Programming language: Scala
License: Apache License 2.0
Tags: Big Data    
Latest version: v0.10.0-beta2

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Ecclesiastical Latin IPA: /ˈʃi.o/, [ˈʃiː.o], [ˈʃi.i̯o] Verb: I can, know, understand, have knowledge.

Scio is a Scala API for Apache Beam and Google Cloud Dataflow inspired by Apache Spark and Scalding.

Scio 0.3.0 and future versions depend on Apache Beam (org.apache.beam) while earlier versions depend on Google Cloud Dataflow SDK (com.google.cloud.dataflow). See this page for a list of breaking changes.


  • Scala API close to that of Spark and Scalding core APIs
  • Unified batch and streaming programming model
  • Fully managed service*
  • Integration with Google Cloud products: Cloud Storage, BigQuery, Pub/Sub, Datastore, Bigtable
  • JDBC, TensorFlow TFRecords, Cassandra, Elasticsearch and Parquet I/O
  • Interactive mode with Scio REPL
  • Type safe BigQuery
  • Integration with Algebird and Breeze
  • Pipeline orchestration with Scala Futures
  • Distributed cache

* provided by Google Cloud Dataflow

Quick Start

Download and install the Java Development Kit (JDK) version 8.

Install sbt.

Use our giter8 template to quickly create a new Scio job repository:

sbt new spotify/scio.g8

Switch to the new repo (default scio-job) and build it:

cd scio-job
sbt stage

Run the included word count example:

target/universal/stage/bin/scio-job --output=wc

List result files and inspect content:

ls -l wc
cat wc/part-00000-of-00004.txt


Getting Started is the best place to start with Scio. If you are new to Apache Beam and distributed data processing, check out the Beam Programming Guide first for a detailed explanation of the Beam programming model and concepts. If you have experience with other Scala data processing libraries, check out this comparison between Scio, Scalding and Spark. Finally check out this document about the relationship between Scio, Beam and Dataflow.

Example Scio pipelines and tests can be found under scio-examples. A lot of them are direct ports from Beam's Java examples. See this page for some of them with side-by-side explanation. Also see Big Data Rosetta Code for common data processing code snippets in Scio, Scalding and Spark.


Scio includes the following artifacts:

  • scio-core: core library
  • scio-test: test utilities, add to your project as a "test" dependency
  • scio-avro: add-on for Avro, can also be used standalone
  • scio-google-cloud-platform: add-on for Google Cloud IO's: BigQuery, Bigtable, Pub/Sub, Datastore, Spanner
  • scio-cassandra*: add-ons for Cassandra
  • scio-elasticsearch*: add-ons for Elasticsearch
  • scio-extra: extra utilities for working with collections, Breeze, etc., best effort support
  • scio-jdbc: add-on for JDBC IO
  • scio-neo4j: add-on for Neo4J IO
  • scio-parquet: add-on for Parquet
  • scio-tensorflow: add-on for TensorFlow TFRecords IO and prediction
  • scio-redis: add-on for Redis
  • scio-smb: add-on for Sort Merge Bucket operations
  • scio-repl: extension of the Scala REPL with Scio specific operations


Copyright 2021 Spotify AB.

Licensed under the Apache License, Version 2.0: http://www.apache.org/licenses/LICENSE-2.0

*Note that all licence references and agreements mentioned in the Scio README section above are relevant to that project's source code only.