DataProcessing

Big Data: Concepts, Tools and Use Cases Big data describes datasets that are too large or too complex for traditional software to handle. It comes from many sources: sensors, apps, logs, social media, and transactions. When stored and analyzed well, this data helps organizations learn, plan, and act faster. Key ideas include how fast data arrives (velocity), how many kinds of data exist (variety), and how reliable the data is (veracity). In practice, teams look for value: useful insights that improve decisions or actions. ...

Big Data Tools: Hadoop, Spark, and Beyond Big data tools help teams turn large amounts of information into useful answers. They cover storage, processing, and fast queries. The field grows quickly, so a simple choice today may change later. A clear plan helps you stay useful as data needs evolve. Hadoop gave a reliable way to store huge files and run many jobs at once. It uses HDFS, a scalable file system, and a processing layer such as MapReduce or Tez. It also has YARN for resource management. Many companies use Hadoop for batch workloads that run overnight or on weekends. ...