76 Performance Optimisation courses delivered Online

Duration 5 Days 30 CPD hours Overview By the end of the course, you should be able to meet the following objectives: List the operational challenges for rolling out and operating telco services including 5G. Identify the role of VMware Telco Cloud products in supporting telco services. Discuss the role of VMware technologies such as vSphere, NSX, and Tanzu etc. in implementing telco services. Outline the role of native tools and other VMware monitoring tools such as vRealize Operations and vRealize Log Insight in maintaining network services. Choose the VMware Telco Cloud products that meet your application requirements. Deploy a solution architecture that meet VMware best practices for delivering services using VMware Telco Cloud products. Implement and maintain VMware Telco Cloud products in a secure manner. Identify the tools and remediation pathways for maintaining the availability and performance of your applications and infrastructure using VMware Telco Cloud and vRealize Suite products. Follow specific steps to resolve application performance and availability problems Scale your VMware Telco Cloud products to meet operational requirements in line with VMware best practices. Optimize the operation of VMware Telco Cloud products to ensure SLAs are met. This five-day, hands-on training course provides the knowledge to operate and scale VMware Telco Cloud version 2.x products in a Telco cloud provider environment. In this course, you are exposed to the entire VMware Telco Cloud portfolio, and the tools and methodologies available to ensure they operate effectively. In addition, you are presented with various scenarios where you will be guided through the process of identifying, analyzing, and formulating solutions to performance and other problems. Course Introduction Introductions and course logistics Course objectives Overview of Network Transformation Reviews the technologies that enable modern networks Lists the components of modern service provider networks Outlines characteristics of modern service provider networks in meeting customer application needs Service Delivery with VMware Telco Cloud Outlines the components of the VMware Telco Cloud portfolio Reviews the role each VMware Telco Cloud product plays in delivering telco services Specifies the dependencies each product has on underlying technologies Supporting VMware Telco Cloud Service Delivery Reviews the products that implements the virtualization, management, platform, and orchestration layers Outlines the role played by other VMware products such as NSX in delivering cloud services Outline opensource integration options with VMware Telco Cloud products Securing VMware Telco Cloud Reviews security threats that affect telco services Identifies the critical telco assets that are prone to attack Outlines best practice for securing VMware Telco Cloud products and underlying technology Provides overview of appropriate security controls for VMware Telco Cloud products Assessing Service Provision Reviews tools and methodologies used to gather requirements Outlines how to assess cloud-native capabilities Documenting findings Identifying security vulnerabilities with Helm. Reviews VMware?s Customer Engagement process Capturing infrastructure requirements from TCA Designing a VMware Telco Cloud Solution Selecting appropriate deployment topology Pros and cons of design choices How a design choice might be affected by other factors such as NSX and TKG deployment, or data center architecture Outlines typical scenarios where specific products align with identified requirements Documenting a design Designing for availability Ensuring a design aligns with best practice Specifying monitoring options Implementing VMware Telco Cloud Products Review deployment options for VMware products Integrating new products with existing ones Outline post-installation tasks Adding the new products as data sources in monitoring tools such as vRealize Operations and vRealize Operations Network Insight Ensuring products meet security requirements Configuring monitoring software Outlines the xNF onboarding process in TCA Maintaining Telco Services Outlines typical administrative tasks in ensuring services are maintained Use of native and other VMware performance monitoring tools Reviewing performance data Role of SLAs in service maintenance Reviews scenarios where known behaviors indicate problems Troubleshooting Deployed Telco Services Reviews the troubleshooting tools available Using tools to gather useful data Outlines how event correlation can be used to isolate problems Using a methodology to determine the root cause of a problem Steps to identify and resolve a problem Reviews scenarios where known problems are isolated and resolution steps identified Scaling VMware Telco Cloud Products Assessing if operations are impacted by lack of resources now or will be in the future Reviews performance optimization options Identifies implications for other products if you scale VMware Telco Cloud products Reviews VMware sizing guidelines Additional course details:Notes Delivery by TDSynex, Exit Certified and New Horizons an VMware Authorised Training Centre (VATC) Nexus Humans VMware Telco Cloud: Day 2 Operate and Scale [V2.x] 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 VMware Telco Cloud: Day 2 Operate and Scale [V2.x] 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.

Unlock your athletic potential with our cutting-edge Sports Nutrition course. Learn how to optimize macronutrients, micronutrients, and hydration for peak performance. Master diet planning, weight control, and specialized nutritional strategies for game day and beyond. Elevate your sports performance through scientific nutrition.

