quine
Scio
Our great sponsors
quine | Scio | |
---|---|---|
6 | 7 | |
281 | 2,523 | |
5.7% | 0.5% | |
9.3 | 9.6 | |
7 days ago | 4 days ago | |
Scala | Scala | |
GNU General Public License v3.0 or later | 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.
quine
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Create a Quine Icon Library with Python
Quine
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Postgres: The Graph Database You Didn't Know You Had
Re [5]'s asssertion under "blunders" of the diminish usecases post sql/pgq, what do you think of sometime like Quine?
https://github.com/thatdot/quine
Their claim to fame is progressive incremental computation - each node is an actor responding to events -- and I'm not sure how a relational db could do that and match the latencies. That usecase is pretty much pattern matching and forensics and stuff like that.
https://docs.quine.io/core-concepts/architecture.html
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Use Quine Graph ETL to reduce SIEM storage costs.
Download Quine - JAR file | Docker Image | Github
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Standing Queries: Turning Data-Driven Events into Event-Driven Data
The first step to making a Standing Query is determining the graph pattern you want to watch for. You may have deployed Quine in your data pipeline to perform a series of tasks to isolate data, implement a specific feature, or monitor the stream to find a specific pattern in real time. In any case, Quine will implement your logic using Cypher. The recipe for this example is included in the Quine repo if you'd like to follow along.
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Ingesting From Multiple Data Sources into Quine Streaming Graphs
Quine is open source if you want to run this analysis for yourself. Download a precompiled version or build it yourself from the codebase (Quine Github). I published the recipe that I developed at https://quine.io/recipes. The page has instructions for downloading the CSV files and running the recipe.
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Ingesting Internet Data into Quine Streaming Graph
I welcome your feedback! Drop in to Quine Slack and let me know what you think. I'm always happy to discuss Quine or answer questions.
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.
What are some alternatives?
lila-ws - Lichess' websocket server
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
AkkaGRPC - Akka gRPC
Apache Flink - Apache Flink
Scala Graph - Graph for Scala is intended to provide basic graph functionality seamlessly fitting into the Scala Collection Library. Like the well known members of scala.collection, Graph for Scala is an in-memory graph library aiming at editing and traversing graphs, finding cycles etc. in a user-friendly way.
Apache Kafka - Mirror of Apache Kafka
fs2-kafka - Functional Kafka Streams for Scala
beam - Apache Beam is a unified programming model for Batch and Streaming data processing.
Iteratee - Iteratees for Cats
Reactive-kafka - Alpakka Kafka connector - Alpakka is a Reactive Enterprise Integration library for Java and Scala, based on Reactive Streams and Akka.
ldbc_snb_bi - Reference implementations for the LDBC Social Network Benchmark's Business Intelligence (BI) workload
metorikku - A simplified, lightweight ETL Framework based on Apache Spark