Oversikt over relevante kurs innen Kurs i programvare og applikasjoner i Jølster

Fleksible kurs for fremtiden Ny 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.   MagiCAD VVS for AutoCAD grunnkurs Her er et utvalg av temaene du vil lære på kurset: Etablering av prosjekt Prosjektering av ventilasjonsanlegg, varmeanlegg, og sanitæranlegg Sammenkobling av systemer gjennom flere tegninger Tekstefunksjoner, snitt, tegninger til utskrift Beregninger, utbalansering, lyd, mengdeberegning Bruk av leverandørspesifike produkter Kollisjonskontroll Automatisk generering av utsparinger Deltakerne skal lære å håndtere tegninger i et prosjekt; arkitekt, VVS-tegninger etc. De skal lære å berike en VVS-modell slik at mest mulig informasjon kan nyttes med hensyn til BIM, 2D-tegninger, strømningstekniske beregninger og lydberegninger. Tilpassete kurs for bedrifter Vi vil at kundene våre skal være best på det de gjør - hele tiden.  Derfor tenker vi langsiktig om kompetanseutvikling og ser regelmessig kunnskapsløft som en naturlig del av en virksomhet. Vårt kurskonsept bygger på et moderne sett av ulike læringsmiljøer, som gjør det enkelt å finne riktig løsning uansett behov. Ta kontakt med oss på telefon 483 12 300, epost: salg@nticad.no eller les mer på www.nticad.no [-]

Objectives Understand how software containers work Understand the architecture of Kubernetes Understand the architecture of Google Cloud Understand how pod networking works in Google Kubernetes Engine Create and manage Kubernetes Engine clusters using the Google Cloud Console and gcloud/kubectl commands   Course Outline Module 1: Introduction to Google Cloud -Use the Google Cloud Console-Use Cloud Shell-Define Cloud Computing-Identify Google Cloud compute services-Understand Regions and Zones-Understand the Cloud Resource Hierarchy-Administer your Google Cloud Resources Module 2: Containers and Kubernetes in Google Cloud -Create a Container Using Cloud Build-Store a Container in Container Registry-Understand the Relationship Between Kubernetes and Google Kubernetes Engine (GKE)-Understand how to Choose Among Google Cloud Compute Platforms Module 3: Kubernetes Architecture -Understand the Architecture of Kubernetes: Pods, Namespaces-Understand the Control-plane Components of Kubernetes-Create Container Images using Cloud Build-Store Container Images in Container Registry-Create a Kubernetes Engine Cluster Module 4: Introduction to Kubernetes Workloads -The kubectl Command-Introduction to Deployments-Pod Networking-Volumes Overview [-]

COURSE OVERVIEW  You will learn to deliver secure remote access to apps and desktops integrating Citrix Virtual Apps and Citrix Desktops with Citrix Gateway.  This course includes an exam. TARGET AUDIENCE Built for IT Professionals working with Citrix ADC and Gateway, with little or no previous Citrix networking experience. Potential students include administrators, engineers, and architects interested in learning how to deploy or manage Citrix ADC or Citrix Gateway environments. COURSE OBJECTIVES  Identify the functionality and capabilities of Citrix ADC and Citrix Gateway Explain basic Citrix ADC and Gateway network architecture Identify the steps and components to secure Citrix ADC Configure Authentication, Authorization, and Auditing Integrate Citrix Gateway with Citrix Virtual Apps, Citrix Virtual Desktops and other Citrix components COURSE CONTENT Module 1: Getting Started Introduction to Citrix ADC Feature and Platform Overview Deployment Options Architectural Overview Setup and Management Module 2: Basic Networking Networking Topology Citrix ADC Components Routing Access Control Lists Module 3: ADC Platforms Citrix ADC MPX Citrix ADC VPX Citrix ADC CPX Citrix ADC SDX Citrix ADC BLX Module 4: High Availability Citrix ADC High Availability High Availability Configuration Managing High Availability In Service Software Upgrade Troubleshooting High Availability Module 5: Load balancing Load Balancing Overview Load Balancing Methods and Monitors Load Balancing Traffic Types Load Balancing Protection Priority Load Balancing Load Balancing Troubleshooting Module 6: SSL Offloading SSL Overview SSL Configuration SSL Offload Troubleshooting SSL Offload SSL Vulnerabilities and Protections Module 7: Security Authentication, Authorization, and Auditing Configuring External Authentication Admin Partitions Module 8: Monitoring and Troubleshooting Citrix ADC Logging Monitoring with SNMP Reporting and Diagnostics AppFlow Functions Citrix Application Delivery Management Troubleshooting Module 9: Citrix Gateway Introduction to Citrix Gateway Advantages and Utilities of Citrix Gateway Citrix Gateway Configuration Common Deployments Module 10: AppExpert Expressions Introduction to AppExpert Policies Default Policies Explore Citrix ADC Gateway Policies Policy Bind Points Using AppExpert with Citrix Gateway Module 11: Authentication, Authorization, and Secure Web Gateway Authentication and Authorization Multi-Factor Authentication nFactor Visualizer SAML authentication Module 12: Managing Client Connections Introduction to Client Connections Session Policies and Profiles Pre and Post Authentication Policies Citrix Gateway Deployment Options Managing User Sessions Module 13: Integration for Citrix Virtual Apps and Desktops Virtual Apps and Desktop Integration Citrix Gateway Integration Citrix Gateway WebFront ICA Proxy Clientless Access and Workspace App Access Fallback SmartControl and SmartAccess for ICA Module 14: Configuring Citrix Gateway Working with Apps on Citrix Gateway RDP Proxy Portal Themes and EULA [-]

