Integrations

Easily access and analyze large volumes of data with CrateDB and Tableau

Create online data-driven reports and dashboards with CrateDB and Power BI

Get insights from various types of data with CrateDB and Metabase

Explore and visualize your data with Apache Superset / Preset and CrateDB

Visualize and monitor time series data with CrateDB and Grafana

Data Analysis with CrateDB and Cluvio

Customer-facing Analytics with Explo and CrateDB

Start querying your data instantly with CrateDB and DataStation

Connect CrateDB with the PostgreSQL ODBC Driver

DataGrip connects to CrateDB to run queries, explore schemas, and manage data, enabling efficient database development and real-time analytics

DBeaver connects to CrateDB to query, manage, and visualize data, enabling streamlined database administration and real-time analytics

SQLPad connects to CrateDB to run queries and build dashboards, enabling collaborative, real-time data exploration and analytics

Create a Machine Learning pipeline with CrateDB and R

QueryZen makes it easier to manage SQL query statements over HTTP.

Load data from PostgreSQL into CrateDB with the CrateDB toolkit

Load data from the well-known open-source relational database systems MySQL and MariaDB into CrateDB

Oracle databases can be integrated with CrateDB to replicate or migrate data, enabling real-time analytics and operational insights on large-scale datasets

Learn how to configure Trino, a fast, distributed SQL query engine for analytics, to run queries against CrateDB.

Ingest data from Rising Wave, a stream processing and management platform, into a CrateDB table for long-term persistence and efficient querying, even at large scale.

With Rill you can turn your CrateDB datasets into powerful, opinionated dashboards with SQL.

Use Plotly Open Source Graphing Libraries to make interactive, publication-quality graphs of your data in CrateDB. Use Dash for rapidly building data apps in Python, based on Plotly.

Ingest event data seamlessly and get enriched analysis and visualization with CrateDB and Kafka

Unlock the full potential of your data pipeline with CrateDB and Apache NiFi

Leverage CrateDB’s scalable storage and real-time query capabilities with StreamSets Data Collector

Collect, store, and analyze real-time data with CrateDB and Telegraf

Build scalable applications with CrateDB and Node-RED

Create an efficient IoT platform with CrateDB and balena

Use Amazon Kinesis Data Streams to collect and process large data streams in real time and relay them into CrateDB

Build your real-time streaming applications with Apache Flink and CrateDB

Using Apache Spark with CrateDB is a powerful combination for processing andanalyzing large datasets

Apache Hop orchestrates and transforms data pipelines that load, process, and stream data into CrateDB, enabling scalable real-time analytics and operational insights on large, distributed datasets

Estuary provides real-time data integration and modern ETL and ELT data pipelines. Build scalable, fault-tolerant streaming data pipelines with Estuary and CrateDB

Debezium captures real-time database changes and streams them into CrateDB, enabling up-to-date analytics and event-driven applications on continuously changing data

AWS Database Migration Service (AWS DMS) is a managed migration and replication service that helps move your database and analytics workloads quickly to CrateDB

ingestr is a command-line application for copying data from any source to any destination database. It supports CrateDB on both the source and destination sides

The dlt data load tool simplifies extracting and loading data into CrateDB, enabling reliable, automated pipelines for analytics-ready data with minimal engineering effort

AMQP enables message-based data streams to be ingested into CrateDB

MQTT streams lightweight IoT and event data into CrateDB, enabling real-time analytics and monitoring of high-volume, low-latency telemetry data

Collectd collects system and application metrics and sends them to CrateDB, enabling real-time performance monitoring and historical analytics at scale

StatsD collects application metrics and forwards them to CrateDB, enabling real-time monitoring, alerting, and analytics of system and application performance

Rsyslog streams logs and system events into CrateDB, enabling centralized, real-time log analysis and operational monitoring at scale

Connect to CrateDB with Dapr for efficient data management and storage

Automate work in CrateDB and integrate with other applications

AWS Lambda streams serverless event data into CrateDB, enabling real-time analytics and automated processing of dynamically generated workloads

Azure Functions streams serverless event and telemetry data into CrateDB, enabling real-time analytics and automated insights from scalable cloud workloads

Django integrates with CrateDB to store and query application data, enabling real-time analytics and scalable data-driven web applications

Gradio connects interactive machine learning apps to CrateDB, enabling real-time collection, storage, and analysis of user interactions and model outputs

You can use MCP with CrateDB, either by selecting the CrateDB MCP Server for Text-to-SQL and documentation retrieval, or by using community MCP servers compatible with PostgreSQL databases.

