Amazon Deequ on EMR/Glue

1. Introduction

Amazon Deequ is an open-source library developed by Amazon that helps in maintaining data quality in data lakes. It provides facilities to define "checks" on your data, and it can be used with AWS Glue and EMR for large scale data processing.

2. Key Concepts

3. Setup Process

Follow these steps to set up Amazon Deequ on AWS EMR and Glue:

1. Launch an EMR cluster with Spark and Deequ installed.
2. Upload your data to Amazon S3.
3. Use Spark to load your data from S3.
4. Define checks using Deequ.
5. Run the checks and analyze the results.

3.1 Example Code

import com.amazon.deequ.checks.Check
import com.amazon.deequ.VerificationSuite
import org.apache.spark.sql.SparkSession

val spark = SparkSession.builder()
    .appName("Deequ Example")
    .getOrCreate()

val data = spark.read.json("s3://your-bucket/data.json")

val verificationResult = VerificationSuite()
    .onData(data)
    .addCheck(
        Check(CheckLevel.Error, "Data Quality Check")
            .hasSize(_ >= 1000) // Check for size
            .isUnique("id")      // Check uniqueness on column 'id'
            .isComplete("name")  // Check completeness on column 'name'
    ).run()

verificationResult.checks.foreach { check =>
    println(check)
}
            

4. Best Practices

• Define clear data quality metrics based on your use case.
• Run checks regularly to ensure data integrity over time.
• Utilize AWS Glue Data Catalog to manage metadata effectively.
• Leverage the parallel processing capabilities of Spark to handle large datasets.

5. FAQ