ClickHouse
PostgreSQL
MySQL
Apache Doris
VeloDB
Snowflakepowe.red
MongoDB
StarRocks
Hadoop
Apache Spark
Apache Cassandra
Apache Flink
PostgreSQL
MongoDB
Apache Storm
Apache Kafka
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Based on our record, ClickHouse should be more popular than Hadoop. It has been mentiond 43 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
For the third, examples here might be analytics plugins in specialized databases like Clickhouse, data-transformations in places like your ETL pipeline using Airflow or Fivetran, or special integrations in your authentication workflow with Auth0 hooks and rules. - Source: dev.to / 3 months ago
Online analytical processing (OLAP) databases like Apache Druid, Apache Pinot, and ClickHouse shine in addressing user-initiated analytical queries. You might write a query to analyze historical data to find the most-clicked products over the past month efficiently using OLAP databases. When contrasting with streaming databases, they may not be optimized for incremental computation, leading to challenges in... - Source: dev.to / 3 months ago
To achieve seamless real-time data ingestion, transformation, and analytics, a powerful combination to explore is RisingWave and ClickHouse. RisingWave is a PostgreSQL-compatible database specifically designed for stream processing. It excels at ingesting real-time data streams, performing diverse transformations, and enabling instant querying of results. ClickHouse® is a high-performance, column-oriented SQL... - Source: dev.to / 4 months ago
You can export the whole dataset as described here: https://github.com/ClickHouse/ClickHouse/issues/29693- Source: Hacker News / 5 months agocurl https://clickhouse.com/ | sh.
Nowadays I am looking at the clickhouse and how it might help me maybe you can check it out: https://clickhouse.com/. Source: 5 months ago
Did you check out tools like https://hadoop.apache.org/ ? Source: about 1 year ago
There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: about 1 year ago
There are several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS. - Source: dev.to / over 1 year ago
A copy of Hadoop installed on each of these machines. You can download Hadoop from the Apache website, or you can use a distribution like Cloudera or Hortonworks. - Source: dev.to / over 1 year ago
The Apache™ Hadoop™ project develops open-source software for reliable, scalable, distributed computing. - Source: dev.to / over 1 year ago
PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
MySQL - The world's most popular open source database
Apache Cassandra - The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance.
Apache Doris - Apache Doris is an open-source real-time data warehouse for big data analytics.
Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
