ActiveBatch
Stonebranch
Control-M
JAMS Scheduler
GoAnywhere MFT
Apache Oozie
Cleo Integration Cloud
IBM Workload Automation
Apache Flink
Apache Spark
Amazon Kinesis
Spring Framework
Spark Mail
Apache Airflow
Confluent
Grails
Orchestrate your entire tech stack with ActiveBatch Workload Automation and Enterprise Job Scheduling. Build and centralize end-to-end workflows under a single pane of glass. Seamlessly manage systems, applications, and services across your organization. Eliminate manual workflows with ActiveBatch so you can focus on higher value activities that drive your company forward.
Limitless Endpoints: Use native integrations and our low-code REST API adapter to connect to any server, any application, any service.
Proactive Support Model: 24/7- US-based support and predictive diagnostics.
Low Code Drag-and-Drop GUI: Easily build reliable, customizable, end-to-end processes.
ActiveBatch Apache FlinkBased on our record, Apache Flink seems to be more popular. It has been mentiond 29 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.
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 / 9 days 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 / 23 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 2 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 / 4 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
Stonebranch - Stonebranch builds IT orchestration and automation solutions that transform business IT environments from simple IT task automation into sophisticated, real-time business service automation.
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Control-M - Control‑M simplifies and automates diverse batch application workloads while reducing failure rates, improving SLAs, and accelerating application deployment.
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
JAMS Scheduler - Enterprise workload automation software supporting processes on Windows, Linux, UNIX, iSeries, SAP, Oracle, SQL, ERPs and more.
Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.
