Oversikt over relevante kurs innen IT-kurs i Mandal

Understand the purpose and key concepts of Service Configuration Management, including its role in maintaining accurate and reliable information about configuration items (CIs) within the IT infrastructure. This eLearning is: Interactive Self-paced Device-friendly 2-3 hours content Mobile-optimised Practical exercises   Exam: 20 questions Multiple Choice 30 Minutes Closed book Pass Mark: 65% [-]

Understand the purpose and key concepts of the Monitor, Support, and Fulfil practices, elucidating their importance in maintaining, supporting, and delivering IT services effectively.InteractiveOur eLearning:Self-pacedDevice-friendly12 hour contentMobile-optimised Exam:60 questionsMultiple Choice90 minutesClosed bookMinimum required score to pass: 65%  [-]

ITIL® 4 Foundation-kurset er en introduksjon til ITIL® 4. Kurset lar kandidater se på IT-tjenestestyring gjennom en ende-til-ende driftsmodell, som inkluderer oppretting, levering og kontinuerlig forbedring av IT-relaterte produkter og tjenester. E-læringskurset inneholder 12 timer med undervisning, og er delt inn i 8 moduler. Les mer om ITIL® 4 på AXELOS sine websider. Inkluderer: Tilgang til ITIL® 4 Foundation e-læring (engelsk) i 12 måneder. ITIL® Foundation online voucher til sertifiseringstest + digital ITIL Foundation bok Du vil motta en e-post med tilgang til e-læringen, sertifiseringsvoucher og digital bok fra Peoplecert. Du avtaler tid for sertifiseringen som beskrevet i e-posten fra Peoplecert. Sertifiseringen består av: 40 spørsmål Multiple Choice 60 minutter + 15 minutter til rådighet dersom du ikke har engelsk som morsmål For å bestå må du ha minimum 26 riktige (65%) Ingen hjelpemidler tillatt ITIL®/PRINCE2®/MSP®/MoP® are registered trademarks of AXELOS Limited, used under permission of AXELOS Limited. All rights reserved. [-]

Kurset fokuserer på konvertering av etterspørsel til verdi via IT-relaterte tjenester. Modulen dekker sentrale emner som SLA-design, styring av flere leverandører, kommunikasjon, relasjonsstyring, CX- og UX-design, kartlegging av kunder og mer. E-læringskurset inneholder 18 timer med undervisning, og er delt inn i 8 moduler. Les mer om ITIL® 4 på  AXELOS sine websider. Du vil motta en e-post med tilgang til e-læringen, sertifiseringsvoucher og digital bok fra Peoplecert. Du avtaler tid for sertifiseringen som beskrevet i e-posten fra Peoplecert. [-]

