Skills Required For Big Data & Hadoop Jobs | Big Data Career, Skills & Roles | Simplilearn
In this presentation, we will be learning about Big Data & Hadoop, challenges of Big Data, what is Spark, job roles in Big Data, companies hiring in 2020 and lastly how Simplilearn can help you in achieving your Big Data job role. With our advanced technology today, machines have become capable of acquiring and processing large sets of data. Big data is the term used to define large amounts of data that can be processed to reveal patterns, trends, and associations, especially relating to human behavior and interactions. We will be covering the below topics in this Big Data & Hadoop live session: 1. What is Big Data? 2. Challenges of Big Data 3. What is Hadoop? 4. What is Spark? 5. Job roles in Big Data 6. Companies hiring in 2020 7. How can Simplilearn help you? What is this Big Data Hadoop training course about? The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab. What are the course objectives? This course will enable you to: 1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark 2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management 3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts 4. Get an overview of Sqoop and Flume and describe how to ingest data using them 5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning 6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution 7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations 8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS 9. Gain a working knowledge of Pig and its components 10. Do functional programming in Spark 11. Understand resilient distribution datasets (RDD) in detail 12. Implement and build Spark applications 13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques 14. Understand the common use-cases of Spark and the various interactive algorithms 15. Learn Spark SQL, creating, transforming, and querying Data frames Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
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