Could you explain more about the data flow from source to downstream and the technical issues involved?

Data flow from source to downstream typically follows an ETL (Extract, Transform, Load) or ELT pipeline. First, data is extracted from source systems such as databases, APIs, or log files. Extraction can be batch or streaming; batch pulls data at scheduled intervals, while streaming captures changes in real time using change data capture (CDC) or message queues. Next, the data is transformed: cleaned, enriched, aggregated, and converted to a target schema. Transformation may involve handling missing values, type conversions, deduplication, and business logic. Finally, the data is loaded into downstream systems like data warehouses, data lakes, or analytics platforms. Technical issues that arise include latency (delays between source changes and downstream availability), data quality (inconsistent or incomplete records), schema evolution (changes in source structure), and scalability (handling high volume or velocity). Mitigation strategies involve using incremental loads, robust error handling, schema versioning, and monitoring pipelines for failures and performance bottlenecks.