Building Data Analytics Solutions Using Amazon Redshift (BDASAR)

 

Resumen del Curso

This course uses an Amazon Redshift data warehouse as part of the data analytics solution. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will design and build data analytics solutions for data warehousing use cases. You will learn how a data warehouse can be integrated into a data lake or a modern data architecture. You will also learn to apply best practices to support security, performance, and cost optimization of Amazon Redshift.

Quién debería asistir

This course is intended for:

  • Data warehouse engineers
  • Data platform engineers
  • Architects and operators who build and manage data analytics pipelines

Prerrequisitos

Students familiar with combining AWS technologies to support data lakes or other data-driven workloads will benefit from this course. We recommend that attendees of this course have:

Objetivos del curso

In this course, you will learn to:

  • Compare the features and benefits of data warehouses, data lakes, and modern data architectures
  • Design and implement a data warehouse analytics solution
  • Identify and apply appropriate techniques, including compression, to optimize data storage
  • Select and deploy appropriate options to ingest, transform, and store data
  • Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case
  • Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights
  • Secure data at rest and in transit
  • Monitor analytics workloads to identify and remediate problems
  • Apply cost management best practices

Contenido del curso

Module A: Overview of Data Analytics and the Data Pipeline
  • Data analytics use cases
  • Using the data pipeline for analytics
Module 1: Using Amazon Redshift in the Data Analytics Pipeline
  • Why Amazon Redshift for data warehousing?
  • Overview of Amazon Redshift
Module 2: Introduction to Amazon Redshift
  • Amazon Redshift architecture
  • Interactive Demo 1: Touring the Amazon Redshift console
  • Amazon Redshift features
  • Practice Lab 1: Setting up your data warehouse using Amazon Redshift
Module 3: Ingestion and Storage
  • Ingestion
  • Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API
  • Data distribution and storage
  • Interactive Demo 3: Analyzing semi-structured data using the SUPER data type
  • Querying data in Amazon Redshift
  • Practice Lab 2: Data analytics using Amazon Redshift Spectrum
Module 4: Processing and Optimizing Data
  • Data transformation
  • Advanced querying
  • Practice Lab 3: Data transformation and querying in Amazon Redshift
  • Resource management
  • Interactive Demo 4: Applying mixed workload management on Amazon Redshift
  • Automation and optimization
Module 5: Security and Monitoring of Amazon Redshift Clusters
  • Securing the Amazon Redshift cluster
  • Monitoring and troubleshooting Amazon Redshift clusters
Module 6: Designing Data Warehouse Analytics Solutions
  • Data warehouse use case review
  • Activity: Designing a data warehouse analytics workflow
Module B: Developing Modern Data Architectures on AWS
  • Modern data architectures

Precios & Delivery methods

Entrenamiento en línea

Duración
1 día

Precio
  • Consulta precio y disponibilidad
Classroom training

Duración
1 día

Precio
  • Consulta precio y disponibilidad

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Fecha garantizada:   Fast Lane llevará a cabo todos los cursos garantizados sin importar el número de participantes, excepto por razones de fuerza mayor u otros eventos inesperados, como e.g. accidentes o enfermedad del instructor, que eviten que el curso se realice.
Instructor-led Online Training:   Este es un curso en línea Guiado por un Instructor
Este es un curso FLEX, que es entregado tanto virtualmente como en el salón de clase.

Europa

Alemania

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Italia

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Polonia

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Reino Unido

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Suiza

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