205 SQL courses in Leeds delivered Live Online

Duration 2 Days 12 CPD hours This course is intended for This is an introductory-level course for Administrators who are new to Jira (this is NOT for experienced Jira admin or users). Students should have a background in basic administration. Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Working in a hands-on learning environment, exploring several practical use cases that provide context as to where and when to use Jira, students will learn about: user management global and project permissions project roles schemes configuration of issue types, workflows, and screens Tracking issues is a critical component of any project management strategy. JIRA provides a web based single repository for creating, tracking and reporting on feature requests, bugs reported, or managing workflow. Geared for administrators new to Jira, JumpStart to Jira for Administrators | Jira Administration is a two-day, hands-on course that explores the most important tasks required to set up Jira, providing students with ample hands-on experience using common administration tasks. This hands-on course enables the Student to administer a JIRA instance and ?learn by doing?. The focus of this course is on Best Practices, and practical skills. Getting started with JIRA Administration JIRA conceptual review Core concepts Terminology Infrastructure JIRA roles Groups vs Roles Overview Project roles Creating a role Project scaling JIRA User management Project Resolution Project status Resolved status Resolution date Schemes Overview Project scope schemes Adding users to schemes Issue type schemes Notification schemes Permission schemes Issue security schemes working with schemes JIRA as a Platform Overview What can be configured Basic JIRA project setup Advanced project setup Workflows Overview Designing a workflow Defining a workflow Implementing a workflow Deploying workflows Workflow events Transitions and sub-tasks Custom Fields Overview Field types Field context Limiting contexts Adding contexts Screens and field configuration Best practices for custom fields User Lifecycle Overview Adding users Adding third-party users Modifying users Deactivating users Remote JIRA Access Overview Emails SQL REST Webhooks XML and RSS Command Line Interface Integrating JIRA with other applications Migrating Data into JIRA Overview Migration steps The CSV importer JIRA cloud migration Summary and Best Practices Looking back at the ?Big Picture? Optional - Jira Certification Prep Review Additional course details: Nexus Humans JumpStart to Jira for Administrators | Jira Administration (TTDV7540) 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 JumpStart to Jira for Administrators | Jira Administration (TTDV7540) 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 introductory-level Python course is geared for experienced web developers new to Python who want to use Python and Django for full stack web development projects. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to: Develop full-stack web sites based on content stored in an RDMS Use python data types appropriately Define data models Understand the architecture of a Django-based web site Create Django templates for easy-to-modify views Map views to URLs Take advantage of the built-in Admin interface Provide HTML form processing Geared for experienced web developers new to Python, Introduction to Full Stack Web Development with Python and Django is a five-day hands-on course that teaches students how to develop Web applications using the Django framework. Students will explore the basics of creating basic applications using the MVC (model-view-controller) design pattern, as well as more advanced topics such as administration, session management, authentication, and automated testing. This comprehensive, practical course provides an in-depth exploration of working with the programming language, not an academic overview of syntax and grammar. Students will immediately be able to use Python to complete tasks in the real world. The Python Environment Starting Python Using the interpreter Running a Python script Getting help Editors and IDEs Getting Started Using variables Built in functions Strings Numbers Converting among types Writing to the screen Command line parameters Flow Control About flow control Conditional expressions Relational and Boolean operators while loops Lists and Tuples About sequences Lists and list methods Tuples Indexing and slicing Iterating through a sequence Sequence functions, keywords, and operators List comprehensions Working with Files File overview The with statement Opening a file Reading/writing files Dictionaries and Sets About dictionaries Creating and using