Dagster
Apache Airflow
Prefect.io
Luigi
Kestra.io
AWS Step Functions
Apache NiFi
Make.com
ActiveBatch
Stonebranch
Control-M
JAMS Scheduler
Cleo Integration Cloud
GoAnywhere MFT
GlobalSCAPE Enhanced File Transfer
OpCon
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.
Dagster ActiveBatchBased on our record, Dagster seems to be more popular. It has been mentiond 6 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.
At my organization, which collects large volumes of public web data, weโve developed a robust system for automated data quality checks using two powerful open-source tools: Dagster and Great Expectations. These tools are the cornerstone of our approach to data quality management, allowing us to efficiently validate and monitor our data pipelines at scale. - Source: dev.to / 11 months ago
Data orchestration tools are key for managing data pipelines in modern workflows. When it comes to tools, Apache Airflow, Dagster, and Flyte are popular tools serving this need, but they serve different purposes and follow different philosophies. Choosing the right tool for your requirements is essential for scalability and efficiency. In this blog, I will compare Apache Airflow, Dagster, and Flyte, exploring... - Source: dev.to / over 1 year ago
This article demonstrates how to work with near real-time and historical data using the dlt package. Whether you need to scale data access across the enterprise or provide historical data for post-event analysis, you can use the same framework to provide customer data. In a future article, I'll demonstrate how to use dlt with a workflow orchestrator such as Apache Airflow or Dagster.``. - Source: dev.to / almost 2 years ago
Instead of the custom orchestrator I used, a proper orchestration tool should replace it like Apache Airflow, Dagster, ..., etc. - Source: dev.to / about 2 years ago
Level 1 of MLOps is when you've put each lifecycle stage and their intefaces in an automated pipeline. The pipeline could be a python or bash script, or it could be a directed acyclic graph run by some orchestration framework like Airflow, dagster or one of the cloud-provider offerings. AI- or data-specific platforms like MLflow, ClearML and dvc also feature pipeline capabilities. - Source: dev.to / over 2 years ago
Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
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.
Prefect.io - Prefect offers modern workflow orchestration tools for building, observing & reacting to data pipelines efficiently.
Control-M - ControlโM simplifies and automates diverse batch application workloads while reducing failure rates, improving SLAs, and accelerating application deployment.
Luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs.
JAMS Scheduler - Enterprise workload automation software supporting processes on Windows, Linux, UNIX, iSeries, SAP, Oracle, SQL, ERPs and more.