You're facing conflicting data sources in Data Warehousing. How do you streamline ETL processes effectively?

Understand the data provenance, technical ecosystem and capture: Unique ID Application name Three Letter Acronyms ( TLAs) Commercial name of system Vendor URL Details of further context Status of the system Data classification Business owner Business contact Technical contact Physical location Link to data inventory Link to data model Last updated date Changes made to metadata A data inventory can be a useful tool when dealing with multiple types and sources of data Creating a data inventory is an important part of creating a data management plan When published under an open licence, a data inventory can help in finding and utilizing the needed data A data inventory improves: Data discovery Data governance Data management

Conflicting data sources are inevitable in modern warehousing. I tackle them by building resilient, explainable ETL pipelines that combine: Source confidence scoring to rank data reliability dynamically AI-driven anomaly detection to flag silent data regressions early Business logic as code using dbt, ensuring every transformation is versioned, tested, and reproducible Stakeholder feedback loops embedded into the pipeline design—treating ETL like a product, not a process Automated reconciliation reports that surface mismatches with root-cause metadata To me, effective ETL isn’t just technical—it’s about scaling trust and visibility across the entire data value chain.

To streamline your ETL processes effectively, start by assessing the data sources you're working with. Conduct an inventory to understand the formats, usage, and how each source fits into your overall data ecosystem. This will help you identify where conflicts occur, whether it's due to inconsistent formats, missing data, or different data definitions. Having a clear view of your data landscape allows you to make better decisions on how to harmonize these data sets. Once you understand the sources, you can develop strategies to integrate them smoothly and eliminate bottlenecks in your ETL pipeline.

1. Perform Data Source Analysis Catalog all sources: What systems provide which data? (CRM, ERP, spreadsheets, APIs, etc.) 2. Define a Source of Truth Establish a Single Source of Truth (SSOT) for each entity (e.g., Customer, Product, Transaction). 3. Standardize Data Early in ETL (Normalize) Build a standardization layer early in your ETL/ELT Apply Business Rules Transparently Create a Data Transformation Rules Engine (can be SQL scripts, dbt models, or a custom tool). 4.Automate Conflict Detection Build data validation check 5. Implement Modular ETL Pipelines Break ETL into small, logical steps

I start by identifying trusted source systems through stakeholder input and data profiling. Then, I define clear source-of-truth rules and apply data mapping and transformation logic in ETL to standardize formats. I document all assumptions, use version control, and add validation checks to catch issues early. This keeps ETL consistent, auditable, and aligned with business needs.

When facing conflicting data sources in data warehousing, the key is setting clear standards from the start. By defining naming conventions, standardizing data formats, and agreeing on metrics and dimensions, you create consistency that simplifies the ETL process. It’s like laying down the tracks before the train rolls in - it saves time and minimizes errors. This way, you don’t have to build complex transformation logic to reconcile differences. With everyone on the same page, the whole system runs smoother, and you can focus on extracting valuable insights rather than fixing data messes. What’s your go-to strategy for handling this in your work?