43518 Courses

Duration 3 Days 18 CPD hours This course is intended for IT Professionals who deploy small-to- medium scale enterprise network solutions based on Aruba products and technologies Overview Upon completion of this course, you will be able to:Explain how Aruba networking solutions meet customers? requirementsExplain how Aruba AirWave provides unified wireless and wired network managementDescribe in-band management and out-of-band managementComplete the initial setup on ArubaOS switchesControl access to switches for both in-band and out-of-band managementManage software and configuration files on ArubaOS switchesExplain use cases for VLANs and configure port-based VLANs on ArubaOS switchesUnderstand and configure Rapid Spanning Tree Protocol (RSTP)Understand and configure Multiple Spanning Tree Protocol (MSTP)Differentiate between different types of link aggregation and understand the benefits of Link Aggregation Control Protocol (LACP)Configure and troubleshoot link aggregation on ArubaOS switchesConfigure static routes on ArubaOS switches and interpret IP routing tablesConfigure a basic Open Shortest Path First (OSPF) solutionDescribe how Virtual Switching Framework (VSF) works and the advantages that it providesConfigure and verify a simple VSF fabricDescribe the basics of wireless communications and 802.11 standardsDefine a wireless LAN (WLAN) and differentiate between wireless security optionsConfigure basic settings on Aruba Instant APsConfigure AirWave management settings on an IAP clusterConfigure SNMP v2c settings on ArubaOS switchesDiscover ArubaOS switches in AirWave and bring switches and IAPs under monitoring and managementImplement zero touch provisioning (ZTP) for Aruba IAPs and ArubaOS switches This course teaches you the fundamental skills necessary to configure and manage modern, open standards-based networking solutions. This course consists of approximately 20% lecture and 80% hands-on lab exercises to help you learn how to implement and validate small to medium enterprise network solutions. This 3-day course prepares network professionals for the HPE ATP - Aruba Mobile First Solutions V1 certification exam.In this course, participants learn about ArubaOS switch technologies including: VLANs, securing access, redundancy technologies such as MSTP, link aggregation techniques including LACP, and switch virtualization with Aruba?s Virtual Switching Framework (VSF). You also learn about IP Routing including static and dynamic IP routing with OSPF. This course teaches you how to deploy Aruba wireless Access Points and configure Aruba Clustering technology. It also teaches you how to configure, manage and monitor the network with the Aruba AirWave management solution. Introduction to Aruba, a Hewlett Packard Enterprise companySwitch CLI (Command Line Interface) NavigationProtecting Management AccessManagement of Software and ConfigurationsVLANSSpanning Tree Protocol (STP)Link AggregationIP RoutingVirtual Switching Framework (VSF)Wireless for Small-to-Medium Businesses (SMBs)Aruba AirWave

Duration 4 Days 24 CPD hours This course is intended for This course is designed for technical professionals who require the skills to administer IBM© MQ queue managers on distributed operating systems, in the Cloud, or on the IBM© MQ Appliance. Overview After completing this course, you should be able to:Describe the IBM© MQ deployment optionsPlan for the implementation of IBM© MQ on-premises or in the CloudUse IBM© MQ commands and the IBM© MQ Explorer to create and manage queue managers, queues, and channelsUse the IBM© MQ sample programs and utilities to test the IBM© MQ networkEnable a queue manager to exchange messages with another queue managerConfigure client connections to a queue managerUse a trigger message and a trigger monitor to start an application to process messagesImplement basic queue manager restart and recovery proceduresUse IBM© MQ troubleshooting tools to identify the cause of a problem in the IBM© MQ networkPlan for and implement basic IBM© MQ security featuresUse accounting and statistics messages to monitor the activities of an IBM© MQ systemDefine and administer a simple queue manager cluster This course provides technical professionals with the skills that are needed to administer IBM© MQ queue managers on distributed operating systems and in the Cloud. In addition to the instructor-led lectures, you participate in hands-on lab exercises that are designed to reinforce lecture content. The lab exercises use IBM© MQ V9.0, giving you practical experience with tasks such as handling queue recovery, implementing security, and problem determination. Note: This course does not cover any of the features of MQ for z/OS or MQ for IBM© i. Course introductionIBM© MQ reviewIBM© MQ installation and deployment optionsCreating a queue manager and queuesExercise: Using commands to create a queue manager and queuesIntroduction to IBM© MQ ExplorerExercise: Using IBM© MQ Explorer to create queue managers and queuesTesting the IBM© MQ implementationExercise: Using IBM© MQ sample programs to test the configurationImplementing distributed queuingExercise: Connecting queue managersIBM© MQ clientsExercise: Connecting an IBM© MQ clientImplementing trigger messages and monitorsExercise: Implementing a trigger monitorDiagnosing problemsExercise: Running an IBM© MQ traceImplementing basic security in IBM© MQExercise: Controlling access to IBM© MQBacking up and restoring IBM© MQ messages and object definitionsExercise: Using a media image to restore a queueExercise: Backing up and restoring IBM© MQ object definitionsIntroduction to queue manager clustersExercise: Implementing a basic clusterMonitoring and configuring IBM© MQ for performanceExercise: Monitoring IBM© MQ for performanceCourse summary

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 5 Days 30 CPD hours This course is intended for This course is intended for IT Professionals who are already experienced in general Windows Server and Windows Client administration, and who want to learn more about using Windows PowerShell for administration. No prior experience with any version of Windows PowerShell, or any scripting language, is assumed. This course is also suitable for IT Professionals already experienced in server administration, including Exchange Server, SharePoint Server, SQL Server, System Center, and others. Overview After completing this course, students will be able to: Describe the functionality of Windows PowerShell and use it to run and find basic commands. Identify and run cmdlets for server administration. Work with Windows PowerShell pipeline. Describe the techniques Windows PowerShell pipeline uses. Use PSProviders and PSDrives to work with other forms of storage. Query system information by using WMI and CIM. Work with variables, arrays, and hash tables. Write basic scripts in Windows PowerShell. Write advanced scripts in Windows PowerShell. Administer remote computers. Use background jobs and scheduled jobs. Use advanced Windows PowerShell techniques. This course provides students with the fundamental knowledge and skills to use Windows PowerShell for administering and automating administration of Windows based servers. Getting Started with Windows PowerShell Overview and Background Understanding command syntax Finding commands Lab : Configuring Windows PowerShell Lab : Finding and Running Basic Commands Cmdlets for administration Active Directory administration cmdlets Network configuration cmdlets Other server administration cmdlets Lab : Windows Administration Working with the Windows PowerShell pipeline Understanding the Pipeline Selecting, Sorting, and Measuring Objects Filtering Objects Out of the Pipeline Enumerating Objects in the Pipeline Sending pipeline data as output Lab : Using the Pipeline Lab : Filtering Objects Lab : Enumerating Objects Lab : Sending output to a file Understanding How the Pipeline Works Passing the pipeline data Advanced considerations for pipeline data Lab : Working with Pipeline Parameter Binding Using PSProviders and PSDrives Using PSProviders Using PSDrives Lab : Using PSProviders and PSDrives Querying Management Information by Using WMI and CIM Understanding WMI and CIM Querying Data with WMI and CIM Making changes with WMI/CIM Lab : Working with WMI and CIM Working with variables, arrays, and hash tables Using variables Manipulating variables Manipulating arrays and hash tables Lab : Working with variables Basic scripting Introduction to scripting Scripting constructs Importing data from files Lab : Basic scripting Advanced scripting Accepting user input Overview of script documentation Troubleshooting and error handling Functions and modules Lab : Accepting data from users Lab : Implementing functions and modules Administering Remote Computers Using basic Windows PowerShell remoting Using advanced Windows PowerShell remoting techniques Using PSSessions Lab : Using basic remoting Lab : Using PSSessions Using Background Jobs and Scheduled Jobs Using Background Jobs Using Scheduled Jobs Lab : Using Background Jobs and Scheduled Jobs Using advanced Windows PowerShell techniques Creating profile scripts Using advanced techniques Lab : Practicing advanced techniques Lab : Practicing script development (optional)