Objectives This course teaches participants the following skills: Identify the purpose and value of each of the Google Cloud Platform products and services Interact with Google Cloud Platform services Describe ways in which customers have used Google Cloud Platform Choose among and use application deployment environments on Google Cloud Platform: Google App Engine, Google Kubernetes Engine, and Google Compute Engine Choose among and use Google Cloud Platform storage options: Google Cloud Storage, Google Cloud SQL, Google Cloud Bigtable, and Google Cloud Datastore Make basic use of BigQuery, Google’s managed data warehouse for analytics Make basic use of Cloud Deployment Manager, Google’s tool for creating and managing cloud resources through templates Make basic use of Google Stackdriver, Google’s monitoring, logging, and diagnostics system All courses will be delivered in partnership with ROI Training, Google Cloud Premier Partner, using a Google Authorized Trainer. Course Outline Module 1: Introducing Google Cloud Platform -Explain the advantages of Google Cloud Platform-Define the components of Google's network infrastructure, including: Points of presence, data centers, regions, and zones-Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) Module 2: Getting Started with Google Cloud Platform -Identify the purpose of projects on Google Cloud Platform-Understand the purpose of and use cases for Identity and Access Management-List the methods of interacting with Google Cloud Platform-Lab: Getting Started with Google Cloud Platform Module 3: Virtual Machines and Networks in the Cloud -Identify the purpose of and use cases for Google Compute Engine.-Understand the various Google Cloud Platform networking and operational tools and services.-Lab: Compute Engine Module 4: Storage in the Cloud -Understand the purpose of and use cases for: Google Cloud Storage, Google Cloud SQL, Google Cloud Bigtable, and Google Cloud Datastore.-Learn how to choose between the various storage options on Google Cloud Platform.-Lab: Cloud Storage and Cloud SQL Module 5: Containers in the Cloud -Define the concept of a container and identify uses for containers.-Identify the purpose of and use cases for Google Kubernetes Engine and Kubernetes.-Lab: Kubernetes Engine Module 6: Applications in the Cloud -Understand the purpose of and use cases for Google App Engine.-Contrast the App Engine Standard environment with the App Engine Flexible environment.-Understand the purpose of and use cases for Google Cloud Endpoints.-Lab: App Engine Module 7: Developing, Deploying, and Monitoring in the Cloud -Understand options for software developers to host their source code.-Understand the purpose of template-based creation and management of resources.-Understand the purpose of integrated monitoring, alerting, and debugging.-Lab: Deployment Manager and Stackdriver Module 8: Big Data and Machine Learning in the Cloud -Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms.-Lab: BigQuery [-]

Excel controllere/økonomer kurs vinklet fra en Controllers hverdag med fokus på gode metoder for å arbeide med Excel-lister. Påfyllet kurset gir deg, gjør deg i stand til å jobbe mer effektivt i Excel. Kursets mange eksempler, viser hva du virkelig kan få til i Excel, enten det er å jobbe med formler / funksjoner, lister eller store Excel-modeller. Kurset er utviklet av controllere, med det for øye at du skal kunne angripe dine Excel-utfordringer på en smart og effektiv måte. Kurset kan også spesialtilpasses og holdes bedriftsinternt i deres eller våre lokaler.   Kursinnhold:     Datautveksling/Klargjøring av data Mulige inndatametoder: Importering av tekstfil, webspørringer, innliming av data fra WWW. Importere data med ulikt dataformat: tekst - eller databaseformat Formatproblemer ifm. dataimport: ”feil” formater, fjerne deler av informasjon i en celle, fjerne duplikater, kjapt finne skrivefeil   Data/Konsolider Konsolider data: hvor data ligger i samme regneark Konsolidering data: hvor dataene skal behandles på tvers av regneark. Konsolidering data: hvor data ligger i ulike arbeidsbøker   Tabeller/Listefunksjonalitet Definisjonen av en tabell: Viktig å huske når tabellen skal behandles videre. Jobbe med en liste: få gode råd angående arbeid med lister Sortere tabellen: kjapt og enkelt med egendefinerte sorteringsnøkler Filter med Autofilter: filtrere ut bare de data du ønsker eller slette tomme rader. Filter med Avansert filter: få råd om i hvilke situasjoner det er smart å bruke avansert filter og hvordan du enklest bruker dette filteret. Beregninger i tabeller og lister Beregninger med flere variabler: Funksjonene Dsummer, Dantall og DGjennomsnitt Organiser og beregne data : Ved hjelp av delsammendrag. Lær hvordan resultatet kan kopieres til et annet sted. Oppslag i en liste: Funksjonene Finn.Kolonne og Finn.Rad Lag rapporter ved hjelp av Pivotteknikk: Opplev hvor enkelt det er å lage rapporter ved hjelp av pivotteknikk!   "Hva hvis"-analyser og optimalisering Sensitivitetsanalyse: Målsøking er en måte å regne ”baklengs” på. Sensitivitetsanalyse: Tabeller med 1 og 2 variabler Optimalisering: Ved hjelp av Problemløseren   Samarbeid med andre Deling av arbeidsbøker: ved ønske om å jobbe samtidig i samme arbeidsbok Sporing av endringer: når du ønsker å plukke opp de endringer andre har gjort     4 gode grunner til å velge KnowledgeGroup 1. Best practice kursinnhold 2. Markedets beste instruktører 3. Små kursgrupper 4. Kvalitets- og startgaranti   [-]

På webinaret går vi gjennom oppsett og presentasjonsopplevelser i Microsoft Teams møter. Teams er i rask utvikling og spesielt på presentasjonsfronten har det skjedd store endringer i det siste. Dette webinaret tar for seg alternativer i oppsett av møtet, tildele riktige roller og skape den beste presentasjonsopplevelsen for deltakerne. Opprette Teams-møter – via Outlook eller Teams | Ad Hoc møter | Kanalmøter | Innstillinger for møtet – i innkallelsen | Innstillinger og administrasjon av møtet – når møtet er i gang i Teams | Visninger | Presentere/dele – hva og hvordan| Presentasjonsvisninger | Teksting | Opptak | Møtenotater – før, under og etter Pris: 1690 kroner [-]

Kurset er rettet mot deg som skal armere i Autodesk Revit. Dette er et praktisk kurs som gjør deg i stand til å armere betongkonstruksjoner, lage armeringstegninger og bøyelister. Hensikten med kurset er å gjøre deg i stand til bruke armerinsgverktøyene i Revit samt lage armeringstegninger og bøyelister ved hjelp av verktøyene som ligger i Revit-applikasjonen Focus RAT Bygg. Du vil lære hvordan manuelt armere betongkonstruksjoner. Du vil også lære verktøyene for å lage løpemeterarmering, armeringsnett og kantarmering. Du vil lære å bruke Revit Extensions for å armere konstruksjoner automatisk. Vi skal også lage armeringstegninger og bøyelister i henhold til NS 3766. Kursinnhold: Manuell armering av betongkonstruksjoner Løpemeterarmering Kantarmering Armeringsnett Automatisk armering av betongkonstruksjoner med Revit Extensions Armere avanserte betongkonstruksjoner Lage armeringstegninger Lage bøyelister [-]

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 Kurset passer for deg som har basisferdighetene på plass men som ønsker å lære mer. Kurset passer også for deg som er selvlært og som ønsker å jobbe mer effektivt. Bruk av stiler gir profesjonelle og flotte dokumenter. Lær å lage innholdsfortegnelse, stikkordliste og figurliste automatisk. Profesjonelt sideoppsett med spalter, marger, sidefarger, sidekantlinjer og dokumenttemaer. Auto korrektur, byggeblokker, egenskaper og felt gjør det enklere å gjenbruke tekst. Flere deldokumenter kan samles i et hoved dokument ved hjelp av hoveddokumentvisning. I lange dokumenter kan du ha uliketopp- og bunntekster og selv bestemme side nummerering. For å friske opp et dokument kan du sette inn utklipp, figurer, SmartArt og diagram. Med tekstbokser kan du presentere sitater eller sammendrag fra dokumentet. Tabeller kan brukes til å presentere informasjon på en oversiktlig måte men kan også sorteres og inneholde beregninger. Maler brukes for å sikre at dokumenter av samme type får en ensartet formatering. Felt, innholdskontroller og skjemakontroller kan settes inn for å effektivisere bruken av maler. Med makroer kan du effektivisere avanserte oppgaver som består av serie med handlinger. Med fletting kan du masseprodusere brev, konvolutter, etiketter og e-post. 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 Word erfaring som de gjerne deler med deg! Meld deg på Word-kurs allerede i dag og sikre deg plass! Lær deg: behandling av stiler rask og enkel opprettelse av innholdsfortegnelse sette inn forsider samarbeid om felles dokument spalter beregninger i tabeller innsetting av diagram sett inn bilder og bildetekst grafikk og tegning maler og skjema bruk av makroer integrasjon med Excel og andre programmer [-]