COURSE OVERVIEW If you are new to Citrix or if you are planning a move to Citrix Cloud, this course is a necessary step in enabling you with the right training and skills to manage and deploy Citrix Workspace successfully. This foundational administration course covers the aspects of installing, configuring and managing a Citrix Virtual Apps and Desktops 7 environment, how to manage an on-premises Citrix solution and migrate from an on-premises solution to cloud using the Citrix Cloud management plane. This five-day course will teach you how to deploy, install, configure, setup profile management, configure policies, printing and basic security features for on-premises Virtual Apps and Desktop solution building, and then migrating to Citrix Cloud. This course includes the exam voucher. TARGET AUDIENCE Experienced IT Professionals who want to be familiar with Citrix Virtual Apps and Desktops 7 in an on-premises environment and Citrix Cloud. Potential students include administrators or engineers responsible for the end user workspace and overall health and performance of the solution. COURSE OBJECTIVES After completing this course you should be able to: Install, configure, and manage a Citrix Virtual Apps and Desktops 7 site and Cloud connectors Identify the considerations between Citrix Virtual Apps and Desktops on-premises and the Citrix Virtual Apps and Desktops Service Deliver app and desktop resources COURSE CONTENT Architecture Overview Introduction to Citrix Virtual Apps and Desktops Architecture Overview Features Hosting Platform Considerations Citrix Virtual Apps and Desktops Service Connection Flow Process Introduction Deploy the Site Pre-Deployment Considerations Citrix Licensing Setup Delivery Controller Setup Site Setup And Management Redundancy Considerations The Apps and Desktops Images Consider Master Image Creation Methods Master Image Requirements Provision and Deliver App and Desktop Resources Machine Catalogs and Delivery Groups Provisioning Methods and Considerations Machine Creation Services (MCS) Deep Dive MCS Environment Considerations Resource Locations Provide Access to App and Desktop Resources  Consider Workspace Experience versus StoreFront  Workspace Experience User Authentication  Workspace App  Communication Flow Manage the User Experience Methods to Manage the User Experience Common User Experience Settings Published App and Desktop Presentation and Management  Published App Properties Server OS Published App Optimizations Published App Presentation Application Groups Apps and Desktops Presentation Manage Printing for User Sessions Map Printers to the User Session Printer Drivers Print Environment Considerations Citrix Profile Management Introduction and Considerations Configure Citrix Profile Management Manage the Site Delegated Administration Use PowerShell with Citrix Virtual Apps and Desktops Power Management Considerations Citrix Virtual Apps and Desktops Basic Security Considerations Citrix Admin Security Considerations XML Service Security Considerations Secure HDX External Traffic Monitor the Site Citrix Director Introduction Monitor and Interact with User Sessions Published Apps Analysis Monitor the Machines Running the VDA Site Specific Common Monitoring Alerts and Notifications Optimize Citrix Director Monitoring with Citrix ADM Introduction to Supporting and Troubleshooting Citrix Virtual Apps and Desktops Introduction to Supporting a Citrix Virtual Apps and Desktops Site Tools Proactive Administration Common Tasks Migrate To Citrix Cloud Migration Considerations Citrix Cloud Connector Deployment Citrix Virtual Apps and Desktops with an On-Premises Resource Location The Migration Process Citrix Analytics Citrix Analytics Introduction Prepare to Use Citrix Analytics Types of Analytics TEST CERTIFICATION Recommended as preparation for the following exams: CCA-V Certification exam. [-]

  Fleksible kurs for fremtidenNy kunnskap skal gi umiddelbar effekt, og samtidig være holdbar og bærekraftig på lang sikt. NTI AS har 30 års erfaring innen kurs og kompetanseheving, og utdanner årlig rundt 10.000 personer i Nord Europa innen CAD, BIM, industri, design og konstruksjon.   Solibri Office Avansert På kurset vil du lære å: Konfigurere optimal eksport av IFC-filer fra Revit Skape egne klassifikasjoner og bruke disse i mengdeuttak og egendefinerte regler Bli kjent med eksisterende regelsett og tilpasning av disse Opprette egne regelsett Opprette mengdeuttak og basere disse på klassifikasjoner. Skape rapporter Spesialtilpasning av Solibri Office Håndtere saker i BCF-format Spesialtilpasset kurs: NTI anbefaler spesialtilpassede kurs for bedrifter som planlegger å sende to eller flere deltakere på Solibri-kurs. Grunnen til dette er at Solibri brukes av mange forskjellige aktører og profesjoner i BAE-bransjen, og følgelig blir de åpne kursene ofte for generelle for enkelte kursdeltakere. I et spesialtilpasset kurs vil vår kurskonsulent kartlegge fokusområdene i forkant av kurset, og gjennomføre kurset i henhold til selskapets behov, gjerne basert på kundens egne modeller. Utbyttet av kurset blir følgelig mye større.   Ta kontakt med oss på telefon 483 12 300, epost: salg-no@nticad.biz eller les mer på www.nti.biz [-]

