Integrating with AWS Data Lakehouse: Real-Time and Batch mode AI Notes

Integrating with AWS Data Lakehouse: Real-Time and Batch

AWS offers a suite of services to build a data lakehouse, enabling both real-time and batch data integration. The core of the data lakehouse is typically Amazon S3, with services like AWS Glue, Amazon Athena, and Amazon Redshift providing ETL, querying, and data warehousing capabilities.

Real-Time (Streaming) Integration

Real-time integration focuses on ingesting and processing data as it arrives, making it available for near-instantaneous analysis within your data lakehouse.

1. Amazon Kinesis Data Streams

Details: A scalable and durable real-time data streaming service. Producers can continuously push data into Kinesis streams, and consumers (like AWS Lambda, Amazon Kinesis Data Analytics, or custom applications) can process this data in real time and land it in your data lake (S3) or other data stores.

Key Features: High throughput, low latency, scalable, durable, can be used for real-time analytics and ETL.

2. Amazon Kinesis Data Firehose

Details: An ETL service that can reliably load streaming data into data lakes, data stores, and analytics services. It can automatically convert, transform, and compress data before loading it into destinations like S3, Redshift, and OpenSearch Service.

Key Features: Simple to use, automatic scaling, data transformation and conversion, supports various destinations.

3. AWS IoT Core (for IoT Data)

Details: If your real-time data originates from IoT devices, AWS IoT Core can ingest, secure, process, and route device data to your data lake in real time using its Rules Engine.

Key Features: Device connectivity and management, secure communication, message brokering, rules engine for data transformation and routing.

4. Amazon Managed Streaming for Apache Kafka (MSK)

Details: A fully managed service that makes it easy to build and run applications that use Apache Kafka to process streaming data. You can use Kafka Connect to stream data into your data lake or other AWS services in near real-time.

Key Features: Fully managed Kafka, scalable, highly available, integrates with the Kafka ecosystem.

Batch Integration

Batch integration involves processing larger volumes of data at scheduled intervals or in response to specific events to populate your data lakehouse.

1. AWS Glue

Details: A fully managed ETL (extract, transform, and load) service that makes it easy to prepare and load data for analytics. You can create batch ETL jobs using a visual interface (AWS Glue Studio) or code (PySpark) to read data from various sources and write it to your data lake (S3) in formats like Parquet or ORC.

Key Features: Serverless, scalable, supports various data sources and transformations, integrated with AWS Glue Data Catalog for metadata management.

2. AWS Glue DataBrew

Details: A visual data preparation tool that allows data analysts and data scientists to clean and normalize data without writing code. You can create recipes and jobs to perform batch data transformations on data residing in S3 and other data sources.

Key Features: Visual interface, over 250 built-in transformations, data profiling, integration with AWS Glue.

3. AWS DataSync

Details: A data transfer service that simplifies, automates, and accelerates moving and replicating data between on-premises storage systems and AWS storage services like S3. It’s useful for one-time migrations or scheduled batch transfers of large datasets.

Key Features: Fast and secure data transfer, supports various storage systems, scheduling options.

4. AWS Transfer Family

Details: A fully managed service that enables you to transfer files into and out of Amazon S3 over protocols like SFTP, FTPS, and FTP. This is suitable for batch data ingestion from systems that rely on these protocols.

Key Features: Managed file transfer service, secure protocols, direct integration with S3.

Choosing the right integration method for your AWS data lakehouse depends on your specific data sources, volume, velocity, transformation requirements, and latency needs. Often, a combination of real-time and batch integration techniques is used to build a comprehensive and efficient data lakehouse solution.

Integrating with AWS Data Lakehouse: Real-Time and Batch mode