Duration 5 Days 30 CPD hours This course is intended for This course is intended for IT Professionals who are already experienced in general Windows Server and Windows Client administration, and who want to learn more about using Windows PowerShell for administration. No prior experience with any version of Windows PowerShell, or any scripting language, is assumed. This course is also suitable for IT Professionals already experienced in server administration, including Exchange Server, SharePoint Server, SQL Server, System Center, and others. Overview After completing this course, students will be able to:Describe the functionality of Windows PowerShell and use it to run and find basic commands.Identify and run cmdlets for server administration.Work with Windows PowerShell pipeline.Describe the techniques Windows PowerShell pipeline uses.Use PSProviders and PSDrives to work with other forms of storage.Query system information by using WMI and CIM.Work with variables, arrays, and hash tables.Write basic scripts in Windows PowerShell.Write advanced scripts in Windows PowerShell.Administer remote computers.Use background jobs and scheduled jobs.Use advanced Windows PowerShell techniques. This course provides students with the fundamental knowledge and skills to use Windows PowerShell for administering and automating administration of Windows based servers. Getting Started with Windows PowerShell Overview and Background Understanding command syntax Finding commands Lab : Configuring Windows PowerShell Lab : Finding and Running Basic Commands Cmdlets for administration Active Directory administration cmdlets Network configuration cmdlets Other server administration cmdlets Lab : Windows Administration Working with the Windows PowerShell pipeline Understanding the Pipeline Selecting, Sorting, and Measuring Objects Filtering Objects Out of the Pipeline Enumerating Objects in the Pipeline Sending pipeline data as output Lab : Using the Pipeline Lab : Filtering Objects Lab : Enumerating Objects Lab : Sending output to a file Understanding How the Pipeline Works Passing the pipeline data Advanced considerations for pipeline data Lab : Working with Pipeline Parameter Binding Using PSProviders and PSDrives Using PSProviders Using PSDrives Lab : Using PSProviders and PSDrives Querying Management Information by Using WMI and CIM Understanding WMI and CIM Querying Data with WMI and CIM Making changes with WMI/CIM Lab : Working with WMI and CIM Working with variables, arrays, and hash tables Using variables Manipulating variables Manipulating arrays and hash tables Lab : Working with variables Basic scripting Introduction to scripting Scripting constructs Importing data from files Lab : Basic scripting Advanced scripting Accepting user input Overview of script documentation Troubleshooting and error handling Functions and modules Lab : Accepting data from users Lab : Implementing functions and modules Administering Remote Computers Using basic Windows PowerShell remoting Using advanced Windows PowerShell remoting techniques Using PSSessions Lab : Using basic remoting Lab : Using PSSessions Using Background Jobs and Scheduled Jobs Using Background Jobs Using Scheduled Jobs Lab : Using Background Jobs and Scheduled Jobs Using advanced Windows PowerShell techniques Creating profile scripts Using advanced techniques Lab : Practicing advanced techniques Lab : Practicing script development (optional)

Duration 5 Days 30 CPD hours This course is intended for Professionals who need to maintain or set up a Kubernetes cluster Container Orchestration Engineers DevOps Professionals Overview Cluster architecture, installation, and configuration Rolling out and rolling back applications in production Scaling clusters and applications to best use How to create robust, self-healing deployments Networking configuration on cluster nodes, services, and CoreDNS Persistent and intelligent storage for applications Troubleshooting cluster, application, and user errors Vendor-agnostic cloud provider-based Kubernetes Kubernetes is a Cloud Orchestration Platform providing reliability, replication, and stability while maximizing resource utilization for applications and services. By the conclusion of this hands-on, vendor agnostic training you will go back to work with the knowledge, skills, and abilities to design, implement, and maintain a production-grade Kubernetes cluster. We prioritize covering all objectives and concepts necessary for passing the Certified Kubernetes Administrator (CKA) exam. You will be provided the components necessary to assemble your own high availability Kubernetes environment and configure, expand, and control it to