Potential Growth: Building a Robust HubSpot Data Lake on AWS

Introduction
In today’s data-driven world, organizations rely on robust data analytics solutions to drive business decisions and gain valuable insights. Creating a data lake to store and analyse data from various sources, including HubSpot, can be a game-changer for businesses looking to leverage their data effectively. In this article, we will explore the steps and architecture involved in creating a HubSpot Data Lake on AWS, utilising services such as Amazon S3, AWS Glue, and AWS Athena.

Overview
The data pipeline starts by extracting data from the HubSpot API and storing it in an Amazon S3 bucket in a structured format. This process is managed using AWS Glue, a fully managed ETL service that handles the Extract, Transform, Load operations efficiently. The pipeline retrieves different module objects like CRM, CMS, Marketing etc. and related information from HubSpot, processes the data, and stores it in Amazon S3 for further analysis.

Key Components

• HubSpot API: The source of the data, providing access to module related objects such as CRM- contacts, companies, deals etc.
• AWS Glue: A managed ETL service that facilitates data extraction, transformation, and loading into Amazon S3.
• Amazon S3: The destination for storing raw and processed data in the data lake.
• AWS Secrets Manager: Securely stores and retrieves HubSpot API credentials.

Key Steps in the Data Ingestion Process

1. Retrieve HubSpot API Credentials: Fetch the necessary API credentials like the base URL and bearer token from AWS Secrets Manager for authentication.
2. API Data Fetching: Pull data for different objects like CRM, Marketing etc. from the HubSpot API, implementing pagination to handle large datasets efficiently.
3. Data Transformation: Use tools like Pandas to transform the API data into a structured format suitable for storage in Amazon S3.
4. Write Data to Amazon S3: Store the processed data in specific S3 bucket paths in JSON format, as the API returns JSON data. We can change the target based on requirement.
5. Incremental and Full Data Loads: Configure the pipeline for incremental loads to update only new or modified records and full loads for schema changes or initial data loads.

Conclusion
Creating a HubSpot Data Lake in AWS provides a scalable and efficient solution for businesses to ingest, store, and analyse data from HubSpot for strategic decision-making. By leveraging AWS services like AWS Glue and Amazon S3, organisations can streamline their data processing workflows, optimise data integration, and drive actionable insights from their data.