Apache Doris
ClickHouse
StarRocks
Apache Hive
Presto DB
PostgreSQL
SQLite
IBM Db2 Warehouse on Cloud
Apache Flink
Apache Spark
Amazon Kinesis
Spring Framework
Apache Airflow
Spark Mail
Confluent
Apache Kafka
Apache Doris Apache FlinkNo Apache Doris videos yet. You could help us improve this page by suggesting one.
Based on our record, Apache Flink seems to be a lot more popular than Apache Doris. While we know about 28 links to Apache Flink, we've tracked only 1 mention of Apache Doris. 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.
As an open-source real-time data warehouse, Apache Doris provides semi-structured data processing capabilities, and the newly-released version 2.1.0 makes a stride in this direction. Before V2.1, Apache Doris stores semi-structured data as JSON files. However, during query execution, the real-time parsing of JSON data leads to high CPU and I/O consumption in addition to high query latency, especially when the... - Source: dev.to / about 2 months ago
You should let the Apache Flink team know, they mention exactly-once processing on their home page (under "correctness guarantees") and in their list of features. [0] https://flink.apache.org/ [1] https://flink.apache.org/what-is-flink/flink-applications/#building-blocks-for-streaming-applications. - Source: Hacker News / 13 days ago
Data scientists often prefer Python for its simplicity and powerful libraries like Pandas or SciPy. However, many real-time data processing tools are Java-based. Take the example of Kafka, Flink, or Spark streaming. While these tools have their Python API/wrapper libraries, they introduce increased latency, and data scientists need to manage dependencies for both Python and JVM environments. For example,... - Source: dev.to / about 1 month ago
Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 3 months ago
Also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc. - Source: dev.to / 5 months ago
Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features. - Source: dev.to / 5 months ago
ClickHouse - ClickHouse is an open-source column-oriented database management system that allows generating analytical data reports in real time.
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
StarRocks - StarRocks offers the next generation of real-time SQL engines for enterprise-scale analytics. Learn how we make it easy to deliver real-time analytics.
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
Apache Hive - Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage.
Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.