meet the demands made of cluster administrators. Your week of intensive, hands-on training will conclude with a mock CKA exam that simulates the real exam. Cluster Architecture, Installation & Configuration Each student will be given an environment that allows them to build a Kubernetes cluster from scratch. After a detailed discussion on key architectural components and primitives, students will install and compare two production grade Kubernetes clusters. Review: Kubernetes Fundamentals After successfully instantiating their own Kubernetes Cluster, students will be guided through foundational concepts of deploying and managing applications in a production environment. Workloads & Scheduling After establishing a solid Kubernetes command line foundation, students will be led through discussion and hands-on labs which focus on effectively creating applications that are easy to configure, simple to manage, quick to scale, and able to heal themselves. Services & Networking Thoroughly understanding the underlying physical and network infrastructure of a Kubernetes cluster is an essential skill for a Certified Kubernetes Administrator. After an in-depth discussion of the Kubernetes Networking Model, students explore the networking of their cluster?s Control Plane, Workers, Pods, and Services. Storage Certified Kubernetes Administrators are often in charge of designing and implementing the storage architecture for their clusters. After discussing many common cluster storage solutions and how to best use each, students practice incorporating stateful storage into their applications. Troubleshooting A Certified Kubernetes Administrator is expected to be an effective troubleshooter for their cluster. The lecture covers a variety of ways to evaluate and optimize available log information for efficient troubleshooting, and the labs have students practice diagnosing and resolving several typical issues within their Kubernetes Cluster. Certified Kubernetes Administrator Practice Exam Just like the Cloud Native Computing Foundation CKA Exam, the students will be given two hours to complete hands-on tasks in their own Kubernetes environment. Unlike the certification exam, students taking the Alta3 CKA Practice Exam will have scoring and documented answers available immediately after the exam is complete, and will have built-in class time to re-examine topics that they wish to discuss in greater depth.

Duration 3 Days 18 CPD hours This course is intended for This course is for information technology professionals, security professionals, network, system managers and administrators tasked with installing, configuring and maintaining Symantec Data Center Security: Server Advanced. Overview At the completion of the course, you will be able to: Describe the major components of Symantec Data Center Security: Server Advanced and how they communicate. Install the management server, console and agent. Define, manage and create assets, policies, events and configurations. Understand policy creation and editing in depth. course is an introduction to implementing and managing a Symantec Data Center Security: Server Advanced 6.0 deployment. Introduction Course Overview The Classroom Lab Environment Introduction to Security Risks and Risk Security Risks Security Risk Management Managing and Protecting Systems Corporate Security Policies and Security Assessments Host-Based Computer Security Issues SDCS:Server Advanced Overview SDCS: Server Advanced Component Overview Policy Types and Platforms Management Console Overview Agent User Interface Overview DEMO of Management Console Installation and Deployment Planning the Installation Deploying SDCS:SA for High Availability Scalability Installing the Management Server Installing the Management Console Installing a Windows Agent Installing a UNIX Agent LAB: Install Manager and Agents Configuring Assets Asset and Agent Overview Viewing Agents and Assets Managing Agents Managing Agents on Assets LAB: Create Asset Groups LAB: Examine Agent Interface Policy Overview Policies Defined Prevention Policy Overview Process Sets Resource Access Policy Options Detection Policy Overview IDS Capabilities Rules Collectors Policy Management Workspace User Interface on Agent Example Use Cases LAB: Paper Based Scenarios LAB: What type of security strategy should be used? Detailed Prevention Policies Policy Editor Policy Structure Global Policy Options Service Options Program Options Policy Processing Order Network Rules File Rules Registry Rules Process Sets Predefined Policies LAB: Deploy Strict policy LAB: Examine Functionality Advanced Prevention Profiling Applications Customizing