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Big Data Hadoop and Spark Developer training courses provided by iLEARN
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In this showcase you will find a selection of training courses and exams in the Big Data Hadoop and Spark Developer context.
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BIG DATA HADOOP AND SPARK DEVELOPER
The world is getting increasingly digital and the importance of big data and data analytics will continue to grow in the coming years. Choosing a career in the field of big data and analytics might just be what you have been trying to find to meet your career expectations.
The Big Data Hadoop training course will teach you the concepts of the Hadoop framework, its formation in a cluster environment, and prepares you for Cloudera's CCA175 Big Data certification.
BIG DATA HADOOP AND SPARK DEVELOPER CERTIFICATION
There is no exam available, but you must complete 85% of the course, one project and one simulation test, with a minimum score of 80%, to obtain a certificate.
BIG DATA HADOOP AND SPARK DEVELOPER COURSE
With this Big Data Hadoop course, you will learn the big data framework using Hadoop and Spark, including HDFS, YARN, and MapReduce. The course will also cover Pig, Hive, and Impala to process and analyse large datasets stored in the HDFS and use Sqoop and Flume for data ingestion.
You will be shown real-time data processing using Spark, including functional programming in Spark, implementing Spark applications, understanding parallel processing in Spark, and using Spark RDD optimisation techniques. You will also learn the various interactive algorithms in Spark and use Spark SQL for creating, transforming, and querying data forms.
Finally, you will be required to execute real-life, industry-based projects using CloudLab in the domains of banking, telecommunication, social media, insurance, and e-commerce.
Here below you can read the course sheet with information about Big Data Hadoop and Spark Developer.
Objectives
By the end of the course you will be able to understand:
- The different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
- Hadoop Distributed File System (HDFS) and YARN architecture
- MapReduce and its characteristics and assimilate advanced MapReduce concepts
- Different types of file formats, Avro schema, using Avro with Hive, and Sqoop and Schema evolution
- Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
- HBase, its architecture and data storage, and learn the difference between HBase and RDBMS
- Resilient distribution datasets (RDD) in detail
- The common use cases of Spark and various interactive algorithms
You will also be able to:
- Ingest data using Sqoop and Flume
- Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
- Gain a working knowledge of Pig and its components
- Do functional programming in Spark, and implement and build Spark applications
- Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimisation techniques
- Create, transform and query data frames with Spark SQL
Who it is aimed at
Big data career opportunities are on the rise and Hadoop is quickly becoming a must-know technology in big data architecture. Big Data training is suitable for IT, data management, and analytics professionals, including:
- Software developers and architects
- Analytics professionals
- Senior IT professionals
- Testing and mainframe professionals
- Data management professionals
- Business intelligence professionals
- Project managers
- Aspiring data scientists
- Graduates looking to build a career in big data analytics
Contents
The course covers the following topics:
- Course introduction
- Lesson 1 - Introduction to big data and Hadoop ecosystem
- Lesson 2 - HDFS and YARN
- Lesson 3 - MapReduce and Sqoop
- Lesson 4 - Basics of Hive and Impala
- Lesson 5 - Working with Hive and Impala
- Lesson 6 - Types of data formats
- Lesson 7 - Advanced Hive concept and data file partitioning
- Lesson 8 - Apache Flume and HBase
- Lesson 9 - Pig
- Lesson 10 - Basics of Apache Spark
- Lesson 11 - RDDs in Spark
- Lesson 12 - Implementation of Spark applications
- Lesson 13 - Spark parallel processing
- Lesson 14 - Spark RDD optimisation techniques
- Lesson 15 - Spark algorithm
- Lesson 16 - Spark SQL
- FREE COURSE - Apache Kafka
- FREE COURSE - Core Java
Prerequisites
There are no prerequisites for this course. However, it's beneficial to have some knowledge of Core Java and SQL. We offer a complimentary self-paced online course "Java essentials for Hadoop" if you need to brush up your Core Java skills.
Duration
Online course duration:
- 1 year platform access