dictionaries About sets Creating and using sets Functions Returning values Function parameters Variable Scope Sorting with functions Errors and Exception Handling Exception overview Using try/catch/else/finally Handling multiple exceptions Ignoring exceptions Modules and Packages Creating Modules The import statement Module search path Creating packages Classes About OO programming Defining classes Constructors Properties Instance methods and data Class/static methods and data Inheritance Django Architecture Django overview Sites and apps Shared configuration Minimal Django layout Built in flexibility Configuring a Project Executing manage.py Starting the project Generating app files App configuration Database setup The development server Using cookiecutter Creating models Defining models Related objects SQL Migration Simplel model access Login for Nothing and Admin for Free Setting up the admin user Using the admin interface Views What is a view HttpResponse URL route configuration Shortcut: get_object_or_404() Class-based views Templates About templates Variable lookups The url tag Shortcut: render() Querying Models QuerySets Field lookups Chaining filters Slicing QuerySets Related fields Q objects Advanced Templates Use Comments Inheritance Filters Escaping HTML Custom filters Forms Forms overview GET and POST The Form class Processing the form Widgets Validation Forms in templates Automated Testing Why create tests? When to create tests Using Django's test framework Using the test client Running tests Checking code coverage

There is a lot to learn in Power BI, this course takes a comprehensive look at the fundamentals of analysing data and includes a balanced look at the four main components that make up Power BI Desktop: Report view, Data view, Model view, and the Power Query Editor. It also demonstrates how to utilise the online Power BI service. It looks at authoring tools that enable you to connect to and transform data from a variety of sources, allowing you to produce detailed reports through a range of visualisations, in an interactive and dynamic way. It also includes a detailed look at formulas by writing both M functions in Power Query, and DAX functions in Desktop view. This knowledge will allow you to take your reports to the next level. The aim of this course is to provide a complete introduction to understanding the Power BI analysis process, by working hands-on with examples that will equip you with the necessary skills to start applying your learning straight away. 1 Getting Started The Power BI ecosystem Opening Power BI Desktop Power BI's four views Introduction to Dashboards 2 Importing Files Importing data sources Importing an Excel file Importing a CSV file Importing a database Connect to an SQL Server Database Import vs. Direct Query Importing from the web Importing a folder of files Managing file connections 3 Shape Data in the Query Editor The process of shaping data Managing data types Keeping and removing rows Add a custom column Appending tables together Hiding queries in reports Fixing error issues Basic maths operations 4 The Data Model Table relationships Relationship properties 5 Merge Queries Table join kinds Merging tables 6 Inserting Dashboard Visuals Things to keep in mind Inserting maps Formatting Maps Inserting charts Formatting Charts Inserting a tree map Inserting a table, matrix, and card Controlling number formats About report themes Highlighting key points Filter reports with slicers Sync slicers across dashboards Custom web visuals 7 Publish and share Reports Publishing to Power BI service Editing online reports Pinning visuals to a dashboard What is Q&A? Sharing dashboards Exporting reports to PowerPoint Exporting reports as PDF files 8 The Power Query Editor Fill data up and down Split column by delimiter Add a conditional column More custom columns Merging columns 9 The M Functions Inserting text functions Insert an IF function Create a query group 10 Pivoting Tables Pivot a table Pivot and append tables Pivot but don't aggregate Unpivot tables Append mismatched headers 11 Data Modelling Expanded Understanding relationships Mark a date table 12 DAX New Columns New columns and measures New column calculations Insert a SWITCH function 13 Introduction to DAX Measures Common measure functions Insert a SUM function Insert a COUNTROWS function Insert a DISTINCTCOUNT function Insert a DIVIDE function DAX rules 14 The CALCULATE Measure The syntax of CALCULATE Insert a CALCULATE function Control field summarisation Things of note 15 The SUMX measure X iterator functions Anatomy of SUMX Insert a SUMX function When to use X functions 16 Time Intelligence Measures Importance of a calendar table Insert a TOTALYTD function Change financial year end date Comparing historical data Insert a DATEADD function 17 Hierarchies and Groups Mine data using hierarchies Compare data in groups