Predefined Policies LAB: Modify Policy Previously Deployed LAB: Re-examine Functionality LAB: Preparing for Policy deployment LAB: Best Practice - Covering Basics LAB: Further Enhance Strict Policy LAB: Create Custom Process Set LAB :Secure an FTP Server LAB: Troubleshoot Policy/pset Assignment Using CLI Detection Policies Detection Policies Structure Collectors Rules Predefined Detection Policies Creating a Detection Policy Using the Template Policy LAB: Deploy Baseline Policy LAB: Create Custom Policy Event Management Events Defined Viewing Events Reports and Queries Overview Creating Queries and Reports Creating Alerts LAB: View Monitor Types and Search Events LAB: Create Real Time Monitor Agent Management and Troubleshooting Configurations Defined Creating and Editing Configurations Common Parameters Prevention Settings Detection Settings Analyzing Agent Log Files Diagnostic Policies Local Agent Tool ? sisipsconfig LAB: Create Custom Configurations LAB: Implement Bulk Logging LAB: Disable Prevention on Agent Using CLI LAB: Use Diagnostic Policy to Gather Logs LAB: Troubleshoot a Policy System Management Managing Users and Roles Server Security Viewing and Managing Server Settings Viewing and Managing Database Settings Viewing and Managing Tomcat Settings LAB: Create a New User LAB: View System Settings

Duration 5 Days 30 CPD hours This course is intended for Linux system administrators, site reliability engineers, and other IT professionals with some Ansible experience who are interested in learning how to manage and automate the deployment, configuration, and operation of key network services included with Red Hat Enterprise Linux 8. Overview Provide key network services using software included with Red Hat Enterprise Linux 8, including DNS with Unbound and BIND9, DHCP and DHCPv6, client e-mail transmission, printing service, NFS and SMB protocol file sharing, SQL database service with MariaDB, and web services using Apache HTTPD, nginx, Varnish, and HAProxy. Configure advanced networking for server use cases, including device teaming. Use Red Hat Ansible Engine to automate the manual deployment and configuration tasks covered in this course. Learn how to configure, manage, and scale key services used in the data center Red Hat Services Management and Automation (RH358) is designed for IT professionals with some experience managing Linux© systems and want to learn more about how to manage and deploy network services included with Red Hat© Enterprise Linux which are particularly important in the modern IT data center. You will learn how to install, configure, and manage basic configurations of these services manually, and then use Red Hat Ansible© Engine to automate your work in a scalable, repeatable manner. This course is based on Red Hat Ansible Engine 2.9 and Red Hat Enterprise Linux 8.1. 1 - Manage network services Discuss and review key tools and skills needed to manage network services. 2 - Configure link aggregation Improve the redundancy or throughput of network connections of servers by configuring Linux network teaming between multiple network interfaces. 3 - Manage DNS and DNS servers Explain the operation of DNS service, troubleshoot DNS issues, and configure servers to act as a DNS caching nameserver or as an authoritative name server. 4 - Manage DHCP and IP address assignment Explain and configure services used for IPv4 and IPv6 address assignment including DHCP, DHCPv6, and SLAAC. 5 - Manage printers and printing files Configure systems to print to a network printer that supports IPP Everywhere, as well as manage existing printer queues. 6 - Configure email transmission Discuss how mail servers operate, then configure a server to use system tools and Postfix to send email messages through an outbound mail relay. 7 - Configure MariaDB SQL databases Discuss the basic operation of SQL-based relational databases, perform basic SQL queries for troubleshooting, and be able to set up a simple MariaDB database service. 8 - Configure web servers Provide web content from Apache HTTPD or Nginx web servers, then configure them with virtual hosts and TLS-based encryption. 9 - Optimize web server traffic Improve performance of your web servers by using Varnish to cache static content being served and HAProxy to terminate TLS connections and balance load between servers. 10 - Provide file-based network storage Deliver simple file-based network shares to clients using the NFS and SMB protocols. 11 - Access block-based network storage Configure iSCSI initiators on your servers to access block-based storage devices provided by network storage arrays or Ceph storage clusters.

