|4 days ago||4 days ago|
|Apache License 2.0||Apache License 2.0|
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Show HN: Box – Data Transformation Pipelines in Rust DataFusion
4 projects | news.ycombinator.com | 30 Nov 2021
A while ago I posted a link to [Arc](https://news.ycombinator.com/item?id=26573930) a declarative method for defining repeatable data pipelines which execute against [Apache Spark](https://spark.apache.org/).
Today I would like to present a proof-of-concept implementation of the [Arc declarative ETL framework](https://arc.tripl.ai) against [Apache Datafusion](https://arrow.apache.org/datafusion/) which is an Ansi SQL (Postgres) execution engine based upon Apache Arrow and built with Rust.
The idea of providing a declarative 'configuration' language for defining data pipelines was planned from the beginning of the Arc project to allow changing execution engines without having to rewrite the base business logic (the part that is valuable to your business). Instead, by defining an abstraction layer, we can change the execution engine and run the same logic with different execution characteristics.
The benefit of the DataFusion over Apache Spark is a significant increase in speed and reduction in execution resource requirements. Even through a Docker-for-Mac inefficiency layer the same job completes in ~4 seconds with DataFusion vs ~24 seconds with Apache Spark (including JVM startup time). Without Docker-for-Mac layer end-to-end execution times of 0.5 second for the same example job (TPC-H) is possible. * the aim is not to start a benchmarking flamewar but to provide some indicative data *.
The purpose of this post is to gather feedback from the community whether you would use a tool like this, what features would be required for you to use it (MVP) or whether you would be interested in contributing to the project. I would also like to highlight the excellent work being done by the DataFusion/Arrow (and Apache) community for providing such amazing tools to us all as open source projects.
1 project | reddit.com/r/dataengineering | 3 Nov 2021
Have a look at Apache Spark
Spark is lit once again
6 projects | dev.to | 29 Oct 2021
Here at Exacaster Spark applications have been used extensively for years. We started using them on our Hadoop clusters with YARN as an application manager. However, with our recent product, we started moving towards a Cloud-based solution and decided to use Kubernetes for our infrastructure needs.
What is B2D Sector?
13 projects | dev.to | 17 Oct 2021
Example tools:\ Tensorflow, Tableau, Apache Spark, Matlab, Jupyter
Why should I invest in raptoreum? What makes it different
1 project | reddit.com/r/raptoreum | 25 Sep 2021
For your first question, if you are interested I encourage you to read the smart contracts paper here: https://docs.raptoreum.com/_media/Raptoreum_Contracts_EN.pdf and then to dig into what Apache Spark can do here: https://spark.apache.org/
How to use Spark and Pandas to prepare big data
3 projects | dev.to | 21 Sep 2021
Apache Spark is one of the most actively developed open-source projects in big data. The following code examples require that you have Spark set up and can execute Python code using the PySpark library. The examples also require that you have your data in Amazon S3 (Simple Storage Service). All this is set up on AWS EMR (Elastic MapReduce).
Google Colab, Pyspark, Cassandra remote cluster combine these all together
2 projects | dev.to | 13 Sep 2021
How to Run Spark SQL on Encrypted Data
3 projects | dev.to | 10 Aug 2021
For those of you who are new, Apache Spark is a popular distributed computing framework used by data scientists and engineers for processing large batches of data. One of its modules, Spark SQL, allows users to interact with structured, tabular data. This can be done through a DataSet/DataFrame API available in Scala or Python, or by using standard SQL queries. Here you can see a quick example of both below:
Machine Learning Tools and Algorithms
3 projects | reddit.com/r/u_Snoo36930 | 29 Jul 2021
Apache Spark :- A massive data processing engine with built-in modules for streaming, SQL, Machine Learning (ML), and graph processing, Apache Spark is recognized for being quick, simple to use, and general. It is also known for being fast, simple to use, and generic.
Strategies for running multiple Spark jobs simultaneously?
1 project | reddit.com/r/apachespark | 25 Jul 2021
What are some alternatives?
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
Scalding - A Scala API for Cascading
luigi - Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
mrjob - Run MapReduce jobs on Hadoop or Amazon Web Services
Smile - Statistical Machine Intelligence & Learning Engine
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Scio - A Scala API for Apache Beam and Google Cloud Dataflow.
dpark - Python clone of Spark, a MapReduce alike framework in Python
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.
Summingbird - Streaming MapReduce with Scalding and Storm
Apache Calcite - Apache Calcite