactly add up, as we explored in an earlier article: Benchmarking Amazon Aurora vs. PostgreSQL.

Timescale on AWS

Timescale offers a few products in the TSDB space: an open-source TSDB PostgreSQL extension that is free to use (TimescaleDB) and a fully managed solution that offers advanced features and support (Timescale Cloud, with TimescaleDB at its core).

TimescaleDB is built on top of PostgreSQL, which provides a familiar interface and features of PostgreSQL but adds a powerful TSDB over it. In several tests, we’ve observed Timescale surpassing InfluxDB with time-series data. Timescale supports columnar compression for even more efficient data storage, ultimately saving time and money on storage and data transfer.

Conclusion

Time-series databases have become critical tools for teams dealing with large volumes of time-series data. As we've seen, they offer high-performance solutions specifically designed to handle the unique challenges of time series data, such as high write throughput, efficient storage, and optimized time-based queries. There are a lot of choices in the ecosystem for TSDBs, with the information above, you should be well-equipped to make the decision best for you and your organization.

You can host all types of PostgreSQL workloads in Timescale. But especially if you have time-series data, Timescale will suit you better than other AWS time-series databases. Plus, by choosing Timescale Cloud, you’ll enjoy the following benefits:

  • Our mature cloud platform is supercharged PostgreSQL (simpler, faster, and more cost-effective). If you work with time-series data, you know how quickly your data will grow—avoid hitting that scalability wall, and definitely don’t be scared to pay for top performance every time you check your monthly bill.

  • Enhanced performance with less compute and storage resources, thanks to specialized optimizations tailored for time series and analytics workloads. For a 1 TB dataset with almost one billion rows, Timescale outperforms Amazon RDS for PostgreSQL, with up to 44 percent higher ingest rates and queries running up to 350x faster.

  • Substantial storage cost savings: our advanced compression algorithms enable a remarkable 90 percent reduction in disk storage. You can tier your older data to object storage built on S3 for an extra savings boost while remaining fully queryable.

  • Expert technical support is included in our pricing, ensuring you receive the assistance you need whenever you need it. Learn how we're revolutionizing hosted database support at no extra cost.

You can try Timescale Cloud for free for 30 days to see if it’s right for you: sign up and see for yourself.

On this page

    Try for free

    Start supercharging your PostgreSQL today.