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Big Data Hadoop training course lets you master the concepts of the Hadoop framework and prepares you for Cloudera’s CCA175 Big data certification. With our online Hadoop training, you’ll learn how the components of the Hadoop ecosystem, such as Hadoop 3.4, Yarn, MapReduce, HDFS, Pig, Impala, HBase, Flume, Apache Spark, etc. fit in with the Big Data processing lifecycle. Implement real-life projects in banking, telecommunication, social media, insurance, and e-commerce on CloudLab.
Why Learn Big Data and Hadoop?
The world is getting increasingly digital, and this means big data is here to stay. In fact, the importance of big data and data analytics is going to continue growing in the coming years. Choosing a career in the field of big data and analytics might just be the type of role that you have been trying to find to meet your career expectations. Professionals who are working in this field can expect an impressive salary, with the median salary for data scientists being $116,000. Even those who are at the entry level will find high salaries, with average earnings of $92,000. As more and more companies realize the need for specialists in big data and analytics, the number of these jobs will continue to grow. Close to 80% of data scientists say there is currently a shortage of professionals working in the field.
What are the objectives of our Big Data Hadoop Online Course?
The Big Data Hadoop Certification course is designed to give you an in-depth knowledge of the Big Data framework using Hadoop and Spark, including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion with our big data training.
You will master real-time data processing using Spark, including functional programming in Spark, implementing Spark applications, understanding parallel processing in Spark, and using Spark RDD optimization techniques. With our big data course, you will also learn the various interactive algorithms in Spark and use Spark SQL for creating, transforming, and querying data forms.
As a part of the Big Data course, 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. This Big Data Hadoop training course will prepare you for the Cloudera CCA175 big data certification.
What skills will you learn with our Big Data Hadoop Certification Training?
Big Data Hadoop training will enable you to master the concepts of the Hadoop framework and its deployment in a cluster environment. You will learn to:
- Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark with this Hadoop course.
- Understand Hadoop Distributed File System (HDFS) and YARN architecture, and learn how to work with them for storage and resource management
- Understand MapReduce and its characteristics and assimilate advanced MapReduce concepts
- Ingest data using Sqoop and Flume
- Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
- Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
- Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
- Understand and work with HBase, its architecture and data storage, and learn the difference between HBase and RDBMS
- Gain a working knowledge of Pig and its components
- Do functional programming in Spark, and implement and build Spark applications
- Understand resilient distribution datasets (RDD) in detail
- Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
- Understand the common use cases of Spark and various interactive algorithms
- Learn Spark SQL, creating, transforming, and querying data frames
- Prepare for Cloudera CCA175 Big Data certification
Who should take this Big Data Hadoop Training Course?
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 best suited for IT, data management, and analytics professionals looking to gain expertise in Big Data, 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
How will Big Data Training help your career?
The field of big data and analytics is a dynamic one, adapting rapidly as technology evolves over time. Those professionals who take the initiative and excel in big data and analytics are well-positioned to keep pace with changes in the technology space and fill growing job opportunities.
How will I execute projects in this Hadoop training course?
You will use Zeedup’s CloudLab to complete projects.
What is CloudLab?
CloudLab is a cloud-based Hadoop and Spark environment lab that Zeedup offers with the Hadoop Training course to ensure a hassle-free execution of your hands-on projects. There is no need to install and maintain Hadoop or Spark on a virtual machine. Instead, you’ll be able to access a preconfigured environment on CloudLab via your browser. This environment is very similar to what companies are using today to optimize Hadoop installation scalability and availability. You’ll have access to CloudLab from your own computer or other devices for the duration of the course.
What types of jobs require Big Data Hadoop trained professionals?
The jobs that require Big Data Hadoop trained professionals include:
- IT professionals
- Data scientists
- Data engineers
- Data analysts
- Project managers
- Program managers
Introduction to Bigdata and Hadoop Ecosystem In this lesson you will learn about traditional systems, problems associated with traditional large scale systems, what is Hadoop and its ecosystem. 0/4
HDFS and Hadoop Architecture 0/5
MapReduce and Sqoop 0/7
Basics of Impala and Hive In this lesson you will be introduced to Hive and Impala, why to use Hive and Impala, differences between Hive and Impala, how Hive and Impala works and comparison of Hive to traditional databases. 0/2
Working with Hive and Impala In this lesson you will learn about metastore, how to create databases and table in Hive and Impala, loading data into tables of Hive and Impala, HCatalog and how impala works on cluster. 0/4
Type of Data Formats In this lesson you will learn about different types of file formats which are available, Hadoop tool support for file format, avro schemas, using avro with Hive and Swoop and Avro schema evolution. 0/5
Advanced HIVE concept and Data File Partitioning In this lesson you will learn about partitioning in Hive and Impala, partitioning in Impala and Hive, when to use partition, bucketing in Hive and more advanced concepts in Hive 0/3
Apache Flume and HBase 0/3
Apache Pig In this lesson you will learn about pig, components of Pig, Pig vs SQL and we will learn how to work with Pig. 0/4
Basics of Apache Spark In this lesson you will learn about apache spark, how to use spark shell, RDDs, functional programing in Spark. 0/4
RDDs in Spark In this lesson you will learn RDD in detail and all operation associated with it, key value Pair RDD and few more other pair RDD operations. 0/3
Implementation of Spark Applications In this lesson you will learn about spark applications vs spark shell, how to create a sparkcontext, building a spark application, how spark run on YARN in client and cluster mode, dynamic resource allocation and configuring spark properties. 0/5
Spark Parallel Processing In this lesson you will learn about how spark run on cluster, RDD partitions, how to create partitioning on File based RDD, HDFS and data locality, parallel operations on spark, spark and stages and how to control the level of parallelism 0/2
Spark RDD Optimization Techniques In this lesson you will learn about RDD lineage, overview on caching, distributed persistence, storage levels of RDD persistence, how to choose the correct RDD persistence storage level and RDD fault tolerance. 0/2
Spark Algorithm In this lesson you will learn common spark use cases, interactive algorithms in spark, graph processing and analysis, machine learning and k-means algorithm. 0/5
Spark SQL In this lesson you will learn about Spark SQL and SQL Context, creating dataframes, transforming and querying dataframes and comparing spark SQL with Impala. 0/2