Duration 2 Days 12 CPD hours This course is intended for Built for experienced IT Professionals familiar with Citrix Virtual Apps and Desktops 7 in an on-premises environment or Citrix Cloud looking to expand their Citrix Virtual Apps and Desktops skills to an advanced level. Potential students include administrators or engineers responsible for the Citrix Apps and Desktop 7 Site. Overview After completing this course you should be able to: Configure Workspace Environment Management to improve the end user environment and virtual resource consumption Implement App Layering skills, for example how to create and administer OS, platform, application, elastic and user layers Migrate to Workspace Environment Management Students will learn to build and combine App Layers to optimize management of your app and desktop images and to implement Workspace Environment Management to improve log-on times, centralize user settings management, and optimize the performance of machines. Students will leave this course with a good understanding of how to manage more complex solutions and features in their Citrix Virtual Apps and Desktop 7 site. Introduction to App Layering App Layering Introduction Architecture and How it Works The OS Layer Creating an OS Layer The Platform Layer Creating a Platform Layer The App Layers Creating App Layers Creating Elastic App and User Layers Elastic App Layering User Layers Deploying a Layered Image using Citrix Virtual Apps and Desktops Using Templates in App Layering Using Layered Images in a Citrix Virtual Apps and Desktops Site Exploring Layer Priority Layer Priority Maintaining an App Layering Environment Updating Layers Maintaining and Updating the App Layering Environment Common App Layering Considerations and Additional Resources Introduction to Workspace Environment Management (WEM) Workspace Environment Management (WEM) Introduction WEM Administration Using WEM to Centralize Environment Management Managing User Resources with WEM Managing Profiles with WEM Managing Endpoints with the WEM Transformer Feature Using WEM for Performance Optimizing Machine Performance with WEM Optimizing User Experience with WEM Using WEM to Secure Environments WEM Security Features Migrating and Upgrading WEM Migrating to WEM Upgrading a WEM Deployment WEM Multi-Location Considerations Additional course details: Nexus Humans CWS-314 Citrix App Layering and WEM Administration 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 CWS-314 Citrix App Layering and WEM Administration 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 3 Days 18 CPD hours This course is intended for This class is intended for the following customer job roles: Cloud architects, administrators, and SysOps personnel Cloud developers and DevOps personnel Overview This course teaches participants the following skills: Plan and implement a well-architected logging and monitoring infrastructure Define Service Level Indicators (SLIs) and Service Level Objectives (SLOs) Create effective monitoring dashboards and alerts Monitor, troubleshoot, and improve Google Cloud infrastructure Analyze and export Google Cloud audit logs Find production code defects, identify bottlenecks, and improve performance Optimize monitoring costs This course teaches you techniques for monitoring, troubleshooting, and improving infrastructure and application performance in Google Cloud. Guided by the principles of Site Reliability Engineering (SRE), and using a combination of presentations, demos, hands-on labs, and real-world case studies, attendees gain experience with full-stack monitoring, real-time log management and analysis, debugging code in production, tracing application performance bottlenecks, and profiling CPU and memory usage. Introduction to Google Cloud Monitoring Tools Understand the purpose and capabilities of Google Cloud operations-focused components: Logging, Monitoring, Error Reporting, and Service Monitoring Understand the purpose and capabilities of Google Cloud application performance management focused components: Debugger, Trace, and Profiler Avoiding Customer Pain Construct a monitoring base on the four golden signals: latency, traffic, errors, and saturation Measure customer pain with SLIs Define critical performance measures Create and use SLOs and SLAs Achieve developer and operation harmony with error budgets Alerting Policies Develop alerting strategies Define alerting policies Add notification channels Identify types of alerts and common uses for each Construct and alert on resource groups Manage alerting policies programmatically Monitoring Critical Systems Choose best practice monitoring project architectures Differentiate Cloud IAM roles for monitoring Use the default dashboards appropriately Build custom dashboards to show resource consumption and application load Define uptime checks to track aliveness and latency Configuring Google Cloud Services for Observability Integrate logging and monitoring agents into Compute Engine VMs and images Enable and utilize Kubernetes Monitoring Extend and clarify Kubernetes monitoring with Prometheus Expose custom metrics through code, and with the help of OpenCensus Advanced Logging and Analysis Identify and choose among resource tagging approaches Define log sinks (inclusion filters) and exclusion filters Create metrics