1 Courses in Edinburgh

Duration 4 Days 24 CPD hours This course is intended for Successful students have experience and knowledge in IT operations, including networking, virtualization, identity, security, business continuity, disaster recovery, data platforms, and governance. Students also have experience designing and architecting solutions. Before attending this course, students must have previous experience deploying or administering Azure resources and strong conceptual knowledge of: Azure compute technologies such as VMs, containers and serverless solutions Azure virtual networking to include load balancers Azure Storage technologies (unstructured and databases) General application design concepts such as messaging and high availability This course teaches Azure Solution Architects how to design infrastructure solutions. Course topics cover governance, compute, application architecture, storage, data integration, authentication, networks, business continuity, and migrations. The course combines lecture with case studies to demonstrate basic architect design principles. Prerequisites Before attending this course, students must have previous experience deploying or administering Azure resources and conceptual knowledge of: Azure Active Directory Azure compute technologies such as VMs, containers and serverless solutions Azure virtual networking to include load balancers Azure Storage technologies (unstructured and databases) General application design concepts such as messaging and high availability AZ-104T00 - Microsoft Azure Administrator 1 - Design governance Design for governance Design for management groups Design for subscriptions Design for resource groups Design for resource tags Design for Azure Policy Design for role-based access control (RBAC) Design for Azure landing zones 2 - Design an Azure compute solution Choose an Azure compute service Design for Azure Virtual Machines solutions Design for Azure Batch solutions Design for Azure App Service solutions Design for Azure Container Instances solutions Design for Azure Kubernetes Service solutions Design for Azure Functions solutions Design for Azure Logic Apps solutions 3 - Design a data storage solution for non-relational data Design for data storage Design for Azure storage accounts Design for data redundancy Design for Azure Blob Storage Design for Azure Files Design for Azure managed disks Design for storage security 4 - Design a data storage solution for relational data Design for Azure SQL Database Design for Azure SQL Managed Instance Design for SQL Server on Azure Virtual Machines Recommend a solution for database scalability Recommend a solution for database availability Design security for data at rest, data in motion, and data in use Design for Azure SQL Edge Design for Azure Cosmos DB and Table Storage 5 - Design data integration Design a data integration solution with Azure Data Factory Design a data integration solution with Azure Data Lake Design a data integration and analytic solution with Azure Databricks Design a data integration and analytic solution with Azure Synapse Analytics Design strategies for hot, warm, and cold data paths Design an Azure Stream Analytics solution for data analysis 6 - Design an application architecture Describe message and event scenarios Design a messaging solution Design an Azure Event Hubs messaging solution Design an event-driven solution Design a caching solution Design API integration Design an automated app deployment solution Design an app configuration management solution 7 - Design authentication and authorization solutions Design for identity and access management (IAM) Design for Microsoft Entra ID Design for Microsoft Entra business-to-business (B2B) Design for Azure Active Directory B2C (business-to-customer) Design for conditional access Design for identity protection Design for access reviews Design service principals for applications Design managed identities Design for Azure Key Vault 8 - Design a solution to log and monitor Azure resources Design for Azure Monitor data sources Design for Azure Monitor Logs (Log Analytics) workspaces Design for Azure Workbooks and Azure insights Design for Azure Data Explorer 9 - Design network solutions Recommend a network architecture solution based on workload requirements Design patterns for Azure network connectivity services Design outbound connectivity and routing Design for on-premises connectivity to Azure Virtual Network Choose an application delivery service Design for application delivery services Design for application protection services 10 - Design a solution for backup and disaster recovery Design for backup and recovery Design for Azure Backup Design for Azure blob backup and recovery Design for Azure files backup and recovery Design for Azure virtual machine backup and recovery Design for Azure SQL backup and recovery Design for Azure Site Recovery 11 - Design migrations Evaluate migration with the Cloud Adoption Framework Describe the Azure migration framework Assess your on-premises workloads Select a migration tool Migrate your structured data in databases Select an online storage migration tool for unstructured data Migrate offline data 12 - Introduction