ClickHouse
SINK

ClickHouse

Send transformed data from your GlassFlow pipelines directly into a ClickHouse database for real-time analytics

Integration screenshot

Overview

ClickHouse Sink Connector

The ClickHouse Sink Connector allows you to load transformed data from GlassFlow into ClickHouse, a high-performance analytics database.
ClickHouse is a fast, open-source columnar database management system optimized for OLAP (Online Analytical Processing) use cases, enabling efficient real-time data analysis. With this connector, you can power ultra-fast querying and ensure your analytics workflows are fueled with the most up-to-date data.

Key Features

  • Optimized for Performance: Leverages ClickHouse's columnar storage for rapid data ingestion and efficient querying.
  • Seamless Integration: Simplifies the process of transferring transformed data directly into ClickHouse.
  • Scalable Data Handling: Designed to handle large datasets with minimal latency, ensuring performance at scale.
  • Customizable Data Mapping: Easily map data fields and formats to fit your ClickHouse schema.
  • Reliable and Secure: Ensures accurate data delivery with secure connection protocols.

Use Cases

  • Real-Time Analytics: Push data into ClickHouse to power live dashboards and reporting.
  • Data Aggregation: Load pre-aggregated metrics for instant querying and insights.
  • High-Performance Data Processing: Leverage ClickHouse's capabilities for lightning-fast analytics and deep data exploration.

With the ClickHouse Sink Connector, GlassFlow enables you to transform your data into actionable insights with unmatched speed and scalability.
Read the blog to learn more about usecases with Clickhouse and GlassFlow.

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Supported by our users

Do not listen to us. Listen to them.

  • GlassFlow team managed to turn my scattered thoughts regarding the data streaming into a Python masterpiece.
    ⁠Anurag Roy@Doorlabs
  • Creating a data pipeline with GlassFlow is incredibly straightforward
    ⁠Swapnil Gupta@Deloitte
  • I am fed up with overcomplicated event-based pipelines. Finally a team that will make my life easier.
    Philippe Brule@aboutphilippe
  • Love the way they team tackles the complexity of building and sharing streaming pipelines.
    Rakesh Kaswan@KaswanRakesh
  • As a Python dev starting with data streaming, you should absolutely give Glassflow a try.
    ⁠Dinesh Marimuthu@Bilthouse
  • Something is cooking here! Super excited to test your solution.
    Joash Xu@joashxu
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