Bli en mester i videoredigering med Adobe Premiere Pro gjennom dette dyptgående kurset ledet av Espen Faugstad, en erfaren kursholder hos Utdannet.no. Dette kurset krever ingen forkunnskaper og tar deg med fra grunnleggende til avanserte teknikker i Premiere Pro. Det er ideelt for alle som ønsker å lære profesjonell videoredigering, enten for personlig bruk eller for å utvikle karrieren som klipper. Kurset dekker alt fra opprettelse av prosjekter, organisering av filer, redigering av video og lyd, til bruk av effekter, overganger, og fargekorrigering. Du vil også lære å opprette titler, teksting, og bruke animasjon for å gi dine videoer et profesjonelt uttrykk. Ved kursets slutt vil du ha opparbeidet deg all den kunnskapen som trengs for å jobbe som en profesjonell videoredigerer.   Innhold: Kapittel 1: Introduksjon Kapittel 2: Prosjekter Kapittel 3: Importere filer Kapittel 4: Redigere video Kapittel 5: Teknikker Kapittel 6: Redigere lyd Kapittel 7: Effekter og overganger Kapittel 8: Titler, grafikk og teksting Kapittel 9: Animere Kapittel 10: Fargekorrigere Kapittel 11: Eksportere Kapittel 12: Avslutning   Varighet: 6 timer og 5 minutter   Om Utdannet.no: Utdannet.no tilbyr noen av landets beste digitale nettkurs. Tjenesten fungerer på samme måte som strømmetjenester for musikk eller TV-serier. Våre kunder betaler en fast månedspris og får tilgang til alle kursene som er produsert så langt. Plattformen har hatt en god vekst de siste årene og kan skilte med 30.000 registrerte brukere og 1,5 millioner videoavspillinger. Vårt mål er å gjøre kompetanseutvikling moro, spennende og tilgjengelig for alle – og med oss har vi Innovasjon Norge og Forskningsrådet. [-]

Kursinstruktør   Geir Johan Gylseth Geir Johan Gylseth er utdannet ved Universitetet i Oslo med hovedvekt på Informatikk og har over 30 års erfaring som instruktør. Geir sin styrke ligger innenfor MS Office. Han har lang erfaring med skreddersøm av kurs, kursmanualer og oppgaver. Geir er en entusiastisk og dyktig instruktør som får meget gode evalueringer. Kursinstruktør   Jonny Austad Jonny Austad er utdannet som Adjunkt og har jobbet som lærer og instruktør siden 1989. Han har dessuten jobbet mye med support og drifting av nettverk og vet som oftest hva som er vanlige problemer ute i bedriftene. Han var den første Datakort-læreren i landet (høsten 1997), og har Office-pakken med spesielt Excel som sitt hjertebarn. Jonny er en meget hyggelig og utadvendt person som elsker å undervise med smarte løsninger på problemer samt vise smarte tips og triks i de ulike programmene. Kursinnhold Dette kurset lærer deg å håndtere ressurser, aktiviteter og budsjett. Du kan opprette, oppdatere og gjøre enkel oppfølging i et prosjekt. Vi går igjennom hvordan du, både grafisk og i tekst, ser effekten av forandringer i prosjekt og hvordan du kan skrive ut dine prosjektplaner. Målet med kurset er å gi deg en prossessorientert tilnærming i MS Project slik at du er i stand til å arbeide målrettet og effektivt med programvaren etter kurset. Sett opp Project for bruk i din bedrift – tips og triks. Lag egne kalendere for enkeltpersoner og/eller grupper. Hold oversikt over tids- og ressursbruk. Vit hvem som jobber hvor – på tvers av prosjekter. Kontroller kostnadene i prosjektet. Ta hensyn til lønnsøkningerog variable kostnader. Vis og kontroller hvordan prosjektet går i forhold til opprinnelig plan (Baseline). Presenter fremdrift på papir og på nett. Utnytt de nye rapportmulighetene. Ta hensyn til at arbeid noen ganger foregår på kvelden og i helger. Se hvordan du kan få vakre utskrifter med egendefinertekomponenter ved hjelp av Project 2016 sine rapportegenskaper. Lag dine egne tabeller og visninger, skreddersydd til ditt bruk. Gjør rapportering og oppfølging enkel slik at du kan konsentrere deg om å lede prosjektet. Bruk tidslinje for enkelkommunikasjon av fremdrift. Kommunikasjon med andre programmer. I tillegg får du en rekke tips og triks du kan bruke i din arbeidsdag.  Alt du lærer får du repetert gjennom aktiv oppgaveløsning slik at du husker det du har lært når du kommer tilbake på jobb. Kursdokumentasjon, lunsj og pausemat er selvsagt inkludert! Kursholderne har mer enn 20 års Project erfaring som de gjerne deler med deg! Meld deg på Project-kurs allerede i dag og sikre deg plass!  Av innhold kan vi nevne:   Innstilling av programvaren – en reprise fra grunnkurset Hva vil jeg ha ut av mine planer og hvordan får jeg det Effektiv og målrettet planlegging Bruk av ressurspool – Ressursstyring på tvers av prosjekter Integrasjon og kobling mot Excel i rapportering og kostnadsoppfølging En grundig gjennomgang av mulighetene i Project Bygg dine egne rapporter og visninger Bruk av flere kalendere Detaljert budsjettering og kostnadsoppfølging Få hjelp og råd med dine konkrete utfordringer i Project [-]