Duration 2 Days 12 CPD hours This course is intended for This training is ideally suited for data analysts, IT professionals, and software developers who seek to augment their data processing and analytics capabilities. It will also benefit system administrators and data engineers who wish to harness Elastic Stack's functionalities for efficient system logging, monitoring, and robust data visualization. With a focus on practical application, this course is perfect for those aspiring to solve complex data challenges in real-time environments across diverse industry verticals. Overview This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you'll explore: New features and updates introduced in Elastic Stack 7.0 Fundamentals of Elastic Stack including Elasticsearch, Logstash, and Kibana Useful tips for using Elastic Cloud and deploying Elastic Stack in production environments How to install and configure an Elasticsearch architecture How to solve the full-text search problem with Elasticsearch Powerful analytics capabilities through aggregations using Elasticsearch How to build a data pipeline to transfer data from a variety of sources into Elasticsearch for analysis How to create interactive dashboards for effective storytelling with your data using Kibana How to secure, monitor and use Elastic Stack's alerting and reporting capabilities The Elastic Stack is a powerful combination of tools for techniques such as distributed search, analytics, logging, and visualization of data. Elastic Stack 7.0 encompasses new features and capabilities that will enable you to find unique insights into analytics using these techniques. Geared for experienced data analysts, IT professionals, and software developers who seek to augment their data processing and analytics capabilities, Working with Elasticsearch will explore how to use Elastic Stack and Elasticsearch efficiently to build powerful real-time data processing applications. Throughout the two-day hands-on course, you?ll explore the power of this robust toolset that enables advanced distributed search, analytics, logging, and visualization of data, enabled by new features in Elastic Stack 7.0. You?ll delve into the core functionalities of Elastic Stack, understanding the role of each component in constructing potent real-time data processing applications. You?ll gain proficiency in Elasticsearch for distributed searching and analytics, Logstash for logging, and Kibana for compelling data visualization. You?ll also explore the art of crafting custom plugins using Kibana and Beats, and familiarize yourself with Elastic X-Pack, a vital extension for effective security and monitoring. The course also covers essentials like Elasticsearch architecture, solving full-text search problems, data pipeline building, and creating interactive Kibana dashboards. Learn how to deploy Elastic Stack in production environments and explore the powerful analytics capabilities offered through Elasticsearch aggregations. The course will also touch upon securing, monitoring, and utilizing Elastic Stack's alerting and reporting capabilities. Hands-on labs, captivating demonstrations, and interactive group activities enrich your learning journey throughout the course. Introducing Elastic Stack What is Elasticsearch, and why use it? Exploring the components of the Elastic Stack Use cases of Elastic Stack Downloading and installing Getting Started with Elasticsearch Using the Kibana Console UI Core concepts of Elasticsearch CRUD operations Creating indexes and taking control of mapping REST API overview Searching - What is Relevant The basics of text analysis Searching from structured data Searching from the full text Writing compound queries Modeling relationships Analytics with Elasticsearch The basics of aggregations Preparing data for analysis Metric aggregations Bucket aggregations Pipeline aggregations Substantial Lab and Case Study Analyzing Log Data Log analysis challenges Using Logstash The Logstash architecture Overview of Logstash plugins Ingest node Visualizing Data with Kibana Downloading and installing Kibana Preparing data Kibana UI Timelion Using plugins

