Apache Flink
Apache Spark
Amazon Kinesis
Spring Framework
Spark Mail
Apache Airflow
Grails
Apache Kafka
Spring Cloud Data Flow
Google Cloud Dataflow
Spark Streaming
Amazon Kinesis
Confluent
StreamSets Data Collector
Lenses
Striim
Apache Flink Spring Cloud Data FlowBased on our record, Apache Flink seems to be a lot more popular than Spring Cloud Data Flow. While we know about 27 links to Apache Flink, we've tracked only 1 mention of Spring Cloud Data Flow. 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.
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 / 21 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 / 4 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
Due to the technology transformation we want to do recently, we started to investigate Apache Iceberg. In addition, the data processing engine we use in house is Apache Flink, so it's only fair to look for an experimental environment that integrates Flink and Iceberg. - Source: dev.to / 5 months ago
And a Cloudera project: https://www.cloudera.com/products/cdf.html And an Azure feature: https://docs.microsoft.com/en-us/azure/data-factory/control-flow-execute-data-flow-activity And a Spring feature: https://spring.io/projects/spring-cloud-dataflow. - Source: Hacker News / almost 3 years ago
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
Spark Streaming - Spark Streaming makes it easy to build scalable and fault-tolerant streaming applications.
Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.
Spark Mail - Spark helps you take your inbox under control. Instantly see what’s important and quickly clean up the rest. Spark for Teams allows you to create, discuss, and share email with your colleagues