Analyzing with Cloudera Data Warehouse - Italian
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08 Jul 2024 - 11 Jul 2024 | Analyzing with Cloudera Data Warehouse - Italian |
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28 Oct 2024 - 31 Oct 2024 | Analyzing with Cloudera Data Warehouse - Italian |
Description
This Analyzing with Data Warehouse course will teach you how 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.
Audience and prerequisites
This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators. Some knowledge of SQL is assumed, as is basic Linux command-line familiarity.
Objectives
Students who successfully complete this course will be able to:
- Use Apache Hive and Apache Impala to access data through queries
- Identify distinctions between Hive and Impala, such as differences in syntax, data formats, and supported features
- Write and execute queries that use functions, aggregate functions, and subqueries
- Use joins and unions to combine datasets
- Create, modify, and delete tables, views, and databases
- Load data into tables and store query results
- Select file formats and develop partitioning schemes for better performance
- Use analytic and windowing functions to gain insight into their data
- Store and query complex or nested data structures
- Process and analyze semi-structured and unstructured data
- Optimize and extend the capabilities of Hive and Impala
- Determine whether Hive, Impala, an RDBMS, or a mix of these is the best choice for a given task
- Utilize the benefits of CDP Data Storage
- Creating Databases and Tables
- Loading Data
- Altering Databases and Tables
- Public Cloud Data Warehouse
Topics
Foundations for Big Data Analytics
- Big Data Analytics Overview
- Data Storage: HDFS
- Distributed Data Processing: YARN,
- MapReduce, and Spark
- Data Processing and Analysis: Hive and Impala
- Database Integration: Sqoop
- Other Data Tools
- Exercise Scenario Explanation
Introduction to Hive and Impala
- What Is Hive?
- What Is Impala?
- Why Use Hive and Impala?
- Schema and Data Storage
- Comparing Hive to Traditional Databases
- Use Cases
Querying with Hive and Impala
- Databases and Tables
- Basic Hive and Impala Query Language Syntax
- Data Types
- Using Hue to Execute Queries
- Using Beeline (Hive’s Shell)
- Using the Impala Shell
Common Operators and Built-in functions
- Operators
- Scalar Functions
- Aggregate Functions
Data Management
- Simplifying Queries with Views
- Storing Query Results
Data Storage and Performance
- Partitioning Tables
- Loading Data into Partitioned Tables
- When to Use Partitioning
- Choosing a File Format
- Using Avro and Parquet File Formats
Working with Multiple Datasets
- UNION and Joins
- Handling NULL Values in Joins
- Advanced Joins
Analytic Functions and Windowing
- Using Common Analytic Functions
- Other Analytic Functions
- Sliding Windows
Complex Data
- Complex Data with Hive
- Complex Data with Impala
Analyzing Text
- Using Regular Expressions with Hive and Impala
- Processing Text Data with SerDes in Hive
- Sentiment Analysis and n-grams in Hive
Apache Hive Optimization
- Understanding Query Performance
- Cost-Based Optimization and statistics
- Bucketing
- ORC File Optimizations
Apache Impala Optimization
- How Impala Executes Queries
- Improving Impala Performance
Extending Hive and Impala
- User-Defined Functions
- Parameterized Queries
Choosing the Best Tool for the Job
- Comparing MapReduce, Hive, Impala and Relational Databases
- Which to Choose?
CDP Public Cloud Data Warehouse
- Data Warehouse Overview
- Auto-Scaling
- Managing Virtual Warehouses
- Querying Data Using CLI and Third-Party Integration
Appendix: Apache Kudu
- What Is Kudu?
- Kudu Tables
- Using Impala with Kudu
Open calls
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08 Jul 2024 - 11 Jul 2024 | Analyzing with Cloudera Data Warehouse - Italian |
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28 Oct 2024 - 31 Oct 2024 | Analyzing with Cloudera Data Warehouse - Italian |
