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  1. Stackups
  2. Application & Data
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  4. Databases
  5. InterSystems IRIS Data platform vs TimescaleDB

InterSystems IRIS Data platform vs TimescaleDB

OverviewComparisonAlternatives

Overview

TimescaleDB
TimescaleDB
Stacks226
Followers374
Votes44
GitHub Stars20.6K
Forks988
InterSystems IRIS Data platform
InterSystems IRIS Data platform
Stacks11
Followers14
Votes0

InterSystems IRIS Data platform vs TimescaleDB: What are the differences?

Introduction:

InterSystems IRIS Data Platform and TimescaleDB are both popular databases, but they have key differences that make each one suitable for specific use cases.

1. Scalability: InterSystems IRIS Data Platform is designed to scale both vertically and horizontally, offering the ability to handle large amounts of data and processing power. In contrast, TimescaleDB is specifically optimized for time-series data, providing superior scalability and performance for time-oriented use cases.

2. Data Model: InterSystems IRIS Data Platform supports multiple data models, including relational, object, and document models, providing flexibility for diverse data structures. On the other hand, TimescaleDB is focused on time-series data, utilizing relational tables with time-series-specific optimizations.

3. Query Performance: InterSystems IRIS Data Platform offers advanced indexing options and SQL query optimizations for efficient data retrieval and analysis. TimescaleDB excels in time-series data handling with features like time-series hypertables and automatic time-based partitioning, improving query performance for time-series data.

4. Consistency and Transactions: InterSystems IRIS Data Platform provides strong ACID compliance and transactional consistency for data integrity and reliability. TimescaleDB sacrifices some ACID properties for improved scalability and performance in time-series data handling.

5. Ecosystem and Integration: InterSystems IRIS Data Platform is part of a larger software ecosystem with modules for analytics, machine learning, and interoperability with various programming languages. TimescaleDB integrates seamlessly with PostgreSQL, allowing users to leverage its ecosystem of extensions, tools, and libraries for additional functionality.

6. Community and Support: InterSystems IRIS Data Platform has a dedicated support team and a well-established user community for assistance and collaboration. TimescaleDB benefits from being built on PostgreSQL, which has a large community of users and developers, providing additional resources and knowledge sharing opportunities.

In Summary, InterSystems IRIS Data Platform offers scalability and data model flexibility, while TimescaleDB excels in time-series data handling with superior query performance and specialized features.

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Detailed Comparison

TimescaleDB
TimescaleDB
InterSystems IRIS Data platform
InterSystems IRIS Data platform

TimescaleDB: An open-source database built for analyzing time-series data with the power and convenience of SQL — on premise, at the edge, or in the cloud.

It provides a set of APIs to operate with transactional persistent data simultaneously: key-value, relational, object, document, multidimensional. Data can be managed by SQL, Java, node.js, .NET, C++, Python, and native server-side ObjectScript language. It includes an Interoperability engine and modules to build AI solutions. It provides features for horizontal scalability (sharding, ECP) and provides High Availability features, Business intelligence, transaction support, and backup.

Packaged as a PostgreSQL extension;Full ANSI SQL;JOINs (e.g., across PostgreSQL tables);Complex queries;Secondary indexes;Composite indexes;Support for very high cardinality data;Triggers;Constraints;UPSERTS;JSON/JSONB;Ability to ingest out of order data;Ability to perform accurate rollups;Data retention policies;Fast deletes;Integration with PostGIS and the rest of the PostgreSQL ecosystem;
Access Controls/Permissions; Data Dictionary Management; Data Replication; Database Conversion; Multiple Programming Languages Supported; Performance Analysis; Virtualization; Backup and Recovery; Data Migration; Data Storage Management; NOSQL; Relational
Statistics
GitHub Stars
20.6K
GitHub Stars
-
GitHub Forks
988
GitHub Forks
-
Stacks
226
Stacks
11
Followers
374
Followers
14
Votes
44
Votes
0
Pros & Cons
Pros
  • 9
    Open source
  • 8
    Easy Query Language
  • 7
    Time-series data analysis
  • 5
    Established postgresql API and support
  • 4
    Reliable
Cons
  • 5
    Licensing issues when running on managed databases
No community feedback yet
Integrations
Prometheus
Prometheus
Equinix Metal
Equinix Metal
Ruby
Ruby
PostgreSQL
PostgreSQL
Django
Django
Kubernetes
Kubernetes
pgAdmin
pgAdmin
Python
Python
Kafka
Kafka
Datadog
Datadog
Node.js
Node.js
Java
Java
Python
Python
.NET
.NET
C++
C++

What are some alternatives to TimescaleDB, InterSystems IRIS Data platform?

MongoDB

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

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