Duration 5 Days 30 CPD hours This course is intended for Deployment engineer Network engineer Sales engineer Overview After taking this course, you should be able to: Describe the Cisco conferencing architecture including cloud, hybrid, and on-premises conferencing Describe the physical deployment options and deployment models for Cisco Meeting Server, including Cisco Meeting Server 1000, 2000, and virtual machine Configure a Cisco Meeting Server single combined deployment for Web-Real Time Communications (WebRTC) endpoints within the enterprise Use APIs and the Cisco Meeting Server API Guide to configure profiles using Postman and the Webadmin API tool Configure a scalable and resilient deployment of Cisco Meeting Server with three servers for WebRTC endpoints within the enterprise Configure a scalable and resilient deployment of Cisco Meeting Server to support standard Session Initiation Protocol (SIP) and WebRTC connectivity outside the enterprise Configure a scalable and resilient deployment of Cisco Meeting Server to support recording and streaming of conferences Configure Cisco Unified Communications Manager and Cisco Meeting Server to support Rendezvous, Scheduled, and Ad-hoc conferencing for Cisco Unified CM registered endpoints Configure Cisco Meeting Server to integrate with a preconfigured on-premise Microsoft Skype for Business installation Install Cisco TelePresence Management Suite (Cisco TMS) and Cisco TelePresence Management Suite for Microsoft Exchange (Cisco TMSXE) on a single Microsoft Windows 2012 server and connect to an existing SQL environment Install and integrate Cisco Meeting Management with Cisco TMS and Cisco Meeting Server Set up and manage a scheduled conference with Cisco TMS and Cisco Meeting Management Capture and analyze logs from Cisco Meeting Server and Cisco Meeting Manager to diagnose faults, including a SIP connection error The Implementing Cisco Collaboration Conferencing (CLCNF) v1.0 course focuses on Cisco© on-premises conferencing architecture and solutions. You will gain knowledge and skills to design and implement common conferencing deployment scenarios of Cisco Meeting Server, its integration with call control features such as Cisco Unified Communications Manager and Cisco Expressway, and other Cisco collaboration conferencing devices.This course offers lessons and hands-on labs to prepare you for the 300-825 Implementing Cisco Collaboration Conferencing (CLCNF) exam. Course outline Describing Cisco Conferencing Architecture Configuring a Single Combined Deployment Installing Cisco Meeting Server Using APIs with Cisco Meeting Server Configuring a Cisco Meeting Server Scalable and Resilient Deployment Configuring Business to Business (B2B) and WebRTC Firewall Traversal Connectivity for Cisco Meeting Server Configuring Recording and Streaming with Cisco Meeting Server Troubleshooting Cisco Meeting Server Integrating Cisco Meeting Server with Cisco Unified CM Integrating Cisco Meeting Server with Microsoft Skype for Business Installing and Operating Cisco TMS and Cisco TMSXE Installing and Integrating Cisco Meeting Management Additional course details: Nexus Humans Cisco Implementing Cisco Collaboration Conferencing v2.0 (CLCNF) 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 Cisco Implementing Cisco Collaboration Conferencing v2.0 (CLCNF) 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 The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview Overview of data science and machine learning at scale Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue Introduction to Cloudera Data Science Workbench Overview of Apache Spark 2 Reading and writing data Inspecting data quality Cleansing and transforming data Summarizing and grouping data Combining, splitting, and reshaping data Exploring data Configuring, monitoring, and troubleshooting Spark applications Overview of machine learning in Spark MLlib Extracting, transforming, and selecting features Building and evaluating regression models Building and evaluating classification models Building and evaluating clustering models Cross-validating models and tuning hyperparameters Building machine learning pipelines Deploying machine learning models Spark, Spark SQL, and Spark MLlib PySpark and sparklyr Cloudera Data Science Workbench (CDSW) Hue This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview of data science and machine learning at scaleOverview of the Hadoop ecosystemWorking with HDFS data and Hive tables using HueIntroduction to Cloudera Data Science WorkbenchOverview of Apache