to the Microsoft Azure Well-Architected Framework Azure Well-Architected Framework pillars Cost optimization Operational excellence Performance efficiency Reliability Security 13 - Microsoft Azure Well-Architected Framework - Cost Optimization Develop cost-management discipline Design with a cost-efficiency mindset Design for usage optimization Design for rate optimization Monitor and optimize over time 14 - Microsoft Azure Well-Architected Framework - Operational excellence Embrace DevOps culture Establish development standards Evolve operations with observability Deploy with confidence Automate for efficiency Adopt safe deployment practices 15 - Microsoft Azure Well-Architected Framework - Performance efficiency Negotiate realistic performance targets Design to meet capacity requirements Achieve and sustain performance Improve efficiency through optimization 16 - Microsoft Azure Well-Architected Framework - Reliability Design for business requirements Design for resilience Design for recovery Design for operations Keep it simple 17 - Microsoft Azure Well-Architected Framework - Security Plan your security readiness Design to protect confidentiality Design to protect integrity Design to protect availability Sustain and evolve your security posture 18 - Getting started with the Microsoft Cloud Adoption Framework for Azure Customer narrative Common blockers 19 - Prepare for successful cloud adoption with a well-defined strategy Customer narrative Capture strategic motivation Define objectives and key results Evaluate financial considerations Understand technical considerations Create a business case 20 - Prepare for cloud adoption with a data-driven plan Customer narrative 21 - Choose the best Azure landing zone to support your requirements for cloud operations Customer narrative Common operating models Design areas for Azure landing zones Design principles for Azure landing zones Journey to the target architecture Choose an Azure landing zone option Deploy the Azure landing zone accelerator Enhance your landing zone 22 - Migrate to Azure through repeatable processes and common tools Customer narrative Migration process Migration tools Common tech platforms 23 - Address tangible risks with the Govern methodology of the Cloud Adoption Framework for Azure Customer narrative Govern methodology Corporate policies Governance disciplines Deploy a cloud governance foundation The Cost Management discipline 24 - Ensure stable operations and optimization across all supported workloads deployed to the cloud Establish business commitments Deploy an operations baseline Protect and recover Enhance an operations baseline Manage platform and workload specialization 25 - Innovate applications by using Azure cloud technologies Follow the innovation lifecycle Azure technologies for the build process Infuse your applications with AI Azure technologies for measuring business impact Azure technologies for the learn process 26 - Prepare for cloud security by using the Microsoft Cloud Adoption Framework for Azure Customer narrative Methodology Security roles and responsibilities Simplify compliance and security Simplify security implementation Security tools and policies Additional course details: Nexus Humans AZ-305T00: Designing Microsoft Azure Infrastructure Solutions training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the AZ-305T00: Designing Microsoft Azure Infrastructure Solutions course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Course Objectives At the end of this course you will be able to: Identify the common objects of an Access database Design and build the structure of a database Input and maintain data Design user-friendly data entry forms Search the database using queries Generate reports from your data 1 year email support service Take a look at the consistent excellent feedback from our corporate clients visiting our site ms-officetraining co uk With more than 20 years experience, we deliver courses on all levels of the Desktop version of Microsoft Office and Office 365; ranging from Beginner, Intermediate, Advanced to the VBA level. Our trainers are Microsoft certified professionals with a proven track record with several years experience in delivering public, one to one, tailored and bespoke courses. Tailored in company training courses: You can choose to run the course exactly as they are outlined by us or we can customise it so that it meets your specific needs. A tailored or bespoke course will follow the standard outline but may be adapted to your specific organisational needs. Introducing Microsoft Access The Access interface Database terminology Access database objects Fields and records Properties Designing a database Planning and designing a table Creating fields Setting common field properties Defining key fields Working with table data Entering and editing data Sorting and filtering table records Deleting records Relationships Why use table relationships? Relationship types Referential Integrity Working with table subdatasheets Querying a database Creating simple Select Queries Specifying query criteria Sorting query results Adding calculated fields Designing Forms Creating simple forms for data entry Designing custom forms Working with form sections Creating sub forms Designing Reports Creating basic list reports Working with grouping and sorting in a report Adding totals to a report Who is this course for? Who is this course for? This course is intended for the user that wants to explore the creation of a relational database. It will focus on the structuring of the database itself and the creation of the basic functional elements of a database in order to manage data. Requirements Requirements Microsoft Office know-how can instantly increase your job prospects as well as your salary. 