Oversikt over relevante kurs innen IT-kurs i Svelvik

Understand the purpose and key concepts of Information Security Management, elucidating its significance in safeguarding the confidentiality, integrity, and availability of organisational information assets. This eLearning is: Interactive Self-paced   Device-friendly   2-3 hours content   Mobile-optimised   Practical exercises   Exam: 20 questions Multiple choise 30 minutes Closed book Minimum required score to pass: 65% [-]

Agenda Module 1: Implement VMs for Windows and Linux -Select Virtual Machine Size-Configure High Availability-Implement Azure Dedicated Hosts-Deploy and Configure Scale Sets-Configure Azure Disk Encryption Module 2: Automate Deployment and Configuration of Resources -Azure Resource Manager Templates-Save a Template for a VM-Evaluate Location of New Resources-Configure a Virtual Hard Disk Template-Deploy from a Template-Create and Execute an Automation Runbook Module 3: Implement Virtual Networking -Virtual Network Peering-Implement VNet Peering Module 4: Implement Load Balancing and Network Security -Implement Azure Load Balancer-Implement an Application Gateway-Understand Web Application Firewall-Implement Azure Firewall-Implement Azure Front Door-Implementing Azure Traffice Manager-Implement Network Security Groups and Application Security Grou-Implement Azure Bastion Module 5: Implement Storage Accounts -Storage Accounts-Blob Storage-Storage Security-Managing Storage-Accessing Blobs and Queues using AAD-Configure Azure Storage Firewalls and Virtual Networks Module 6: Implement Azure Active Directory -Overview of Azure Active Directory-Users and Groups-Domains and Custom Domains-Azure AD Identity Protection-Implement Conditional Access-Configure Fraud Alerts for MFA-Implement Bypass Options-Configure Trusted IPs-Configure Guest Users in Azure AD-Manage Multiple Directori Module 7: Implement and Manage Azure Governance -Create Management Groups, Subscriptions, and Resource Groups-Overview of Role-Based Access Control (RBAC)-Role-Based Access Control (RBAC) Roles-Azure AD Access Reviews-Implement and Configure an Azure Policy-Azure Blueprints Module 8: Implement and Manage Hybrid Identities -Install and Configure Azure AD Connect-Configure Password Sync and Password Writeback-Configure Azure AD Connect Health Module 9: Manage Workloads in Azure -Migrate Workloads using Azure Migrate-VMware - Agentless Migration-VMware - Agent-Based Migration-Implement Azure Backup-Azure to Azure Site Recovery-Implement Azure Update Management Module 10: Implement Cloud Infrastructure Monitoring -Azure Infrastructure Security Monitoring-Azure Monitor-Azure Workbooks-Azure Alerts-Log Analytics-Network Watcher-Azure Service Health-Monitor Azure Costs-Azure Application Insights-Unified Monitoring in Azure Module 11: Manage Security for Applications -Azure Key Vault-Azure Managed Identity Module 12: Implement an Application Infrastructure -Create and Configure Azure App Service-Create an App Service Web App for Containers-Create and Configure an App Service Plan-Configure Networking for an App Service-Create and Manage Deployment Slots-Implement Logic Apps-Implement Azure Functions Module 13: Implement Container-Based Applications -Azure Container Instances-Configure Azure Kubernetes Service Module 14: Implement NoSQL Databases -Configure Storage Account Tables-Select Appropriate CosmosDB APIs Module 15: Implement Azure SQL Databases -Configure Azure SQL Database Settings-Implement Azure SQL Database Managed Instances-High-Availability and Azure SQL Database [-]

