A Customer Data Platform (CDP) is essential for organizations looking to centralize, analyze, and leverage data to improve customer engagement, marketing strategies, and overall business intelligence. AWS offers a robust set of tools that can be used to build a scalable, efficient, and secure CDP. In this post, we’ll walk through the architecture and key steps for building a CDP on AWS, based on the diagram provided.
1. Data Sources
A successful CDP begins with integrating various data sources to create a comprehensive view of customer behavior and interactions. In this architecture, we have:
Apache Web Server Log Files: Collected from four Apache web servers, these logs capture website activity and user interactions. Using Kinesis Agent, the logs are converted to JSON format and ingested via Amazon Kinesis Firehose.
Database Data: Key business data such as customers, products, returns, and orders, stored in a SQL Server database. This data is accessed using Amazon Database Migration Service (DMS) to replicate the data to an Amazon S3 raw zone in Parquet format.
Weather Data: External weather data, which can be valuable for understanding customer behavior in response to weather patterns, is available via AWS Data Exchange. The data is loaded into an Amazon S3 raw zone upon availability.
2. Ingestion Tools
AWS provides powerful ingestion tools to automate data collection and transformation:
Amazon Kinesis Firehose: This service collects streaming data, such as web server logs, and can automatically transform and load it into Amazon S3 in JSON and Parquet formats. Kinesis Firehose also allows for data validation through Lambda functions, ensuring only high-quality data reaches the data lake.
Amazon Database Migration Service (DMS): DMS handles the continuous replication of SQL Server data to Amazon S3, keeping the raw zone in sync with the source database.
AWS Data Exchange: AWS Data Exchange simplifies subscribing to external data sources, such as weather data, and allows for automatic ingestion into Amazon S3 when new data is available.
3. Data Transformation and Storage
To create a usable and analytics-ready dataset, raw data needs to be processed and stored in an organized manner. AWS S3 and Lambda functions enable this process through multiple zones:
Raw Zone: This is the initial landing area in Amazon S3 where unprocessed data is stored. Database and weather data are replicated here, and each new file triggers a Lambda function that performs data quality checks. Web server logs are stored directly in the raw zone after ingestion from Kinesis Firehose.
Clean Zone: Data in the raw zone undergoes quality checks and is then moved to the clean zone, where it is partitioned by date (yyyy/mm/dd). This step ensures data consistency and quality, making it ready for more complex analysis. Web server logs, which have already passed through initial checks, are stored directly in this zone.
Curated Zone: This is the most refined data layer, where business logic and enrichment (e.g., adding weather data to transactional data) are applied. The curated data is partitioned by day for databases and weather data, and by hour for web server logs. This structure supports efficient querying and minimizes data duplication.
4. Data Cataloging
An AWS Glue Data Catalog is used to maintain metadata about each dataset. This catalog allows for efficient data discovery, helping users and applications locate and query relevant data from the data lake. By centralizing metadata, the Glue Data Catalog also simplifies data governance and security.
5. Data Access for Different Consumers
AWS provides a variety of tools to meet the specific needs of different types of data consumers in the organization:
Marketing Specialists: These users need visual insights into customer interactions, ad campaign effectiveness, and user behavior patterns. Data visualization tools like Tableau and Amazon QuickSight can be connected to the curated data zone for interactive dashboards and reports.
Data Analysts: Analysts require SQL access to the data for creating reports and ad-hoc insights. Amazon Athena provides an easy way to run SQL queries directly on the data stored in S3 without requiring additional infrastructure. The frequency of updates allows analysts to work with fresh data daily for databases and weather data, and hourly for web server logs.
Data Scientists: Data scientists may need extensive access to databases, weather, and web server logs for machine learning projects. They can leverage SparkML or Amazon SageMaker for building, training, and deploying ML models. SageMaker also supports direct access to data in Amazon S3, which facilitates real-time model training and predictions.
6. Building the CDP on AWS: Step-by-Step Summary
Step 1: Set Up Data Sources
- Configure Apache web servers to send log data to Kinesis Agent.
- Set up DMS replication for SQL Server data.
- Subscribe to the external weather data feed on AWS Data Exchange.
Step 2: Configure Ingestion
- Set up Amazon Kinesis Firehose to ingest web server logs, transforming them to JSON and then to Parquet format.
- Use DMS for continuous replication of database data into the raw zone on S3.
- Load weather data into Amazon S3 as it becomes available.
Step 3: Data Transformation and Storage
- Store incoming data in the raw zone.
- Set up Lambda functions to perform data quality checks before moving data to the clean zone.
- Process data in the clean zone, and enrich and denormalize it for the curated zone.
Step 4: Set Up the Glue Data Catalog
- Use AWS Glue to catalog each data source, tagging datasets with metadata such as source, date of ingestion, and transformation history.
Step 5: Data Access and Analysis
- Use Athena for SQL-based querying, QuickSight/Tableau for visualizations, and SageMaker for ML model development and deployment.
Conclusion
Building a CDP on AWS allows for a streamlined, centralized approach to data management, from ingestion to analysis. By using services like Kinesis, DMS, S3, and Glue, organizations can ensure their data is high-quality, organized, and ready to support data-driven decisions. The combination of Amazon Athena, QuickSight, and SageMaker offers flexibility for various types of data consumers, empowering them to draw actionable insights and enhance customer engagement.
This AWS-based CDP architecture provides a strong foundation for organizations to maximize the value of their data and stay competitive in a data-driven world.
