Again "Communication is the key" and accepting/acknowledging the gaps in understanding is equally important. Having a frequent status call with stakeholders has helped me to keep everyone on same page. Good sync between the client, Solution architects, Data governance specialists and a Dev team (Yes, including Infra :) ) makes life easier!!

Next 7 steps I would take when working on a "brown field " data Warehouse project . For " blue field " and "green field " first 3 step can be omitted - STEP 1- Why are we developing or re developing - i.e. pain area i)Cost ii) Simplicity iii) Futuristic improvement iv) Scale STEP 2- next try to see which of the below make sense - i) Re engineering ii) Re architecture iii) Retrofit STEP 3 - Engage stakeholders on plan STEP 4 - Next is data hydration strategies e2e - it nvolves source tracking, volumetrics , frequency check, transformations possible, reusability of code STEP 5 - Test runs of cloud ETL STEP 6 - Benchmark run basis of STEP 1 factors STEP 7 - Engage the stakeholders again on UAT testing and overall pain point relook

Conflicts are inevitable in any project, especially in data warehousing, where multiple teams—engineers, analysts, and business stakeholders—work together with different priorities. A few things that helped for me: Keeping communication open – Many issues arise because teams aren't on the same page. Encouraging open discussions early on prevents misunderstandings. Defining clear roles and responsibilities – When ownership is unclear, work overlaps, and gaps appear. Setting clear accountability from the start avoids confusion. Having a structured way to resolve conflicts – Not every disagreement is bad, but letting them drag on is. Would love to hear your thoughts—what has worked for you in handling project conflicts

To resolve data warehousing conflicts, first identify the root cause—schema changes, data quality, performance, or business logic. Use database versioning to manage schema changes and automated validation checks for data quality. Optimize ETL scheduling, indexing, and partitioning for performance. Align stakeholders with data governance meetings and documented data contracts. Implement CI/CD, Git branching, and rollback strategies to prevent deployment conflicts. Track issues in JIRA, perform RCA, validate fixes with automation, and monitor post-deployment to ensure stability.

I make sure everyone feels comfortable sharing concerns early, so we can solve problems before they grow. Clear roles help avoid confusion, and having a simple process for resolving conflicts keeps things running smoothly. Regular check-ins and data validation also help us stay on the same page.