Building Data Analytics Solutions using Amazon Redshift

Kurskod GK7379

Building Data Analytics Solutions using Amazon Redshift

In 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.

Pris
10450 kr (exklusive moms)
Längd
1 dag
Ort och datum
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Kursen hålls på begäran

Kontakta oss för mer information.

Telefon: 08-562 557 50 
E-post: kursbokning@cornerstone.se

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.


Target audience and prerequisites

This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines.

Students with a minimum one-year experience managing data warehouses will benefit from this course.

We recommend that attendees of this course have:

Detaljerad information


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: 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

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