Apache Airflow is recommended for data engineers, data scientists, and IT professionals who need to automate and manage workflows. It is particularly suited for organizations handling large-scale data processing tasks, requiring integration with various systems, and those looking to deploy machine learning pipelines or ETL processes.
Based on our record, Apache Airflow should be more popular than AWS Database Migration Service. It has been mentiond 79 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.
The major infrastructure providers offer CDC products that work within their ecosystem. Tools like AWS DMS, GCP Datastream, and Azure Data Factory can be configured to stream changes from Postgres to other infrastructure. - Source: dev.to / 10 months ago
The second big drawback is speed. There will be more latency in this scenario. How much latency depends upon the environment. If there is RDBMS in the source, AWS Data Migration Service will at worst take around 60 seconds to replicate. That cost needs to be accounted for. Secondarily, many triggering events are leveraged which happen fairly quickly but they do add up. - Source: dev.to / over 1 year ago
Amazon Database Migration Service might initially seem like a perfect tool for a smooth and straightforward migration to RDS. However, our overall experience using it turned out to be closer to an open beta product rather than a production-ready tool for dealing with a critical asset of any company, which is its data. Nevertheless, with the extra adjustments, we made it work for almost all our needs. - Source: dev.to / over 1 year ago
Does AWS DMS make sense here? Doesn't the aforementioned "snapshot+restore to provisioned and upgrade" method suffice? I wanted to get some opinions before deep diving into the docs for yet another AWS service. Source: about 2 years ago
One easy solution is AWS DMS. I use it for on-going CDC replication with custom transforms, but you can use it for simple replication too. Source: over 2 years ago
There is a lot of stuff for Python which follows the "express computation as a dag" approach, especially Apache Airflow https://airflow.apache.org/. - Source: Hacker News / 4 days ago
Doing ingestion or data processing with Airflow, a very popular open-source platform for developing and running workflows, is a fairly common setup. DataHub's automatic lineage extraction works great with Airflow - provided you configure the Airflow connection to DataHub correctly. - Source: dev.to / about 2 months ago
Apache Airflow represents the open-source workflow orchestration approach to MongoDB ETL. By combining Airflow's powerful scheduling and dependency management with a Python library like PyMongo, you can build highly customized ETL workflows that integrate seamlessly with MongoDB. - Source: dev.to / 2 months ago
You appear to be making the mistake of assuming that the only valid definition for the term "workflow" is the definition used by software such as https://airflow.apache.org/ https://www.merriam-webster.com/dictionary/workflow thinks the word dates back to 1921. There no reason Anthropic can't take that word and present their own alternative definition for it in the context of LLM tool usage, which is what they've... - Source: Hacker News / 4 months ago
Is this really true? Something that can be supported by clear evidence? Iโve seen this trotted out many times, but it seems like there are interesting Apache projects: https://airflow.apache.org/ https://iceberg.apache.org/ https://kafka.apache.org/ https://superset.apache.org/. - Source: Hacker News / 7 months ago
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