A well-planned data integration strategy is essential for a successful data merge, especially when dealing with different data sources and formats... Data profiling: Perform thorough data profiling to understand the structure, content and quality of the two data sets. Identify inconsistencies, missing values and data quality issues. Data transformation: Apply appropriate data transformation techniques such as data cleansing, normalization and standardization to ensure data consistency and compatibility. Data integration tools: Use robust data integration tools to automate the process of data merging. These tools can handle complex data transformations and mapping rules, reducing manual effort and increasing efficiency.

Merging data will have multiple aspects like 1. Data Profiling and Quality Assessment: Understand the data's strengths and weaknesses. 2. Data Cleansing and Standardization: Ensure consistency and accuracy. 3. Data Mapping: Connect corresponding fields. 4. Data Transformation: Modify data to fit a unified structure. 5.Data Validation and Testing: Verify data integrity. 6.Data Integration: Combine datasets efficiently. 7. Data Governance: Maintain data quality and consistency.

My perspective to think beyond the prompts is: 1. Build an industry standard data mapping. Identify your sources attributes and create a documentation to align with industry standard metrics. 2. Cross cloud platform integration : Make sure your unified cloud platform is compatible with variety of formats and structure of the data including relational data. 3. Define unified strategy:Leverage Uniform from Databricks. 4. Define your enterprise common data model: CDM will help you with data definitions and data mapping. Leverage mapping and modeling tools to ensure cross company models speak the same language. 5. Ensure business validations: involving business during the modeling phase will ensure data mapping accuracy and data reliability.

Data is critical to every organisation be it financial, healthcare or manufacturing. Processing data using right tool and at right time should be priority to get more insights when needed. Modern data warehousing now a days providing a vide range of tools to ingest, process, computer and report it to right platform. I found Synapse and ADF tools now a days which are playing critical role here with the help of ML and other analytics. Currently we are also working on such requirements only, dealing with huge unstructured data to process, compute and generating insights and reporting it via BI for proactive measures and decision making.