based on logs Define custom metrics Link application errors to Logging using Error Reporting Export logs to BigQuery Monitoring Network Security and Audit Logs Collect and analyze VPC Flow logs and Firewall Rules logs Enable and monitor Packet Mirroring Explain the capabilities of Network Intelligence Center Use Admin Activity audit logs to track changes to the configuration or metadata of resources Use Data Access audit logs to track accesses or changes to user-provided resource data Use System Event audit logs to track GCP administrative actions Managing Incidents Define incident management roles and communication channels Mitigate incident impact Troubleshoot root causes Resolve incidents Document incidents in a post-mortem process Investigating Application Performance Issues Debug production code to correct code defects Trace latency through layers of service interaction to eliminate performance bottlenecks Profile and identify resource-intensive functions in an application Optimizing the Costs of Monitoring Analyze resource utilization cust for monitoring related components within Google Cloud Implement best practices for controlling the cost of monitoring within Google Cloud

Duration 2.5 Days 15 CPD hours This course is intended for This course is intended for those with a basic understanding of Tableau who want to pursue mastery of the advanced features. Overview The goal of this course is to present essential Tableau concepts and its advanced functionalities to help better prepare and analyze data. This course will use Tableau Hyper, Tableau Prep and more. Getting Up to Speed ? a Review of the Basics Connecting Tableau to your data Connecting to Tableau Server Connecting to saved data sources Measure Names and Measure Values Three essential Tableau concepts Exporting data to other devices Summary All About Data ? Getting Your Data Ready Data mining and knowledge discovery process models CRISP?DM All About Data ? Joins, Blends, and Data Structures All About Data - Joins, Blends, and Data Structures Introduction to joins Introduction to complex joins Exercise: observing join culling Introduction to join calculations Introduction to spatial joins Introduction to unions Understanding data blending Order of operations No dimensions from a secondary source Introduction to scaffolding Introduction to data structures Exercise: adjusting the data structure for different questions Summary Table Calculations Table Calculations A definition and two questions Introduction to functions Directional and non-directional table calculations Application of functions Summary Level of Detail Calculations Level of Detail Calculations Building playgrounds Playground I: FIXED and EXCLUDE Playground II: INCLUDE Practical application Exercise: practical FIXED Exercise: practical INCLUDE Exercise: practical EXCLUDE Summary Beyond the Basic Chart Types Beyond the Basic Chart Types Improving popular visualizations Custom background images Tableau extensions Summary Mapping Mapping Extending Tableau's mapping capabilities without leaving Tableau Extending Tableau mapping with other technology Exercise: connecting to a WMS server Exploring the TMS file Exploring Mapbox Accessing different maps with a dashboard Creating custom polygons Converting shape files for Tableau Exercise: polygons for Texas Heatmaps Summary Tableau for Presentations Tableau for Presentations Getting the best images out of Tableau From Tableau to PowerPoint Embedding Tableau in PowerPoint Animating Tableau Story points and dashboards for Presentations Summary Visualization Best Practices and Dashboard Design Visualization Best Practices and Dashboard Design Visualization design theory Formatting rules Color rules Visualization type rules Compromises Keeping visualizations simple Dashboard design Dashboard layout Sheet selection Summary Advanced Analytics Advanced Analytics Self-service Analytics Use case ? Self-service Analytics Use case ? Geo-spatial Analytics Summary Improving Performance Improving Performance Understanding the performance-recording dashboard Exercise: exploring performance recording in Tableau desktop Performance-recording dashboard events Behind the scenes of the performance- recording dashboard Hardware and on-the-fly techniques Hardware considerations On-the-fly-techniques Single Data Source > Joining > Blending Three ways Tableau connects to data Using referential integrity when joining Advantages of blending Efficiently working with data sources Tuning data sources Working efficiently with large data sources Intelligent extracts Understanding the Tableau data extract Constructing an extract for optimal performance Exercise: summary aggregates for improved performance Optimizing extracts Exercise: materialized calculations Using filters wisely Extract filter performance Data source filter performance Context filters Dimension and measure filters Table-calculation filters Efficient calculations Boolean/Numbers > Date > String Additional performance considerations Avoid overcrowding a dashboard Fixing dashboard sizing Setting expectations Summary Additional course details: Nexus Humans Advanced Tableau 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 Advanced Tableau 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 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.