Azure Logic Apps
Apache Kafka
MuleSoft Anypoint Platform
RabbitMQ
Google Cloud Pub/Sub
Apache ActiveMQ
KubeMQ
ZeroMQ
Apache Flink
Apache Spark
Amazon Kinesis
Spring Framework
Spark Mail
Apache Airflow
Confluent
Grails
Azure Logic Apps Apache FlinkBased on our record, Apache Flink seems to be a lot more popular than Azure Logic Apps. While we know about 30 links to Apache Flink, we've tracked only 1 mention of Azure Logic Apps. 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.
Over the last couple of years, I had projects involving Excel. In this post, I will dive into the details of implementations (use cases) concerning Excel Workbooks. One project involved processing Excel files in a Container running on an Azure Kubernetes Service (AKS) cluster, the other generating an Excel Workbook for reporting purposes orchestrated by an Azure Logic App. Source: almost 2 years ago
Restate is built as a sharded replicated state machine similar to how TiKV (https://tikv.org/), Kudu (https://kudu.apache.org/kudu.pdf) or CockroachDB (https://github.com/cockroachdb/cockroach) since it makes it possible to tune the system more easily for different deployment scenarios (on-prem, cloud, cost-effective blob storage). Moreover, it allows for some other cool things like seamlessly moving from one log... - Source: Hacker News / 15 days ago
I’ve recently started my journey with Apache Flink. As I learn certain concepts, I’d like to share them. One such "learning" is the expansion of array type columns in Flink SQL. Having used ksqlDB in a previous life, I was looking for functionality similar to the EXPLODE function to "flatten" a collection type column into a row per element of the collection. Because Flink SQL is ANSI compliant, it’s no surprise... - Source: dev.to / about 1 month 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 / about 2 months 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 / 3 months 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 / 5 months ago
Apache Kafka - Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala.
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
MuleSoft Anypoint Platform - Anypoint Platform is a unified, highly productive, hybrid integration platform that creates an application network of apps, data and devices with API-led connectivity.
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
RabbitMQ - RabbitMQ is an open source message broker software.
Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.