COURSE OVERVIEW Students will begin by understanding the core compute and storage technologies that are used to build an analytical solution. They will then explore how to design an analytical serving layers and focus on data engineering considerations for working with source files. 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. TARGET AUDIENCE The primary audience for this course is data professionals, data architects, and business intelligence professionals who want to learn about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure. The secondary audience for this course data analysts and data scientists who work with analytical solutions built on Microsoft Azure. COURSE OBJECTIVES   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 CONTENT 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. Introduction to Azure Synapse Analytics Describe Azure Databricks Introduction to Azure Data Lake storage Describe Delta Lake architecture Work with data streams by using Azure Stream Analytics Lab 1: Explore compute and storage options for data engineering workloads Combine streaming and batch processing with a single pipeline Organize the data lake into levels of file transformation Index data lake storage for query and workload acceleration After completing module 1, students will be able to: Describe Azure Synapse Analytics Describe Azure Databricks Describe Azure Data Lake storage Describe Delta Lake architecture Describe Azure Stream Analytics 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. Design a multidimensional schema to optimize analytical workloads Code-free transformation at scale with Azure Data Factory Populate slowly changing dimensions in Azure Synapse Analytics pipelines Lab 2: Designing and Implementing the Serving Layer Design a star schema for analytical workloads Populate slowly changing dimensions with Azure Data Factory and mapping data flows After completing module 2, students will be able to: Design a star schema for analytical workloads Populate a slowly changing dimensions with Azure Data Factory and mapping data flows 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. Design a Modern Data Warehouse using Azure Synapse Analytics Secure a data warehouse in Azure Synapse Analytics Lab 3: Data engineering considerations Managing files in an Azure data lake Securing files stored in an Azure data lake After completing module 3, students will be able to: Design a Modern Data Warehouse using Azure Synapse Analytics Secure a data warehouse in Azure Synapse Analytics 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). Explore Azure Synapse serverless SQL pools capabilities Query data in the lake using Azure Synapse serverless SQL pools Create metadata objects in Azure Synapse serverless SQL pools Secure data and manage users in Azure Synapse serverless SQL pools Lab 4: Run interactive queries using serverless SQL pools Query Parquet data with serverless SQL pools Create external tables for Parquet and CSV files Create views with serverless SQL pools Secure access to data in a data lake when using serverless SQL pools Configure data lake security using Role-Based Access Control (RBAC) and Access Control List After completing module 4, students will be able to: Understand Azure Synapse serverless SQL pools capabilities Query data in the lake using Azure Synapse serverless SQL pools Create metadata objects in Azure Synapse serverless SQL pools Secure data and manage users in Azure Synapse serverless SQL pools 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. Understand big data engineering with Apache Spark in Azure Synapse Analytics Ingest data with Apache Spark notebooks in Azure Synapse Analytics Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics Integrate SQL and Apache Spark pools in Azure Synapse Analytics Lab 5: Explore, transform, and load data into the Data Warehouse using Apache Spark Perform Data Exploration in Synapse Studio Ingest data with Spark notebooks in Azure Synapse Analytics Transform data with DataFrames in Spark pools in Azure Synapse Analytics Integrate SQL and Spark pools in Azure Synapse Analytics After completing module 5, students will be able to: Describe big data engineering with Apache Spark in Azure Synapse Analytics Ingest data with Apache Spark notebooks in Azure Synapse Analytics Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics Integrate SQL and Apache Spark pools in Azure Synapse Analytics 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. Describe Azure Databricks Read and write data in Azure Databricks Work with DataFrames in Azure Databricks Work with DataFrames advanced methods in Azure Databricks Lab 6: Data Exploration and Transformation in Azure Databricks Use DataFrames in Azure Databricks to explore and filter data Cache a DataFrame for faster subsequent queries Remove duplicate data Manipulate date/time values Remove and rename DataFrame columns Aggregate data stored in a DataFrame After completing module 6, students will be able to: Describe Azure Databricks Read and write data in Azure Databricks Work with DataFrames in Azure Databricks Work with DataFrames advanced methods in Azure Databricks 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. Use data loading best practices in Azure Synapse Analytics Petabyte-scale ingestion with Azure Data Factory Lab 7: Ingest and load Data into the Data Warehouse Perform petabyte-scale ingestion with Azure Synapse Pipelines Import data with PolyBase and COPY using T-SQL Use data loading best practices in Azure Synapse Analytics After completing module 7, students will be able to: Use data loading best practices in Azure Synapse Analytics Petabyte-scale ingestion with Azure Data Factory 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. Data integration with Azure Data Factory or Azure Synapse Pipelines Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines