Apache Spark

Apache Spark - A unified analytics engine for large-scale data processing (by apache)

Apache Spark Alternatives

Similar projects and alternatives to Apache Spark

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better Apache Spark alternative or higher similarity.

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Reviews and mentions

Posts with mentions or reviews of Apache Spark. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-09-21.
  • Why should I invest in raptoreum? What makes it different
    reddit.com/r/raptoreum | 2021-09-25
    For your first question, if you are interested I encourage you to read the smart contracts paper here: https://docs.raptoreum.com/_media/Raptoreum_Contracts_EN.pdf and then to dig into what Apache Spark can do here: https://spark.apache.org/
  • How to use Spark and Pandas to prepare big data
    dev.to | 2021-09-21
    Apache Spark is one of the most actively developed open-source projects in big data. The following code examples require that you have Spark set up and can execute Python code using the PySpark library. The examples also require that you have your data in Amazon S3 (Simple Storage Service). All this is set up on AWS EMR (Elastic MapReduce).
  • Google Colab, Pyspark, Cassandra remote cluster combine these all together
    dev.to | 2021-09-13
  • How to Run Spark SQL on Encrypted Data
    dev.to | 2021-08-10
    For those of you who are new, Apache Spark is a popular distributed computing framework used by data scientists and engineers for processing large batches of data. One of its modules, Spark SQL, allows users to interact with structured, tabular data. This can be done through a DataSet/DataFrame API available in Scala or Python, or by using standard SQL queries. Here you can see a quick example of both below:
  • Machine Learning Tools and Algorithms
    Apache Spark :- A massive data processing engine with built-in modules for streaming, SQL, Machine Learning (ML), and graph processing, Apache Spark is recognized for being quick, simple to use, and general. It is also known for being fast, simple to use, and generic.
  • Strategies for running multiple Spark jobs simultaneously?
  • Python VS Scala
    reddit.com/r/scala | 2021-07-02
    Actually, it does. Scala has Spark for data science and some ML libs like Smile.
  • Best library for CSV to XML or JSON.
    reddit.com/r/javahelp | 2021-07-01
    Apache Beam may be what you're looking for. It will work with both Python and Java. It's used by GCP in the Cloud Dataflow service as a sort of streaming ETL tool. It occupies a similar niche to Spark, but is a little easier to use IMO.
  • 5 Best Big Data Frameworks You Can Learn in 2021
    dev.to | 2021-06-18
    Both Fortune 500 and small companies are looking for competent people who can derive useful insight from their huge pile of data and that's where Big Data Framework like Apache Hadoop, Apache Spark, Flink, Storm, and Hive can help.
  • Difference between reduce(), fold() and aggregate()?
    If you look at the code reduce, fold, aggregate
  • What is Cost-based Optimization?
    dev.to | 2021-06-02
    In Catalyst, the Apache Spark optimizer, the cost is a vector of the number of rows and the number of bytes being processed. The vector is converted into a scalar value during comparison.
  • The way to launch Apache Spark + Apache Zeppelin + InterSystems IRIS
    dev.to | 2021-05-28
    The official site of Apache Spark
  • Hi we have a stranege error while moving to spark 3.0.2
    Checking the code that seems to throw this error (here) this seems to hint at having some form of column name repeated. It may be an internal issue, and that you are reusing a name that is already present in one of the base tables, for instance. It's hard to know with the rewritten query you provide, since you may have rewritten it "correctly", also, the error could actually be propagated from some naming before, since this is a whole plan rewrite stage after all.
  • On explaining technical stuff in a non-technical way — (Py)Spark
    dev.to | 2021-04-23
    The homework example illustrates, as I understand it, the over-simplified basic thinking behind Apache Spark (and many similar frameworks and systems, e.g. horizontal or vertical data “sharding”), splitting the data into reasonable groups (called “partitions” in Spark’s case), given the fact that you know what kind of tasks you have to perform on the data, so that you are efficient, and distribute those partitions to ideally equal number of workers (or as many workers as your system can provide). These workers can be in the same machine or in different ones, e.g. each worker on one machine (node). There must be a coordinator of all this effort, to collect all the necessary information that is needed to perform the task and to redistribute the load in case of failure. It is also necessary to have a (network) connection between the coordinator and the workers to communicate and exchange data and information. Or even re-partition the data in case of either failure or when the computations require it (e.g. we need to calculate something on each row of data independently but then we need to group those rows by a key). There is also the concept of doing things in a “lazy” way and use caching to keep track of intermediate results and not having to calculate everything from scratch all the time.
  • A Scala rant
    reddit.com/r/scala | 2021-03-31
    yep, nailed it: https://github.com/apache/spark/blob/master/pom.xml#L122


Basic Apache Spark repo stats
1 day ago

apache/spark is an open source project licensed under Apache License 2.0 which is an OSI approved license.

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