Kurs i hypotesetesting I forbedringsarbeid og problemløsning tester vi hypoteser for å bestemme rotårsaker og årsakssammenhenger. Dette kurset lærer deg å utforme og teste hypoteser. Du får svar på spørsmål som: Er det signifikante forskjeller i gjennomsnitt eller variasjon? Har endringen vi har gjort medført en signifikant forbedring?   Kurset er for deg som vil: utforme hypotese basert på egne teorier om rotårsak eller årsakssammenhenger bestemme om datasett har signifikante forskjelliger i gjennomsnitt eller variasjon avgjøre om forbedringsarbeid har gitt signifikante forskjeller forstå årsakssammenhenger ved hjelp av statistikk   Du lærer følgende: Bruk av statistisk hypotesetesting Praktisk og statistisk signifikans Statistikk og sannsynlighet Utforme hypotese Velge Hypotesetest (type data, fordeling, statistikk av interesse, # populasjoner) Trekke konklusjon basert på p-verdi Type I og type II feil Vurdering av datautvalg og prøveantall Bruke av p-verdi Vi bruker praktiske eksempler og øvelser i undervisningen.     Kursholder Kursholder Sissel Pedersen Lundeby er IASSC (International association for Six Sigma certification) akkreditert kursholder (eneste i Norge per januar 2022): "This accreditation publically reflects that you have met the standards established by IASSC such that those who participate in a training program led by you can expect to receive an acceptable level of knowledge transfer consistent with the Lean Six Sigma belt Bodies of Knowledge as established by IASSC."  Hypotesetesting er et av verktøyene som benyttes innen Lean Six Sigma, og Sissel har bred erfaring med anvendelse av dette verktøyet.  Sissel er utdannet sivilingeniør i kjemiteknikk fra NTNU, og har mer enn 20 års erfaring innen produksjon og miljøteknologi. Hennes Lean Six Sigma opplæring startet i 2002, hos et amerikansk firma, hvor hun ble Black Belt sertifisert. I 2017 ble hun også Black Belt sertifisert gjennom IASSC. Sissel har svært god erfaring med å bruke Lean Six Sigma til forbedringer, og fokuserer på å skape målbare resultater. Kursene bruker praktiske, gjenkjennelige eksempler, og formidler Lean Six Sigma på en enkel, forståelig måte.      Tilbakemeldinger "Inspirerende, faglig dyktig, folkeliggjør et teoretisk fagområde" Espen Fjeld, Kommersiell direktør hos Berendsen "Faglig meget dyktig og klar fremføring. Morsom og skaper tillit" Jon Sørensen, Produksjonsleder hos Berendsen "10/10 flink til å nå alle" Erlend Stene, Salgsleder hos Berendsen "Tydelig og bra presentert. God til å kontrollspørre og lytte (sjekke forståelse)" Morten Bodding, Produksjonsleder hos Berendsen "Utgjorde en forskjell, engasjert og dyktig" Kursdeltager fra EWOS "Du er inspirerende, positiv og dyktig i faget" Kursdeltager fra EWOS "Jeg var veldig imponert over Sissels Lean Six Sigma kunnskap. Hun gjør det enkelt å identifisere forbedringer og skape resultater" Daryl Powell, Lean Manager, Kongsberg Maritime Subsea   Praktisk informasjon Kurset arrangeres på forespørsel fra bedrifter. Åpne kurs arrangeres ihht kurskalenderen. Kurset består av et nettmøte på 2 timer. [-]

Filer i SharePoint lagres i bibliotek. Her tar vi en grundig gjennomgang av bibliotek og tilpasningsmuligheter for disse, som versjonering, maler og Office-integrasjon. Webinaret varer i 2 timer og består av to økter à 45 min. Etter hver økt er det 10 min spørsmålsrunde. Mellom øktene er det 10 min pause. Webinaret kan også spesialtilpasses og holdes bedriftsinternt kun for din bedrift.   Kursinnhold:   Om bibliotek Møt biblioteksmalene i SharePoint Opplasting, nedlasting Office-programmene og bibliotek Områdepapirkurv   Tilpasse bibliotek Endre Office-mal for et bibliotek Tilpass kolonner og metadata   Tips til bibliotek Bruke kolonner i Word Bibliotek i Windows Utforsker   Utvidet om bibliotek Gjennomgang av versjonering Bli kjent med godkjenning Arkivering og Send til   Veien videre Introduksjon til innholdstyper Introduksjon til dokumentsenter og innholds-sortering 3 gode grunner til å delta 1. Møt SharePoint sine bibliotek-apper og lær måter å åpne og lagre i bibliotek og håndtere innholdet 2. Forstå mer om versjonering, godkjenning og arkivering 3. Bli kjent med dokumentsenter og innholds-sortering   [-]

