| jet-train | nifi | |
|---|---|---|
| 4 | 35 | |
| 14 | 4,410 | |
| - | 1.8% | |
| 0.0 | 9.9 | |
| 8 months ago | 6 days ago | |
| Kotlin | Java | |
| - | 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.
jet-train
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Updating data files, commits vs. pull requests
My Jet Train project makes use of GTFS. GTFS stands for General Transit Feed Specification. It models public transportation schedules and their associated geographic information.
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An example of overengineering - keep it WET
I had to rewrite my Jet Train demo to use another data provider, switching from a Swiss one to a Bay Area one. One of the main components of the demo is a streaming pipeline. The pipeline:
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"Once you see the world in terms of data and events, your imagination is the limit!" — Nicolas Frankel.
My favorite project at the moment is Jet Trains, even if right now it has nothing to do with trains. When I joined Hazelcast, my colleague Neil Stevenson showed me a demo project of his. He had recorded the position of the train he was riding on in Italy some time ago and he had used Jet to display the position on the map. I felt it had a lot of unused potentials: the position markers were never removed, there was a single train, etc. So I thought about displaying many trains on the map and moving them "in real-time" (or close to, as there's no such thing as real-time).
- Looking for open source projects that use data pipelines and big data flows
nifi
- FLaNK Stack Weekly 19 Feb 2024
- Ask HN: What are some unpopular technologies you wish people knew more about?
- FLaNK Stack Weekly for 13 November 2023
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Ask HN: What low code platforms are worth using?
Apache NIFI (https://nifi.apache.org/).
It uses the concept of Flow-based programming. Also its so underacknolged but this tool is very flexible. I have used as an Event Bus all the 3rd-Party Integrations.
- Apache Nifi: easy to use, powerful, reliable system to process, distribute data
- Tool decision - What architecture would you choose and why?
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Help with choosing techstack for a new DE team
Presently setting up Apache Nifi + Apache MiNiFi for the ETL portion of my work. NiFi was easy enough to figure out; but the docs for MiNiFi have been a pain due to differences between the Java and C++ versions. I then entirely configured it with the Java version so that it was easier to search for answers for the MiNiFi yaml syntax.
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MS SQL Change Data Capture
Found it
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Is there something like airflow but written in Scala/Java?
Apache Camel Apache Nifi Spring Cloud
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Json splitting and Rerouting (new to nifi)
NIFI, like most Apache projects does most of its discussion on its mailing lists, but also has a slack.
What are some alternatives?
pipelinewise - Data Pipeline Framework using the singer.io spec
Logstash - Logstash - transport and process your logs, events, or other data
superset - Apache Superset is a Data Visualization and Data Exploration Platform
grouparoo - 🦘 The Grouparoo Monorepo - open source customer data sync framework
meltano
kotlin - The Kotlin Programming Language.
meltano - Meltano: the declarative code-first data integration engine that powers your wildest data and ML-powered product ideas. Say goodbye to writing, maintaining, and scaling your own API integrations.
Hazelcast Jet - Distributed Stream and Batch Processing
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Metabase - The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum: