What you'll learn:
- You will learn about Hadoop, eco system, tools and spark
- Big Data Hadoop Development
This course will make you ready to switch career on big data hadoop and spark.
After this watching this, you will understand about Hadoop, HDFS, YARN, Map reduce, python, pig, hive, oozie, sqoop, flume, HBase, No SQL, Spark, Spark sql, Spark Streaming.
This is the one stop course. so dont worry and just get started.
You will get all possible support from my side.
For any queries, feel free to message me here.
Note: All programs and materials are provided.
About Hadoop Ecosystem, NoSQL and Spark:
Hadoop and its Ecosystem: Hadoop is an open-source framework for distributed storage and processing of large data sets. Its core components include the Hadoop Distributed File System (HDFS) for data storage and the MapReduce programming model for data processing. Hadoop's ecosystem comprises various tools and frameworks designed to enhance its capabilities. Notable components include Apache Pig for data scripting, Apache Hive for data warehousing, Apache HBase for NoSQL database functionality, and Apache Spark for faster, in-memory data processing. These tools collectively form a robust ecosystem that enables organizations to tackle big data challenges efficiently, making Hadoop a cornerstone in the world of data analytics and processing.
NoSQL: NoSQL, short for "not only SQL," represents a family of database management systems designed to handle large and unstructured data. Unlike traditional relational databases, NoSQL databases offer flexibility, scalability, and agility. They are particularly well-suited for applications involving social media, e-commerce, and real-time analytics. Prominent NoSQL databases include Hbase for columnar storage used extensively in Hadoop Ecosystem.
Spark: Apache Spark is an open-source, lightning-fast data processing framework designed for big data analytics. It offers in-memory processing, which significantly accelerates data analysis and machine learning tasks. Spark supports various programming languages, including Java, Scala, and Python, making it accessible to a wide range of developers. With its ability to process both batch and streaming data, Spark has become a preferred choice for organizations seeking high-performance data analytics and machine learning capabilities, outpacing traditional MapReduce-based solutions for many use cases.