80 percent of job openings require spreadsheet and word-processing software skills Career path Career path Microsoft Office know-how can instantly increase your job prospects as well as your salary. 80 percent of job openings require spreadsheet and word-processing software skills

Duration 4 Days 24 CPD hours This course is intended for This course is for experienced cloud security engineers who have taken a previous certification in the security, compliance and identity portfolio. Specifically, students should have advanced experience and knowledge in a wide range of security engineering areas, including identity and access, platform protection, security operations, securing data, and securing applications. They should also have experience with hybrid and cloud implementations. Beginning students should instead take the course SC-900: Microsoft Security, Compliance, and Identity Fundamentals. This is an advanced, expert-level course. Although not required to attend, students are strongly encouraged to have taken and passed another associate level certification in the security, compliance and identity portfolio (such as AZ-500, SC-200 or SC-300) before attending this class. This course prepares students with the expertise to design and evaluate cybersecurity strategies in the following areas: Zero Trust, Governance Risk Compliance (GRC), security operations (SecOps), and data and applications. Students will also learn how to design and architect solutions using zero trust principles and specify security requirements for cloud infrastructure in different service models (SaaS, PaaS, IaaS). Prerequisites Highly recommended to have attended and passed one of the associate level certifications in the security, compliance and identity portfolio (such as AZ-500T00 Microsoft Azure Security Technologies, SC-200T00: Microsoft Security Operations Analyst, or SC-300T00: Microsoft Identity and Access Administrator.) Advanced experience and knowledge in identity and access, platform protection, security operations, securing data and securing applications. Experience with hybrid and cloud implementations. 1 - Introduction to Zero Trust and best practice frameworks Zero Trust initiatives Zero Trust technology pillars part 1 Zero Trust technology pillars part 2 2 - Design solutions that align with the Cloud Adoption Framework (CAF) and Well-Architected Framework (WAF) Define a security strategy Cloud Adoption Framework secure methodology Design security with Azure Landing Zones The Well-Architected Framework security pillar 3 - Design solutions that align with the Microsoft Cybersecurity Reference Architecture (MCRA) and Microsoft cloud security benchmark (MCSB) Design solutions with best practices for capabilities and controls Design solutions with best practices for attack protection 4 - Design a resiliency strategy for common cyberthreats like ransomware Common cyberthreats and attack patterns Support business resiliency Ransomware protection Configurations for secure backup and restore Security updates 5 - Case study: Design solutions that align with security best practices and priorities Case study description Case study answers Conceptual walkthrough Technical walkthrough 6 - Design solutions for regulatory compliance Translate compliance requirements into a security solution Address compliance requirements with Microsoft Purview Address privacy requirements with Microsoft Priva Address security and compliance requirements with Azure policy Evaluate infrastructure compliance with Defender for Cloud 7 - Design solutions for identity and access management Design cloud, hybrid and multicloud access strategies (including Azure AD) Design a solution for external identities Design modern authentication and authorization strategies Align conditional access and Zero Trust Specify requirements to secure Active Directory Domain Services (AD DS) Design a solution to manage secrets, keys, and certificates 8 - Design solutions for securing privileged access The enterprise access model Design identity governance solutions Design a solution to secure tenant administration Design a solution for cloud infrastructure entitlement management (CIEM) Design a solution for privileged access workstations and bastion services 9 - Design solutions for security operations Design security operations capabilities in hybrid and multicloud environments Design centralized logging and auditing Design security information and event management (SIEM) solutions Design solutions for detection and response Design a solution for security orchestration, automation, and response (SOAR) Design security workflows