Hybrid architectures that combine batch and streaming data processing are designed to handle both real-time data ingestion and processing, as well as larger, more complex batch operations. This type of architecture is beneficial for systems that need to make decisions on-the-fly while also performing deep analyses of historical data.

In the context of AWS, such hybrid systems can be built using services like AWS Lambda, AWS Glue, Amazon Redshift, and Amazon S3. Here’s how these components can work together in a pipeline:

1. **Data Ingestion and Initial Processing:**
– **AWS Lambda:** Functions as the event-driven service that reacts to real-time data events. For example, new data coming into the system via Amazon Kinesis Data Streams or directly uploaded files to Amazon S3 can trigger a Lambda function. Lambda processes individual data events instantly, performs lightweight transformations, and stores processed data or pushes transformed streams back into a further processing component.
– **Amazon Kinesis (Optional Component):** Can be used for handling streaming data before it gets picked up by Lambda or other processing tools.

2. **Storage and Batch Processing:**
– **Amazon S3:** Operates as the primary storage layer where both raw and processed data can be stored. Real-time data processed by Lambda can be written to S3, creating a durable and scalable data lake that can be accessed for batch processing.
– **AWS Glue:** Acts as the ETL (Extract, Transform, Load) service that runs on batch mode. Glue can periodically (e.g., daily, hourly) extract data from S3, perform complex transformations, cleanse the data, and prepare it for deeper analysis. It can also be scheduled to run at specific intervals or triggered by events.
– **Amazon Redshift:** The data warehouse component that enables fast querying and analysis. Once Glue has transformed the data, it can be loaded into Redshift for further analytics, leveraging powerful processing capabilities for large datasets.

3. **Analytics and Query:**
– **Amazon Redshift Spectrum:** Can be employed to query the data residing in S3 directly, allowing Redshift queries on the S3 data without loading it into the database, improving flexibility and speed for some use cases.

4. **Use Case Example:**
– Imagine an e-commerce platform tracking customer interactions (clicks, views, purchases). The platform receives clickstream data through a streaming service like Kinesis.
– AWS Lambda functions are triggered by incoming data streams, processing logs, and storing them for immediate anomaly detection (e.g., transaction fraud).
– Processed logs are stored in S3, where AWS Glue picks up data daily to perform ETL operations, cleansing and enriching data before loading into Redshift.
– Analysts and business intelligence tools then use Redshift to generate insights, reports, and dashboards providing historical trends and patterns, effectively combining real-time data signals with batch-processed data for a comprehensive view.

This hybrid architecture balances the requirement for immediate real-time data handling with robust batch processing, making it effective for large, dynamic environments like those found in e-commerce, finance, or IoT applications.