Spark 2Reading and writing dataInspecting data qualityCleansing and transforming dataSummarizing and grouping dataCombining, splitting, and reshaping dataExploring dataConfiguring, monitoring, and troubleshooting Spark applicationsOverview of machine learning in Spark MLlibExtracting, transforming, and selecting featuresBuilding and evauating regression modelsBuilding and evaluating classification modelsBuilding and evaluating clustering modelsCross-validating models and tuning hyperparametersBuilding machine learning pipelinesDeploying machine learning models Additional course details: Nexus Humans Cloudera Data Scientist Training 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 Cloudera Data Scientist Training 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 participants: Cloud architects, administrators, and SysOps/DevOps personnel Individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure with the Google Cloud Platform. Overview This course teaches participants the following skills: Understand how software containers work Understand the architecture of Kubernetes Understand the architecture of Google Cloud Platform Understand how pod networking works in Kubernetes Engine Create and manage Kubernetes Engine clusters using the GCP Console and gcloud/ kubectl commands Launch, roll back and expose jobs in Kubernetes Manage access control using Kubernetes RBAC and Google Cloud IAM Managing pod security policies and network policies Using Secrets and ConfigMaps to isolate security credentials and configuration artifacts Understand GCP choices for managed storage services Monitor applications running in Kubernetes Engine This class introduces participants to deploying and managing containerized applications on Google Kubernetes Engine (GKE) and the other services provided by Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as pods, containers, deployments, and services; as well as networks and application services. This course also covers deploying practical solutions including security and access management, resource management, and resource monitoring. Introduction to Google Cloud Platform Use the Google Cloud Platform Console Use Cloud Shell Define cloud computing Identify GCPs compute services Understand regions and zones Understand the cloud resource hierarchy Administer your GCP resources Containers and Kubernetes in GCP Create a container using Cloud Build Store a container in Container Registry Understand the relationship between Kubernetes and Google Kubernetes Engine (GKE) Understand how to choose among GCP compute platforms Kubernetes Architecture Understand the architecture of Kubernetes: pods, namespaces Understand the control-plane components of Kubernetes Create container images using Google Cloud Build Store container images in Google Container Registry Create a Kubernetes Engine cluster Kubernetes Operations Work with the kubectl command Inspect the cluster and Pods View a Pods console output Sign in to a Pod interactively Deployments, Jobs, and Scaling Create and use Deployments Create and run Jobs and CronJobs Scale clusters manually and automatically Configure Node and Pod affinity Get software into your cluster with Helm charts and Kubernetes Marketplace GKE Networking Create Services to expose applications that are running within Pods Use load balancers to expose Services to external clients Create Ingress resources for HTTP(S) load balancing Leverage container-native load balancing to improve Pod load balancing Define Kubernetes network policies to allow and block traffic to pods Persistent Data and Storage Use Secrets to isolate security credentials Use ConfigMaps to isolate configuration artifacts Push out and roll back updates to Secrets and ConfigMaps Configure Persistent Storage Volumes for Kubernetes Pods Use StatefulSets to ensure that claims on persistent storage volumes persist across restarts Access Control and Security in Kubernetes and Kubernetes Engine Understand Kubernetes authentication and authorization Define Kubernetes RBAC roles and role bindings for accessing resources in namespaces Define Kubernetes RBAC cluster roles and cluster role bindings for accessing cluster-scoped resources Define Kubernetes pod security policies Understand the structure of GCP IAM Define IAM roles and policies for Kubernetes Engine cluster administration Logging and Monitoring Use Stackdriver to monitor and manage availability and performance Locate and inspect Kubernetes logs Create probes for wellness checks on live applications Using GCP Managed Storage Services from Kubernetes Applications Understand pros and cons for using a managed storage service versus self-managed containerized storage Enable applications running in GKE to access GCP storage services Understand use cases for Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Bigtable, Cloud Firestore, and Bigquery from within a Kubernetes application

