{"course":{"productid":26441,"modality":1,"active":true,"language":"fr","title":"Building Data Analytics Solutions Using Amazon Redshift","productcode":"BDASAR","vendorcode":"AW","vendorname":"Amazon Web Services","fullproductcode":"AW-BDASAR","courseware":{"has_ekit":true,"has_printkit":false,"language":""},"url":"https:\/\/portal.flane.ch\/course\/amazon-bdasar","objective":"<p>In this course, you will learn to:<\/p>\n<ul>\n<li>Compare the features and benefits of data warehouses, data lakes, and modern data architectures<\/li><li>Design and implement a data warehouse analytics solution<\/li><li>Identify and apply appropriate techniques, including compression, to optimize data storage<\/li><li>Select and deploy appropriate options to ingest, transform, and store data<\/li><li>Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case<\/li><li>Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights<\/li><li>Secure data at rest and in transit<\/li><li>Monitor analytics workloads to identify and remediate problems<\/li><li>Apply cost management best practices<\/li><\/ul>","essentials":"<p>Students with a minimum one-year experience managing data warehouses will benefit from this course.<\/p>\n<p>We recommend that attendees of this course have:\n<\/p>\n<ul>\n<li>Completed either <span class=\"cms-link-marked\"><a class=\"fl-href-prod\" href=\"\/swisscom\/fr\/course\/amazon-awse\"><svg role=\"img\" aria-hidden=\"true\" focusable=\"false\" data-nosnippet class=\"cms-linkmark\"><use xlink:href=\"\/css\/img\/icnset-linkmarks.svg#linkmark\"><\/use><\/svg>AWS Technical Essentials <span class=\"fl-prod-pcode\">(AWSE)<\/span><\/a><\/span> or <span class=\"cms-link-marked\"><a class=\"fl-href-prod\" href=\"\/swisscom\/fr\/course\/amazon-awsa\"><svg role=\"img\" aria-hidden=\"true\" focusable=\"false\" data-nosnippet class=\"cms-linkmark\"><use xlink:href=\"\/css\/img\/icnset-linkmarks.svg#linkmark\"><\/use><\/svg>Architecting on AWS <span class=\"fl-prod-pcode\">(AWSA)<\/span><\/a><\/span><\/li><li>Completed <span class=\"cms-link-marked\"><a class=\"fl-href-prod\" href=\"\/swisscom\/fr\/course\/amazon-bdla\"><svg role=\"img\" aria-hidden=\"true\" focusable=\"false\" data-nosnippet class=\"cms-linkmark\"><use xlink:href=\"\/css\/img\/icnset-linkmarks.svg#linkmark\"><\/use><\/svg>Building Data Lakes on AWS <span class=\"fl-prod-pcode\">(BDLA)<\/span><\/a><\/span><\/li><\/ul>","audience":"<p>This course is intended for:<\/p>\n<ul>\n<li>Data warehouse engineers<\/li><li>Data platform engineers<\/li><li>Architects and operators who build and manage data analytics pipelines<\/li><\/ul>","contents":"<h5>Module A: Overview of Data Analytics and the Data Pipeline<\/h5><ul>\n<li>Data analytics use cases<\/li><li>Using the data pipeline for analytics<\/li><\/ul><h5>Module 1: Using Amazon Redshift in the Data Analytics Pipeline<\/h5><ul>\n<li>Why Amazon Redshift for data warehousing?<\/li><li>Overview of Amazon Redshift<\/li><\/ul><h5>Module 2: Introduction to Amazon Redshift<\/h5><ul>\n<li>Amazon Redshift architecture<\/li><li>Interactive Demo 1: Touring the Amazon Redshift console<\/li><li>Amazon Redshift features<\/li><li>Practice Lab 1: Setting up your data warehouse using Amazon Redshift<\/li><\/ul><h5>Module 3: Ingestion and Storage<\/h5><ul>\n<li>Ingestion<\/li><li>Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API<\/li><li>Data distribution and storage<\/li><li>Interactive Demo 3: Analyzing semi-structured data using the SUPER data type<\/li><li>Querying data in Amazon Redshift<\/li><li>Practice Lab 2: Data analytics using Amazon Redshift Spectrum<\/li><\/ul><h5>Module 4: Processing and Optimizing Data<\/h5><ul>\n<li>Data transformation<\/li><li>Advanced querying<\/li><li>Practice Lab 3: Data transformation and querying in Amazon Redshift<\/li><li>Resource management<\/li><li>Interactive Demo 4: Applying mixed workload management on Amazon Redshift<\/li><li>Automation and optimization<\/li><li>Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster<\/li><\/ul><h5>Module 5: Security and Monitoring of Amazon Redshift Clusters<\/h5><ul>\n<li>Securing the Amazon Redshift cluster<\/li><li>Monitoring and troubleshooting Amazon Redshift clusters<\/li><\/ul><h5>Module 6: Designing Data Warehouse Analytics Solutions<\/h5><ul>\n<li>Data warehouse use case review<\/li><li>Activity: Designing a data warehouse analytics workflow<\/li><\/ul><h5>Module B: Developing Modern Data Architectures on AWS<\/h5><ul>\n<li>Modern data architectures<\/li><\/ul>","summary":"<p>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. 