Acerca de este Curso

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Fechas límite flexibles
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Nivel principiante
Aprox. 18 horas para completar
Inglés (English)
Subtítulos: Inglés (English)

Qué aprenderás

  • Understand the basics of SELECT statements

  • Understand how and why to filter results

  • Explore grouping and aggregation to answer analytic questions

  • Work with sorting and limiting results

Habilidades que obtendrás

Apache HiveApache ImpalaData AnalysisBig DataSQL
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel principiante
Aprox. 18 horas para completar
Inglés (English)
Subtítulos: Inglés (English)

Instructor

ofrecido por

Logotipo de Cloudera

Cloudera

Programa - Qué aprenderás en este curso

Semana
1

Semana 1

3 horas para completar

Orientation to SQL on Big Data

3 horas para completar
9 videos (Total 47 minutos), 5 lecturas, 2 cuestionarios
9 videos
Review and Preparation4m
Using the Hue Query Editors7m
Running SQL Utility Statements6m
Running SQL SELECT Statements5m
Understanding Different SQL Interfaces4m
Overview of Beeline and Impala Shell2m
Using Beeline8m
Using Impala Shell3m
5 lecturas
Instructions for Downloading and Installing the Exercise Environment30m
Troubleshooting the VM5m
(Optional) What about Spark SQL?10m
Expectations for Learners10m
(Optional) Using Other SQL Engines10m
2 ejercicios de práctica
Week 1 Core Quiz30m
Week 1 Honors Quiz5m
Semana
2

Semana 2

3 horas para completar

SQL SELECT Essentials

3 horas para completar
16 videos (Total 83 minutos), 4 lecturas, 2 cuestionarios
16 videos
SQL SELECT Building Blocks2m
Introduction to the SELECT List7m
Expressions and Operators7m
Data Types6m
Column Aliases5m
Built-In Functions7m
Data Type Conversion5m
The DISTINCT Keyword5m
Introduction to the FROM Clause3m
Identifiers7m
Formatting SELECT Statements4m
Using Beeline in Non-Interactive Mode5m
Using Impala Shell in Non-Interactive Mode4m
Formatting the Output of Beeline and Impala Shell4m
Saving Hive and Impala Query Results to a File5m
4 lecturas
Order of Operations5m
Division and Modulo Operators15m
Common String Functions15m
Case (In)Sensitivity in SQL10m
2 ejercicios de práctica
Week 2 Core Quiz30m
Week 2 Honors Quiz5m
Semana
3

Semana 3

3 horas para completar

Filtering Data

3 horas para completar
14 videos (Total 85 minutos), 6 lecturas, 2 cuestionarios
14 videos
About the Datasets4m
Introduction to the WHERE Clause2m
Using Expressions in the WHERE Clause9m
Comparison Operators9m
Data Types and Precision4m
Logical Operators7m
Other Relational Operators4m
Understanding Missing Values8m
Handling Missing Values6m
Conditional Functions9m
Using Variables with Beeline and Impala Shell7m
Calling Beeline and Impala Shell from Scripts6m
Querying Hive and Impala in Scripts and Applications2m
6 lecturas
Data Reference5m
(Optional) Unicode Characters10m
Working with Literal Strings15m
Missing Values with Logical Operators10m
Missing Values in String Columns5m
(Optional Exercise) Change VM Desktop Color30m
2 ejercicios de práctica
Week 3 Core Quiz30m
Week 3 Honors Quiz5m
Semana
4

Semana 4

3 horas para completar

Grouping and Aggregating Data

3 horas para completar
15 videos (Total 82 minutos), 6 lecturas, 2 cuestionarios
15 videos
Introduction to Aggregation2m
Common Aggregate Functions2m
Using Aggregate Functions in the SELECT Statement8m
Introduction to the GROUP BY Clause6m
Choosing an Aggregate Function and Grouping Column4m
Grouping Expressions6m
Grouping and Aggregation, Together and Separately5m
NULL Values in Grouping and Aggregation4m
The COUNT Function7m
Tips for Applying Grouping and Aggregation7m
Filtering on Aggregates2m
The HAVING Clause8m
Understanding Hive and Impala Version Differences10m
Understanding Hue Version Differences2m
6 lecturas
COUNT(*) and SUM(1)5m
Interpreting Aggregates: Populations and Samples10m
The least and greatest Functions5m
Why Aggregate Expressions Ignore NULL Values5m
(Optional) Shortcuts for Grouping10m
How Grouping and Aggregation Can Mislead10m
2 ejercicios de práctica
Week 4 Core Quiz30m
Week 4 Honors Quiz10m

Revisiones

Principales revisiones sobre ANALYZING BIG DATA WITH SQL

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Acerca de Programa especializado: Modern Big Data Analysis with SQL

This Specialization teaches the essential skills for working with large-scale data using SQL. Maybe you are new to SQL and you want to learn the basics. Or maybe you already have some experience using SQL to query smaller-scale data with relational databases. Either way, if you are interested in gaining the skills necessary to query big data with modern distributed SQL engines, this Specialization is for you. Most courses that teach SQL focus on traditional relational databases, but today, more and more of the data that’s being generated is too big to be stored there, and it’s growing too quickly to be efficiently stored in commercial data warehouses. Instead, it’s increasingly stored in distributed clusters and cloud storage. These data stores are cost-efficient and infinitely scalable. To query these huge datasets in clusters and cloud storage, you need a newer breed of SQL engine: distributed query engines, like Hive, Impala, Presto, and Drill. These are open source SQL engines capable of querying enormous datasets. This Specialization focuses on Hive and Impala, the most widely deployed of these query engines. This Specialization is designed to provide excellent preparation for the Cloudera Certified Associate (CCA) Data Analyst certification exam. You can earn this certification credential by taking a hands-on practical exam using the same SQL engines that this Specialization teaches—Hive and Impala....
Modern Big Data Analysis with SQL

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