Best Query Engines for Sematext Cloud

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Compare the Top Query Engines that integrate with Sematext Cloud as of September 2025

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This a list of Query Engines that integrate with Sematext Cloud. Use the filters on the left to add additional filters for products that have integrations with Sematext Cloud. View the products that work with Sematext Cloud in the table below.

What are Query Engines for Sematext Cloud?

Query engines are software tools designed to retrieve and process data from databases or large datasets in response to user queries. They efficiently interpret and execute search requests, optimizing the retrieval process to deliver accurate and relevant results quickly. Query engines can handle structured, semi-structured, and unstructured data, making them versatile for various applications such as data analytics, business intelligence, and search engines. They often support complex query languages like SQL and can integrate with multiple data sources to provide comprehensive insights. By optimizing data retrieval, query engines enhance the performance and usability of data-driven applications and decision-making processes. Compare and read user reviews of the best Query Engines for Sematext Cloud currently available using the table below. This list is updated regularly.

  • 1
    ClickHouse

    ClickHouse

    ClickHouse

    ClickHouse is a fast open-source OLAP database management system. It is column-oriented and allows to generate analytical reports using SQL queries in real-time. ClickHouse's performance exceeds comparable column-oriented database management systems currently available on the market. It processes hundreds of millions to more than a billion rows and tens of gigabytes of data per single server per second. ClickHouse uses all available hardware to its full potential to process each query as fast as possible. Peak processing performance for a single query stands at more than 2 terabytes per second (after decompression, only used columns). In distributed setup reads are automatically balanced among healthy replicas to avoid increasing latency. ClickHouse supports multi-master asynchronous replication and can be deployed across multiple datacenters. All nodes are equal, which allows avoiding having single points of failure.
    1 Rating
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  • 2
    Apache Spark

    Apache Spark

    Apache Software Foundation

    Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.
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