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Ingest Microsoft Access as Parquet

Copy data from Microsoft Access to parquet format in Azure Data Lake Storage Gen2.

Category: Ingest to Lakehouse | Tags: Ingestion

How it works

Execute query '<<ExtractQuery>>' against MS Access databse '<<DataLakeSystemFolder>>' and ingest as parquet into Data Lake location 'raw/<<DataLakeSystemFolder>>/<<DataLakeDatasetFolder>>'

To use this activity within the API, use an ActivityCode of MSACCESS-ADLS.

Available Connections​

SourceConnection:

TargetConnection:

Example JSON​

An example of what the Task Config would look like for a task using this activity. Some of these variables would be set at the group level to avoid duplication between tasks.

{
  "SourceConnection": "MY-SOURCE-CONN",
  "ExtractQuery": "",
  "DataLakeSystemFolder": "my_folder",
  "DataLakeDatasetFolder": "data",
  "TargetConnection": "MY-TARGET-CONN",
  "DeltaSchemaName": "example_schema",
  "DeltaTableName": "my_table"
}

Variable Reference​

The following variables are supported:

  • DatabricksComputeId (Optional) - The Id of the Databricks compute resource to use to run the Notebook.

  • DatabricksJobClusterSpec (Required) - JSON configuration for the job cluster. See help for examples.

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    Job Cluster Spec

    JSON configuration for the ephemeral job cluster. A new cluster is created for each run and terminated on completion.

    Required properties

    • RuntimeVersion — Databricks Runtime version (e.g. "17.3")
    • NodeTypeId or InstancePoolId — one must be specified

    Optional properties

    • NumWorkers — number of worker nodes (omit or set to 0 for single-node)
    • DriverNodeTypeId — override the driver node type
    • DriverInstancePoolId — use a separate pool for the driver node
    • SparkVersionFull — override the full Spark version string (e.g. "17.3.x-gpu-ml-scala2.12")
    • Libraries — additional libraries to install on the cluster

    azure-keyvault-secrets is always installed automatically.

    Example — Standard job cluster

    {
        "RuntimeVersion": "17.3",
        "NodeTypeId": "Standard_DS3_v2",
        "NumWorkers": 4
    }

    Example — Using instance pools

    {
        "RuntimeVersion": "17.3",
        "InstancePoolId": "0101-120000-pool123",
        "NumWorkers": 0,
        "Libraries": [{ "pypi": { "package": "pandas==2.0.0" } }]
    }

  • DatabricksServerlessSpec (Optional) - Optional JSON configuration for serverless job runs. See help for examples.

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    Serverless Job Spec

    Optional JSON configuration for serverless job runs. Allows specifying Python dependencies and an explicit environment version for serverless compute. All fields are optional.

    Required properties

    None.

    Optional properties

    • Libraries — list of pip requirement strings to install
    • EnvironmentVersion — Databricks environment version. If omitted, Databricks uses its default.

    Example — With libraries only. Will use the "default" environment

    {
        "Libraries": ["pandas", "numpy>=1.21.0"]
    }

    Example — With libraries and a pinned environment version

    {
        "Libraries": ["pandas==2.0.0"],
        "EnvironmentVersion": "5"
    }

    Notes

    • The spec is only applied to serverless runs (cluster IDs starting with SERVERLESS). It is ignored for existing clusters and job clusters.
    • Libraries map to environments[].spec.dependencies in the Databricks Jobs API.

  • DataLakeDatasetFolder (Required) - Name of the folder in the Data Lake containing the dataset.

  • DataLakeSystemFolder (Required) - Name of the parent (System) folder in the Data Lake containing the dataset.

  • DeltaSchemaName (Optional) - The name of the Schema this transformation lives in.

  • DeltaTableName (Optional) - The name of the Table representing this transformation.

  • DIUsToUseForCopyActivity (Optional) - Specifies the powerfulness of the copy executor. Value can be between 2 and 256. When left at default, the Data Factory dynamically applies the optimal DIU setting based on the source-sink pair and data pattern.

  • ElevateToDelta (Optional) - Ingest directly to Lakehouse Table

  • ExtractControlVariableName (Optional) - For incremental loads only, the name to assign the Extract Control variable in State Config for the ExtractControl value derived from the Extract Control Query above.

  • ExtractControlVariableSeedValue (Optional) - The initial value to set for the Extract Control variable in State Config - this will have no impact beyond the original seeding of the Extract Control variable in State Config.

  • ExtractQuery (Required) - SQL query to extract data from the source database.

  • IsFederated (Optional) - Makes task available to other Insight Factories within this organisation.

  • Links (Optional)

  • MaximumNumberOfAttemptsAllowed (Optional) - The total number of times the running of this Task can be attempted.

  • MaximumRunTimeHours (Optional) - The maximum number of hours a task run is expected to take. If a run exceeds this duration it will be flagged as stuck.

  • MinutesToWaitBeforeNextAttempt (Optional) - If a Task run fails, the number of minutes to wait before re-attempting the Task.

  • RetainHistory (Optional) - Should the raw files be saved to the History Container to preserve them?

    Show more details

    **Retain History? ** By default, this flag is set to the value assigned in the Configuration item SaveRawFilesToHistory (signalled by the double triangle brackets around the Configuration item name e.g. &lt;&lt;SaveRawFilesToHistory&gt;&gt;). This default behaviour can be overridden here.

  • SourceConnection (Required) - Source connection to use.

  • TableMetadata (Optional) - Column-level documentation, tags, and key/foreign-key definitions for the produced table.

  • TargetConnection (Optional) - Target connection to use.