Push Technology
Ably
Confluent
Lightstreamer
Apache Kafka
Pusher
SignalR
Socket.io
Apache Flink
Apache Spark
Amazon Kinesis
Spring Framework
Spark Mail
Apache Airflow
Grails
Apache Kafka
Push Technology helps companies modernize real-time applications to work under any conditions, removing the boundaries of the internet. Diffusion® Intelligent Data Mesh helps you solve the connectivity, security, scalability, and data distribution challenges of your real-time solutions. Our powerful real-time SDKs and REST API make building applications simple. To enquire more, visit the website.
Push Technology enables companies worldwide to build intelligent real-time applications. With Diffusion®, designed by the most creative & brightest minds in the market, build real-time, secure, high-performance applications that scale easily and satisfy today's consumer expectations under all network conditions. Along with this, build reliable data-efficient IoT, extend your data pipelines such as Kafka & enable a single view of data. Developers can integrate these features into their solution using easy-to-use and simple SDKs and REST API. Diffusion is powered by patented capabilities such as delta-streaming, comprehensive data semantics, in-memory key-value store, and more. To enquire more, visit the website.
Push Technology Apache FlinkBased on our record, Apache Flink seems to be more popular. It has been mentiond 27 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.
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 / 26 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
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
Ably - Serious realtime infrastructure
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Confluent - Confluent offers a real-time data platform built around Apache Kafka.
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
Lightstreamer - Lightstreamer is a server for delivering real-time messages to browser-based and mobile...
Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.