Design threat detection coverage 10 - Case study: Design security operations, identity and compliance capabilities Case study description Case study answers Conceptual walkthrough Technical walkthrough 11 - Design solutions for securing Microsoft 365 Evaluate security posture for collaboration and productivity workloads Design a Microsoft 365 Defender solution Design configurations and operational practices for Microsoft 365 12 - Design solutions for securing applications Design and implement standards to secure application development Evaluate security posture of existing application portfolios Evaluate application threats with threat modeling Design security lifecycle strategy for applications Secure access for workload identities Design a solution for API management and security Design a solution for secure access to applications 13 - Design solutions for securing an organization's data Design a solution for data discovery and classification using Microsoft Purview Design a solution for data protection Design data security for Azure workloads Design security for Azure Storage Design a security solution with Microsoft Defender for SQL and Microsoft Defender for Storage 14 - Case study: Design security solutions for applications and data Case study description Case study answers Conceptual walkthrough Technical walkthrough 15 - Specify requirements for securing SaaS, PaaS, and IaaS services Specify security baselines for SaaS, PaaS, and IaaS services Specify security requirements for web workloads Specify security requirements for containers and container orchestration 16 - Design solutions for security posture management in hybrid and multicloud environments Evaluate security posture by using Microsoft Cloud Security Benchmark Design integrated posture management and workload protection Evaluate security posture by using Microsoft Defender for Cloud Posture evaluation with Microsoft Defender for Cloud secure score Design cloud workload protection with Microsoft Defender for Cloud Integrate hybrid and multicloud environments with Azure Arc Design a solution for external attack surface management 17 - Design solutions for securing server and client endpoints Specify server security requirements Specify requirements for mobile devices and clients Specify internet of things (IoT) and embedded device security requirements Secure operational technology (OT) and industrial control systems (ICS) with Microsoft Defender for IoT Specify security baselines for server and client endpoints Design a solution for secure remote access 18 - Design solutions for network security Design solutions for network segmentation Design solutions for traffic filtering with network security groups Design solutions for network posture management Design solutions for network monitoring 19 - Case study: Design security solutions for infrastructure Case study description Case study answers Conceptual walkthrough Technical walkthrough Additional course details: Nexus Humans SC-100T00 Microsoft Cybersecurity Architect training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the SC-100T00 Microsoft Cybersecurity Architect course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Tableau is an intuitive and simple tool to learn. This Tableau training course is a jumpstart to getting report writers and analysts who are self-taught or have no previous knowledge to being productive. It covers everything from connecting to data, through to creating interactive dashboards with a range of visualisations in three days. Having a quick turnaround from starting to use Tableau, to getting real, actionable insights means that you get a swift return on your investment. At the end of this course, you will be able to communicate insights more effectively, enabling your organisation to make better decisions, quickly. This accelerated approach is key to getting engagement from within your organisation so everyone can immediately see and feel the impact of the data and insights you create. Our Tableau Desktop Fast Track course combines all of our Foundation (Fundamentals) and Analyst (Intermediate) content into a 3 day live online course with added access to online bonus content of 3 additional modules. Gathering Requirements, Bring Your Own Data and Engaging Users. What do you get? This course is delivered live virtually and has all material provided through our online portal, together with email support and live coaching sessions. The full program includes all of the following elements: 3 days of live and interactive instructor-led sessions delivered by an expert Tableau Trainer 6 weeks access to our live coaching program delivered by expert Tableau coaches 50+ practical exercises to practice what you learn 12 months access to video’s that walk you through the theory and exercise solutions Practical advice, tools and resources for using Tableau in the real world The three additional online modules provide:Clarity on the approach to gathering dashboard requirements in a way that can be translated into dashboard designs.An agile and iterative development process that delivers products that meet user needs more quickly and effectively.An understanding of how end users will interact