Query Real Time Data in Kafka Using SQL

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#SQL #Streaming #Kafka #Rust #Database

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  • risingwave

    RisingWave: A Distributed SQL Database for Stream Processing

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

  • 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.

  • CodiumAI

    TestGPT | Generating meaningful tests for busy devs. Get non-trivial tests (and trivial, too!) suggested right inside your IDE, so you can code smart, create more value, and stay confident when you push.

  • 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

    Materialize is a fast, distributed SQL database built on streaming internals. (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.

  • InfluxDB

    Access the most powerful time series database as a service. Ingest, store, & analyze all types of time series data in a fully-managed, purpose-built database. Keep data forever with low-cost storage and superior data compression.

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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