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.

Apache Spark 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 2023-08-12.
  • Integrate Pyspark Structured Streaming with confluent-kafka
    2 projects | dev.to | 12 Aug 2023
    Apache Spark - https://spark.apache.org/
  • Rest in Peas: The Unrecognized Death of Speech Recognition (2010)
    4 projects | news.ycombinator.com | 4 May 2023
  • Gotta write this on my resume
    2 projects | /r/ProgrammerHumor | 2 Apr 2023
    So for example contributing to say spark may better for experience(and resume) than Twitter-the algorithm.
  • Query Real Time Data in Kafka Using SQL
    7 projects | dev.to | 23 Mar 2023
    Additionally, one of the challenges of working with Kafka is how to efficiently analyze and extract insights from the large volumes of data stored in Kafka topics. Traditional batch processing approaches, such as Hadoop MapReduce or Apache Spark, can be slow and expensive, and may not be suitable for real-time analytics. To address this challenge, you can use SQL queries with Kafka to analyze and extract insights from the data in real time.
  • Unveiling the Analytics Industry in Bangalore
    3 projects | /r/u_Khushisondhi7 | 23 Mar 2023
  • Apache Iceberg as storage for on-premise data store (cluster)
    3 projects | /r/dataengineering | 16 Mar 2023
    Spark for your transformation compute engine. Get Spark to talk to Nessie.
  • 5 Best Practices For Data Integration To Boost ROI And Efficiency
    3 projects | /r/ReviewNPrep | 12 Mar 2023
    There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka.
  • Forward Compatible Enum Values in API with Java Jackson
    5 projects | dev.to | 11 Feb 2023
    We’re not discussing the technical details behind the deduplication process. It could be Apache Flink, Apache Spark, or Kafka Streams. Anyway, it’s out of the scope of this article.
  • Uber Interview Experience/Asking Suggestions
    4 projects | /r/dataengineering | 1 Feb 2023
    One place to look are the projects repo's and docs, once you have a good idea of how the system is architected poking around pieces of the codebase can be helpful in letting you really understand their internals. I personally enjoy going through spark repo and trino repo and the documentation for both projects is decent and can answer many of your questions.
  • DataOps 101: An Introduction to the Essential Approach of Data Management Operations and Observability
    3 projects | dev.to | 22 Jan 2023
    DataOps is a collaborative effort within an organization, with many different teams of people working together to ensure that DataOps functions properly and delivers data value [3]. So, before the data is delivered to end users, it is subjected to a number of treatments and refinements from multiple teams. Data scientists first use their data science techniques, such as machine learning and deep learning to build models using software stacks such as Python or R and tools such as Spark or Tensorflow, among others, and the models are then transferred to data engineers, who collect and manage the data used to train and evaluate these models, while data developers and data architects create complete applications that include the models. The data governance team then implements data access controls for training and benchmarking purposes, while the operations team ( "Ops") is in charge of putting everything together and making it available to end users.
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    getonboard.dev | 1 Dec 2023
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