Apache Sqoop
Azure Data Factory
WANdisco Fusion Platform
Mover
Apache NiFi
Data2CRM.Migration
Talend Big Data Platform
Import2
Apache Flink
Apache Spark
Amazon Kinesis
Spring Framework
Spark Mail
Apache Airflow
Confluent
Grails
Apache Sqoop Apache FlinkBased on our record, Apache Flink seems to be a lot more popular than Apache Sqoop. While we know about 28 links to Apache Flink, we've tracked only 2 mentions of Apache Sqoop. 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.
I had never heard of Sqoop and looking at its page sqoop.apache.org, it seems to be legacy. Source: almost 2 years ago
ListItem(name='Apache Sqoop', website='https://sqoop.apache.org/', category='Data Transfer Tools', short_description='Sqoop is a command-line interface application for transferring data between relational databases and Hadoop. The Apache Sqoop project was retired in June 2021 and moved to the Apache Attic.'),. Source: over 2 years 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 / 1 day 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 / 30 days 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
Azure Data Factory - Learn more about Azure Data Factory, the easiest cloud-based hybrid data integration solution at an enterprise scale. Build data factories without the need to code.
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
WANdisco Fusion Platform - WANdisco Fusion is a data replication product for Hadoop.
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
Mover - Mover is a platform for moving files, streamlines the process of transferring users' data to the cloud.
Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.