Ingesting data from streaming and batch sources, transforming and processing data across formats, orchestrating ETL pipelines, and applying programming concepts for data engineering on AWS.
This is the highest-weighted domain on the DEA-C01 exam, covering the end-to-end process of getting data into AWS and preparing it for consumption. It includes performing data ingestion from streaming sources (Amazon Kinesis, Amazon MSK, DynamoDB Streams, AWS DMS) and batch sources (Amazon S3, AWS Glue, Amazon EMR, Amazon Redshift), configuring schedulers and event triggers, managing API consumption with throttling and rate limits, and handling fan-in/fan-out patterns for streaming distribution. The domain also covers transforming and processing data using services like AWS Glue, Amazon EMR, Lambda, and Amazon Redshift, including format conversions (CSV to Parquet), multi-source integration via JDBC/ODBC, and container-based processing with EKS and ECS. Pipeline orchestration with Step Functions, MWAA, and Glue workflows is tested, along with programming concepts such as CI/CD, Infrastructure as Code (CloudFormation, CDK, SAM), distributed computing, and software engineering best practices. (34% of exam)
5 minutes
5 Questions
Data Ingestion and Transformation are fundamental concepts in the AWS Certified Data Engineer - Associate exam, representing critical stages in any data pipeline.
**Data Ingestion** refers to the process of collecting and importing data from various sources into a storage or processing system. AWS offers multiple services for ingestion:
- **Amazon Kinesis** (Data Streams, Data Firehose): Handles real-time streaming data ingestion from sources like IoT devices, clickstreams, and application logs.
- **AWS Glue Crawlers**: Automatically discover and catalog data from sources like S3, RDS, and DynamoDB.
- **AWS Database Migration Service (DMS)**: Migrates data from on-premises or cloud databases to AWS with support for continuous replication.
- **Amazon AppFlow**: Enables SaaS application data integration (e.g., Salesforce, SAP).
- **AWS Transfer Family**: Supports SFTP, FTPS, and FTP-based file ingestion into S3.
- **Amazon MSK (Managed Streaming for Apache Kafka)**: Provides managed Kafka for high-throughput streaming ingestion.
Ingestion can be **batch** (periodic bulk loads) or **real-time/streaming** (continuous data flow), and choosing the right pattern depends on latency requirements and data volume.
**Data Transformation** involves converting raw data into a structured, clean, and usable format. Key AWS services include:
- **AWS Glue (ETL Jobs)**: Serverless Apache Spark-based service for Extract, Transform, Load operations using PySpark or Python.
- **AWS Glue DataBrew**: A visual, no-code data preparation tool for cleaning and normalizing data.
- **Amazon EMR**: Managed Hadoop/Spark clusters for large-scale data transformations.
- **AWS Lambda**: Lightweight, event-driven transformations for smaller datasets.
- **Amazon Athena**: SQL-based transformations using CTAS (Create Table As Select) queries on S3 data.
Common transformations include data cleansing, deduplication, format conversion (CSV to Parquet), schema evolution, partitioning, aggregation, and joining datasets.
Understanding ingestion patterns (batch vs. streaming), choosing appropriate services, optimizing data formats, handling schema changes, and ensuring data quality are essential skills tested in the exam. The goal is building efficient, scalable, and cost-effective data pipelines on AWS.Data Ingestion and Transformation are fundamental concepts in the AWS Certified Data Engineer - Associate exam, representing critical stages in any data pipeline.
**Data Ingestion** refers to the process of collecting and importing data from various sources into a storage or processing system. AWS…
