what is data engineering?

Data engineering involves designing and building systems for collecting, storing, and analyzing data at scale. It ensures that data is accessible, reliable, and ready for analysis by data scientists and business intelligence professionals.

Key Components of Data Engineering with AWS

1. Amazon S3 (Simple Storage Service):

   • Storage: Amazon S3 is used to store large amounts of data in a scalable and cost-effective manner. It can handle various data formats like CSV, JSON, Parquet, etc.

   • Data Lake: S3 acts as a data lake where raw data is ingested and stored before processing.

2. AWS Glue Crawler:

   • Data Discovery: Glue Crawlers automatically discover and catalog metadata about your data stored in S3. They infer the schema and create tables in the AWS Glue Data Catalog.

   • ETL (Extract, Transform, Load): Glue provides ETL capabilities to transform raw data into a structured format suitable for analysis.

3. AWS Glue Data Catalog:

   • Metadata Repository: The Data Catalog stores metadata about data stored in S3 and other data sources. It acts as a central repository for schema and table definitions.

   • Integration: It integrates with other AWS services like Athena, Redshift, and QuickSight, enabling seamless data querying and visualization.

4. Amazon Athena:

   • Query Service: Athena is a serverless query service that allows you to run SQL queries on data stored in S3. It uses the metadata stored in the Glue Data Catalog to understand the structure of the data.

   • Ad-hoc Analysis: Athena is ideal for ad-hoc data analysis and can handle large datasets without the need for complex ETL processes.

5. Amazon QuickSight:

   • Data Visualization: QuickSight is a business intelligence service that allows you to create interactive dashboards and visualizations from your data.

   • Integration with Athena: QuickSight can directly connect to Athena, enabling you to visualize query results and gain insights from your data.

Example Workflow

1. Data Ingestion:

   • Raw data is ingested into Amazon S3 from various sources (e.g., logs, IoT devices, databases).

2. Data Discovery and Cataloging:

   • AWS Glue Crawler scans the data in S3, infers the schema, and updates the Glue Data Catalog with table definitions.

3. Data Transformation:

   • AWS Glue ETL jobs transform the raw data into a structured format, such as Parquet, and store it back in S3.

4. Data Querying:

   • Using Amazon Athena, you can run SQL queries on the transformed data stored in S3. Athena uses the Glue Data Catalog to understand the data schema.

5. Data Visualization:

   • Amazon QuickSight connects to Athena to visualize the query results. You can create dashboards and reports to share insights with stakeholders.

Example .gitlab-ci.yml for Data Engineering Pipeline

stages:
  - ingest
  - catalog
  - transform
  - query
  - visualize

ingest:
  stage: ingest
  script:
    - echo "Ingesting data into S3..."
    - aws s3 cp data.csv s3://your-bucket/data/

catalog:
  stage: catalog
  script:
    - echo "Running Glue Crawler..."
    - aws glue start-crawler --name your-crawler

transform:
  stage: transform
  script:
    - echo "Running Glue ETL job..."
    - aws glue start-job-run --job-name your-etl-job

query:
  stage: query
  script:
    - echo "Running Athena query..."
    - aws athena start-query-execution --query-string "SELECT * FROM your_table" --result-configuration OutputLocation=s3://your-bucket/query-results/

visualize:
  stage: visualize
  script:
    - echo "Creating QuickSight dashboard..."
    - aws quicksight create-dashboard --aws-account-id your-account-id --dashboard-id your-dashboard-id --source-entity file://dashboard-definition.json

This pipeline automates the process of ingesting, cataloging, transforming, querying, and visualizing data, ensuring a seamless data engineering workflow on AWS.