Sources for ETL Pipelines

From a business standpoint, source systems are the backbone of operations. They capture and store data related to various business processes. Key examples include:

• CRM Systems: Contain customer information, sales data, marketing campaign results, and customer interactions.
• ERP Systems: Manage core business processes like finance, HR, supply chain, and production.
• Sales & Marketing Automation Systems: Track lead generation, sales pipeline, marketing campaign performance, and customer engagement.
• Customer Support Systems: Record customer inquiries, issues, and resolutions.
• Financial Systems: Manage accounting, budgeting, and financial reporting.
• Human Capital Management (HCM) Systems: Handle employee data, payroll, benefits, and performance management.
• Operational Systems: Include point-of-sale systems, inventory management systems, and production systems.

Technologically, source systems can be categorized based on their data structure and access methods:

• Relational Databases: Structured data stored in tables with rows and columns (e.g., Oracle, SQL Server, MySQL).
• Flat Files: Unstructured or semi-structured data stored in simple text formats (e.g., CSV, Excel, JSON).
• NoSQL Databases: Highly scalable databases for handling large volumes of unstructured or semi-structured data (e.g., MongoDB, Cassandra).
• Cloud-Based Storage: Object-based storage for storing and retrieving data (e.g., AWS S3, Azure Blob Storage).
• Data Lakes: Repositories for storing raw data in its native format (e.g., Amazon S3, Azure Data Lake Storage).
• Real-time Data Feeds: Continuous streams of data from sensors, social media, or financial markets.

Extracting data from diverse source systems presents challenges:

• Data Quality: Inconsistent data formats, missing values, and errors can impact data reliability.
• Data Volume: Large datasets can slow down extraction and processing.
• Data Access: Restrictions and permissions may limit data availability.
• Data Integration: Combining data from multiple sources requires careful mapping and transformation.

To overcome these challenges, effective data governance, data quality management, and robust ETL processes are essential.