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Duration 1 Days 6 CPD hours This course is intended for This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines. Completed either AWS Technical Essentials or Architecting on AWS Completed Building Data Lakes on AWS Overview 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 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. 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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Duration 2 Days 12 CPD hours This course is intended for People working in an organization aiming to improve performance, especially in response to digital transformation or disruption. Any roles involved in the creation and delivery of products or services: Leadership and CXO, especially CIO, CTO, CPO, and CVO Transformation and evolution leads and change agents Value stream architects, managers, engineers Scrum Masters, agile and DevOps coaches and facilitators Portfolio, product and project managers, and owners Business analysts Architects, developers, and engineers Release and environment managers IT Ops, service and support desk workers Customer experience and success professionals Overview After completing this course, students will be able to: Describe the origins of value stream management and key concepts such as flow, value, and delivery Describe what value stream management is, why it's needed and the business benefits of its practice Describe how lean, agile, DevOps, and ITSM principles contribute to value stream management Identify and describe value streams, where they start and end, and how they interconnect Identify value stream roles and responsibilities Express value streams visually using mapping techniques, define current and target states and hypothesis backlog Write value stream flow and realization optimization hypotheses and experiments Apply metrics such as touch/processing time, wait/idle time, and cycle time to value streams Understand flow metrics and how to access the data to support data-driven conversations and decisions Examine value realization metrics and aligning to business outcomes, and how to sense and respond to them (outcomes versus outputs) Architect a DevOps toolchain alongside a value stream and data connection points Design a continuous inspection and adaptation approach for organizational evolution The Value Stream Management Foundation course from Value Stream Management Consortium, and offered in partnership with DevOps Institute, is an introductory course taking learners through a value stream management implementation journey. It considers the human, process, and technology aspects of this way of working and explores how optimizing value streams for flow and realization positively impacts organizational performance. History and Evolution of Value Stream Management and its Application Value stream management?s origins Definitions of value stream management Flow Lean and systems thinking and practices Agile, DevOps and other frameworks Research and analysis Identifying Value Streams What is a value stream? Identifying value streams Choosing a value stream Digital value streams Value stream thinking Mapping Value Streams Types of maps Value stream mapping The fuzzy front end Artifacts 10 steps to value stream mapping Mapping and management VSM investment case Limitations of value stream mapping Connecting DevOps Toolchains CICD and the DevOps toolchain Value stream management processes Value stream management platforms DevOps tool categories Building an end-to-end DevOps toolchain Common data model and tools integrations Value Stream Metrics The duality of VSM Downtime in technology Lean, DORA and Flow metrics Definition of Done Value metrics Benefits hypotheses Value streams as profit centers KPIs and OKRs Inspecting the Value Stream 3 Pillars of Empiricism Organizational performance Visibility When to inspect Data and discovery Insights and trends Organizing as Value Streams Value stream alignment Team types and topologies Project to product Hierarchy to autonomy Target Operating Model Value stream people Value stream roles Value stream funding Evolving Value Streams Why now? Transitions VSM capability matrix VSM culture iceberg Learning Making local discoveries global improvements Managing value stream interdependencies

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