. This course includes identity content for Azure AD, enterprise application registration, conditional access, identity governance, and other identity tools.   TARGET AUDIENCE This course is for the Identity and Access Administrators who are planning to take the associated certification exam, or who are performing identity and access administration tasks in their day-to-day job. This course would also be helpful to an administrator or engineer that wants to specialize in providing identity solutions and access management systems for Azure-based solutions; playing an integral role in protecting an organization. COURSE OBJECTIVES Implement an identity management solution Implement an authentication and access management solutions Implement access management for apps Plan and implement an identity governancy strategy COURSE CONTENT Module 1: Implement an identity management solution Learn to create and manage your initial Azure Active Directory (Azure AD) implementation and configure the users, groups, and external identities you will use to run your solution. Lessons M1 Implement Initial configuration of Azure AD Create, configure, and manage identities Implement and manage external identities Implement and manage hybrid identity Lab 1a: Manage user roles Lab 1b: Setting tenant-wide properties Lab 1c: Assign licenses to users Lab 1d: Restore or remove deleted users Lab 1e: Add groups in Azure AD Lab 1f: Change group license assignments Lab 1g: Change user license assignments Lab 1h: Configure external collaboration Lab 1i: Add guest users to the directory Lab 1j: Explore dynamic groups After completing module 1, students will be able to: Deploy an initail Azure AD with custom settings Manage both internal and external identities Implement a hybrid identity solution Module 2: Implement an authentication and access management solution Implement and administer your access management using Azure AD. Use MFA, conditional access, and identity protection to manager your identity solution. Lessons M2 Secure Azure AD user with MFA Manage user authentication Plan, implement, and administer conditional access Manage Azure AD identity protection Lab 2a: Enable Azure AD MFA Lab 2b: Configure and deploy self-service password reset (SSPR) Lab 2c: Work with security defaults Lab 2d: Implement conditional access policies, roles, and assignments Lab 2e: Configure authentication session controls Lab 2f: Manage Azure AD smart lockout values Lab 2g: Enable sign-in risk policy Lab 2h: Configure Azure AD MFA authentication registration policy After completing module 2, students will be able to: Configure and manage user authentication including MFA Control access to resources using conditional access Use Azure AD Identity Protection to protect your organization Module 3: Implement access management for Apps Explore how applications can and should be added to your identity and access solution with application registration in Azure AD. Lessons M3 Plan and design the integration of enterprise for SSO Implement and monitor the integration of enterprise apps for SSO Implement app registration Lab 3a: Implement access management for apps Lab 3b: Create a custom role to management app registration Lab 3c: Register an application Lab 3d: Grant tenant-wide admin consent to an application Lab 3e: Add app roles to applications and recieve tokens After completing module 3, students will be able to: Register a new application to your Azure AD Plan and implement SSO for enterprise application Monitor and maintain enterprise applications Module 4: Plan and implement an identity governancy strategy Design and implement identity governance for your identity solution using entitlement, access reviews, privileged access, and monitoring your Azure Active Directory (Azure AD). Lessons M4 Plan and implement entitlement management Plan, implement, and manage access reviews Plan and implement privileged access Monitor and maintain Azure AD Lab 4a: Creat and manage a resource catalog with Azure AD entitlement Lab 4b: Add terms of use acceptance report Lab 4c: Manage the lifecycle of external users with Azure AD identity governance Lab 4d: Create access reviews for groups and apps Lab 4e: Configure PIM for Azure AD roles Lab 4f: Assign Azure AD role in PIM Lab 4g: Assign Azure resource roles in PIM Lab 4h: Connect data from Azure AD to Azure Sentinel After completing module 4, students will be able to: Mange and maintain Azure AD from creation to solution Use access reviews to maintain your Azure AD Grant access to users with entitlement management [-]

