Apache Flink
Apache Hadoop
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Apache Flink | Apache Hadoop | |
---|---|---|
9 | 26 | |
23,158 | 14,316 | |
1.2% | 0.9% | |
9.9 | 9.9 | |
3 days ago | 3 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.
Apache Flink
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First 15 Open Source Advent projects
7. Apache Flink | Github | tutorial
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Pyflink : Flink DataStream (KafkaSource) API to consume from Kafka
Does anyone have fully running Pyflink code snippet to read from Kafka using the new Flink DataStream (KafkaSource) API and just print out the output to console or write it out to a file. Most of the examples and the official Flink GitHubare using the old API (FlinkKafkaConsumer).
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I keep getting build failure when I try to run mvn clean compile package
I'm trying to use https://github.com/mauricioaniche/ck to analyze the ck metrics of https://github.com/apache/flink. I have the latest version of java downloaded and I have the latest version of apache maven downloaded too. My environment variables are set correctly. I'm in the correct directory as well. However, when I run mvn clean compile package in powershell it always says build error. I've tried looking up the errors but there's so many. https://imgur.com/a/Zk8Snsa I'm very new to programming in general so any suggestions would be appreciated.
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How do I determine what the dependencies are when I make pom.xml file?
Looking at the project on github, it seems like they should have a pom in the root dir https://github.com/apache/flink/blob/master/pom.xml
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Akka is moving away from Open Source
Akka is used only as a possible RPC implementation, isn't it?
- We Are Changing the License for Akka
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DeWitt Clause, or Can You Benchmark %DATABASE% and Get Away With It
Apache Drill, Druid, Flink, Hive, Kafka, Spark
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Computation reuse via fusion in Amazon Athena
It took me some time to get a good grasp of the power of SQL; and it really kicked in when I learned about optimization rules. It's a program that you rewrite, just like an optimizing compiler would.
You state what you want; you have different ways to fetch and match and massage data; and you can search through this space to produce a physical plan. Hopefully you used knowledge to weight parts to be optimized (table statistics, like Java's JIT would detect hot spots).
I find it fascinating to peer through database code to see what is going on. Lately, there's been new advances towards streaming databases, which bring a whole new design space. For example, now you have latency of individual new rows to optimize for, as opposed to batch it whole to optimize the latency of a dataset. Batch scanning will be benefit from better use of your CPU caches.
And maybe you could have a hybrid system which reads history from a log and aggregates in a batched manner, and then switches to another execution plan when it reaches the end of the log.
If you want to have a peek at that here are Flink's set of rules [1], generic and stream-specific ones. The names can be cryptic, but usually give a good sense of what is going on. For example: PushFilterIntoTableSourceScanRule makes the WHERE clause apply the earliest possible, to save some CPU/network bandwidth further down. PushPartitionIntoTableSourceScanRule tries to make a fan-out/shuffle happen the earliest possible, so that parallelism can be made use of.
[1] https://github.com/apache/flink/blob/5f8fb304fb5d68cdb0b3e3c...
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Avro SpecificRecord File Sink using apache flink is not compiling due to error incompatible types: FileSink<?> cannot be converted to SinkFunction<?>
[1]: https://mvnrepository.com/artifact/org.apache.avro/avro-maven-plugin/1.8.2 [2]: https://github.com/apache/flink/blob/master/flink-connectors/flink-connector-files/src/main/java/org/apache/flink/connector/file/sink/FileSink.java [3]: https://ci.apache.org/projects/flink/flink-docs-master/docs/connectors/datastream/file_sink/ [4]: https://github.com/apache/flink/blob/c81b831d5fe08d328251d91f4f255b1508a9feb4/flink-end-to-end-tests/flink-file-sink-test/src/main/java/FileSinkProgram.java [5]: https://github.com/rajcspsg/streaming-file-sink-demo
Apache Hadoop
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Getting thousands of files of output back from a container
Did you check out tools like https://hadoop.apache.org/ ?
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Trying to run hadoop using docker
check out the various dockerfiles bundled with hadoop on GitHub. you can point to them from within docker-compose. they haven't been updated in a couple years tho.
- Unveiling the Analytics Industry in Bangalore
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5 Best Practices For Data Integration To Boost ROI And Efficiency
There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka.
- Hadoop or Spark?
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Data Engineering and DataOps: A Beginner's Guide to Building Data Solutions and Solving Real-World Challenges
There are several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS.
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Effortlessly Set Up a Hadoop Multi-Node Cluster on Windows Machines with Our Step-by-Step Guide
A copy of Hadoop installed on each of these machines. You can download Hadoop from the Apache website, or you can use a distribution like Cloudera or Hortonworks.
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In One Minute : Hadoop
The Apache™ Hadoop™ project develops open-source software for reliable, scalable, distributed computing.
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Elon Musk dissolves Twitter's board of directors
So, clearly with your AP CS class and PLC logic knowledge, if you were dumped into a codebase like Hadoop, QT, or TensorFlow you'd be able to quickly and competently analyze what is going on with that code, understand all the libraries used, know the reasons why certain compromises were made, and be able to make suggestions on how to restructure the code in a different way? Because I've been programming for coming up on two decades and unless a system is within the domains that I have experience in, I would not be able to provide any useful information without a massive onboarding timeline, and definitely wouldn't be able to help redesign anything until actually coding within the system for a significant amount of time.
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A peek into Location Data Science at Ola
This requires the use of distributed computation tools such as Spark and Hadoop, Flink and Kafka are used. But for occasional experimentation, Pandas, Geopandas and Dask are some of the commonly used tools.
What are some alternatives?
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
Go IPFS - IPFS implementation in Go [Moved to: https://github.com/ipfs/kubo]
Deeplearning4j - Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learning using automatic differentiation.
Ceph - Ceph is a distributed object, block, and file storage platform
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
Seaweed File System - SeaweedFS is a fast distributed storage system for blobs, objects, files, and data lake, for billions of files! Blob store has O(1) disk seek, cloud tiering. Filer supports Cloud Drive, cross-DC active-active replication, Kubernetes, POSIX FUSE mount, S3 API, S3 Gateway, Hadoop, WebDAV, encryption, Erasure Coding. [Moved to: https://github.com/seaweedfs/seaweedfs]
H2O - Sparkling Water provides H2O functionality inside Spark cluster
Weka
Scio - A Scala API for Apache Beam and Google Cloud Dataflow.
MooseFS - MooseFS – Open Source, Petabyte, Fault-Tolerant, Highly Performing, Scalable Network Distributed File System (Software-Defined Storage)
Apache Kafka - Mirror of Apache Kafka
GlusterFS - Web Content for gluster.org -- Deprecated as of September 2017