Query Real Time Data in Kafka Using SQL

1. risingwave

   1 31 7,731 10.0 Rust

   Stream processing and management platform.

   

   In the demo tutorial, we'll leverage the following GitHub repository where we assume that all necessary things are set up using Docker compose.

2. InfluxDB

   www.influxdata.com featured

   InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.

   

3. Apache Spark

   2 121 41,083 10.0 Scala

   Apache Spark - A unified analytics engine for large-scale data processing

   

   Additionally, one of the challenges of working with Kafka is how to efficiently analyze and extract insights from the large volumes of data stored in Kafka topics. Traditional batch processing approaches, such as Hadoop MapReduce or Apache Spark, can be slow and expensive, and may not be suitable for real-time analytics. To address this challenge, you can use SQL queries with Kafka to analyze and extract insights from the data in real time.

4. redpanda

   3 72 10,320 10.0 C++

   Redpanda is a streaming data platform for developers. Kafka API compatible. 10x faster. No ZooKeeper. No JVM!

   

   RisingWave is an open-source distributed SQL database for stream processing. RisingWave accepts data from sources like Apache Kafka, Apache Pulsar, Amazon Kinesis, Redpanda, and databases via native Change data capture connections to MySQL and PostgreSQL sources. It uses the concept of materialized view that involves caching the outcome of your query operations and it is quite efficient for long-running stream processing queries.

5. Apache Pulsar

   4 34 14,599 9.8 Java

   Apache Pulsar - distributed pub-sub messaging system

   

   RisingWave is an open-source distributed SQL database for stream processing. RisingWave accepts data from sources like Apache Kafka, Apache Pulsar, Amazon Kinesis, Redpanda, and databases via native Change data capture connections to MySQL and PostgreSQL sources. It uses the concept of materialized view that involves caching the outcome of your query operations and it is quite efficient for long-running stream processing queries.

6. materialize

   5 127 5,978 10.0 Rust

   Real-time Data Integration and Transformation: use SQL to transform, deliver, and act on fast-changing data. (by MaterializeInc)

   

   Most streaming database technologies use SQL for these reasons: RisingWave, Materialize, KsqlDB, Apache Flink, and so on offering SQL interfaces. This post explains how to choose the right streaming database.

7. ApacheKafka

   6 135 35 0.0

   A curated re-sources list for awesome Apache Kafka

   

   Apache Kafka is a distributed streaming platform that allows you to store and process real-time data streams. It is commonly used in modern data architectures to capture and analyze user interactions with web and mobile applications, as well as IoT device data, logs, and system metrics. It is often used for real-time data processing, data pipelines, and event-driven applications. However, querying data stored in Kafka can be challenging, especially for users who are more comfortable with SQL than with Kafka's native APIs. This is where the streaming SQL engine and database can be helpful. It is actually possible to run SQL directly on streaming data.

8. flink-statefun

   7 18 515 3.2 Java

   Apache Flink Stateful Functions

   

   Most streaming database technologies use SQL for these reasons: RisingWave, Materialize, KsqlDB, Apache Flink, and so on offering SQL interfaces. This post explains how to choose the right streaming database.

9. SaaSHub

   www.saashub.com featured

   SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

   

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