Scio
circe
Scio | circe | |
---|---|---|
7 | 12 | |
2,523 | 2,474 | |
0.3% | 0.2% | |
9.6 | 8.6 | |
8 days ago | 4 days ago | |
Scala | Scala | |
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.
Scio
- Are there any openly available data engineering projects using Scala and Spark which follow industry conventions like proper folder/package structures and object oriented division of classes/concerns? Most examples I’ve seen have everything in one file without proper separation of concerns.
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For the DE's that choose Java over Python in new projects, why?
I doubt it is possible because I suspect that GIL would like a word. So I could spend nights trying to make it work in Python (and possibly, if not likely, fail). Or I could just use this ready made solution.
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what popular companies uses Scala?
Apache Beam API called Scio. They open sourced it https://spotify.github.io/scio/
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Scala or Python
Generally Python is a lingua franca. I have never met a data engineer that doesn't know Python. Scala isn't used everywhere. Also, you should know that in Apache Beam (data processing framework that's gaining popularity because it can handle both streaming and batch processing and runs on spark) the language choices are Java, Python, Go and Scala. So, even if you "only" know Java, you can get started with Data engineering through apache beam.
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Wanting to move away from SQL
I agree 100%. I haven't used SQL that much in previous data engineering roles, and I refuse to consider jobs that mostly deal with SQL. One of my roles involved using a nice Scala API for apache beam called Scio and it was great. Code was easy to write, maintain, and test. It also worked well with other services like PubSub and BigTable.
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ETL Pipelines with Airflow: The Good, the Bad and the Ugly
If you prefer Scala, then you can try Scio: https://github.com/spotify/scio.
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ELT, Data Pipeline
To counter the above mentioned problem, we decided to move our data to a Pub/Sub based stream model, where we would continue to push data as it arrives. As fluentd is the primary tool being used in all our servers to gather data, rather than replacing it we leveraged its plugin architecture to use a plugin to stream data into a sink of our choosing. Initially our inclination was towards Google PubSub and Google Dataflow as our Data Scientists/Engineers use Big Query extensively and keeping the data in the same Cloud made sense. The inspiration of using these tools came from Spotify’s Event Delivery – The Road to the Cloud. We did the setup on one of our staging server with Google PubSub and Dataflow. Both didn't really work out for us as PubSub model requires a Subscriber to be available for the Topic a Publisher streams messages to, otherwise the messages are not stored. On top of it there was no way to see which messages are arriving. During this the weirdest thing that we encountered was that the Topic would be orphaned losing the subscribers when working with Dataflow. PubSub we might have managed to live with, the wall in our path was Dataflow. We started off with using SCIO from Spotify to work with Dataflow, there is a considerate lack of documentation over it and found the community to be very reserved on Github, something quite evident in the world of Scala for which they came up with a Code of Conduct for its user base to follow. Something that was required from Dataflow for us was to support batch write option to GCS, after trying our hand at Dataflow to no success to achieve that, Google's staff at StackOverflow were quite responsive and their response confirmed that it was something not available with Dataflow and streaming data to BigQuery, Datastore or Bigtable as a datastore was an option to use. The reason we didn't do that was to avoid high streaming cost to these services to store data, as majority of our jobs from the data team are based on batched hourly data. The initial proposal to the updated pipeline is shown below.
circe
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Database abstraction library which allows a clean domain model
Using Circe so I define some classes that contain my custom Encoder[BusinessObject] in a file and I use that whenever I want to save/store a record, or handle a web request or respose. I also represent my mongo queries as JSON objects that I can freely build then pass to the driver.
- Scala Library To Generate Case Classes for JSON
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What companies/startups are using Scala (open source projects on github)?
Circe adopters should be using Scala https://github.com/circe/circe
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what popular companies uses Scala?
If you look at Circe's github repo you will see a very large list of very recognizable companies, that should give you some idea. Circe isn't the ONLY Json parsing library, but it is probably the most popular, so - should give you a rough idea of the types and variety of companies using Scala.
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Every time I sit down to use an HTTP client and JSON parser, I get really frustrated
Has the worst error messages I've ever seen for a parser. "Attempt to decode value on failed cursor" is not helpful when all you have is missing fields. Has been an issue for 5 years.
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It's unsafe to depend on Typelevel Libraries
Circe tries to drop Scala 2.12 support in retaliation for not enough users paying them.
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Building a REST API in Scala 3 using Iron and Cats
Circe: https://circe.github.io/circe/
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[Circe] Renaming fields for value classes during decoding
PR for the same functionality in Scala3: https://github.com/circe/circe/pull/1800
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Scala 3.0 serialization
Otherwise I tend to just use ZIO-JSON or Circe both of which have been updated for Scala 3.
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Performance of 12 JSON parsers for Scala
I've updated results of benchmarks of 12 JSON parsers for Scala: - AVSystem's scala-commons - Borer - Circe - DSL-JSON - Jackson - jsoniter-scala - Play-JSON, - play-json-jsoniter - Spray-JSON - uPickle - weePickle - zio-json
What are some alternatives?
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
json4s - JSON library
Apache Flink - Apache Flink
spray-json - A lightweight, clean and simple JSON implementation in Scala
Apache Kafka - Mirror of Apache Kafka
play-json
beam - Apache Beam is a unified programming model for Batch and Streaming data processing.
zio-json - Fast, secure JSON library with tight ZIO integration.
Reactive-kafka - Alpakka Kafka connector - Alpakka is a Reactive Enterprise Integration library for Java and Scala, based on Reactive Streams and Akka.
jackson-module-scala - Add-on module for Jackson (https://github.com/FasterXML/jackson) to support Scala-specific datatypes
metorikku - A simplified, lightweight ETL Framework based on Apache Spark
jsoniter-scala - Scala macros for compile-time generation of safe and ultra-fast JSON codecs