Apache Spark VS Scio

Compare Apache Spark vs Scio and see what are their differences.

Apache Spark

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

Scio

A Scala API for Apache Beam and Google Cloud Dataflow. (by spotify)
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Apache Spark Scio
48 5
32,962 2,308
1.8% 1.3%
10.0 9.5
1 day ago 8 days ago
Scala Scala
Apache License 2.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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

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 2022-05-10.

Scio

Posts with mentions or reviews of Scio. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-05-14.
  • what popular companies uses Scala?
    3 projects | reddit.com/r/scala | 14 May 2022
    Apache Beam API called Scio. They open sourced it https://spotify.github.io/scio/
  • Scala or Python
    1 project | reddit.com/r/dataengineering | 19 Apr 2022
    Generally Python is a lingua franca. I have never met a data engineer that doesn't know Python. Scala isn't used everywhere. Also, you should know that in Apache Beam (data processing framework that's gaining popularity because it can handle both streaming and batch processing and runs on spark) the language choices are Java, Python, Go and Scala. So, even if you "only" know Java, you can get started with Data engineering through apache beam.
  • Wanting to move away from SQL
    2 projects | reddit.com/r/dataengineering | 25 Feb 2022
    I agree 100%. I haven't used SQL that much in previous data engineering roles, and I refuse to consider jobs that mostly deal with SQL. One of my roles involved using a nice Scala API for apache beam called Scio and it was great. Code was easy to write, maintain, and test. It also worked well with other services like PubSub and BigTable.
  • ETL Pipelines with Airflow: The Good, the Bad and the Ugly
    7 projects | news.ycombinator.com | 8 Oct 2021
    If you prefer Scala, then you can try Scio: https://github.com/spotify/scio.
  • ELT, Data Pipeline
    4 projects | dev.to | 1 Jan 2021
    To counter the above mentioned problem, we decided to move our data to a Pub/Sub based stream model, where we would continue to push data as it arrives. As fluentd is the primary tool being used in all our servers to gather data, rather than replacing it we leveraged its plugin architecture to use a plugin to stream data into a sink of our choosing. Initially our inclination was towards Google PubSub and Google Dataflow as our Data Scientists/Engineers use Big Query extensively and keeping the data in the same Cloud made sense. The inspiration of using these tools came from Spotify’s Event Delivery – The Road to the Cloud. We did the setup on one of our staging server with Google PubSub and Dataflow. Both didn't really work out for us as PubSub model requires a Subscriber to be available for the Topic a Publisher streams messages to, otherwise the messages are not stored. On top of it there was no way to see which messages are arriving. During this the weirdest thing that we encountered was that the Topic would be orphaned losing the subscribers when working with Dataflow. PubSub we might have managed to live with, the wall in our path was Dataflow. We started off with using SCIO from Spotify to work with Dataflow, there is a considerate lack of documentation over it and found the community to be very reserved on Github, something quite evident in the world of Scala for which they came up with a Code of Conduct for its user base to follow. Something that was required from Dataflow for us was to support batch write option to GCS, after trying our hand at Dataflow to no success to achieve that, Google's staff at StackOverflow were quite responsive and their response confirmed that it was something not available with Dataflow and streaming data to BigQuery, Datastore or Bigtable as a datastore was an option to use. The reason we didn't do that was to avoid high streaming cost to these services to store data, as majority of our jobs from the data team are based on batched hourly data. The initial proposal to the updated pipeline is shown below.

What are some alternatives?

When comparing Apache Spark and Scio you can also consider the following projects:

Trino - Official repository of Trino, the distributed SQL query engine for big data, formerly known as PrestoSQL (https://trino.io)

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.

Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

Smile - Statistical Machine Intelligence & Learning Engine

Weka

Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

Apache Arrow - Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing

Apache Calcite - Apache Calcite

Deeplearning4j - Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learning using automatic differentiation.

Apache Flink - Apache Flink