qbeast-spark
Apache Spark
qbeast-spark | Apache Spark | |
---|---|---|
12 | 101 | |
192 | 38,378 | |
9.9% | 1.3% | |
8.6 | 10.0 | |
5 days ago | 4 days ago | |
Scala | Scala | |
Apache License 2.0 | Apache License 2.0 |
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qbeast-spark
- Release 0.3.2 of qbeast-spark!
- Qbeast-Spark Visualizer!
- Release 0.3.1 of Qbeast Spark
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Collaborative roadmap for qbeast-spark: Open Source Table Format
We want to develop qbeast-spark in an open way, so we publish a tentative Roadmap for this summer https://github.com/Qbeast-io/qbeast-spark/discussions/108
- qbeast-spark v0.2.0 available on Maven Central Repository
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Datasource enabling multidimensional indexing and sampling pushdown
If you want to play with it, check out the Qbeast-Spark github
- Apache Spark Datasource enabling multidimensional indexing and sampling pushdown
- New DataSource enabling multi-columnar indexing and efficient data sampling
Apache Spark
- "xAI will open source Grok"
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Groovy 🎷 Cheat Sheet - 01 Say "Hello" from Groovy
Recently I had to revisit the "JVM languages universe" again. Yes, language(s), plural! Java isn't the only language that uses the JVM. I previously used Scala, which is a JVM language, to use Apache Spark for Data Engineering workloads, but this is for another post 😉.
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🦿🛴Smarcity garbage reporting automation w/ ollama
Consume data into third party software (then let Open Search or Apache Spark or Apache Pinot) for analysis/datascience, GIS systems (so you can put reports on a map) or any ticket management system
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Go concurrency simplified. Part 4: Post office as a data pipeline
also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc.
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Five Apache projects you probably didn't know about
Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features.
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Apache Spark VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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Integrate Pyspark Structured Streaming with confluent-kafka
Apache Spark - https://spark.apache.org/
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Spark – A micro framework for creating web applications in Kotlin and Java
A JVM based framework named "Spark", when https://spark.apache.org exists?
- Rest in Peas: The Unrecognized Death of Speech Recognition (2010)
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PySpark SparkSession Builder with Kubernetes Master
I recently saw a pull request that was merged to the Apache/Spark repository that apparently adds initial Python bindings for PySpark on K8s. I posted a comment to the PR asking a question about how to use spark-on-k8s in a Python Jupyter notebook, and was told to ask my question here.
What are some alternatives?
delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
Spark Utils - Basic framework utilities to quickly start writing production ready Apache Spark applications
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
mmlspark - Simple and Distributed Machine Learning [Moved to: https://github.com/microsoft/SynapseML]
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Clustering4Ever - C4E, a JVM friendly library written in Scala for both local and distributed (Spark) Clustering.
Scalding - A Scala API for Cascading
Sparkplug - Spark package to "plug" holes in data using SQL based rules ⚡️ 🔌
mrjob - Run MapReduce jobs on Hadoop or Amazon Web Services
incubator-gluten - Gluten is a middle layer responsible for offloading JVM-based SQL engines' execution to native engines.
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.