First step to analyze this projects is to understand the core concepts of business and current state of procecesses. Also we need to get fsmiliar with data sources, data architect, and infrastructure . For choosing first source to integrate i think we should consider some aspects. *Impact of source on business and importance of the source for the business. *The data source that is used the most in reports, such as shared dimensions, is prioritized *Also, the data sources that currently have the most pressure on the infrastructure can also be a good option *The data sources that accept the least impact from other data sources and have the greatest impact on other data sources are a good option.

Leading ETL tools automate the entire data flow while saving data engineers from the tedious tasks of moving and formatting data A visual, drag-and-drop interface can be used for specifying rules and data flows Provide support for complex data management while assisting it with complex calculations, data integrations and string manipulations Use best ETL tools for encrypting data both in motion and at rest Preferably use ETL tools which are certified compliant with industry or government regulations, including HIPAA and GDPR Application Programming Interfaces(APIs) using Enterprise Application Integration (EAI) can be used in place of ETL. It will provide a more flexible, Scalable solution that includes work flow integration

Decide with source to integrate first should be evaluated on following considerations 1. Business impact Data that is crucial for decision making 2. Data granularity Data that is must for DWH. 3. Data size Data that has lower size 4. Data quality With better Data quality efficiency and effectiveness of DWH is high. 5. Cost effectiveness.

I think the answer is simple: ask the business which business value they want to have first. Then see which source is connected to that business value and start with the relevant part of that data source. Implementing a good agile process makes this choice easy, or at least the choice of the business. It also speeds up delivery and makes the complex project manageable by breaking it up into many small pieces. That always work for me. Ken Collier has a great book about it.

I think it requires a strategic approach. Dependencies: Integrate foundational sources that other data sets depend on, such as master data (e.g., customer, product, or financial data). Incremental Value: Select sources that allow incremental delivery, enabling iterative development and quicker stakeholder feedback. Availability and Accessibility: Ensure the data source is stable, accessible, and supported by existing infrastructure. Compatibility: Verify that the data aligns with the architecture of the data warehouse (e.g., formats, APIs). Transformation Requirements: Start with sources requiring minimal ETL complexity. Relevance to Core Metrics: Focus on data sources that contribute directly to key performance indicators (KPIs).