Apache Spark
mrjob

Apache Spark | mrjob | |
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115 | 1 | |
40,522 | 2,617 | |
0.7% | 0.0% | |
10.0 | 0.0 | |
3 days ago | almost 2 years ago | |
Scala | Python | |
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.
Apache Spark
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Automating Enhanced Due Diligence in Regulated Applications
If you're designing an event-based pipeline, you can use a data streaming tool like Kafka to process data as it's collected by the pipeline. For a setup that already has data stored, you can use tools like Apache Spark to batch process and clean it before moving ahead with the pipeline.
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Run PySpark Local Python Windows Notebook
PySpark is the Python API for Apache Spark, an open-source distributed computing system that enables fast, scalable data processing. PySpark allows Python developers to leverage the powerful capabilities of Spark for big data analytics, machine learning, and data engineering tasks without needing to delve into the complexities of Java or Scala.
- Infraestrutura para análise de dados com Jupyter, Cassandra, Pyspark e Docker
- His Startup Is Now Worth $62B. It Gave Away Its First Product Free
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How to Install PySpark on Your Local Machine
If you’re stepping into the world of Big Data, you have likely heard of Apache Spark, a powerful distributed computing system. PySpark, the Python library for Apache Spark, is a favorite among data enthusiasts for its combination of speed, scalability, and ease of use. But setting it up on your local machine can feel a bit intimidating at first.
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How to Use PySpark for Machine Learning
According to the Apache Spark official website, PySpark lets you utilize the combined strengths of ApacheSpark (simplicity, speed, scalability, versatility) and Python (rich ecosystem, matured libraries, simplicity) for “data engineering, data science, and machine learning on single-node machines or clusters.”
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Top FP technologies
spark
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Why Apache Spark RDD is immutable?
Apache Spark is a powerful and widely used framework for distributed data processing, beloved for its efficiency and scalability. At the heart of Spark’s magic lies the RDD, an abstraction that’s more than just a mere data collection. In this blog post, we’ll explore why RDDs are immutable and the benefits this immutability provides in the context of Apache Spark.
- Spark SQL is getting pipe syntax
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Intro to Ray on GKE
The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as we’ve explored here, but Ray Clusters can also be created independent of Kubernetes.
mrjob
What are some alternatives?
Smile - Statistical Machine Intelligence & Learning Engine
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.
Trino - Official repository of Trino, the distributed SQL query engine for big data, former
dumbo - Python module that allows one to easily write and run Hadoop programs.
Scalding - A Scala API for Cascading
streamparse - Run Python in Apache Storm topologies. Pythonic API, CLI tooling, and a topology DSL.
dpark - Python clone of Spark, a MapReduce alike framework in Python
Weka
murmurhash - 💥 Cython bindings for MurmurHash2
Apache Flink - Apache Flink
mmh3 - Python extension for MurmurHash (MurmurHash3), a set of fast and robust hash functions.