Blended Virtual CourseThe course is a hybrid of virtual training and self-study which will be a mixture of teaching using Microsoft Teams for short bursts at the beginning of the day, then setting work for the rest of the day and then coming back at the end of the day for another on-line session for any questions before setting homework in the form of practice exams for the evening. You do not have to install Microsoft Teams, you will receive a link and can access the course using the web browser.  Remote proctored examTake your exam from any location. Read about iSQI remote proctored exam here Requirements for the exam: The exam will be using Google Chrome and there is a plug-in that needs to be installed  You will need a laptop/PC with a camera and a microphone  A current ID with a picture    KursinnholdDette kurset forklarer det grunnleggende i softwaretesting. Det er basert på ISTQB- pensum og er akkreditert av BCS.    Kurset inneholder øvelser, prøveeksamener og spill for å fremheve sentrale deler av pensum. Dette sammen med kursmateriell og presentasjoner vil bistå i forståelse av begreper og metoder som blir presentert.   Bouvet sine kursdeltakeres testresultater vs ISTQB gjennomsnitt   «Særs godt kurs med mye fokus på praktiske oppgaver som gjør læring vesentlig lettere. Engasjert kursleder hjelper også. Kursleder starter på et nivå som alle føler seg komfortabel med.» Alexander Røstum Course content Fundamentals of Testing This section looks at why testing is necessary, what testing is, and explains general testing principles, the fundamental test process, and psychological aspects of testing.   Skills Gained • Learn about the differences between the testing levels and targets• Know how to apply both black and white box approaches to all levels of testing• Understand the differences between the various types of review and be aware of Static Analysis• Learn aspects of test planning, estimation, monitoring and control• Communicate better through understanding standard definitions of terms• Gain knowledge of the different types of testing tools and the best way of implementing those tools   Testing throughout the software lifecycle Explains the relationship between testing and life cycle development models, including the V-model and iterative development. Outlines four levels of testing:• Component testing• Integration testing• System testing• Acceptance testing Describes four test types, the targets of testing:• functional• non-functional characteristics• structural• change-related Outlines the role of testing in maintenance.   Static Techniques Explains the differences between the various types of review, and outlines the characteristics of a formal review. Describes how static analysis can find defects.   Test Design Techniques This section explains how to identify test conditions (things to test) and how to design test cases and procedures. It also explains the difference between white and black box testing. The following techniques are described in some detail with practical exercises :• Equivalence Partitioning• Boundary Value Analysis• Decision Tables• State Transition testing• Statement and Decision testingIn addition, use case testing and experience-based testing (such as exploratory testing) are described, and advice is given on choosing techniques.   Test Management This section looks at organisational implications for testing and describes test planning and estimation, test monitoring and control. The relationship of testing and risk is covered,and configuration management and incident management.   Tool Support for Testing Different types of tool support for testing are described throughout the course. This session summarises them, and discusses how to use them effectively and how best to introduce a new tool.   The Exam The ISTQB Foundation exam is a 1-hour, 40 question multiple choice exam. There is an extra 15 minutes allowed for candidates whose first language is not English.The pass mark is 65% (26/40) and there are no pre requisites to taking this exam.The exam is a remote proctored exam [-]

Excelkurs Basis - elæringskurs [-]

