Cloudera Data Analyst Training

$4,154.00

Duration: 4 days

Online Option: Only – Open Enrolment

Prerequisites :

Some knowledge of SQL is assumed, as is basic Linux command-line familiarity. Prior knowledge of Apache Hadoop is not required.

Course dates : 

Oct 18-21, 2022

Nov 29-Dec 2, 2022

Per Participant Pricing: $4,154 CAD

Description

Cloudera Administrator Training for Apache Hadoop 101

Cloudera Data Analyst Training

Cloudera Educational Services’ four-day Data Analyst Training course will teach you to apply traditional data analytics and business intelligence skills to big data. This course presents the tools data professionals need to access, manipulate, transform, and analyze complex data sets using SQL and familiar scripting languages.

What is Cloudera?

Talend explains that Cloudera is a software company that, for more than a decade, has provided a structured, flexible, and scalable platform, enabling sophisticated analysis of big data using Apache Hadoop, in any environment. Cloudera allows for a depth of data processing that goes beyond just data accumulation and storage. Cloudera’s enhanced capabilities provide the power to analyze data rapidly and easily while tracking and securing it across all environments. By using Cloudera’s comprehensive audits and lineage tracing, users can know where data originated and why it matters.

Who is this course for?

This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators.

Prerequisites

Some knowledge of SQL is assumed, as is basic Linux command-line familiarity. Prior knowledge of Apache Hadoop is not required.

Skills You’ll Gain

  • How the open-source ecosystem of big data tools addresses challenges not met by traditional RDBMSs
  • Using Apache Hive and Apache Impala to provide SQL access to data
  • Hive and Impala syntax and data formats, including functions and subqueries
  • Create, modify, and delete tables, views, and databases; load data; and store results of queries
  • Create and use partitions and different file formats
  • Combining two or more datasets using JOIN or UNION, as appropriate
  • What analytic and windowing functions are, and how to use them
  • Store and query complex or nested data structures
  • Process and analyze semi-structured and unstructured data
  • Techniques for optimizing Hive and Impala queries
  • Extending the capabilities of Hive and Impala using parameters, custom file formats, and SerDes, and external scripts
  • How to determine whether Hive, Impala, an RDBMS, or a mix of these is best for a given task

For more information, please check this blog from P2L.

 

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