Streaming Visualizations Native to Modern Data Platforms

Arcadia Data delivers real-time analytics and BI directly from streaming data platforms such as Apache Kafka®, Apache Kudu™, and Apache Solr™.

Why Use Streaming Visualizations?

Streaming visualizations give you real-time analytics and BI to see the trends and patterns in your data to help you react more quickly. A wide variety of use cases such as fraud detection, data quality analysis, operations optimization, and more need quick responses, and real-time BI helps users drill down to issues that require immediate attention.

Arcadia Data for Streaming Visualizations

Arcadia Data lets you visualize streaming data in platforms ideal for real-time analysis such as Apache Kafka (plus Confluent KSQL), Apache Kudu, and Apache Solr.

Apache KafkaApache KuduApache Solr

Browse and analyze Apache Kafka® topics with Arcadia Data

Arcadia Data uniquely integrates with Confluent KSQL for the lowest-latency real-time visualizations on Kafka data. Analyze event data at a granular level to investigate the details of your streaming data.

Start visualizing Kafka topics today with Arcadia Instant for KSQL

With our free downloads, you can get started today. Follow these four steps:

Additional resources:

Fast Analytics on Apache Kudu

Kudu is a powerful analytics platform ideal for streaming visualizations and real-time BI because of its ability to quickly update data and also scale out across a large cluster of nodes. Arcadia Data is tightly integrated with Kudu to deliver a streaming data analytics environment featuring:

  • Fast inserts/updates from a streaming data source.
  • Real-time analysis combined with historical analysis.
  • Horizontally scalable analytics in a high-speed columnar format.

Streaming Analytics on Apache Solr

Apache Solr supports fast updates and fast lookups, making it an ideal platform for storing streaming data. Arcadia Data is tightly integrated with Solr to deliver a streaming data analytics and real-time BI environment featuring:

  • Fast inserts/updates from a streaming data source.
  • The ability to index and analyze a wide variety of unstructured data formats.
  • A search interface to quickly look up data associated with text, such as in log files.