Objectives Describe specialized data classifications on Azure Identify Azure data protection mechanisms Implement Azure data encryption methods Secure Internet protocols and how to implement them on Azure Describe Azure security services and features Agenda Module 1: Identity and Access -Configure Azure Active Directory for Azure workloads and subscriptions-Configure Azure AD Privileged Identity Management-Configure security for an Azure subscription Module 2: Platform Protection -Understand cloud security-Build a network-Secure network-Implement host security-Implement platform security-Implement subscription security Module 3: Security Operations -Configure security services-Configure security policies by using Azure Security Center-Manage security alerts-Respond to and remediate security issues-Create security baselines Module 4: Data and applications -Configure security policies to manage data-Configure security for data infrastructure-Configure encryption for data at rest-Understand application security-Implement security for application lifecycle-Secure applications-Configure and manage Azure Key Vault       [-]

Kursinnhold Enkle formler Cellereferanser Gi navn til celler og områder Feilkontroll og formelrevisjon Hente data fra andre ark og arbeidsbøker Egendefinerte tallformater Betinget formatering Utklippstavle og avansert innliming   Det er fordelaktig å ha to skjermer - en til å følge kurset og en til å gjøre det kursholder demonstrerer.   Kurset gjennomføres i sanntid med nettundervisning via Teams. Det blir mulighet for å stille spørsmål, ha diskusjoner, demonstrasjoner og øvelser. Du vil motta en invitasjon til Teams fra kursholder. [-]

Koding automasjon i bygg Denne fagskole utdanningens innhold tilsvarer 5 studiepoeng og utdanning er på nettet.  Maksimalt antall studieplasser er 25. Utdanningen er praktisk tilrettelagt, slik at du kan anvende teori og kunnskap i praksis. Du vil få mulighet til å jobbe med reelle og aktuelle problemstillinger, og du vil få tilbakemelding fra erfarne fagfolk. Læremateriellet består av video, podkaster, resyme av fagstoff, artikler, forskningsrapporter, foredrag, presentasjon av fagstoff, samt quizer og annet. Læremateriellet du får tilgang til er på en LMS som er under kontinuerlig utvikling og oppdatering. Du får ett års tilgang til læremateriell, etter at utdanningen er ferdig, på Learning Management System (LMS) I denne utdanningen lærer du om: Installere Python på egen PC (Spyder): Veiledning for hvordan du installerer Python og Spyder IDE for å utvikle Python-programmer. Introduksjon til programmering i: HTML: Grunnleggende om HTML-strukturer og webutvikling. Python: Introduksjon til grunnleggende programmeringskonsepter, inkludert: Variabler og Datatyper: Opprettelse og bruk av variabler med ulike datatyper som heltall (integers), desimaltall (floats), strenger (strings), lister (lists), tupler (tuples), og dictionaries (dictionaries). Operatorer: Bruk av matematiske, sammenlignings-, og logiske operatorer for beregninger og verdikomparasjoner. Løkker: Implementering av kontrollstrukturer som if-setninger, for- og while-løkker, samt avvikshantering med try og except for å styre programflyten. Funksjoner: Definisjon og anvendelse av funksjoner for å organisere koden i moduler og forbedre lesbarheten og vedlikeholdbarheten. Input og Output: Håndtering av datainnlesning fra bruker og datavisning til skjermen. Moduler og Biblioteker: Utforsking av innebygde og tredjepartsmoduler for å utvide programmets funksjonalitet. Filstyring: Åpning, lesing, skriving, og lukking av filer. Strukturering av kode: Organisering av kode ved hjelp av funksjoner, klasser, og moduler for bedre lesbarhet og vedlikehold. JavaScript: Grunnleggende programmeringskonsepter for å utvikle interaktive webapplikasjoner. Programmere App til mobil telefon: Introduksjon til å kunne programmere Android-apps. Fra sensor til web: Utvikling av programmer fra grunnen av, fra å programmere Arduino UNO som en Modbus RTU slave til å utvikle en Modbus RTU master i Python. Konfigurasjon av egen PC som webserver (IIS) for å støtte webapplikasjoner. Integrert prosjektarbeid som involverer programmering fra sensor til web, som kombinerer hardware og software for å samle, behandle, og presentere data. Inkluderer API-er (Application Programming Interfaces) og tekniske beskrivelser. Du velger selv prosjektoppgave: Oppgaven kan for eksempel innebære å innhente data via API fra https://www.yr.no/ eller en annen nettressurs. Ved å anvende Modbus for I/O på Arduino, er det mulig å utvikle et system som både overvåker og regulerer energiforbruket ditt. Brukergrensesnittet kan være basert på web, og konfigureres på din egen datamaskin. Denne utdanningen danner et solid fundament for videre læring og anvendelse av disse konseptene i automasjon i bygg. Bedriftsinterne utdanning tilpasset din bedrift Denne utdanningen kan tilbys som en bedriftsintern utdanning. Det faglige innholdet er fastsatt, men den faglige tilnærmingen kan tilpasses den enkelte bedrifts behov og ønsker. Ta kontakt for en prat, så kan vi sammen lage et utdanningsløp som passer for deg og din bedrift. Kontaktpersoner er Hans Gunnar Hansen (tlf. 91101824) og Vidar Luth-Hanssen (tlf. 91373153) [-]

