beam
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beam | Apache Accumulo | |
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30 | 2 | |
7,508 | 1,045 | |
1.5% | 3.9% | |
10.0 | 9.7 | |
5 days ago | 6 days ago | |
Java | Java | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
beam
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Ask HN: Does (or why does) anyone use MapReduce anymore?
The "streaming systems" book answers your question and more: https://www.oreilly.com/library/view/streaming-systems/97814.... 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 https://beam.apache.org very much is. Nowadays people often use "map reduce" as a shorthand for whatever batch processing system they're building on top of.
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beam VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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How do Streaming Aggregation Pipelines work?
Apache Beam is one of many tools that you can use
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Releasing Temporian, a Python library for processing temporal data, built together with Google
Flexible runtime ☁️: Temporian programs can run seamlessly in-process in Python, on large datasets using Apache Beam.
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Kafka cluster loses or duplicates messages
To perform the tests I'm using a Kafka cluster on Kubernetes from the Beam repo (here).
- Apache Beam
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Real Time Data Infra Stack
Apache Beam: Streaming framework which can be run on several runner such as Apache Flink and GCP Dataflow
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Google Cloud Reference
Apache Beam: Batch/streaming data processing 🔗Link
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Composer out of resources - "INFO Task exited with return code Negsignal.SIGKILL"
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.
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Pub/Sub parallel processing best practices
That being said, there is a learning curve in understanding how Apache Beam works. Take a look at the beam website for more information.
Apache Accumulo
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In One Minute : Hadoop
Accumulo, a sorted, distributed key/value store that provides robust, scalable data storage and retrieval.
- Apache Accumulo – sorted, distributed, robust, scalable key/value store
What are some alternatives?
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
Presto - The official home of the Presto distributed SQL query engine for big data
Apache Hadoop - Apache Hadoop
Zeppelin - Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more.
Scio - A Scala API for Apache Beam and Google Cloud Dataflow.
Hazelcast - Hazelcast is a unified real-time data platform combining stream processing with a fast data store, allowing customers to act instantly on data-in-motion for real-time insights.
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
Apache Flink - Apache Flink
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Hazelcast Jet - Distributed Stream and Batch Processing
Apache Hive - Apache Hive
Apache Storm - Apache Storm