Building Data Analytics Solutions Using Amazon Redshift
- Référence GK7379
- Durée 1 Jour
Modalité pédagogique
Options de paiement complémentaires
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CPF
Cette formation est éligible au Compte Personnel de Formation pour son financement.
Modalité pédagogique
La formation est disponible dans les formats suivants:
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Classe inter à distance
Depuis n'importe quelle salle équipée d'une connexion internet, rejoignez la classe de formation délivrée en inter-entreprises.
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Classe inter en présentiel
Formation délivrée en inter-entreprises. Cette méthode d'apprentissage permet l'interactivité entre le formateur et les participants en classe.
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Intra-entreprise
Cette formation est délivrable en groupe privé, et adaptable selon les besoins de l’entreprise. Nous consulter.
Demander cette formation dans un format différent
Résumé
Haut de pageIn this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon Redshift.
Company Events
These events can be delivered exclusively for your company at our locations or yours, specifically for your delegates and your needs. The Company Events can be tailored or standard course deliveries.
Prochaines dates
Haut de pagePublic
Haut de pageThis course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines.
Objectifs de la formation
Haut de pageIn 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
Programme détaillé
Haut de pageModule 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: Load and query data in an Amazon Redshift cluster
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
- Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster
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
Pré-requis
Haut de pageStudents with a minimum one-year experience managing data warehouses will benefit from this course.
We recommend that attendees of this course have:
- Completed either AWS Technical Essentials or Architecting on AWS
- Completed Building Data Lakes on AWS
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- <table style="width: 100%; border-collapse: collapse; border-style: solid; border-color: #141c36; float: left;" border="3" cellspacing="2" cellpadding="5" data-mce-style="width: 100%; border-collapse: collapse; border-style: solid; border-color: #141c36; float: left;"><tbody><tr><td style="width: 100%;" data-mce-style="width: 100%;"><p style="text-align: left;" data-mce-style="text-align: left;"><strong><span style="color: #141c36;" data-mce-style="color: #141c36;">Délais d’inscription</span></strong></p><hr /><p style="text-align: left;" data-mce-style="text-align: left;">Avant le début de la formation <strong>jusqu’à 5 jours ouvrés sous réserve de disponibilité </strong>sauf dans le cas d'un financement <a title="compte CPF" href="https://www.globalknowledge.com/fr-fr/formations/compte-personnel-de-formation-cpf" data-mce-href="https://www.globalknowledge.com/fr-fr/formations/compte-personnel-de-formation-cpf">CPF,</a> jusqu'à 11 jours ouvrés. <a rel="noopener noreferrer" title="formations éligibles CPF" href="mailto:cpf@globalknowledge.fr" target="_blank" rel="noopener noreferrer" data-mce-href="mailto:cpf@globalknowledge.fr">Contactez-nous</a> pour connaître les formations éligibles.</p></td></tr></tbody></table><p><br /></p>
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- GK7379
- Building Data Analytics Solutions Using Amazon Redshift
- Data
- Training | Building Data Analytics Solutions Using Amazon Redshift
- Amazon Web Services
