Streambased Documentation
  • Home
  • Overview
    • Key Concepts
  • Streambased Cloud
    • Streambased Cloud UI
      • Create your first Streambased cluster
      • Create your first Streambased API Key
      • Running your first A.S.K Query
      • Exploring your data using S.S.K
    • Iceberg Service for Kafka - I.S.K.
      • Overview
      • Architecture
      • Usage
      • Quick Start
    • Analytics Service for Kafka - A.S.K.
      • Overview
      • Architecture
      • Connecting to Streambased A.S.K.
        • Connect Superset to Streambased A.S.K.
        • Connect Jupyter to Streambased A.S.K.
        • Connect a JDBC Client to Streambased A.S.K.
        • Connect an ODBC client to Streambased A.S.K.
        • Connect a Python Application (SQL Alchemy) to Streambased A.S.K.
    • Storage Service for Kafka - S.S.K.
      • Overview
      • Connecting to Streambased S.S.K.
        • Connecting a S3 compatible client to Streambased S.S.K.
        • Connect a S3manager to Streambased S.S.K.
  • Streambased Platform
    • Overview
    • Requirements
    • Step by Step Installation
    • Configuration
    • Connecting Analytical Applications to Streambased
      • Connect Superset to Streambased
      • Connect Jupyter to Streambased
      • Connect a JDBC Client to Streambased
      • Connect an ODBC client to Streambased
      • Connect a Python Application (SQL Alchemy) to Streambased
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On this page
  • What is Streambased?
  • What can it do?
  • Streambased Acceleration

Overview

What is Streambased?

Streambased is the premier platform for converging the operational and analytical functions within an organisation. Streambased technology provides access to operational data in Kafka via a platform that integrates with analytical tools, like Tableau, Superset, and PowerBI as well as larger analytical powerhouses like Databricks, Snowflake and Trino

What can it do?

Streambased surfaces your Kafka via 3 industry standard mechanisms for consuming Kafka data:

  1. As a SQL engine - use SQL to interface Kafka data using industry standard JDBC, ODBC and SQL Alchemy protocols.

  2. As a file system - navigate your Kafka data as if it were an Amazon S3 compatible file system. Download subsets of data to yuor applications for later analysis, perfect for those one time tasks.

  3. As a table format - represent your Kafka topics as Apache Iceberg tables. Enabling integration with the big names of data analysis.

This expanded reach opens a wealth of new data that provides data analysts, BI engineers and data scientists with increased insight whilst not requiring any changes in the tools and techniques they use today.

Streambased Acceleration

Apache Kafka is not an analytically optimized system and all of the above would be pointless with the existing performance constraints of working with Kafka data for analytics.

Luckily, Streambased' innovative indexing technology allows for up to 100x improvements in read performance for anlytics queries, transforming Kafka from an operational superstar to an analytics powerhouse.

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Last updated 3 months ago