DANA-262: Analyzing with Cloudera Data Warehouse( Analyzing With Cloudera Data Warehouse ) Course Overview

DANA-262: Analyzing with Cloudera Data Warehouse( Analyzing With Cloudera Data Warehouse ) Course Overview

Embark on a transformative four-day journey with DANA-262: Analyzing with Cloudera Data Warehouse, a course designed for data analysts, business intelligence specialists, developers, and database administrators. Through expert instruction and practical, hands-on exercises, you will master the tools to effectively access, manipulate, and analyze big data using SQL and Scripting languages in CDP Public Cloud environments. Learn to optimize data queries with Apache Hive and Apache Impala, understand data storage solutions like HDFS, and handle complex data structures. This course equips you with skills to enhance decision-making processes, ensuring you can tackle modern data challenges efficiently.

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Successfully delivered 1 sessions for over 21 professionals

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Course Prerequisites

Certainly, here are the minimum prerequisites for students wishing to enroll in the DANA-262: Analyzing with Cloudera Data Warehouse course:


  • Basic knowledge of SQL: Familiarity with writing and executing SQL queries is expected as the course includes complex data set manipulations using SQL.
  • Basic Linux command-line skills: Students should be able to navigate and perform operations using the Linux command line as much of the hands-on exercises will involve such interactions.
  • Understanding of basic data processing concepts: While in-depth expertise is not required, a general understanding of how data is stored, processed, and analyzed will be beneficial.
  • Interest in big data and data analytics: Motivation to learn how big data frameworks operate, specifically within the Cloudera ecosystem, will enhance the learning experience.

This foundational knowledge will help ensure that participants can effectively engage with the course content and participate actively in the practical exercises.


Target Audience for DANA-262: Analyzing with Cloudera Data Warehouse( Analyzing With Cloudera Data Warehouse )

DANA-262: Analyzing with Cloudera Data Warehouse is a four-day course ideal for professionals aiming to leverage big data using SQL and scripting languages.


Target Audience for DANA-262:


  • Data Analysts
  • Business Intelligence Specialists
  • Database Administrators
  • System Architects
  • Developers familiar with SQL
  • IT Professionals with basic Linux command-line skills


Learning Objectives - What you will Learn in this DANA-262: Analyzing with Cloudera Data Warehouse( Analyzing With Cloudera Data Warehouse )?

Introduction to the Course’s Learning Outcomes: The DANA-262 course equips participants with the skills to utilize Cloudera Data Warehouse for performing complex data analytics, using SQL and script languages through hands-on exercises.

Learning Objectives and Outcomes:

  • Understand and navigate the ecosystems of Apache Hive and Apache Impala.
  • Execute complex SQL queries involving functions, aggregate functions, and subqueries.
  • Apply joins and unions effectively to merge datasets.
  • Manage database entities such as tables, views, and databases including their creation, modification, and deletion.
  • Optimize data storage and retrieval using various file formats and partitioning schemes.
  • Leverage analytic and windowing functions to derive deeper insights from data.
  • Handle complex and nested data structures within Hive and Impala environments.
  • Improve query performance through optimization techniques specific to Hive and Impala.
  • Decide the appropriate data processing tools (Hive, Impala, RDBMS) based on the analytical needs.
  • Utilize CDP Public Cloud Data Warehouse features effectively for scaling and managing data solutions.

Technical Topic Explanation

SQL

SQL (Structured Query Language) is a programming language used to manage and manipulate databases. It allows you to access, modify, and organize data efficiently. Skills in SQL are fundamental for roles in data analysis, making it a staple topic in many data analyst courses for beginners and data analytics training programs. By learning SQL, you can query large data sets, make updates to data, and create and manage database structures—essential abilities for careers in data science analytics.

Scripting languages

Scripting languages are programming languages designed to automate tasks that would otherwise need to be executed step-by-step by a human operator. They are essential for increasing efficiency and performing repetitive tasks quickly. Common uses include web scripting, task automation, and data analysis. These languages are usually easier to learn compared to more complex system programming languages, making them a great starting point in data analyst courses for beginners or data analytics training programs. By using scripting languages, you can streamline your workflow significantly, which is a valuable skill in the field of data science analytics.

CDP Public Cloud

CDP Public Cloud is a platform provided by Cloudera that allows organizations to manage, secure, and analyze vast amounts of data. Integrated fully with public cloud environments, it helps enable faster, more data-driven decision-making across the entire enterprise. By leveraging cloud storage and computing capabilities, CDP Public Cloud supports various data and analytic functions, promoting a flexible and scalable approach to business intelligence and data management. It's suitable for businesses looking to implement advanced analytics and machine learning without the need for deep technical infrastructure knowledge.

Apache Hive

Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data query and analysis. It allows SQL developers to write Hive Query Language (HQL), which is similar to SQL, to query, summarize, and analyze large datasets stored in Hadoop’s HDFS. Designed to handle big data, Hive helps improve the efficiency of data processing, supporting data science analytics and making it accessible for analysis. It suits a range of data-related tasks, from data mining to business intelligence, becoming an integral tool in many data analyst and data science analytics courses.

Apache Impala

Apache Impala is an open-source query engine that allows users to execute SQL-like queries on data stored in Hadoop clusters in real-time. Designed to perform high-speed data analytics, it provides a direct way to query data without needing data transformation or movement. This makes it highly advantageous for data science analytics and big data processing. Impala supports various data formats and integrates well with the Hadoop ecosystem, making it a valuable tool for data analysts, particularly those engaging in data analytics training or enrolled in data analyst courses for beginners.

Target Audience for DANA-262: Analyzing with Cloudera Data Warehouse( Analyzing With Cloudera Data Warehouse )

DANA-262: Analyzing with Cloudera Data Warehouse is a four-day course ideal for professionals aiming to leverage big data using SQL and scripting languages.


Target Audience for DANA-262:


  • Data Analysts
  • Business Intelligence Specialists
  • Database Administrators
  • System Architects
  • Developers familiar with SQL
  • IT Professionals with basic Linux command-line skills


Learning Objectives - What you will Learn in this DANA-262: Analyzing with Cloudera Data Warehouse( Analyzing With Cloudera Data Warehouse )?

Introduction to the Course’s Learning Outcomes: The DANA-262 course equips participants with the skills to utilize Cloudera Data Warehouse for performing complex data analytics, using SQL and script languages through hands-on exercises.

Learning Objectives and Outcomes:

  • Understand and navigate the ecosystems of Apache Hive and Apache Impala.
  • Execute complex SQL queries involving functions, aggregate functions, and subqueries.
  • Apply joins and unions effectively to merge datasets.
  • Manage database entities such as tables, views, and databases including their creation, modification, and deletion.
  • Optimize data storage and retrieval using various file formats and partitioning schemes.
  • Leverage analytic and windowing functions to derive deeper insights from data.
  • Handle complex and nested data structures within Hive and Impala environments.
  • Improve query performance through optimization techniques specific to Hive and Impala.
  • Decide the appropriate data processing tools (Hive, Impala, RDBMS) based on the analytical needs.
  • Utilize CDP Public Cloud Data Warehouse features effectively for scaling and managing data solutions.