BigData

Big Data Tools: Hadoop, Spark, and Beyond Big data tools come in many shapes. Hadoop started the era of distributed storage and batch processing. It uses HDFS to store large files across machines and MapReduce to run tasks in parallel. Over time, Spark offered faster processing by keeping data in memory and providing friendly APIs for Java, Python, and Scala. Together, these tools let teams scale data work from a few gigabytes to petabytes, while still being affordable for many organizations. ...

Big Data Tools: Hadoop, Spark, and Beyond Hadoop started the era of big data by providing a simple way to store large files and process them across many machines. HDFS stores data in blocks with redundancy, helping survive failures. MapReduce offered a straightforward way to run large tasks in parallel, and YARN coordinates resources. For many teams, Hadoop taught the basics of scale: storage, fault tolerance, and batch processing. Spark changed the game. It runs in memory and can reuse data across steps, which speeds up analytics. Spark includes several components: Spark Core (fundamentals), Spark SQL for structured queries, MLlib for machine learning, GraphX for graphs, and Structured Streaming for near real-time data. Because it works well with the Hadoop file system, Spark teams often mix both, using the same data lake. ...