Simplify your orchestration workflows with seamless automation with CrateDB and Apache Airflow

Prefect orchestrates and automates data workflows into CrateDB, enabling reliable, scheduled, and monitored pipelines for real-time analytics and operational insights.

CrateDB integrates with Kestra via the PostgreSQL plugin to create an efficient, scalable data pipeline

Meltano extracts, transforms, and loads data into CrateDB, enabling automated pipelines for analytics-ready, centralized, and up-to-date datasets adhering to the Singer specification.

Terraform automates the provisioning and management of CrateDB infrastructure, enabling consistent, scalable, and reproducible deployments

Marquez tracks and catalogs data lineage for pipelines feeding CrateDB, enabling visibility, governance, and observability of data flow and analytics processes.

Atlan connects to CrateDB to provide a collaborative data workspace, enabling data discovery, governance, and cataloging for analytics and operational insights.

Empower your applications with data analysis and AI capabilities with CrateDB and LangChain

Streamline your machine learning workflows with CrateDB and MLflow

Optimize your machine learning workflows with CrateDB and PyCaret

LlamaIndex connects AI applications to CrateDB, enabling structured data retrieval and real-time analytics for enhanced language model insights and decision-making

MindsDB connects machine learning models to CrateDB, enabling predictive analytics and AI-driven insights directly on your operational data

TensorFlow integrates with CrateDB to train and deploy machine learning models on your data, enabling real-time predictions and AI-driven analytics at scale

scikit-learn is an open-source Python package that allows predictive data analysis on data in CrateDB

Pandas connects to CrateDB to query, manipulate, and analyze large datasets, enabling efficient data exploration and real-time analytics within Python

Polars connects to CrateDB for fast, memory-efficient data processing, enabling high-performance analytics and transformations on large-scale datasets

Dask integrates with CrateDB to perform scalable, parallel data processing, enabling distributed analytics and real-time insights on large datasets

Datashader visualizes large-scale datasets stored in CrateDB, enabling high-performance, interactive analytics and insights from millions to billions of data points

PyViz integrates with CrateDB to build interactive, scalable data visualizations and dashboards, enabling real-time exploration and analysis of large datasets

Store long-term metrics with Prometheus and CrateDB

OpenTelemetry exports traces, metrics, and logs into CrateDB, enabling unified observability, real-time analytics, and deep insights across distributed systems

Replicate and analyze your time-series data from InfluxDB in CrateDB, enabling scalable, SQL-based analytics and long-term insights on high-volume metrics which can be combined with other data types

dbt transforms and models data stored in CrateDB, enabling analytics-ready datasets with versioned, tested, and documented SQL transformations

Apache Iceberg integrates with CrateDB to query and analyze large-scale data lake tables, enabling SQL-based analytics with transactional consistency and schema evolution

MongoDB integrates with CrateDB to replicate and analyze document data, enabling real-time analytics and scalable querying on semi-structured datasets

Amazon DynamoDB integrates with CrateDB to stream or replicate key-value data, enabling real-time analytics and SQL-based querying on high-velocity operational workloads

Load data from PostgreSQL into CrateDB with the CrateDB toolkit

Load data from the well-known open-source relational database systems MySQL and MariaDB into CrateDB

Oracle databases can be integrated with CrateDB to replicate or migrate data, enabling real-time analytics and operational insights on large-scale, datasets

SQL Server integrates with CrateDB to replicate or stream relational data, enabling real-time analytics and scalable querying

JMeter sends load test metrics and results into CrateDB, enabling real-time analysis and historical insights into application performance and scalability

Locust streams load testing metrics into CrateDB, enabling real-time performance monitoring and analysis of application scalability under stress

Streamlit connects interactive data apps to CrateDB, enabling real-time visualization, analysis, and exploration of large-scale datasets

Use Plotly Open Source Graphing Libraries to make interactive, publication-quality graphs of your data in CrateDB. Use Dash for rapidly building data apps in Python, based on Plotly

Gradio connects interactive machine learning apps to CrateDB, enabling real-time collection, storage, and analysis of user interactions and model outputs

Datashader visualizes large-scale datasets stored in CrateDB, enabling high-performance, interactive analytics and insights from millions to billions of data points