Lab 8: Transform Data with Azure Data Factory or Azure Synapse Pipelines Execute code-free transformations at scale with Azure Synapse Pipelines Create data pipeline to import poorly formatted CSV files Create Mapping Data Flows After completing module 8, students will be able to: Perform data integration with Azure Data Factory Perform code-free transformation at scale with Azure Data Factory 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. Orchestrate data movement and transformation in Azure Data Factory Lab 9: Orchestrate data movement and transformation in Azure Synapse Pipelines Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines After completing module 9, students will be able to: Orchestrate data movement and transformation 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. Optimize data warehouse query performance in Azure Synapse Analytics Understand data warehouse developer features of Azure Synapse Analytics Lab 10: Optimize Query Performance with Dedicated SQL Pools in Azure Synapse Understand developer features of Azure Synapse Analytics Optimize data warehouse query performance in Azure Synapse Analytics Improve query performance After completing module 10, students will be able to: Optimize data warehouse query performance in Azure Synapse Analytics Understand data warehouse developer features of Azure Synapse Analytics 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. Analyze and optimize data warehouse storage in Azure Synapse Analytics Lab 11: Analyze and Optimize Data Warehouse Storage Check for skewed data and space usage Understand column store storage details Study the impact of materialized views Explore rules for minimally logged operations After completing module 11, students will be able to: Analyze and optimize data warehouse storage in Azure Synapse Analytics 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. Design hybrid transactional and analytical processing using Azure Synapse Analytics Configure Azure Synapse Link with Azure Cosmos DB Query Azure Cosmos DB with Apache Spark pools Query Azure Cosmos DB with serverless SQL pools Lab 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link Configure Azure Synapse Link with Azure Cosmos DB Query Azure Cosmos DB with Apache Spark for Synapse Analytics Query Azure Cosmos DB with serverless SQL pool for Azure Synapse Analytics After completing module 12, students will be able to: Design hybrid transactional and analytical processing using Azure Synapse Analytics Configure Azure Synapse Link with Azure Cosmos DB Query Azure Cosmos DB with Apache Spark for Azure Synapse Analytics Query Azure Cosmos DB with SQL serverless for Azure Synapse Analytics 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. Secure a data warehouse in Azure Synapse Analytics Configure and manage secrets in Azure Key Vault Implement compliance controls for sensitive data Lab 13: End-to-end security with Azure Synapse Analytics Secure Azure Synapse Analytics supporting infrastructure Secure the Azure Synapse Analytics workspace and managed services Secure Azure Synapse Analytics workspace data After completing module 13, students will be able to: Secure a data warehouse in Azure Synapse Analytics Configure and manage secrets in Azure Key Vault Implement compliance controls for sensitive data 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. Enable reliable messaging for Big Data applications using Azure Event Hubs Work with data streams by using Azure Stream Analytics Ingest data streams with Azure Stream Analytics Lab 14: Real-time Stream Processing with Stream Analytics Use Stream Analytics to process real-time data from Event Hubs Use Stream Analytics windowing functions to build aggregates and output to Synapse Analytics Scale the Azure Stream Analytics job to increase throughput through partitioning Repartition the stream input to optimize parallelization After completing module 14, students will be able to: Enable reliable messaging for Big Data applications using Azure Event Hubs Work with data streams by using Azure Stream Analytics Ingest data streams with Azure Stream Analytics 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. Process streaming data with Azure Databricks structured streaming Lab 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks Explore key features and uses of Structured Streaming Stream data from a file and write it out to a distributed file system Use sliding windows to aggregate over chunks of data rather than all data Apply watermarking to remove stale data Connect to Event Hubs read and write streams After completing module 15, students will be able to: Process streaming data with Azure Databricks structured streaming Module 16: Build reports using Power BI integration with Azure Synpase 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. Create reports with Power BI using its integration with Azure Synapse Analytics Lab 16: Build reports using Power BI integration with Azure Synpase Analytics Integrate an Azure Synapse workspace and Power BI Optimize integration with Power BI Improve query performance with materialized views and result-set caching Visualize data with SQL serverless and create a Power BI report After completing module 16, students will be able to: Create reports with Power BI using its integration with Azure Synapse Analytics 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. Use the integrated machine learning process in Azure Synapse Analytics Lab 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics Create an Azure Machine Learning linked service Trigger an Auto ML experiment using data from a Spark table Enrich data using trained models Serve prediction results using Power BI After completing module 17, students will be able to: Use the integrated machine learning process in Azure Synapse Analytics     [-]