Duration 3 Days 18 CPD hours This course is intended for Data Analysts, Business Analysts, Business Intelligence professionals Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform Overview This course teaches students the following skills: Derive insights from data using the analysis and visualization tools on Google Cloud Platform Interactively query datasets using Google BigQuery Load, clean, and transform data at scale Visualize data using Google Data Studio and other third-party platforms Distinguish between exploratory and explanatory analytics and when to use each approach Explore new datasets and uncover hidden insights quickly and effectively Optimizing data models and queries for price and performance Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your data grows? Welcome to the Data Insights course! This four-course accelerated online specialization teaches course participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The courses feature interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The courses also cover data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization. This specialization is intended for the following participants: Data Analysts, Business Analysts, Business Intelligence professionals Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform To get the most out of this specialization, we recommend participants have some proficiency with ANSI SQL. Introduction to Data on the Google Cloud Platform Highlight Analytics Challenges Faced by Data Analysts Compare Big Data On-Premises vs on the Cloud Learn from Real-World Use Cases of Companies Transformed through Analytics on the Cloud Navigate Google Cloud Platform Project Basics Lab: Getting started with Google Cloud Platform Big Data Tools Overview Walkthrough Data Analyst Tasks, Challenges, and Introduce Google Cloud Platform Data Tools Demo: Analyze 10 Billion Records with Google BigQuery Explore 9 Fundamental Google BigQuery Features Compare GCP Tools for Analysts, Data Scientists, and Data Engineers Lab: Exploring Datasets with Google BigQuery Exploring your Data with SQL Compare Common Data Exploration Techniques Learn How to Code High Quality Standard SQL Explore Google BigQuery Public Datasets Visualization Preview: Google Data Studio Lab: Troubleshoot Common SQL Errors Google BigQuery Pricing Walkthrough of a BigQuery Job Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs Optimize Queries for Cost Lab: Calculate Google BigQuery Pricing Cleaning and Transforming your Data Examine the 5 Principles of Dataset Integrity Characterize Dataset Shape and Skew Clean and Transform Data using SQL Clean and Transform Data using a new UI: Introducing Cloud Dataprep Lab: Explore and Shape Data with Cloud Dataprep Storing and Exporting Data Compare Permanent vs Temporary Tables Save and Export Query Results Performance Preview: Query Cache Lab: Creating new Permanent Tables Ingesting New Datasets into Google BigQuery Query from External Data Sources Avoid Data Ingesting Pitfalls Ingest New Data into Permanent Tables Discuss Streaming Inserts Lab: Ingesting and Querying New Datasets Data Visualization Overview of Data Visualization Principles Exploratory vs Explanatory Analysis Approaches Demo: Google Data Studio UI Connect Google Data Studio to Google BigQuery Lab: Exploring a Dataset in Google Data Studio Joining and Merging Datasets Merge Historical Data Tables with UNION Introduce Table Wildcards for Easy Merges Review Data Schemas: Linking Data Across Multiple Tables Walkthrough JOIN Examples and Pitfalls Lab: Join and Union Data from Multiple Tables Advanced Functions and Clauses Review SQL Case Statements Introduce Analytical Window Functions Safeguard Data with One-Way Field Encryption Discuss Effective Sub-query and CTE design Compare SQL and Javascript UDFs Lab: Deriving Insights with Advanced SQL Functions Schema Design and Nested Data Structures Compare Google BigQuery vs Traditional RDBMS Data Architecture Normalization vs Denormalization: Performance Tradeoffs Schema Review: The Good, The Bad, and The Ugly Arrays and Nested Data in Google BigQuery Lab: Querying Nested and Repeated Data More Visualization with Google Data Studio Create Case Statements and Calculated Fields Avoid Performance Pitfalls with Cache considerations Share Dashboards and Discuss Data Access considerations Optimizing for Performance Avoid Google BigQuery Performance Pitfalls Prevent Hotspots in your Data Diagnose Performance Issues with the Query Explanation map Lab: Optimizing and Troubleshooting Query Performance Advanced Insights Introducing Cloud Datalab Cloud Datalab Notebooks and Cells Benefits of Cloud Datalab Data Access Compare IAM and BigQuery Dataset Roles Avoid Access Pitfalls Review Members, Roles, Organizations, Account Administration, and Service Accounts