s3-sqs-connector
Jupyter Scala
| s3-sqs-connector | Jupyter Scala | |
|---|---|---|
| 6 | 6 | |
| 16 | 1,564 | |
| - | 0.1% | |
| 0.0 | 9.0 | |
| 19 days ago | 21 days ago | |
| Scala | Scala | |
| Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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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.
s3-sqs-connector
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Upload to S3 -> AWS lambda with some Scala Spark code -> Process -> Write back to S3
Are you planning on uploading and processing many files to S3? If so I would use something like Structured Streaming with the FileSource which can detect new files uploaded to S3 and process them in on a "standard" Spark cluster. You can then build a very easy to deploy and operate cluster on EKS/Kubernetes. I would check out: https://github.com/qubole/s3-sqs-connector once the number of files you upload start to get really large. Glue could also be used to achieve roughly the same thing and without the hassle of managing the EKS/K8s clusters.
Jupyter Scala
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💐 Making VSCode itself a Java REPL 🔁
Checkout almond
- A Python-compatible statically typed language erg-lang/erg
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EDA libraries for Scala and Spark?
What about https://github.com/alexarchambault/plotly-scala and https://almond.sh/
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Is there any editor or IDE that supports Ammonite with inline dependencies?
I use Almond in JupyterLab, which has pretty solid code completion. In IntelliJ, you can create a scratch sc file and run lines of it in the Scala REPL. That's really convenient for code completion and I normally will use that when I'm testing something from a specific project.
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Recommended option for "Java with different syntax"?
The UI part. There's only the scala REPL. I think the closest is a scala kernel for Jupyter notebooks, check this out: https://almond.sh/
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An SQL Solution for Jupyter
We have used https://almond.sh/ to create a Spark SQL interpreter using Jupyter Notebooks - plus a whole lot more which you can see here: https://arc.tripl.ai/tutorial
After seeing many companies writing ETL using code we decided it was too hard to manage at scale so provided this abstraction layer - which is heavily centered around expressing business logic in SQL - to standardise development (JupyterLab) and allow rapid deployments.
What are some alternatives?
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
sparkmagic - Jupyter magics and kernels for working with remote Spark clusters
deequ - Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.
Metals - Scala language server with rich IDE features 🚀
LearningSparkV2 - This is the github repo for Learning Spark: Lightning-Fast Data Analytics [2nd Edition]
Vegas - The missing MatPlotLib for Scala + Spark
Spark Utils - Basic framework utilities to quickly start writing production ready Apache Spark applications
Apache Flink - Apache Flink
mmlspark - Simple and Distributed Machine Learning [Moved to: https://github.com/microsoft/SynapseML]
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.
Scio - A Scala API for Apache Beam and Google Cloud Dataflow.
Hail - Cloud-native genomic dataframes and batch computing