Through a combination of presentations, demos, and hands-on labs, participants get an overview of the Google Cloud platform and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud Platform. Learning Objectives This course teaches participants the following skills: Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform Employ BigQuery and Cloud Datalab to carry out interactive data analysis Train and use a neural network using TensorFlow Employ ML APIs Choose between different data processing products on the Google Cloud Platform Course Outline Module 1: Introducing Google Cloud Platform -Google Platform Fundamentals Overview-Google Cloud Platform Big Data Products Module 2: Compute and Storage Fundamentals -CPUs on demand (Compute Engine)-A global filesystem (Cloud Storage)-CloudShell-Lab: Set up an Ingest-Transform-Publish data processing pipeline Module 3: Data Analytics on the Cloud -Stepping-stones to the cloud-CloudSQL: your SQL database on the cloud-Lab: Importing data into CloudSQL and running queries-Spark on Dataproc-Lab: Machine Learning Recommendations with Spark on Dataproc Module 4: Scaling Data Analysis -Fast random access-Datalab-BigQuery-Lab: Build machine learning dataset Module 5: Machine Learning -Machine Learning with TensorFlow-Lab: Carry out ML with TensorFlow-Pre-built models for common needs-Lab: Employ ML APIs Module 6: Data Processing Architectures -Message-oriented architectures with Pub/Sub-Creating pipelines with Dataflow-Reference architecture for real-time and batch data processing Module 7: Summary -Why GCP?-Where to go from here-Additional Resources [-]

MD-101: Managing Modern Desktops [-]

ITIL® 4 Practitioner: Change Enablement [-]

Er du på jakt etter mer avansert funksjonalitet på forsidene dine? På dette webinaret lærer du mer om å sette inn innhold fra andre kilder og å sy sammen komponentene på siden. Webinaret varer i 2 timer og består av to økter à 45 min. Etter hver økt er det 10 min spørsmålsrunde. Mellom øktene er det 10 min pause. Webinaret kan også spesialtilpasses og holdes bedriftsinternt kun for din bedrift.   Kursinnhold:   Sider og sideoppsett Bli kjent med Webdel-sider og oppsett Hvordan legge til skriptsnutter og elementer fra andre nettsider   Bygg inn innhold Legg inn embed-kode Forberede og presentere en PowerPoint-presentasjon på forsiden ved hjelp av Office Web Apps/Office Online   Forsider og dashboards Forberede og presentere en Excel-bok på forsiden med Excel Services Forberede og presentere en Visio-tegning som forsidemeny med Visio Services   Dynamiske sider Målgrupper Koble sammen webdeler og la innhold i en webdel påvirke innholdet i en annen   3 gode grunner til å velge KnowledgeGroup 1. Best practice kursinnhold 2. Markedets beste instruktører 3. Gratis support [-]