with dashboards to ensure that designers deliver actionable results. THE SYLLABUS PHASE 1: DESIGN MODULE 1: UNDERSTAND TABLEAU What is possible How does Tableau deal with data Know your way around Review of Type Conversions How do we format charts Dashboard basics – My first Dashboard MODULE 2: TRANSFORM DATA Connecting to and setting up data in Tableau Modifying data attributes How Do I Structure my Data – Groups & Hierarchies, Visual Groups How Tableau Deals with Dates – Using Discrete and Continuous Dates, Custom Dates How do I create calculated fields and why? – Creating Calculated Fields, Types of calculated fields, Row Level v Aggregations, Aggregating dimensions in calculations, Changing the Level of Detail (LOD) of calculations – What, Why, How MODULE 3: GATHER REQUIREMENTS(ONLINE CONTENT ONLY) Brainstorm and assess possible priorities Pitfalls to avoid Gather requirements PHASE 2: DEVELOP MODULE 4: CREATE CHARTS Charts that Compare Multiple Measures – Measure Names and Measure Values, Shared Axis Charts, Dual Axis Charts, Scatter Plots Showing progress over time Creating Tables – Creating Tables, Highlight Tables, Heat Maps Showing Relational & Proportional Data – Pie Charts, Donut Charts, Tree Maps Making things dynamic with parameters MODULE 5: COMBINE DATA Relationships Joining Tables – Join Types, Joining tables within the same database, cross database joins, join calculations Blending – How to create a blend with common fields, Custom defined Field relationships and mismatched element names, Calculated fields in blended data sources Unions – Manual Unions and mismatched columns, Wildcard unions Data Extracts – Creating & Editing Data extracts MODULE 6: ANALYSE INFORMATION Table Calculations Sets, Reference Lines, Trends and Forecasting Answering spatial questions – Mapping, Creating a choropleth (filled) map, Using your own images for spatial analysis, Mapping with spatial files Advanced charts Bar in Bar charts Bullet graphs Creating Bins and Histograms Creating a Box & Whisker plot Viz in Tooltips PHASE 3: DESIGN MODULE 7: BUILD DASHBOARDS Using the Dashboard Interface Device layouts Dashboard Actions – Set actions, Parameter actions Viz in Tooltips for Dashboards Dashboard containers – Horizontal & Vertical containers, Hidden containers Navigate between dashboards Telling data driven stories MODULE 8: BRING YOUR OWN DATA Design Best Practices & Resources Wireframe templates Questions Process Start building and testing MODULE 9: EMPOWER STAKEHOLDERS What is Tableau Server Publishing & permissions How can your users engage with content The Tableau ecosystem Review your progress Your next steps HOW MUCH OF YOUR TIME WILL THIS TAKE? Delegates are also provided 6 weeks access to our Tableau Coaching. We run Live Q&A sessions from 4pm-5pm on a Monday (Connecting to Data and Calculated Fields), 2pm-3pm Friday (Creating Charts) & 4pm-5pm Friday (Dashboard Design). The coaching helps delegates to transition from the theory of using Tableau to its practical use. We’d be expecting them to apply the exercises you’ll be doing during the course, onto your own data after the course. In our experience, this is the best way to increase both understanding and long term memory retention. The live coaching also acts as a troubleshooting platform for any practical issues that delegates need to overcome in the real world. Delegates also have 12 months access to all of the training material covered in the course in the form of an online portal (this includes theory videos, exercise solution videos, exercise materials and even quizzes). We have a growing LinkedIn community that delegates are encouraged to join and participate in. We regularly post useful blog posts and additional training that will enhance the Tableau journey and understanding. We help teams using Tableau to transform in the following ways : From a disjointed understanding of Tableau – To being familiar with Tableau terminology and capability From ad-hoc data uploads and error prone calculations – To reusable data connections and robust metrics From disjointed stakeholder questions – To clear and concise requirements that lead to decision making From being unsure how difficult Tableau will be to learn – To being able to develop standard charts and tables in Tableau with dynamic reporting capabilities From manually combining data for each analytical task – To dynamically combining data from multiple tables for analysis From being unsure how to answer analytical questions and what options there are – To being equipped with multiple actionable, dynamic, analytical use cases From not knowing Why, When and How to create Dashboards or Story’s – To being able to combine analysis to answer complex questions and tell data driven stories From using demo data theory – To Delivering value [Answering questions] on their own data From spending lots of time answering colleagues ad-hoc (data) questions – To empowering stakeholders in answering ad-hoc queries and reducing the time to analyse and steer the business

This course will take you through the key tools of Power BI. You will learn how to use them to clean and visualize data and create impressive reports and dashboards with ease.