  An Azure IoT Developer is responsible for implementing and then maintaining the cloud and edge portions of an Azure IoT solution. In addition to configuring and maintaining devices by using Azure IoT services and other Microsoft tools, the IoT Developer also sets up the physical devices and is responsible for maintaining the devices throughout the life cycle. The IoT Developer implements designs for IoT solutions, including device topology, connectivity, debugging and security. For Edge device scenarios, the IoT Developer also deploys compute/containers and configures device networking, which could include various edge gateway implementations. The IoT Developer implements designs for solutions to manage data pipelines, including monitoring and data transformation as it relates to IoT. The IoT Developer works with data engineers and other stakeholders to ensure successful business integration. IoT Developers should have a good understanding of Azure services, including data storage options, data analysis, data processing, and the Azure IoT PaaS versus SaaS options. After completing this course, students will be able to: Create, configure, and manage an Azure IoT hub. Provision devices by using IoT Hub and DPS, including provisioning at scale. Establish secure 2-way communication between devices and IoT Hub. Implement message processing by using IoT Hub routing and Azure Stream Analytics. Configure the connection to Time Series Insights and support business integration requirements. Implement IoT Edge scenarios using marketplace modules and various edge gateway patterns. Implement IoT Edge scenarios that require developing and deploying custom modules and containers. Implement device management using device twins and direct methods. Implement solution monitoring, logging, and diagnostics testing. Recognize and address security concerns and implement Azure Security Center for IoT. Build an IoT Solution by using Azure IoT Central and recongize SaaS opportunities for IoT. Course prerequisites IoT Developers should have basic programming skills in at least one Azure-supported language, including C#, Node.js, C, Python, or Java. Software development experience is a prerequisite for this course, but no specific software language is required, and the experience does not need to be at a professional level. Data Processing Experience: General understanding of data storage and data processing is a recommended but not required.  Cloud Solution Awareness: Students should have a basic understanding of PaaS, SaaS, and IaaS implementations. Microsoft Azure Fundamentals (M-AZ-900T00/M-AZ900), or equivalent skills, is recommended.  This course helps to prepare for exam AZ-220.   Agenda Module 1: Introduction to IoT and Azure IoT Services -Business Opportunities for IoT-Introduction to IoT Solution Architecture-IoT Hardware and Cloud Services Module 2: Devices and Device Communication -IoT Hub and Devices-IoT Developer Tools-Device Configuration and Communication Module 3: Device Provisioning at Scale -Device Provisioning Service Terms and Concepts-Configure and Manage the Device Provisioning Service-Device Provisioning Tasks Module 4: Message Processing and Analytics -Messages and Message Processing-Data Storage Options-Azure Stream Analytics Module 5: Insights and Business Integration -Business Integration for IoT Solutions-Data Visualization with Time Series Insights-Data Visualization with Power BI Module 6: Azure IoT Edge Deployment Process -Introduction to Azure IoT Edge-Edge Deployment Process-Edge Gateway Devices Module 7: Azure IoT Edge Modules and Containers -Develop Custom Edge Modules-Offline and Local Storage Module 8: Device Management -Introduction to IoT Device Management-Manage IoT and IoT Edge Devices-Device Management at Scale Module 9: Solution Testing, Diagnostics, and Logging -Monitoring and Logging-Troubleshooting Module 10: Azure Security Center and IoT Security Considerations -Security Fundamentals for IoT Solutions-Introduction to Azure Security Center for IoT-Enhance Protection with Azure Security Center for IoT Agents Module 11: Build an IoT Solution with IoT Central -Introduction to IoT Central-Create and Manage Device Templates-Manage Devices in Azure IoT Central [-]