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.<\/p>","objective_plain":"In this course, you will learn to:\n\n\n- Compare the features and benefits of data warehouses, data lakes, and modern data architectures\n- Design and implement a data warehouse analytics solution\n- Identify and apply appropriate techniques, including compression, to optimize data storage\n- Select and deploy appropriate options to ingest, transform, and store data\n- Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case\n- Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights\n- Secure data at rest and in transit\n- Monitor analytics workloads to identify and remediate problems\n- Apply cost management best practices","essentials_plain":"Students with a minimum one-year experience managing data warehouses will benefit from this course.\n\nWe recommend that attendees of this course have:\n\n\n\n- Completed either AWS Technical Essentials (AWSE) or Architecting on AWS (AWSA)\n- Completed Building Data Lakes on AWS (BDLA)","audience_plain":"This course is intended for:\n\n\n- Data warehouse engineers\n- Data platform engineers\n- Architects and operators who build and manage data analytics pipelines","contents_plain":"Module A: Overview of Data Analytics and the Data Pipeline\n\n\n- Data analytics use cases\n- Using the data pipeline for analytics\nModule 1: Using Amazon Redshift in the Data Analytics Pipeline\n\n\n- Why Amazon Redshift for data warehousing?\n- Overview of Amazon Redshift\nModule 2: Introduction to Amazon Redshift\n\n\n- Amazon Redshift architecture\n- Interactive Demo 1: Touring the Amazon Redshift console\n- Amazon Redshift features\n- Practice Lab 1: Setting up your data warehouse using Amazon Redshift\nModule 3: Ingestion and Storage\n\n\n- Ingestion\n- Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API\n- Data distribution and storage\n- Interactive Demo 3: Analyzing semi-structured data using the SUPER data type\n- Querying data in Amazon Redshift\n- Practice Lab 2: Data analytics using Amazon Redshift Spectrum\nModule 4: Processing and Optimizing Data\n\n\n- Data transformation\n- Advanced querying\n- Practice Lab 3: Data transformation and querying in Amazon Redshift\n- Resource management\n- Interactive Demo 4: Applying mixed workload management on Amazon Redshift\n- Automation and optimization\n- Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster\nModule 5: Security and Monitoring of Amazon Redshift Clusters\n\n\n- Securing the Amazon Redshift cluster\n- Monitoring and troubleshooting Amazon Redshift clusters\nModule 6: Designing Data Warehouse Analytics Solutions\n\n\n- Data warehouse use case review\n- Activity: Designing a data warehouse analytics workflow\nModule B: Developing Modern Data Architectures on AWS\n\n\n- Modern data architectures","summary_plain":"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. 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.","skill_level":"Intermediate","version":"1.0","duration":{"unit":"d","value":1,"formatted":"1 jour"},"pricelist":{"List Price":{"DE":{"country":"DE","currency":"EUR","taxrate":19,"price":795},"AT":{"country":"AT","currency":"EUR","taxrate":20,"price":795},"SE":{"country":"SE","currency":"EUR","taxrate":25,"price":750},"SI":{"country":"SI","currency":"EUR","taxrate":20,"price":795},"IT":{"country":"IT","currency":"EUR","taxrate":20,"price":725},"PL":{"country":"PL","currency":"PLN","taxrate":23,"price":2000},"US":{"country":"US","currency":"USD","taxrate":null,"price":675},"AE":{"country":"AE","currency":"USD","taxrate":5,"price":650},"GR":{"country":"GR","currency":"EUR","taxrate":null,"price":795},"MK":{"country":"MK","currency":"EUR","taxrate":null,"price":795},"HU":{"country":"HU","currency":"EUR","taxrate":20,"price":795},"NL":{"country":"NL","currency":"EUR","taxrate":21,"price":795},"BE":{"country":"BE","currency":"EUR","taxrate":21,"price":795},"GB":{"country":"GB","currency":"GBP","taxrate":20,"price":900},"CH":{"country":"CH","currency":"CHF","taxrate":8.1,"price":870},"CA":{"country":"CA","currency":"CAD","taxrate":null,"price":930}}},"lastchanged":"2025-08-27T17:03:23+02:00","parenturl":"https:\/\/portal.flane.ch\/swisscom\/fr\/json-courses","nexturl_course_schedule":"https:\/\/portal.flane.ch\/swisscom\/fr\/json-course-schedule\/26441","source_lang":"fr","source":"https:\/\/portal.flane.ch\/swisscom\/fr\/json-course\/amazon-bdasar"}}