Overview: Visualizing Data: Designing Informative Graphics   Welcome to 'Visualizing Data: Designing Informative Graphics,' a comprehensive course designed to equip you with the skills needed to create compelling and informative visualizations from raw data. In today's data-driven world, the ability to effectively communicate insights through visualizations is crucial for professionals across various industries.   Module 1: Introduction to Data Visualization In this module, you'll embark on your journey into the world of data visualization. Understand the importance of data visualization, its applications, and the fundamental principles behind creating impactful visuals.   Module 2: Choosing the Right Visualization Types Discover the diverse range of visualization types available and learn how to select the most suitable ones for different data sets and objectives. Gain insights into when to use bar charts, line graphs, scatter plots, and more.   Module 3: Data Preparation and Cleaning Master the art of preparing and cleaning data for visualization. Learn essential techniques to ensure data accuracy, completeness, and consistency, laying a solid foundation for effective visualization creation.   Module 4: Design Principles for Effective Visualizations Unlock the secrets of designing visually appealing and informative graphics. Explore principles such as color theory, typography, layout, and visual hierarchy to create engaging and user-friendly visualizations.   Module 5: Basic Charts and Graphs Dive into the world of basic charts and graphs, including bar charts, pie charts, histograms, and line graphs. Understand how to construct these fundamental visualizations accurately to convey your message effectively.   Module 6: Advanced Charts and Graphs Take your visualization skills to the next level with advanced chart types such as heatmaps, treemaps, and network diagrams. Explore complex data structures and learn to visualize them in a clear and intuitive manner.   By the end of this course, you'll have the knowledge and confidence to transform raw data into visually compelling stories that drive understanding and decision-making. Whether you're a data analyst, business professional, or aspiring data visualization expert, 'Visualizing Data: Designing Informative Graphics' is your gateway to mastering the art of data visualization. Don't miss out on this opportunity to elevate your skills and make a lasting impact with your data presentations. Enroll now and embark on your journey towards becoming a proficient data visualization practitioner! Course Curriculum Module 1_ Introduction to Data Visualization Introduction to Data Visualization 00:00 Module 2_ Choosing the Right Visualization Types Choosing the Right Visualization Types 00:00 Module 3_ Data Preparation and Cleaning Data Preparation and Cleaning 00:00 Module 4_ Design Principles for Effective Visualizations Design Principles for Effective Visualizations 00:00 Module 5_ Basic Charts and Graphs Basic Charts and Graphs 00:00 Module 6_ Advanced Charts and Graphs Advanced Charts and Graphs 00:00

Duration 2 Days 12 CPD hours This course is intended for Experienced DataStage developers seeking training in more advanced DataStage job techniques and who seek techniques for working with complex types of data resources. Overview Use Connector stages to read from and write to database tables Handle SQL errors in Connector stages Use Connector stages with multiple input links Use the File Connector stage to access Hadoop HDFS data Optimize jobs that write to database tables Use the Unstructured Data stage to extract data from Excel spreadsheets Use the Data Masking stage to mask sensitive data processed within a DataStage job Use the Hierarchical stage to parse, compose, and transform XML data Use the Schema Library Manager to import and manage XML schemas Use the Data Rules stage to validate fields of data within a DataStage job Create custom data rules for validating data Design a job that processes a star schema data warehouse with Type 1 and Type 2 slowly changing dimensions This course is designed to introduce you to advanced parallel job data processing techniques in DataStage v11.5. In this course you will develop data techniques for processing different types of complex data resources including relational data, unstructured data (Excel spreadsheets), and XML data. In addition, you will learn advanced techniques for processing data, including techniques for masking data and techniques for validating data using data rules. Finally, you will learn techniques for updating data in a star schema data warehouse using the DataStage SCD (Slowly Changing Dimensions) stage. Even if you are not working with all of these specific types of data, you will benefit from this course by learning advanced DataStage job design techniques, techniques that go beyond those utilized in the DataStage Essentials course. Accessing databases Connector stage overview - Use Connector stages to read from and write to relational tables - Working with the Connector stage properties Connector stage functionality - Before / After SQL - Sparse lookups - Optimize insert/update performance Error handling in Connector stages - Reject links - Reject conditions Multiple input links - Designing jobs using Connector stages with multiple input links - Ordering records across