Target Audience for Cloudera Data Analyst Training for Apache Hadoop
Cloudera Data Analyst Training for Apache Hadoop equips participants with essential skills for big data analytics using Hadoop tools.
- Data Analysts interested in big data and Hadoop
- Business Intelligence Professionals seeking to understand Hadoop ecosystems
- Database Administrators looking to expand into Hadoop-based systems
- Data Engineers who require proficiency in Hive and Impala
- IT Professionals aiming to specialize in big data analytics
- Software Developers who need to understand Data Processing on Hadoop
- System Architects planning to design big data solutions
- Technical Managers overseeing data analytics projects
- Data Scientists seeking to enhance their Data Processing capabilities
- Hadoop Developers and Engineers looking to deepen their expertise in Hive and Impala
Learning Objectives - What you will Learn in this Cloudera Data Analyst Training for Apache Hadoop?
Introduction to the Course's Learning Outcomes and Concepts Covered
Gain a comprehensive understanding of Hadoop and its ecosystem, including HDFS, YARN, MapReduce, Spark, Hive, and Impala, to effectively store, process, manage, and analyze big data.
Learning Objectives and Outcomes
- Understand the motivation behind using Apache Hadoop and its core components like HDFS for data storage and YARN for resource management.
- Learn the fundamentals of distributed Data Processing with MapReduce and Spark, and how to analyze data using Pig, Hive, and Impala.
- Gain proficiency in schema design, data storage, and query execution with Apache Hive and Impala, and understand their use cases and advantages over traditional databases.
- Develop skills in writing, executing, and optimizing HiveQL and Impala queries to perform data analysis and manipulation tasks.
- Master Data Management techniques including creating, loading, altering databases/tables, and simplifying queries with views in Hive and Impala.
- Learn about data storage optimization through partitioning, choosing efficient file formats like Avro and Parquet, and understanding their impact on performance.
- Acquire the ability to work with multiple datasets using UNIONs, joins, and handling NULL values in data analysis processes.
- Understand and apply analytic functions and windowing in Hive and Impala to perform advanced data analysis.
- Gain insights into text Data Processing, including the use of regular expressions and SerDes for sentiment analysis in Hive.
- Optimize Apache Hive and Impala performance by understanding query execution plans, bucketing, and specific optimization techniques for each tool.
- Extend the capabilities of Hive and Impala by integrating custom SerDes, file formats, scripting for data transformation, and user-defined functions.
- Evaluate and choose the most appropriate tool between Hive, Impala, and traditional relational databases for various Data Processing tasks.
