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  • Installing DSS
    • Requirements
    • Installing a new DSS instance
    • Upgrading a DSS instance
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      • Install on macOS
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      • Install on Azure
      • Install a virtual machine
    • Setting up Hadoop and Spark integration
    • Setting up R integration
    • Customizing DSS installation
    • Installing database drivers
    • Java runtime environment
    • The Python environment
    • Installing a DSS plugin
    • Configuring LDAP authentication
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  • DSS concepts
  • Connecting to data
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    • SQL databases
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  • Exploring your data
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  • Schemas, storage types and meanings
    • Definitions
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    • Schema for data preparation
    • Creating schemas of datasets
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  • Data preparation
    • Processors reference
      • Extract from array
      • Fold an array
      • Sort array
      • Concatenate JSON arrays
      • Discretize (bin) numerical values
      • Change coordinates system
      • Copy column
      • Rename columns
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      • Count occurrences
      • Convert currencies
      • Extract date elements
      • Compute difference between dates
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      • Split e-mail addresses
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      • Filter rows/cells with formula
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      • Filter rows/cells on value
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      • Flag rows with formula
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      • Fold multiple columns
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      • Extract from geo column
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      • Resolve GeoIP
      • Create GeoPoint from lat/lon
      • Extract lat/lon from GeoPoint
      • Flag holidays
      • Split invalid cells into another column
      • Join with other dataset (memory-based)
      • Extract with JSONPath
      • Group long-tail values
      • Translate values using meaning
      • Normalize measure
      • Negate boolean value
      • Force numerical range
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      • Nest columns
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      • Extract with regular expression
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      • Python function
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      • Remove rows where cell is empty
      • Round numbers
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      • Split and fold
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      • Split column
      • Transform string
      • Tokenize text
      • Transpose rows to columns
      • Triggered unfold
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      • Convert a UNIX timestamp to a date
      • Fill empty cells with previous/next value
      • Split URL (into protocol, host, port, ...)
      • Classify User-Agent
      • Generate a best-effort visitor id
      • Zip JSON arrays
    • Filtering and flagging rows
    • Managing dates
    • Reshaping
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    • Sampling and charts engines
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  • Machine learning
    • Prediction (Supervised ML)
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    • Features handling
    • The machine learning engines
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    • MLLib (Spark) engine
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  • The Flow
  • Visual recipes
    • Sync: Copying datasets
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    • Join: joining datasets
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    • Stacking datasets
    • Sampling datasets
  • Recipes based on code
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  • Working with partitions
    • Partitioning files-based datasets
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  • DSS and Hadoop
    • Setting up Hadoop integration
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      • Cloudera CDH
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    • Using multiple Hadoop filesystems
  • DSS and Spark
    • Usage of Spark in DSS
    • Setting up Spark integration
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    • Usage notes per dataset type
    • Spark pipelines
    • Limitations and attention points
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  • Real-time predictions
    • Introduction
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    • Sampling methods
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  • DSS APIs
    • The DSS public API
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      • The REST API
    • The internal Python API
      • Interacting with datasets
      • Advanced Python API usage
      • Performing SQL, Hive and Impala queries
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  • Release notes
    • DSS 4.0 Release notes
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  • Other Documentation
  • Third-party acknowledgements
 
Dataiku DSS
  • Docs »
  • DSS and Spark

DSS and SparkΒΆ

  • Usage of Spark in DSS
    • SparkSQL recipes
    • Visual recipes
    • Python code
      • Note about Spark code in Python notebooks
    • R code
      • Note about Spark code in R notebooks
    • Scala code
      • Spark Scala, PySpark & SparkR recipes
      • Spark Scala, PySpark & SparkR notebooks
      • Note about Spark code in Scala notebooks
    • Machine Learning with MLLib
    • Machine Learning with H2O Sparkling Water
  • Setting up Spark integration
    • Set up your Spark environment
    • Set up Spark integration with DSS
      • Case 1: Spark integrated in your Hadoop distribution
      • Case 2: Manual installation of Spark
      • Caveat for RedHat / CentOS 6.x clusters
      • Verify the installation
    • Configure Spark logging
  • Spark configurations
  • Usage notes per dataset type
  • Spark pipelines
    • Enabling Spark pipelines
    • Creating a Spark pipeline
    • Configuring behavior for intermediate datasets
  • Limitations and attention points

Spark is a general engine for distributed computation. You can think of Spark as a faster and more convenient replacement for MapReduce. Once Spark integration is setup, DSS will offer settings to choose Spark as a job’s execution engine in various components.

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