multiple input links File Connector stage - Read and write data to Hadoop file systems Demonstration 1: Handling database errors Demonstration 2: Parallel jobs with multiple Connector input links Demonstration 3: Using the File Connector stage to read and write HDFS files Processing unstructured data Using the Unstructured Data stage in DataStage jobs - Extract data from an Excel spreadsheet - Specify a data range for data extraction in an Unstructured Data stage - Specify document properties for data extraction. Demonstration 1: Processing unstructured data Data masking Using the Data Masking stage in DataStage jobs - Data masking techniques - Data masking policies - Applying policies for masquerading context-aware data types - Applying policies for masquerading generic data types - Repeatable replacement - Using reference tables - Creating custom reference tables Demonstration 1: Data masking Using data rules Introduction to data rules - Using the Data Rules Editor - Selecting data rules - Binding data rule variables - Output link constraints - Adding statistics and attributes to the output information Use the Data Rules stage to valid foreign key references in source data Create custom data rules Demonstration 1: Using data rules Processing XML data Introduction to the Hierarchical stage - Hierarchical stage Assembly editor - Use the Schema Library Manager to import and manage XML schemas Composing XML data - Using the HJoin step to create parent-child relationships between input lists - Using the Composer step Writing Hierarchical data to a relational table Using the Regroup step Consuming XML data - Using the XML Parser step - Propagating columns Topic 6: Transforming XML data - Using the Aggregate step - Using the Sort step - Using the Switch step - Using the H-Pivot step Demonstration 1: Importing XML schemas Demonstration 2: Compose hierarchical data Demonstration 3: Consume hierarchical data Demonstration 4: Transform hierarchical data Updating a star schema database Surrogate keys - Design a job that creates and updates a surrogate key source key file from a dimension table Slowly Changing Dimensions (SCD) stage - Star schema databases - SCD stage Fast Path pages - Specifying purpose codes - Dimension update specification - Design a job that processes a star schema database with Type 1 and Type 2 slowly changing dimensions Demonstration 1: Build a parallel job that updates a star schema database with two dimensions Additional course details: Nexus Humans KM423 IBM InfoSphere DataStage v11.5 - Advanced Data Processing training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the KM423 IBM InfoSphere DataStage v11.5 - Advanced Data Processing course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 5 Days 30 CPD hours This course is intended for This course is designed for business professionals who leverage data to address business issues. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming. However, there is not necessarily a single organizational role that this course targets. A prospective student might be a programmer looking to expand their knowledge of how to guide business decisions by collecting, wrangling, analyzing, and manipulating data through code; or a data analyst with a background in applied math and statistics who wants to take their skills to the next level; or any number of other data-driven situations. Ultimately, the target student is someone who wants to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business. Overview In this course, you will learn to: Use data science principles to address business issues. Apply the extract, transform, and load (ETL) process to prepare datasets. Use multiple techniques to analyze data and extract valuable insights. Design a machine learning approach to address business issues. Train, tune, and evaluate classification models. Train, tune, and evaluate regression and forecasting models. Train, tune, and evaluate clustering models. Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance. For a business to thrive in our data-driven world, it must treat data as one of its most important assets. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, it can also inform?by guiding decisions and influencing day-to-day operations. This calls for a robust workforce of professionals who can analyze, understand, manipulate, and present data within an effective and repeatable process framework. In other words, the business world needs data science practitioners. This course will enable you to bring value to the business by putting data science concepts into practice Addressing Business Issues with Data Science Topic A: Initiate a Data Science Project Topic B: Formulate a Data Science Problem Extracting, Transforming, and Loading Data Topic A: Extract Data Topic B: Transform Data Topic C: Load Data Analyzing Data Topic A: Examine Data Topic B: Explore the Underlying Distribution of Data Topic C: Use Visualizations to Analyze Data Topic D: Preprocess Data Designing a Machine Learning Approach Topic A: Identify Machine Learning Concepts Topic B: Test a Hypothesis Developing Classification Models Topic A: Train and Tune Classification Models Topic B: Evaluate Classification Models Developing Regression Models Topic A: Train and Tune Regression Models Topic B: Evaluate Regression Models Developing Clustering Models Topic A: Train and Tune Clustering Models Topic B: Evaluate Clustering Models Finalizing a Data Science Project Topic A: Communicate Results to Stakeholders Topic B: Demonstrate Models in a Web App Topic C: Implement and Test Production Pipelines