Duration 3 Days 18 CPD hours This course is intended for Application developers who want to build cloud-native applications or redesign existing applications that will run on Google Cloud Platform Overview This course teaches participants the following skills: Use best practices for application development. Choose the appropriate data storage option for application data. Implement federated identity management. Develop loosely coupled application components or microservices. Integrate application components and data sources. Debug, trace, and monitor applications. Perform repeatable deployments with containers and deployment services. Choose the appropriate application runtime environment; use Google Container Engine as a runtime environment and later switch to a no-ops solution with Google App Engine flexible environment. Learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem. This course uses lectures, demos, and hands-on labs to show you how to use Google Cloud services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications. Best Practices for Application Development Code and environment management. Design and development of secure, scalable, reliable, loosely coupled application components and microservices. Continuous integration and delivery. Re-architecting applications for the cloud. Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK How to set up and use Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK. Lab: Set up Google Client Libraries, Cloud SDK, and Firebase SDK on a Linux instance and set up application credentials. Overview of Data Storage Options Overview of options to store application data. Use cases for Google Cloud Storage, Cloud Firestore, Cloud Bigtable, Google Cloud SQL, and Cloud Spanner. Best Practices for Using Cloud Firestore Best practices related to using Cloud Firestore in Datastore mode for:Queries, Built-in and composite indexes, Inserting and deleting data (batch operations),Transactions,Error handling. Bulk-loading data into Cloud Firestore by using Google Cloud Dataflow. Lab: Store application data in Cloud Datastore. Performing Operations on Cloud Storage Operations that can be performed on buckets and objects. Consistency model. Error handling. Best Practices for Using Cloud Storage Naming buckets for static websites and other uses. Naming objects (from an access distribution perspective). Performance considerations. Setting up and debugging a CORS configuration on a bucket. Lab: Store files in Cloud Storage. Handling Authentication and Authorization Cloud Identity and Access Management (IAM) roles and service accounts. User authentication by using Firebase Authentication. User authentication and authorization by using Cloud Identity-Aware Proxy. Lab: Authenticate users by using Firebase Authentication. Using Pub/Sub to Integrate Components of Your Application Topics, publishers, and subscribers. Pull and push subscriptions. Use cases for Cloud Pub/Sub. Lab: Develop a backend service to process messages in a message queue. Adding Intelligence to Your Application Overview of pre-trained machine learning APIs such as Cloud Vision API and Cloud Natural Language Processing API. Using Cloud Functions for Event-Driven Processing Key concepts such as triggers, background functions, HTTP functions. Use cases. Developing and deploying functions. Logging, error reporting, and monitoring. Managing APIs with Cloud Endpoints Open API deployment configuration. Lab: Deploy an API for your application. Deploying Applications Creating and storing container images. Repeatable deployments with deployment configuration and templates. Lab: Use Deployment Manager to deploy a web application into Google App Engine flexible environment test and production environments. Execution Environments for Your Application Considerations for choosing an execution environment for your application or service:Google Compute Engine (GCE),Google Kubernetes Engine (GKE), App Engine flexible environment, Cloud Functions, Cloud Dataflow, Cloud Run. Lab: Deploying your application on App Engine flexible environment. Debugging, Monitoring, and Tuning Performance Application Performance Management Tools. Stackdriver Debugger. Stackdriver Error Reporting. Lab: Debugging an application error by using Stackdriver Debugger and Error Reporting. Stackdriver Logging. Key concepts related to Stackdriver Trace and Stackdriver Monitoring. Lab: Use Stackdriver Monitoring and Stackdriver Trace to trace a request across services, observe, and optimize performance.