The students will learn how to interactively explore data stored in files in a data lake. They will learn the various ingestion techniques that can be used to load data using the Apache Spark capability found in Azure Synapse Analytics or Azure Databricks, or how to ingest using Azure Data Factory or Azure Synapse pipelines. The students will also learn the various ways they can transform the data using the same technologies that is used to ingest data. The student will spend time on the course learning how to monitor and analyze the performance of analytical system so that they can optimize the performance of data loads, or queries that are issued against the systems. They will understand the importance of implementing security to ensure that the data is protected at rest or in transit. The student will then show how the data in an analytical system can be used to create dashboards, or build predictive models in Azure Synapse Analytics. After completing this course, students will be able to: Explore compute and storage options for data engineering workloads in Azure Design and Implement the serving layer Understand data engineering considerations Run interactive queries using serverless SQL pools Explore, transform, and load data into the Data Warehouse using Apache Spark Perform data Exploration and Transformation in Azure Databricks Ingest and load Data into the Data Warehouse Transform Data with Azure Data Factory or Azure Synapse Pipelines Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines Optimize Query Performance with Dedicated SQL Pools in Azure Synapse Analyze and Optimize Data Warehouse Storage Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link Perform end-to-end security with Azure Synapse Analytics Perform real-time Stream Processing with Stream Analytics Create a Stream Processing Solution with Event Hubs and Azure Databricks Build reports using Power BI integration with Azure Synpase Analytics Perform Integrated Machine Learning Processes in Azure Synapse Analytics Course prerequisites Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions.Recommended prerequisites:M-DP900 - Microsoft Azure Data FundamentalsM-AZ900 - Microsoft Azure Fundamentals Agenda Module 1: Explore compute and storage options for data engineering workloads This module provides an overview of the Azure compute and storage technology options that are available to data engineers building analytical workloads. This module teaches ways to structure the data lake, and to optimize the files for exploration, streaming, and batch workloads. The student will learn how to organize the data lake into levels of data refinement as they transform files through batch and stream processing. Then they will learn how to create indexes on their datasets, such as CSV, JSON, and Parquet files, and use them for potential query and workload acceleration. Module 2: Design and implement the serving layer This module teaches how to design and implement data stores in a modern data warehouse to optimize analytical workloads. The student will learn how to design a multidimensional schema to store fact and dimension data. Then the student will learn how to populate slowly changing dimensions through incremental data loading from Azure Data Factory. Module 3: Data engineering considerations for source files This module explores data engineering considerations that are common when loading data into a modern data warehouse analytical from files stored in an Azure Data Lake, and understanding the security consideration associated with storing files stored in the data lake. Module 4: Run interactive queries using Azure Synapse Analytics serverless SQL pools In this module, students will learn how to work with files stored in the data lake and external file sources, through T-SQL statements executed by a serverless SQL pool in Azure Synapse Analytics. Students will query Parquet files stored in a data lake, as well as CSV files stored in an external data store. Next, they will create Azure Active Directory security groups and enforce access to files in the data lake through Role-Based Access Control (RBAC) and Access Control Lists (ACLs). Module 5: Explore, transform, and load data into the Data Warehouse using Apache Spark This module teaches how to explore data stored in a data lake, transform the data, and load data into a relational data store. The student will explore Parquet and JSON files and use techniques to query and transform JSON files with hierarchical structures. Then the student will use Apache Spark to load data into the data warehouse and join Parquet data in the data lake with data in the dedicated SQL pool. Module 6: Data exploration and transformation in Azure Databricks This module teaches how to use various Apache Spark DataFrame methods to explore and transform data in Azure Databricks. The student will learn how to perform standard DataFrame methods to explore and transform data. They will also learn how to perform more advanced tasks, such