Big data fundamentals for analysts and engineers
Big data fundamentals for analysts and engineers Big data is not only about size. It blends data variety, velocity, and value. For both analysts and engineers, the goal is to collect data from many sources, store it reliably, and run computations that support decisions. A simple mental model is: collect, store, process, and share results with the team. Core layers matter. Data sources can be logs, transactions, sensors, or social feeds. Storage choices include data lakes for raw data and data warehouses for structured, fast queries. Processing engines vary from batch tools like Spark to streaming engines such as Flink or Spark Structured Streaming. The final step is consumption: dashboards, reports, or machine learning models that make use of the data. ...