BDASARAWAmazon Web ServicesAW-BDASAR1.0<p>Sie sollten mindestens ein Jahr Erfahrung in der Verwaltung von Data Warehouses mitbringen und folgende Kurse vorher besucht haben:</p> <ul> <li>Entweder <span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/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> oder <span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/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><span class="cms-link-marked"><a class="fl-href-prod" href="/swisscom/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><p>Dieser Kurs richtet sich an Data Warehouse Engineers, Data Platform Engineers, sowie Architects und Operators, die Datenanalyse-Pipelines erstellen und verwalten.</p><ul> <li>Module A: Overview of Data Analytics and the Data Pipeline</li><li>Module 1: Using Amazon Redshift in the Data Analytics Pipeline</li><li>Module 2: Introduction to Amazon Redshift</li><li>Module 3: Ingestion and Storage</li><li>Module 4: Processing and Optimizing Data</li><li>Module 5: Security and Monitoring of Amazon Redshift Clusters</li><li>Module 6: Designing Data Warehouse Analytics Solutions</li><li>Module B: Developing Modern Data Architectures on AWS</li></ul><h5>Module A: Overview of Data Analytics and the Data Pipeline</h5> <ul> <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> <li>Why Amazon Redshift for data warehousing?</li><li>Overview of Amazon Redshift</li></ul><h5>Module 2: Introduction to Amazon Redshift</h5> <ul> <li>Amazon Redshift architecture</li><li>Interactive Demo 1: Touring the Amazon Redshift console</li><li>Amazon Redshift features</li><li>Practice Lab 1: Load and query data in an Amazon Redshift cluster</li></ul><h5>Module 3: Ingestion and Storage</h5> <ul> <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> <li>Data transformation</li><li>Advanced querying</li><li>Practice Lab 3: Data transformation and querying in Amazon Redshift</li><li>Resource management</li></ul><p> Interactive Demo 4: Applying mixed workload management on Amazon Redshift </p> <ul> <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> <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> <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> <li>Modern data architectures</li></ul>Sie sollten mindestens ein Jahr Erfahrung in der Verwaltung von Data Warehouses mitbringen und folgende Kurse vorher besucht haben: - Entweder AWS Technical Essentials (AWSE) oder Architecting on AWS (AWSA) - Building Data Lakes on AWS (BDLA)Dieser Kurs richtet sich an Data Warehouse Engineers, Data Platform Engineers, sowie Architects und Operators, die Datenanalyse-Pipelines erstellen und verwalten.- Module A: Overview of Data Analytics and the Data Pipeline - Module 1: Using Amazon Redshift in the Data Analytics Pipeline - Module 2: Introduction to Amazon Redshift - Module 3: Ingestion and Storage - Module 4: Processing and Optimizing Data - Module 5: Security and Monitoring of Amazon Redshift Clusters - Module 6: Designing Data Warehouse Analytics Solutions - Module B: Developing Modern Data Architectures on AWSModule 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 architectures1 Tag795.00795.00750.00795.00725.002000.00675.00650.00795.00795.00795.00795.00795.00900.00870.00930.00