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Smart Data Lake Builder is still under heavy development so new features are added all the time. The following list will give you a rough overview of current and planned features. More details on the roadmap will follow shortly.

Filebased metadata

  • Easy to version with a VCS for DevOps
  • Flexible structure by splitting over multiple files and subdirectories
  • Easy to generate from third party metadata (e.g. source system table catalog) to automate transformation of large number of DataObjects

Support for complex workflows & streaming

  • Fork, join, parallel execution, multiple start- & end-nodes possible
  • Recovery of failed runs
  • Switch a workflow between batch or streaming execution by using just a command line switch

Execution Engines

  • Spark (DataFrames)
  • Snowflake (DataFrames)
  • File (Input&OutputStream)
  • Future: Kafka Streams, Flink, …


  • Spark: diverse connectors (HadoopFS, Hive, DeltaLake, JDBC, Kafka, Splunk, Webservice, JMS) and formats (CSV, JSON, XML, Avro, Parquet, Excel, Access …)
  • File: SFTP, Local, Webservice
  • Easy to extend by implementing predefined scala traits
  • Support for getting secrets from different secret providers
  • Support for SQL update & merge (Jdbc, DeltaLake)
  • Support for integration of Airbyte sources

Generic Transformations

  • Spark based: Copy, Historization, Deduplication (incl. incremental update/merge mode for streaming)
  • File based: FileTransfer
  • Easy to extend by implementing predefined scala traits
  • Future: applying MLFlow machine learning models

Customizable Transformations

  • Spark Transformations:
    • Chain predefined standard transformations (e.g. filter, row level data validation and more) and custom transformations within the same action
    • Custom Transformation Languages: SQL, Scala (Class, compile from config), Python
    • Many input DataFrames to many outputs DataFrames (but only one output recommended normally, in order to define dependencies as detailed as possible for the lineage)
    • Add metadata to each transformation to explain your data pipeline.
  • File Transformations:
    • Language: Scala
    • Only one to one (one InputStream to one OutputStream)

Early Validation

(see execution phases for details)

  • Execution in 3 phases before execution
    • Load Config: validate configuration
    • Prepare: validate connections
    • Init: validate Spark DataFrame Lineage (missing columns in transformations of later actions will stop the execution)

Execution Modes

(see execution Modes for details)

  • Process all data
  • Partition parameters: give partition values to process for start nodes as parameter
  • Partition Diff: search missing partitions and use as parameter
  • Incremental: use stateful input DataObject, or compare sortable column between source and target and load the difference
  • Spark Streaming: asynchronous incremental processing by using Spark Structured Streaming
  • Spark Streaming Once: synchronous incremental processing by using Spark Structured Streaming with Trigger=Once mode

Schema Evolution

  • Automatic evolution of data schemas (new column, removed column, changed datatype)
  • Support for changes in complex datatypes (e.g. new column in array of struct)
  • Automatic adaption of DataObjects with fixed schema (Jdbc, DeltaLake)


  • Number of rows written per DataObject
  • Execution duration per Action
  • Arbitrary custom metrics defined by aggregation expressions
  • StateListener interface to get notified about progress & metrics

Data Catalog

  • Report all DataObjects attributes (incl. foreign keys if defined) for visualisation of data catalog in BI tool
  • Metadata support for categorizing Actions and DataObjects
  • Custom metadata attributes


  • Report all dependencies between DataObjects for visualisation of lineage in BI tool

Data Quality

  • Metadata support for primary & foreign keys
  • Check & report primary key violations by executing primary key checker action
  • Define and validate row-level Constraints before writing DataObject
  • Define and evaluate Expectations when writing DataObject, trigger warning or error, collect result as custom metric
  • Future: Report data quality (foreign key matching & expectations) by executing data quality reporter action


  • Support for CI
    • Config validation
    • Custom transformation unit tests
    • Spark data pipeline simulation (acceptance tests)
  • Support for Deployment
    • Dry-run

Spark Performance

  • Execute multiple Spark jobs in parallel within the same Spark Session to save resources
  • Automatically cache and release intermediate results (DataFrames)


  • Delete, or archive & compact partitions according to configurable expressions