A report by Forbes estimates that big data & Hadoop market is growing at a CAGR of 42.1% from 2015 and it will touch the mark of $99.31 billion by 2022. Another report from Mckinsey estimates a shortage of some 1.5 million big data experts by 2018. The findings of both the reports clearly suggest that market for big data analytics is growing worldwide at a massive rate and this trend looks to benefit IT professionals in a big way. After all, a big data hadoop certification is about gaining in-depth knowledge of the big data framework and becoming familiar with the Hadoop ecosystem.
More so, the objective of the training is to learn the use of Hadoop and Spark, together with gaining familiarity with HDFS, YARN and MapReduce. The participants learn to process and analyze big datasets, and also gain information in regard to data ingestion with the use of Sqoop and Flume. The training will offer the knowledge and mastery of real-time data processing to trainees who can also learn the ways to create, query and transform data forms of any scale. Anyone to the training will be able to master the concepts of Hadoop framework and learn its deployment in any environment.
Similarly, an enrollment in big data Hadoop training will help IT professionals learn different major components of Hadoop ecosystems such as Pig, Hive, Impala, Flume, Sqoop, Apache Spark and Yarn and implement them on projects. They will also learn about the ways to work with HDFS and YARN architecture for storage and resource management. The course is designed to also enrich trainees with the knowledge of MapReduce, its characteristics and its assimilation. The participants can also get to know how to ingest data with the help of Flume and Sqoop and how to create tables and database in Hive and Impala.
What's more, the training teaches about Impala and Hive for portioning purposes and also imparts knowledge about different types of file formats to work with. The trainees can expect to understand all about Flume, including its configurations and then become familiar with HBase and its architecture and data storage. Some of other major aspects to learn in the training include Pig components, Spark applications and RDD in detail. The training is also good for understanding Spark SQL and knowing about various interactive algorithms. All this information will be particularly helpful to those IT professionals planning to move into the big data domain
More so, the objective of the training is to learn the use of Hadoop and Spark, together with gaining familiarity with HDFS, YARN and MapReduce. The participants learn to process and analyze big datasets, and also gain information in regard to data ingestion with the use of Sqoop and Flume. The training will offer the knowledge and mastery of real-time data processing to trainees who can also learn the ways to create, query and transform data forms of any scale. Anyone to the training will be able to master the concepts of Hadoop framework and learn its deployment in any environment.
Similarly, an enrollment in big data Hadoop training will help IT professionals learn different major components of Hadoop ecosystems such as Pig, Hive, Impala, Flume, Sqoop, Apache Spark and Yarn and implement them on projects. They will also learn about the ways to work with HDFS and YARN architecture for storage and resource management. The course is designed to also enrich trainees with the knowledge of MapReduce, its characteristics and its assimilation. The participants can also get to know how to ingest data with the help of Flume and Sqoop and how to create tables and database in Hive and Impala.
What's more, the training teaches about Impala and Hive for portioning purposes and also imparts knowledge about different types of file formats to work with. The trainees can expect to understand all about Flume, including its configurations and then become familiar with HBase and its architecture and data storage. Some of other major aspects to learn in the training include Pig components, Spark applications and RDD in detail. The training is also good for understanding Spark SQL and knowing about various interactive algorithms. All this information will be particularly helpful to those IT professionals planning to move into the big data domain
No comments:
Post a Comment