A strategic approach to data integration is critical to efficient processing and analysis when overwhelmed by the flood of external data ... Prioritize data sources: Identify key data sources that align with your business goals and focus on integrating them first. Implement a data ingestion framework: Use a robust data ingestion framework to automate the process of extracting, transforming and loading data from different sources. Use modern data platforms: Consider using modern data platforms such as Databricks to simplify data integration and processing. Databricks' Lakehouse architecture provides a unified platform that enables efficient data ingestion and transformation.

My perspective : Drowning in external data can be overwhelming. Streamlining integration is key for optimal performance. I would begin by identifying relevant data sources and discarding irrelevant ones. Utilizing data integration tools that automate the process of data extraction, transformation, and loading (ETL). Implementing data management best practices, like data cleansing and duplication, to maintain data hygiene. Using cloud storage solutions for better scalability and accessibility. Lastly, establishing a robust data governance framework to regulate data access and usage. By doing these, you can efficiently manage external data and improve performance.

𝗗𝗮𝘁𝗮 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Identify data sources that directly align with your business objectives. Assess data accuracy, completeness, and consistency. 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻 Batch vs. Real-time Processing: Choose the appropriate approach based on latency and throughput requirements. Standardize data formats, handle missing values, and remove outliers. Load only new or changed data to reduce processing time. 𝗗𝗮𝘁𝗮 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 𝗮𝗻𝗱 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 Data Lake vs. Data Warehouse: Select the appropriate storage solution based on data structure and query patterns. Optimize query performance by creating indexes and partitioning data. Caching: Store frequently accessed data in memory to reduce query latency.