AWS Services for data engineering

AWS services for data engineering can be categorized into different groups based on their primary functionalities and use cases.

These categories represent the key AWS services that data engineers can leverage to build robust and scalable data engineering solutions. Depending on the project's specific requirements, data engineers may choose a combination of these services to create end-to-end data pipelines and analytical systems on the AWS platform.

Data Storage and Data Lake:

• Amazon S3 (Simple Storage Service): Scalable object storage for storing raw and processed data in a data lake.

• Amazon DynamoDB: Fully managed NoSQL database for fast and scalable storage of structured data.

• Amazon Redshift: A fully managed data warehouse for high-performance analytics and reporting.

• Amazon RDS (Relational Database Service): Managed relational databases for structured data storage.

• Amazon DocumentDB: Fully managed MongoDB-compatible document database.

• Amazon Neptune: Fully managed graph database for highly connected data.

Data Ingestion and Streaming

• Amazon Kinesis: Real-time data streaming and processing services, including Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics.

• AWS IoT Core: For ingesting and processing data from Internet of Things (IoT) devices.

Data Processing and ETL

• AWS Glue: Fully managed extract, transform, load (ETL) service for data preparation and transformation.

• Amazon EMR (Elastic MapReduce): Managed Hadoop, Spark, and other big data processing frameworks for large-scale data processing.

• AWS Lambda: Serverless compute service for running code in response to events, commonly used for real-time data processing.

  

  Data Visualization and Business Intelligence

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Workflow Orchestration

• AWS Data Pipeline: Orchestration service for automating data workflows across various AWS services and on-premises data sources.

• AWS Step Functions: Serverless workflow service for coordinating distributed applications and microservices.

Data Security and Governance

• AWS IAM (Identity and Access Management): Manages access to AWS services and resources.

• AWS Key Management Service (KMS): Securely manages encryption keys.

• AWS CloudTrail: Auditing service for monitoring API activity and user actions in AWS.

• AWS Lake Formation: Includes data access controls and governance features for data lakes.

Machine Learning Integration:

• Amazon SageMaker: Fully managed machine learning service for building, training, and deploying ML models.

• AWS Glue DataBrew: Visual data preparation tool with ML-based data transformations.