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This presentation will help you understand how to become a Big Data Engineer. First, you will learn who is a Big Data Engineer and what are their roles and responsibilities. Then, you will see the seven essential skills you need to have to become a Big Data Engineer. You will understand the different range of salaries and job roles of a Big Data Engineer. Finally, this video will tell you the necessary certifications you can opt for after becoming a Big Data Engineer. Now, let’s get started with learning the steps to become a Big Data Engineer.
Below topics are explained this "how to become a Big Data Engineer" presentation:
1. Who is a Big Data Engineer
2. Responsibilities of a Big Data Engineer
3. Skills to become a Big Data Engineer
4. Big Data Engineer's salary and roles
5. Certifications for a Big Data Engineer
6. Simplilearn certifications for a Big Data Engineer
YouTube Link: https://www.youtube.com/watch?v=yHf7qzFV6Qg
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
This presentation will help you understand how to become a Big Data Engineer. First, you will learn who is a Big Data Engineer and what are their roles and responsibilities. Then, you will see the seven essential skills you need to have to become a Big Data Engineer. You will understand the different range of salaries and job roles of a Big Data Engineer. Finally, this video will tell you the necessary certifications you can opt for after becoming a Big Data Engineer. Now, let’s get started with learning the steps to become a Big Data Engineer.
Below topics are explained this "how to become a Big Data Engineer" presentation:
1. Who is a Big Data Engineer
2. Responsibilities of a Big Data Engineer
3. Skills to become a Big Data Engineer
4. Big Data Engineer's salary and roles
5. Certifications for a Big Data Engineer
6. Simplilearn certifications for a Big Data Engineer
YouTube Link: https://www.youtube.com/watch?v=yHf7qzFV6Qg
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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