Delta Lake

Creating a data generator for Delta Lake. You will have the ability to generate and validate Delta Lake files.

Requirements

• 10 minutes
• Git
• Gradle
• Docker

Get Started

First, we will clone the data-caterer-example repo which will already have the base project setup required.

JavaScalaYAMLUI

git clone git@github.com:data-catering/data-caterer-example.git

git clone git@github.com:data-catering/data-caterer-example.git

git clone git@github.com:data-catering/data-caterer-example.git

Run Data Caterer UI via the 'Quick Start' found here.

Plan Setup

Create a file depending on which interface you want to use.

• Java: src/main/java/io/github/datacatering/plan/MyDeltaLakeJavaPlan.java
• Scala: src/main/scala/io/github/datacatering/plan/MyDeltaLakePlan.scala
• YAML: docker/data/custom/plan/my-delta-lake.yaml

JavaScalaYAMLUI

import io.github.datacatering.datacaterer.java.api.PlanRun;

public class MyDeltaLakeJavaPlan extends PlanRun {
}

import io.github.datacatering.datacaterer.api.PlanRun

class MyDeltaLakePlan extends PlanRun {
}

In docker/data/custom/plan/my-delta-lake.yaml:

name: "my_delta_lake_plan"
description: "Create account data in Delta Lake"
tasks:
  - name: "delta_lake_task"
    dataSourceName: "my_delta_lake"

1. Click on Connection towards the top of the screen
2. For connection name, set to my_delta_lake
3. Click on Select data source type.. and select Delta Lake
4. Set Path as /tmp/custom/delta_lake/accounts
   1. Optionally, we could set the number of partitions and columns to partition by
5. Click on Create
6. You should see your connection my_delta_lake show under Existing connections
7. Click on Home towards the top of the screen
8. Set plan name to my_delta_lake_plan
9. Set task name to delta_lake_task
10. Click on Select data source.. and select my_delta_lake

This class defines where we need to define all of our configurations for generating data. There are helper variables and methods defined to make it simple and easy to use.

Connection Configuration

Within our class, we can start by defining the connection properties to read/write from/to Delta Lake.

JavaScalaYAMLUI

var accountTask = delta(
        "customer_accounts",            //name
        "/opt/app/data/customer/delta", //path
        Map.of()                        //additional options
);

Additional options can be found here.

val accountTask = delta(
  "customer_accounts",            //name
  "/opt/app/data/customer/delta", //path
  Map()                           //additional options
)

Additional options can be found here.

In docker/data/custom/application.conf:

delta_lake {
    my_delta_lake {
        "spark.databricks.delta.properties.defaults.appendOnly" = "true"
    }
}

1. We have already created the connection details in this step

Schema

Depending on how you want to define the schema, follow the below:

• Manual schema guide
• Automatically detect schema from the data source, you can simply enable configuration.enableGeneratePlanAndTasks(true)
• Automatically detect schema from a metadata source

Additional Configurations

At the end of data generation, a report gets generated that summarises the actions it performed. We can control the output folder of that report via configurations. We will also enable the unique check to ensure any unique fields will have unique values generated.

JavaScalaYAMLUI

var config = configuration()
        .generatedReportsFolderPath("/opt/app/data/report")
        .enableUniqueCheck(true);

execute(myPlan, config, accountTask, transactionTask);

val config = configuration
  .generatedReportsFolderPath("/opt/app/data/report")
  .enableUniqueCheck(true)

execute(myPlan, config, accountTask, transactionTask)

In docker/data/custom/application.conf:

flags {
  enableUniqueCheck = true
}
folders {
  generatedReportsFolderPath = "/opt/app/data/report"
}

1. Click on Advanced Configuration towards the bottom of the screen
2. Click on Flag and click on Unique Check
3. Click on Folder and enter /tmp/data-caterer/report for Generated Reports Folder Path

Run

Now we can run via the script ./run.sh that is in the top level directory of the data-caterer-example to run the class we just created.

JavaScalaYAMLUI

./run.sh MyDeltaLakeJavaPlan

./run.sh MyDeltaLakePlan

./run.sh my-delta-lake.yaml

1. Click the button Execute at the top
2. Progress updates will show in the bottom right corner
3. Click on History at the top
4. Check for your plan name and see the result summary
5. Click on Report on the right side to see more details of what was executed

Congratulations! You have now made a data generator that has simulated a real world data scenario. You can check the DeltaLakeJavaPlan.java or DeltaLakePlan.scala files as well to check that your plan is the same.

Validation

If you want to validate data from a Delta Lake source, follow the validation documentation found here to help guide you.