Duration 3 Days 18 CPD hours This course is intended for Cloud Solutions Architects, DevOps Engineers. Individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure with a focus on Google Compute Engine. Overview Configure VPC networks and virtual machines Administer Identity and Access Management for resources Implement data storage services in GCP Manage and examine billing of GCP resources Monitor resources using Stackdriver services Connect your infrastructure to GCP Configure load balancers and autoscaling for VM instances Automate the deployment of GCP infrastructure services Leverage managed services in GCP This class introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud Platform, with a focus on Compute Engine. Through a combination of presentations, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, systems, and application services. This course also covers deploying practical solutions including securely interconnecting networks, customer-supplied encryption keys, security and access management, quotas and billing, and resource monitoring. Introduction to Google Cloud Platform List the different ways of interacting with GCP Use the GCP Console and Cloud Shell Create Cloud Storage buckets Use the GCP Marketplace to deploy solutions Virtual Networks List the VPC objects in GCP Differentiate between the different types of VPC networks Implement VPC networks and firewall rules Design a maintenance server Virtual Machines Recall the CPU and memory options for virtual machines Describe the disk options for virtual machines Explain VM pricing and discounts Use Compute Engine to create and customize VM instances Cloud IAM Describe the Cloud IAM resource hierarchy Explain the different types of IAM roles Recall the different types of IAM members Implement access control for resources using Cloud IAM Storage and Database Services Differentiate between Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Firestore and Cloud Bigtable Choose a data storage service based on your requirements Implement data storage services Resource Management Describe the cloud resource manager hierarchy Recognize how quotas protect GCP customers Use labels to organize resources Explain the behavior of budget alerts in GCP Examine billing data with BigQuery Resource Monitoring Describe the Stackdriver services for monitoring, logging, error reporting, tracing, and debugging Create charts, alerts, and uptime checks for resources with Stackdriver Monitoring Use Stackdriver Debugger to identify and fix errors Interconnecting Networks Recall the GCP interconnect and peering services available to connect your infrastructure to GCP Determine which GCP interconnect or peering service to use in specific circumstances Create and configure VPN gateways Recall when to use Shared VPC and when to use VPC Network Peering Load Balancing and Autoscaling Recall the various load balancing services Determine which GCP load balancer to use in specific circumstances Describe autoscaling behavior Configure load balancers and autoscaling Infrastructure Automation Automate the deployment of GCP services using Deployment Manager or Terraform Outline the GCP Marketplace Managed Services Describe the managed services for data processing in GCP Additional course details: Nexus Humans Architecting with Google Compute Engine 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 Architecting with Google Compute Engine 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 2 Days 12 CPD hours This course is intended for This course is designed for professionals in a variety of job roles who are currently using desktop or web-based data management tools such as Microsoft Excel or SQL Server reporting services to perform numerical or general data analysis. They are responsible for connecting to cloud-based data sources, as well as shaping and combining data for the purpose of analysis. They are also looking for alternative ways to analyze business data, visualize insights, and share those insights with peers across the enterprise. This includes capturing and reporting on data to peers, executives, and clients. Overview In this course, you will analyze data with Microsoft Power BI. You will: Analyze data with self-service BI. Connect to data sources. Perform data cleaning, profiling, and shaping. Visualize data with Power BI. Enhance data analysis by adding and customizing visual elements. Model data with calculations. Create interactive visualizations. As technology progresses and becomes more interwoven with our businesses and lives, more data is collected about business and personal activities. This era of 'big data' is a direct result of the popularity and growth of cloud computing, which provides an abundance of computational power and storage, allowing organizations of all sorts to capture and store data. Leveraging that data effectively can provide timely insights and competitive advantages. Creating data-backed visualizations is key for data scientists, or any professional, to explore, analyze, and report insights and trends from data. Microsoft© Power BI© software is designed for this purpose. Power BI was built to connect to a wide range of data sources, and it enables users to quickly create visualizations of connected data to gain insights, show trends, and create reports. Power BI's data connection capabilities and visualization features go far beyond those that can be found in spreadsheets, enabling users to create compelling and interactive worksheets, dashboards, and stories that