Duration 4 Days 24 CPD hours This course is intended for This class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, loading, transforming, cleaning, and validating data. Designing pipelines and architectures for data processing. Creating and maintaining machine learning and statistical models. Querying datasets, visualizing query results and creating reports Overview Design and build data processing systems on Google Cloud Platform. Leverage unstructured data using Spark and ML APIs on Cloud Dataproc. Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow. Derive business insights from extremely large datasets using Google BigQuery. Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML. Enable instant insights from streaming data Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data. Introduction to Data Engineering Explore the role of a data engineer. Analyze data engineering challenges. Intro to BigQuery. Data Lakes and Data Warehouses. Demo: Federated Queries with BigQuery. Transactional Databases vs Data Warehouses. Website Demo: Finding PII in your dataset with DLP API. Partner effectively with other data teams. Manage data access and governance. Build production-ready pipelines. Review GCP customer case study. Lab: Analyzing Data with BigQuery. Building a Data Lake Introduction to Data Lakes. Data Storage and ETL options on GCP. Building a Data Lake using Cloud Storage. Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions. Securing Cloud Storage. Storing All Sorts of Data Types. Video Demo: Running federated queries on Parquet and ORC files in BigQuery. Cloud SQL as a relational Data Lake. Lab: Loading Taxi Data into Cloud SQL. Building a Data Warehouse The modern data warehouse. Intro to BigQuery. Demo: Query TB+ of data in seconds. Getting Started. Loading Data. Video Demo: Querying Cloud SQL from BigQuery. Lab: Loading Data into BigQuery. Exploring Schemas. Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA. Schema Design. Nested and Repeated Fields. Demo: Nested and repeated fields in BigQuery. Lab: Working with JSON and Array data in BigQuery. Optimizing with Partitioning and Clustering. Demo: Partitioned and Clustered Tables in BigQuery. Preview: Transforming Batch and Streaming Data. Introduction to Building Batch Data Pipelines EL, ELT, ETL. Quality considerations. How to carry out operations in BigQuery. Demo: ELT to improve data quality in BigQuery. Shortcomings. ETL to solve data quality issues. Executing Spark on Cloud Dataproc The Hadoop ecosystem. Running Hadoop on Cloud Dataproc. GCS instead of HDFS. Optimizing Dataproc. Lab: Running Apache Spark jobs on Cloud Dataproc. Serverless Data Processing with Cloud Dataflow Cloud Dataflow. Why customers value Dataflow. Dataflow Pipelines. Lab: A Simple Dataflow Pipeline (Python/Java). Lab: MapReduce in Dataflow (Python/Java). Lab: Side Inputs (Python/Java). Dataflow Templates. Dataflow SQL. Manage Data Pipelines with Cloud Data Fusion and Cloud Composer Building Batch Data Pipelines visually with Cloud Data Fusion. Components. UI Overview. Building a Pipeline. Exploring Data using Wrangler. Lab: Building and executing a pipeline graph in Cloud Data Fusion. Orchestrating work between GCP services with Cloud Composer. Apache Airflow Environment. DAGs and Operators. Workflow Scheduling. Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery. Monitoring and Logging. Lab: An Introduction to Cloud Composer. Introduction to Processing Streaming Data Processing Streaming Data. Serverless Messaging with Cloud Pub/Sub Cloud Pub/Sub. Lab: Publish Streaming Data into Pub/Sub. Cloud Dataflow Streaming Features Cloud Dataflow Streaming Features. Lab: Streaming Data Pipelines. High-Throughput BigQuery and Bigtable Streaming Features BigQuery Streaming Features. Lab: Streaming Analytics and Dashboards. Cloud Bigtable. Lab: Streaming Data Pipelines into Bigtable. Advanced BigQuery Functionality and Performance Analytic Window Functions. Using With Clauses. GIS Functions. Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz. Performance Considerations. Lab: Optimizing your BigQuery Queries for Performance. Optional Lab: Creating Date-Partitioned Tables in BigQuery. Introduction to Analytics and AI What is AI?. From Ad-hoc Data Analysis to Data Driven Decisions. Options for ML models on GCP. Prebuilt ML model APIs for Unstructured Data Unstructured Data is Hard. ML APIs for Enriching Data. Lab: Using the Natural Language API to Classify Unstructured Text. Big Data Analytics with Cloud AI Platform Notebooks What's a Notebook. BigQuery Magic and Ties to Pandas. Lab: BigQuery in Jupyter Labs on AI Platform. Production ML Pipelines with Kubeflow Ways to do ML on GCP. Kubeflow. AI Hub. Lab: Running AI models on Kubeflow. Custom Model building with SQL in BigQuery ML BigQuery ML for Quick Model Building. Demo: Train a model with BigQuery ML to predict NYC taxi fares. Supported Models. Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML. Lab Option 2: Movie Recommendations in BigQuery ML. Custom Model building with Cloud AutoML Why Auto ML? Auto ML Vision. Auto ML NLP. Auto ML Tables.