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   [-]

Datagrunnlaget Gruppering Formatering Tallformater Visningsalternativer Feltinnstillinger Beregnede felt Bruk av flere pivottabeller i samme arbeidsbok Slicere som virker på flere pivottabeller eller pivotdiagrammer Tabellfunksjonalitet   Analyse av pivotdiagram Bruk av pivotdiagram i andre programmer   et 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.   [-]

Start din reise som frontend-utvikler med kurset "HTML: Komplett" ledet av Espen Faugstad hos Utdannet.no. Dette kurset er første trinn i læringsstien «Frontend-utvikler», designet for å gi deg alle nødvendige ferdigheter for å bygge raske og moderne nettsider. Vi antar ingen tidligere kunnskap om HTML, CSS eller JavaScript, og du vil lære alt fra grunnleggende til avanserte teknikker. Kurset begynner med HTML, hjertet av webutvikling, og vil ta deg gjennom grunnleggende syntaks, tekstformatering, oppretting av lenker, håndtering av multimedia, og mye mer. Du vil også lære å strukturere nettsider effektivt, jobbe med tabeller og skjemaer, og til slutt lage et prosjekt som demonstrerer dine nye ferdigheter. Etter å ha fullført dette kurset, vil du være godt rustet til å fortsette med CSS og JavaScript for å skape vakre, responsive og lynraske nettsider som fungerer utmerket på alle enheter.   Innhold: Kapittel 1: Introduksjon Kapittel 2: Internett Kapittel 3: Syntaks Kapittel 4: Tekst Kapittel 5: Link Kapittel 6: Multimedia Kapittel 7: Tabell Kapittel 8: Skjema Kapittel 9: Struktur Kapittel 10: Prosjekt Kapittel 11: Avslutning   Varighet: 5 timer og 55 minutter   Om Utdannet.no: Utdannet.no tilbyr noen av landets beste digitale nettkurs. Tjenesten fungerer på samme måte som strømmetjenester for musikk eller TV-serier. Våre kunder betaler en fast månedspris og får tilgang til alle kursene som er produsert så langt. Plattformen har hatt en god vekst de siste årene og kan skilte med 30.000 registrerte brukere og 1,5 millioner videoavspillinger. Vårt mål er å gjøre kompetanseutvikling moro, spennende og tilgjengelig for alle – og med oss har vi Innovasjon Norge og Forskningsrådet. [-]

