spark-cassandra-connector
Apache Spark
spark-cassandra-connector | Apache Spark | |
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1 | 101 | |
1,930 | 38,378 | |
-0.1% | 0.6% | |
5.1 | 10.0 | |
7 days ago | 7 days ago | |
Scala | Scala | |
Apache License 2.0 | Apache License 2.0 |
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spark-cassandra-connector
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Reading from cassandra in Spark does not return all the data when using JoinWithCassandraTable
This works perfectly fine and I get all the data I'm expecting. However if I change spark.cassandra.sql.inClauseToJoinConversionThreshold(see https://github.com/datastax/spark-cassandra-connector/blob/master/doc/reference.md) to something lower like 20 which means I hit the threshold (my cross-product is 10*10=100) and JoinWithCassandraTable will be used. I suddenly do not get all the data, and on top of that I get duplicated rows for some of the data. It looks like I'm completely missing some of the partition keys, and some of the partition keys return duplicated rows (this quick-analysis might however be wrong).
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?
deequ - Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.
Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Scalding - A Scala API for Cascading
mrjob - Run MapReduce jobs on Hadoop or Amazon Web Services
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.
Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing
Weka
Smile - Statistical Machine Intelligence & Learning Engine
Apache Calcite - Apache Calcite
Scio - A Scala API for Apache Beam and Google Cloud Dataflow.