Query Real Time Data in Kafka Using SQL

This page summarizes the projects mentioned and recommended in the original post on dev.to

InfluxDB - Power Real-Time Data Analytics at Scale
Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives
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  • risingwave

    SQL stream processing, analytics, and management. We decouple storage and compute to offer instant failover, dynamic scaling, speedy bootstrapping, and efficient joins.

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

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

    InfluxDB logo
  • Apache Spark

    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.

  • redpanda

    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.

  • Apache Pulsar

    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.

  • materialize

    The data warehouse for operational workloads. (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.

  • ApacheKafka

    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.

    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.

  • SaaSHub

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

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NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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