bring data to life and turn data into thoughtful action. Analyzing Data with Self-Service BI Topic A: Data Analysis and Visualization for Business Intelligence Topic B: Self-Service BI with Microsoft Power BI Connecting to Data Sources Topic A: Create Data Connections Topic B: Configure and Manage Data Relationships Topic C: Save Files in Power BI Performing Data Cleaning, Profiling, and Shaping Topic A: Clean, Transform, and Load Data with the Query Editor Topic B: Profile Data with the Query Editor Topic C: Shape Data with the Query Editor Topic D: Combine and Manage Data Rows Visualizing Data with Power BI Topic A: Create Visualizations in Power BI Topic B: Chart Data in Power BI Enhancing Data Analysis Topic A: Customize Visuals and Pages Topic B: Incorporate Tooltips Modeling Data with Calculations Topic A: Create Calculations with Data Analysis Expressions (DAX) Topic B: Create Calculated Measures and Conditional Columns Creating Interactive Visualizations Topic A: Create and Manage Data Hierarchies Topic B: Filter and Slice Reports Topic C: Create Dashboards Additional course details: Nexus Humans Microsoft Power BI: Data Analysis Practitioner (Second Edition) (v1.3) 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 Microsoft Power BI: Data Analysis Practitioner (Second Edition) (v1.3) 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 course is designed for professionals in a variety of job roles who are currently using desktop or web-based data management tools such as Microsoft Excel or SQL Server reporting services to perform numerical or general data analysis. They are responsible for connecting to cloud-based data sources, as well as shaping and combining data for the purpose of analysis. They are also looking for alternative ways to analyze business data, visualize insights, and share those insights with peers across the enterprise. This includes capturing and reporting on data to peers, executives, and clients. This course is also designed for professionals who want to pursue the Microsoft Power BI Data Analyst (Exam PL-300) certification. Overview In this course, you will analyze data with Microsoft Power BI. You will: Analyze data with self-service BI. Connect to data sources. Perform data cleaning, profiling, and shaping. Visualize data with Power BI. Enhance data analysis by adding and customizing visual elements. Model data with calculations. Create interactive visualizations. Use advanced analysis techniques. Enhance reports and dashboards. Publish and share reports and dashboards. Extend Power BI beyond the desktop. As technology progresses and becomes more interwoven with our businesses and lives, more data is collected about business and personal activities. This era of 'big data' is a direct result of the popularity and growth of cloud computing, which provides an abundance of computational power and storage, allowing organizations of all sorts to capture and store data. Leveraging that data effectively can provide timely insights and competitive advantages. Creating data-backed visualizations is key for data scientists, or any professional, to explore, analyze, and report insights and trends from data. Microsoft© Power BI© software is designed for this purpose. Power BI was built to connect to a wide range of data sources, and it enables users to quickly create visualizations of connected data to gain insights, show trends, and create reports. Power BI's data connection capabilities and visualization features go far beyond those that can be found in spreadsheets, enabling users to create compelling and interactive worksheets, dashboards, and stories that bring data to life and turn data into thoughtful action. Analyzing Data with Self-Service BI Topic A: Data Analysis and Visualization for Business Intelligence Topic B: Self-Service BI with Microsoft Power BI Connecting to Data Sources Topic A: Create Data Connections Topic B: Configure and Manage Data Relationships Topic C: Save Files in Power BI Performing Data Cleaning, Profiling, and Shaping Topic A: Clean, Transform, and Load Data with the Query Editor Topic B: Profile Data with the Query Editor Topic C: Shape Data with the Query Editor Topic D: Combine and Manage Data Rows Visualizing Data with Power BI Topic A: Create Visualizations in Power BI Topic B: Chart Data in Power BI Enhancing Data Analysis Topic A: Customize Visuals and Pages Topic B: Incorporate Tooltips Modeling Data with Calculations Topic A: Create Calculations with Data Analysis Expressions (DAX) Topic B: Create Calculated Measures and Conditional Columns Creating Interactive Visualizations Topic A: Create and Manage Data Hierarchies Topic B: Filter and Slice Reports Topic C: Create Dashboards Using Advanced Analysis Techniques Topic A: Create Calculated Tables, Variables, and Parameters Topic B: Enhance Visuals with Statistical Analysis Topic C: Perform Advanced Analysis Enhancing Reports and Dashboards Topic A: Enhance Reports Topic B: Enhance Dashboards Publishing and Sharing Reports and Dashboards Topic A: Publish Reports Topic B: Create and Manage Workspaces Topic C: Share Reports and Dashboards Extending Power BI Beyond the Desktop Topic A: Use Power BI Mobile Topic B: Extend Access with the Power BI API Additional course details: Nexus Humans Microsoft Power BI: Data Analysis Professional (Second Edition) (v1.3) 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 Microsoft Power BI: Data Analysis Professional (Second Edition) (v1.3) 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.