Apache Beam VS Delta Lake

The "streaming systems" book answers your question and more: https://www.oreilly.com/library/view/streaming-systems/9781491983867/. It gives you a history of how batch processing started with MapReduce, and how attempts at scaling by moving towards streaming systems gave us all the subsequent frameworks (Spark, Beam, etc.). As for the framework called MapReduce, it isn't used much, but its descendant... - Source: Hacker News / 3 months ago

Apache Beam is one of many tools that you can use. Source: 5 months ago

Apache Beam: Streaming framework which can be run on several runner such as Apache Flink and GCP Dataflow. - Source: dev.to / over 1 year ago

Apache Beam: Batch/streaming data processing 🔗Link. - Source: dev.to / over 1 year ago

What you are looking for is Dataflow. It can be a bit tricky to wrap your head around at first, but I highly suggest leaning into this technology for most of your data engineering needs. It's based on the open source Apache Beam framework that originated at Google. We use an internal version of this system at Google for virtually all of our pipeline tasks, from a few GB, to Exabyte scale systems -- it can do it all. Source: over 1 year ago

Delta is pretty great, let's you do upserts into tables in DataBricks much easier than without it. I think the website is here: https://delta.io. - Source: Hacker News / 4 months ago

Apache Iceberg is one of the three types of lakehouse, the other two are Apache Hudi and Delta Lake. - Source: dev.to / 5 months ago

The Apache Spark / Databricks community prefers Apache parquet or Linux Fundation's delta.io over json. Source: 5 months ago

Databricks provides Jupyter lab like notebooks for analysis and ETL pipelines using spark through pyspark, sparkql or scala. I think R is supported as well but it doesn't interop as well with their newer features as well as python and SQL do. It interfaces with cloud storage backend like S3 and offers some improvements to the parquet format of data querying that allows for updating, ordering and merged through... - Source: Hacker News / 10 months ago

Structured, Semi-structured and Unstructured can be stored in one single format, a lakehouse storage format like Delta, Iceberg or Hudi (assuming those don't require low-latency SLAs like subsecond). Source: 11 months ago