How to Build a Data Ingestion and Analytics Pipeline using AWS Redshift, Kinesis Data Firehose, Lambda, and S3

This pipeline will allow you to capture, transform, and load streaming data into a data warehouse for analysis and visualization.

Overview

The following diagram illustrates the high-level architecture of the pipeline:

+-----------------+     +-----------------+     +-----------------+     +-----------------+
|                 |     |                 |     |                 |     |                 |
|  Data Source    +---->+ Kinesis Data    +---->+ Lambda Function +---->+ Redshift        |
|                 |     | Firehose        |     |                 |     | Cluster         |
|                 |     | Delivery Stream |     |                 |     |                 |
+-----------------+     +-----------------+     +-----------------+     +-----------------+
                                    |
                                    |
                                    v
                            +-----------------+
                            |                 |
                            | S3 Bucket       |
                            |                 |
                            +-----------------+

The pipeline consists of the following components:

• Data Source: This is the source of the streaming data that you want to ingest and analyze. For example, this could be a web application that generates clickstream data, a sensor network that produces IoT data, or a social media platform that generates user activity data. You can use any data source that can send data to Kinesis Data Firehose via the PutRecord or PutRecordBatch API calls.
• Kinesis Data Firehose Delivery Stream: This is the service that captures, and loads the streaming data into downstream services such as Redshift or S3. You can write Lambda functions to request additional, customized processing of the data before it is sent downstream.
• Lambda Function: This is the function that performs any custom logic or transformation on the data before it is loaded into Redshift. For example, you can use Lambda to filter, enrich, aggregate, or format the data according to your business requirements. You can also use Lambda to perform any de-aggregation or parsing of the data if it is aggregated or contains multiple records in a single API call.
• Redshift Cluster: This is the data warehouse that stores and analyzes the streaming data. You can use Redshift to run SQL queries on the data and generate insights and reports. You can also connect Redshift to various BI tools or dashboards for visualization and exploration.
• S3 Bucket: This is the optional destination for storing the raw or processed data in case you want to keep a backup or archive of the data for future reference or analysis. You can also use S3 as a source for loading data into Redshift using the COPY command.

How to Build the Pipeline

1. Create a Kinesis Data Firehose delivery stream and configure its destination settings. You can choose either Redshift or S3 as the destination, or both if you want to store the data in both places (as a backup or raw data source). You also need to specify an S3 bucket prefix for storing any data that fails to be delivered or partitioned by Kinesis Data Firehose.
2. Create a Lambda function and write your custom code for processing the data. You can use any programming language that is supported by Lambda, such as Python, Node.js, Java, etc. You need to follow the input and output format expected by Kinesis Data Firehose.
3. Enable and configure dynamic partitioning on your Kinesis Data Firehose delivery stream. This feature allows you to partition your data based on keys or expressions that you define in your Lambda function. This way, you can organize your data into different folders or prefixes in S3 or Redshift based on attributes such as date, time, event type, user ID, etc.
4. Create a Redshift cluster and configure its connection settings. You need to provide the cluster endpoint, database name, user name, password, table name, and copy options for loading data from Kinesis Data Firehose. You also need to create a table schema that matches the format of your processed data.
5. Send your streaming data to Kinesis Data Firehose using PutRecord or PutRecordBatch API calls from your data source. You can also use tools such as Kinesis Agent or Kinesis Producer Library (KPL) to simplify this process.
6. Monitor your pipeline using CloudWatch metrics and logs. You can check the status and performance of your delivery stream, Lambda function, and Redshift cluster using the AWS console or CLI. You can also set up alarms or notifications for any errors or issues that may occur.
7. Query your data in Redshift using SQL commands or BI tools. You can use the AWS console, CLI, or any SQL client to connect to your Redshift cluster and run queries on your data. You can also use services such as QuickSight, Athena, or Glue to visualize or analyze your data further.

Conclusion

In this blog post, I have shown you how to build a data ingestion and analytics pipeline using AWS services such as Redshift, Kinesis Data Firehose, Lambda, and S3. This pipeline can help you capture, transform, and load streaming data into a data warehouse for analysis and visualization. You can also customize and extend this pipeline to suit your specific use cases and requirements.

I hope you found this post useful and informative. Thank you for reading!

References:

Using AWS Lambda with Amazon Kinesis Data Firehose
Destination Settings – Amazon Kinesis Data Firehose