as removing duplicate data, manipulate date/time values, rename columns, and aggregate data. Module 7: Ingest and load data into the data warehouse This module teaches students how to ingest data into the data warehouse through T-SQL scripts and Synapse Analytics integration pipelines. The student will learn how to load data into Synapse dedicated SQL pools with PolyBase and COPY using T-SQL. The student will also learn how to use workload management along with a Copy activity in a Azure Synapse pipeline for petabyte-scale data ingestion. Module 8: Transform data with Azure Data Factory or Azure Synapse Pipelines This module teaches students how to build data integration pipelines to ingest from multiple data sources, transform data using mapping data flowss, and perform data movement into one or more data sinks. Module 9: Orchestrate data movement and transformation in Azure Synapse Pipelines In this module, you will learn how to create linked services, and orchestrate data movement and transformation using notebooks in Azure Synapse Pipelines. Module 10: Optimize query performance with dedicated SQL pools in Azure Synapse In this module, students will learn strategies to optimize data storage and processing when using dedicated SQL pools in Azure Synapse Analytics. The student will know how to use developer features, such as windowing and HyperLogLog functions, use data loading best practices, and optimize and improve query performance. Module 11: Analyze and Optimize Data Warehouse Storage In this module, students will learn how to analyze then optimize the data storage of the Azure Synapse dedicated SQL pools. The student will know techniques to understand table space usage and column store storage details. Next the student will know how to compare storage requirements between identical tables that use different data types. Finally, the student will observe the impact materialized views have when executed in place of complex queries and learn how to avoid extensive logging by optimizing delete operations. Module 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link In this module, students will learn how Azure Synapse Link enables seamless connectivity of an Azure Cosmos DB account to a Synapse workspace. The student will understand how to enable and configure Synapse link, then how to query the Azure Cosmos DB analytical store using Apache Spark and SQL serverless. Module 13: End-to-end security with Azure Synapse Analytics In this module, students will learn how to secure a Synapse Analytics workspace and its supporting infrastructure. The student will observe the SQL Active Directory Admin, manage IP firewall rules, manage secrets with Azure Key Vault and access those secrets through a Key Vault linked service and pipeline activities. The student will understand how to implement column-level security, row-level security, and dynamic data masking when using dedicated SQL pools. Module 14: Real-time Stream Processing with Stream Analytics In this module, students will learn how to process streaming data with Azure Stream Analytics. The student will ingest vehicle telemetry data into Event Hubs, then process that data in real time, using various windowing functions in Azure Stream Analytics. They will output the data to Azure Synapse Analytics. Finally, the student will learn how to scale the Stream Analytics job to increase throughput. Module 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks In this module, students will learn how to ingest and process streaming data at scale with Event Hubs and Spark Structured Streaming in Azure Databricks. The student will learn the key features and uses of Structured Streaming. The student will implement sliding windows to aggregate over chunks of data and apply watermarking to remove stale data. Finally, the student will connect to Event Hubs to read and write streams. Module 16: Build reports using Power BI integration with Azure Synapase Analytics In this module, the student will learn how to integrate Power BI with their Synapse workspace to build reports in Power BI. The student will create a new data source and Power BI report in Synapse Studio. Then the student will learn how to improve query performance with materialized views and result-set caching. Finally, the student will explore the data lake with serverless SQL pools and create visualizations against that data in Power BI. Module 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics This module explores the integrated, end-to-end Azure Machine Learning and Azure Cognitive Services experience in Azure Synapse Analytics. You will learn how to connect an Azure Synapse Analytics workspace to an Azure Machine Learning workspace using a Linked Service and then trigger an Automated ML experiment that uses data from a Spark table. You will also learn how to use trained models from Azure Machine Learning or Azure Cognitive Services to enrich data in a SQL pool table and then serve prediction results using Power BI. [-]