Dette grunnleggende kurset om Microsoft Azure gir deg en solid forståelse av fundamentale konsepter i Azure-skymiljøet. Kursholderen, seniorutvikler John Inge Muldal Erlandsen, vil veilede deg gjennom nøkkelkomponentene og mulighetene som Azure tilbyr. Microsoft Azure er en ledende skyplattform og konkurrerer direkte med Amazon Web Services (AWS). Azure inneholder et bredt spekter av enheter, funksjoner og tjenester som du kan bruke for å oppfylle ulike behov innen skytjenester. Dette kurset er spesielt rettet mot forberedelse til AZ-900-eksamen, som fører til Microsoft-sertifiseringen "Microsoft Certified Fundamentals". Målet med kurset er å gi deg tilstrekkelig kunnskap og forberedelse til å bestå denne eksamenen med suksess. Kurset vil dekke følgende emner: Kapittel 1: Introduksjon Kapittel 2: Tjenester Kapittel 3: Verktøy Kapittel 4: Sikkerhet Kapittel 5: Styring Kapittel 6: Administrasjon Kapittel 7: Avslutning   Varighet: 4 timer og 32 minutter   Om Utdannet.no: Utdannet.no tilbyr noen av landets beste digitale nettkurs. Vår tjeneste fungerer på samme måte som strømmetjenester for musikk eller TV-serier, der våre kunder betaler en fast månedspris for tilgang til alle kursene vi har tilgjengelig. Vi har opplevd betydelig vekst de siste årene, med over 30 000 registrerte brukere og 1,5 millioner videoavspillinger. Vårt mål er å gjøre kompetanseutvikling engasjerende, spennende og tilgjengelig for alle, og vi har støtte fra Innovasjon Norge og Forskningsrådet. [-]

 This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. TARGET AUDIENCE This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. COURSE CONTENT Module 1: Introduction to Azure Machine Learning In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace. Getting Started with Azure Machine Learning Azure Machine Learning Tools Lab : Creating an Azure Machine Learning WorkspaceLab : Working with Azure Machine Learning Tools After completing this module, you will be able to Provision an Azure Machine Learning workspace Use tools and code to work with Azure Machine Learning Module 2: No-Code Machine Learning with Designer This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume. Training Models with Designer Publishing Models with Designer Lab : Creating a Training Pipeline with the Azure ML DesignerLab : Deploying a Service with the Azure ML Designer After completing this module, you will be able to Use designer to train a machine learning model Deploy a Designer pipeline as a service Module 3: Running Experiments and Training Models In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models. Introduction to Experiments Training and Registering Models Lab : Running ExperimentsLab : Training and Registering Models After completing this module, you will be able to Run code-based experiments in an Azure Machine Learning workspace Train and register machine learning models Module 4: Working with Data Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments. Working with Datastores Working with Datasets Lab : Working with DatastoresLab : Working with Datasets After completing this module, you will be able to Create and consume datastores Create and consume datasets Module 5: Compute Contexts One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you'll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs. Working with Environments Working with Compute Targets Lab : Working with EnvironmentsLab : Working with Compute Targets After completing this module, you will be able to Create and use environments Create and use compute targets Module 6: Orchestrating Operations with Pipelines Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it's time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you'll explore how to define and run them in this module. Introduction to Pipelines Publishing and Running Pipelines Lab : Creating a PipelineLab : Publishing a Pipeline After completing this module, you will be able to Create pipelines to automate machine learning workflows Publish and run pipeline services Module 7: Deploying and Consuming Models Models are designed to help decision making through predictions, so they're only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing. Real-time Inferencing Batch Inferencing Lab : Creating a Real-time Inferencing ServiceLab : Creating a Batch Inferencing Service After completing this module, you will be able to Publish a model as a real-time inference service Publish a model as a batch inference service Module 8: Training Optimal Models By this stage of the course, you've learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you'll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data. Hyperparameter Tuning Automated Machine Learning Lab : Tuning HyperparametersLab : Using Automated Machine Learning After completing this module, you will be able to Optimize hyperparameters for model training Use automated machine learning to find the optimal model for your data Module 9: Interpreting Models Many of the decisions made by organizations and automated systems today are based on predictions made by machine learning models. It's increasingly important to be able to understand the factors that influence the predictions made by a model, and to be able to determine any unintended biases in the model's behavior. This module describes how you can interpret models to explain how feature importance determines their predictions. Introduction to Model Interpretation using Model Explainers Lab : Reviewing Automated Machine Learning ExplanationsLab : Interpreting Models After completing this module, you will be able to Generate model explanations with automated machine learning Use explainers to interpret machine learning models Module 10: Monitoring Models After a model has been deployed, it's important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data. Monitoring Models with Application Insights Monitoring Data Drift Lab : Monitoring a Model with Application InsightsLab : Monitoring Data Drift After completing this module, you will be able to Use Application Insights to monitor a published model Monitor data drift   [-]