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This section provides guides and references to use the Delta Lake connector. Configure and schedule Delta Lake metadata and profiler workflows from the Collate UI:

Requirements

Delta Lake requires Python 3.9 or 3.10. Collate does not yet support the Delta connector on Python 3.11. The Delta Lake connector can extract information from a metastore or directly from storage. When extracting directly from storage, extra requirements apply depending on the storage type.

S3 Permissions

To execute metadata extraction, the AWS account must have enough access to fetch the required data. The Bucket Policy in AWS requires at least these permissions:

Metadata Ingestion

To ingest metadata from DeltaLake, you need to create a service connection. The service connects DeltaLake with Collate. Once you create a service, Collate automatically starts ingesting metadata.

Step 1: Add New Service

  1. In the left navigation, click Connections.
  2. On the Connections page, click Add New Service.
Add New Service

Step 2: Select a Service and Connector

From the service type dropdown, select Database Services, then click the DeltaLake connector tile. Select Service

Step 3: Add Service Name and Description

  • Enter a unique, descriptive Service Name. Collate identifies services by their service name. Enter a name that distinguishes this deployment from other DeltaLake services you are ingesting metadata from.
  • Optional: Enter a Description for the service.
Add New Service Name
Note: The service name cannot be changed after it is set.

Step 4: Configure Connection Options

Specify where ingestion runs, provide your source credentials, and verify the connection.

Select Ingestion Runner

Select an Ingestion Runner: the runner where the ingestion pipeline will execute. Add Name and Select Ingestion Runner

Enter Connection Details

Enter the connection details for DeltaLake. The right-hand panel in the UI displays inline help for each field. Configure Service Connection
  • Metastore Host Port: Enter the Host & Port of Hive Metastore Service to configure the Spark Session. Either of metastoreHostPort, metastoreDb or metastoreFilePath is required.
  • Metastore File Path: Enter the file path to local Metastore in case Spark cluster is running locally. Either of metastoreHostPort, metastoreDb or metastoreFilePath is required.
  • Metastore DB: The JDBC connection to the underlying Hive metastore DB. Either of metastoreHostPort, metastoreDb or metastoreFilePath is required.
  • appName (Optional): Enter the app name of spark session.
  • Connection Arguments (Optional): Key-value pairs used to pass extra config elements to the Spark Session builder. Collate internally runs pyspark 3.X and delta-lake 2.0.0, so Spark configuration options must target 3.X. Metastore Host Port When connecting to an external metastore using the Metastore Host Port parameter, Collate prepares a Spark Session with the following configuration:
Collate then uses the catalog functions from the Spark Session to pick up the metadata exposed by the Hive Metastore. Metastore File Path If a local file path contains the metastore information instead (for example, for local testing with the default metastore_db directory), set the following:
This updates the Derby information. For more information, see this Stack Overflow thread.
  • For all supported configurations, see the Spark configuration documentation.
  • For more information about the Hive metastore, see the Spark Hive tables documentation and The Internals of Spark SQL book. Metastore Database Connect to the metastore directly by pointing to the Hive Metastore db — for example, jdbc:mysql://localhost:3306/demo_hive. This requires the common database settings (url, username, password) and the driver class name for the JDBC metastore. Provide the driver to the ingestion image and pass the classpath used in the Spark Configuration under spark.driver.extraClassPath.

Connection Details for StorageConfig - S3

  • AWS Access Key ID and AWS Secret Access Key: When you interact with AWS, you specify your AWS security credentials to verify who you are and whether you have permission to access the resources that you are requesting. AWS uses the security credentials to authenticate and authorize your requests (docs). Access keys consist of two parts: An access key ID (for example, AKIAIOSFODNN7EXAMPLE), and a secret access key (for example, wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY). You must use both the access key ID and secret access key together to authenticate your requests. You can find further information on how to manage your access keys.
  • AWS Region: Each AWS Region is a separate geographic area in which AWS clusters data centers (docs). As AWS can have instances in multiple regions, we need to know the region the service you want to reach belongs to. Note that the AWS Region is the only required parameter when configuring a connection. When connecting to the services programmatically, there are different ways in which we can extract and use the rest of AWS configurations. You can find further information about configuring your credentials.
  • AWS Session Token (optional): If you are using temporary credentials to access your services, you will need to inform the AWS Access Key ID and AWS Secrets Access Key. Also, these will include an AWS Session Token. You can find more information on Using temporary credentials with AWS resources.
  • Endpoint URL (optional): To connect programmatically to an AWS service, you use an endpoint. An endpoint is the URL of the entry point for an AWS web service. The AWS SDKs and the AWS Command Line Interface (AWS CLI) automatically uses the default endpoint for each service in an AWS Region. But you can specify an alternate endpoint for your API requests. Find more information on AWS service endpoints.
  • Profile Name: A named profile is a collection of settings and credentials that you can apply to an AWS CLI command. When you specify a profile to run a command, the settings and credentials are used to run that command. Multiple named profiles can be stored in the config and credentials files. You can inform this field if you’d like to use a profile other than default. Find more information about Named profiles for the AWS CLI.
  • Assume Role Arn: Typically, you use AssumeRole within your account or for cross-account access. In this field you’ll set the ARN (Amazon Resource Name) of the policy of the other account. A user who wants to access a role in a different account must also have permissions that are delegated from the account administrator. The administrator must attach a policy that allows the user to call AssumeRole for the ARN of the role in the other account. This is a required field if you’d like to AssumeRole. Find more information on AssumeRole.
  • Assume Role Session Name: An identifier for the assumed role session. Use the role session name to uniquely identify a session when the same role is assumed by different principals or for different reasons. By default, we’ll use the name OpenMetadataSession. Find more information about the Role Session Name.
  • Assume Role Source Identity: The source identity specified by the principal that is calling the AssumeRole operation. You can use source identity information in AWS CloudTrail logs to determine who took actions with a role. Find more information about Source Identity.

