Databricks Delta Live Migration

Problem Statement:

Client is struggling with disparate data sources like DynamoDB, JustCall, Google Analytics, Big Query, and HubSpot, highlighting the need for a unified, scalable, and cost-effective solution.

They plan to consolidate data from these sources into Databricks Delta Live to address these challenges:

1) Data Volume & Slowness
2) Manual Cluster Management
3) Integration Complexity
4) Inefficient Resource Allocation
5) Lack of Scalability & Efficiency Streamline data integration, ensure quality & consistency, leverage advanced analytics for actionable insights, and address challenges effectively.

Solution Overview:

A data pipeline architecture using Databricks, which integrates multiple data sources and processes data through various stages to enable reporting and analytics in Qlik.
Breakdown:

1. Data Sources:
   o Various data sources are depicted on the left, including:
   Dynatrace
   HubSpot
   Google BigQuery
   JustCall
   Google Analytics
   These sources are connected to the Databricks platform using APIs.
2. Databricks File Store:
   o Data from the sources is initially ingested and stored in the Databricks File Store.
3. Data Extraction & Load (Bronze Layer):
   o Raw data is extracted from the Databricks File Store.
   o This raw data is loaded into the Bronze Layer, which serves as the repository for raw, unprocessed data.
4. Data Preprocessing/Cleaning (Silver Layer):
   o The raw data in the Bronze Layer undergoes preprocessing and cleaning.
   o The cleaned data is then moved to the Silver Layer, representing cleaned and processed data.
5. Data Transformation (Gold Layer):
   o The cleaned data from the Silver Layer is further transformed based on specific business requirements.
   o The transformed data is stored in the Gold Layer, which contains the final, polished data ready for analysis and reporting.
6. Data Load to Qlik:
   o The final transformed data from the Gold Layer is pulled into Qlik using Databricks Connectors.
   o This data is used for reporting and analytics in Qlik.

Tech Stack Leveraged:

Data Sources:
• HubSpot
• Google BigQuery
• JustCall
• Google Analytics

Data Processing and Orchestration:
• .NET Core
• RabbitMQ
• SignalR
• Redis BackPlane

Databricks Delta Live Tables Data Transformation:
• Bronze Layer (Raw Data)
• Silver Layer (Preprocessed/Cleaned Data)
• Gold Layer (Transformed Data)

Data Analytics and Reporting:
• Qlik (for advanced analytics and reporting)

Benefits Delivered:

• Optimized Performance and Cost Efficiency: This solution accelerates query processing and analytics, significantly reducing the time required for data insights. By leveraging cost-effective models, it also minimizes expenses compared to traditional approaches, ensuring that organizations can achieve high performance without inflating their budgets.
• Scalable and Future-Ready Data Management: The solution centralizes data into a unified platform, enabling comprehensive and consistent analytics. It is designed to scale seamlessly with varying data volumes, ensuring that businesses are equipped to handle growth. Additionally, the adaptable infrastructure ensures that organizations remain future-ready, with the flexibility to integrate new technologies and processes as they evolve. Automated data processes further enhance operational efficiency, reducing manual intervention and errors.