Advanced Configuration

Database Services have an Advanced Configuration section, where you can pass extra arguments to the connector and, if needed, change the connection Scheme. This would only be required to handle advanced connectivity scenarios or customizations.
  • Connection Options (Optional): Enter the details for any additional connection options that can be sent to database during the connection. These details must be added as Key-Value pairs.
  • Connection Arguments (Optional): Enter the details for any additional connection arguments such as security or protocol configs that can be sent during the connection. These details must be added as Key-Value pairs.

Test Connection

Once the credentials have been added, click on Test Connection and Save the changes. Test Connection

Step 5: Configure Ingestion Options

In the What to Ingest step, use filter patterns to control which assets Collate ingests from your database service. Filter patterns use regular expressions applied to asset names.

How Filter Patterns Work

  • Include: Add one or more comma-separated regular expressions. Collate ingests only assets whose names match at least one expression. Leave blank to include all assets.
  • Exclude: Add one or more comma-separated regular expressions. Collate skips any asset whose name matches an expression. Leave blank to exclude nothing.
Rules match asset names using one of five expressions:
  • contains: matches any name containing the value. For example, sales matches my_sales_data and sales_2024.
  • starts with: matches names beginning with the value. For example, prod_ matches prod_db and prod_schema.
  • ends with: matches names ending with the value. For example, _raw matches events_raw and logs_raw.
  • is exactly: matches the exact name only. For example, analytics matches only analytics.
  • matches regex: matches names using a regular expression. For example, ^prod_.*_v\d+$ matches prod_events_v1.
When both Include and Exclude are set, Exclude takes priority.
Tip: Leave all filter patterns empty to ingest all databases, schemas, and tables available in the source.
Filter Options The Database, Schema, Table, and Stored Procedure sections each include the following filter options:
  • Database: Controls which databases Collate ingests from the source.
  • Schema: Controls which schemas within the ingested databases are included.
  • Table: Controls which tables and views within the ingested schemas are included.
  • Stored Procedure: Controls which stored procedures are included in metadata ingestion.
Each section provides the following controls:
  • Scan Mode: You can choose between the following scan modes:
    • Scan all: Ingests every asset of that type the connector can access. This is the default.
    • Only specific: Enables include rules so only assets matching at least one rule are ingested.
  • Exclude system toggle: Use this toggle to automatically filter out system-reserved names defined by the connector — for example, Exclude system databases for the Databases section.
  • Always exclude: Add permanent exclusion rules (shown in red). Assets matching these rules are never ingested, regardless of include rules.
  • Preview: Shows a real-time summary of what will be in scope based on your current rules.
  • Include rules (available only in Only specific mode): Click + Add to define a rule. Added rules appear as chips; an asset is included if it matches any rule.
Tip: If AutoPilot is enabled, usage tracking, data lineage, and other downstream workflows start automatically after the first metadata ingestion completes.

Step 6: Create & Deploy

Click Create & Deploy to deploy the agent and start the first metadata ingestion run. Collate saves the service configuration and immediately begins pulling metadata from the source. To monitor ingestion progress or view the service you just added, go to Connections in the left navigation and select your service.

Configure Metadata Agent and Schedule Ingestion

The Metadata Agent extracts schemas, tables, columns, and other structural metadata from your source and keeps your Collate catalog in sync. It powers discovery, lineage, and governance across your data assets. When you click Create & Deploy, Collate automatically deploys a Metadata Agent for this service and triggers the first ingestion run. View its status and run history from the Agents tab on the service detail page. To configure the additional Metadata Agent and schedule ingestion, follow these steps:
  1. In the left navigation, click Connections and select your service.
  2. Click the Agents tab.
  3. Click Add Agent and select Metadata from the dropdown. Add Metadata Agent For some services, the dropdown is not available and clicking Add Agent takes you directly to the agent configuration page.
  4. On the Configure Ingestion page, do the following and click Next.
    • Name this Ingestion: Enter a unique recognizable name for this ingestion pipeline. Name this Ingestion
    • Agent Setup: Configure core parameters for metadata extraction. The following fields are available: Agent Setup
    • Filter Patterns: Apply include or exclude rules to scope which databases, schemas, tables, and stored procedures this agent ingests. For more information about various filter options, see Step 5: Configure Ingestion Options. Filter Patterns
    • Scope & Behaviour: Control how the agent handles metadata during ingestion. Toggle each option on or off based on your needs:
      Note: Available toggles vary by connector. Stored procedure options only appear for connectors that support stored procedures.
      Scope & Behaviour
    • Advanced Config: Optional connector-specific settings such as Include Views and Extract JSON Schema. Advanced Config
  5. On the Schedule Interval page, set when the agent runs:
    • Schedule: Choose a preset interval (Hourly, Daily, Weekly, Monthly) or enter a custom cron expression.
    • On-Demand: No automatic schedule; trigger the agent manually when needed.
    Schedule Interval
  6. Click Add to deploy the agent.

Troubleshooting

Delta Lake Troubleshooting

Learn more about how to troubleshoot common Delta Lake connector issues and resolve configuration or ingestion errors.