I kurset ”Måling og mengdebereging” vil du lære hvordan Revu brukes til å kalibrere og måle på PDF-tegninger, samt hvordan du kan opprette, spare og dele tilpassede markeringsverktøy. Disse kan så brukes til effektiv beregning av mengder og priser på alt fra vegg- og gulvarealer, til prising av utstyr på en riggplan. Å lære å bruke Revu til måling og mengdeberegning vil bl.a. gi følgende fordeler: Stor tidsbesparelse Større nøyaktighet og mindre feil Bedre dokumentasjon av mengdeberegningen Oppnå optimal utnyttelse av Bluebeam Revu i prosjektene [-]

Excel Videregående [-]

  Webinaret passer for deg som allerede vet hvordan du skal lage enkle presentasjoner i PowerPoint, men som ønsker å gå et trinn videre. Vi vil gi deg gode tips om hvordan du kan lage bedre presentasjoner, og jobbe mer effektivt med å lage lysbilder som engasjerer publikum og får frem budskapet. Temaer på webinaret: Bruk av maler og lysbildeoppsett Gjenbruk av eksisterende lysbilder Inndelinger i presentasjonen Effektiv bruk av punktmerker SmartArt – hvordan presentere mer grafisk Innsetting og redigering av fotografier og andre illustrasjoner, samt informasjon om bildebibliotek og opphavsrett Innsetting av film Bruk av fotoalbumoverganger mellom lysbildene Mål med webinaret: Etter endt webinar skal du kunne jobbe mer smart og effektivt i PowerPoint. Forkunnskap: Du må ha brukt PowerPoint på et grunnleggende nivå. Kurset passer for deg som ønsker å jobbe smartere med å lage lysbildepresentasjoner i PowerPoint.  Målgruppe: Dette kurset er for deg som vet litt om PowerPoint og som ønsker å lære mer effektiv bruk av verktøyet. Undervisningsform: Dette er et webinar som du følger via din PC eller nettbrett. Vi vil sende deg påloggingsinformasjon etter at du har meldt deg på kurset. Pris: 1600 kroner (Ansatte og studenter ved UiS har egne betingelser) Varighet: 3 timer [-]

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     [-]

AZ-140: Configuring and Operating Microsoft Azure Virtual Desktop [-]

Dette er et spesialkurs som fokuserer på analyse av store datasett ved hjelp av Pivottabell og Power Pivot, samt formelbasert analyse. Målet er å få frem styrker og svakheter ved de forskjellige metodene, og å se litt på hvilke forutsetninger som påvirker valg av løsning. For å ha utbytte av dette kurser forutsettes at man er vant bruker av Excel. Pivot og Power Pivot blir gjennomgått fra begynnelsen, så man trenger ikke være kjent med disse verktøyene fra før. Betingede formler kan være ganske krevende, så det er en fordel å være litt trygg på formelskriving. I en kurssituasjon blir selvsagt kurset tilpasset deltagernes nivå og forkunnskaper. I kurset gjennomgås bl.a.: Kontroll/gjennomgang av en del sentral funksjonalitet – bl.a. absolutte, relative og blandede referanser. Sammendrag av data fra flere ark i samme eller flere arbeidsbøker, bl.a. gjennomgående summering og tabulering v.hj.a. INDIREKTE-funksjonen. Betingende sammendrag v.hj.a. matriseformler og funksjoner Modifisere datasett med FINN.RAD, FINN.KOLONNE, matriseformler og andre teknikker Pivottabell, hvor vi bl.a. ser på: Sette sammen data fra forskjellige grunnlag før pivotering Vise dataserie på forskjellige måter (sum, gjennomsnitt, prosentfordelt, etc.) Hvordan foreta beregninger rett i pivottabellen, f.ex. inntekter – kostnader = resultat Pivottabell hvor datagrunnlaget er oppdelt i flere forskjellige Pivottabell rett mot en spørring i en database Power Pivot Forskjeller (og likheter) med «vanlig» Pivottabell Når forlater vi den vanlige pivottabellen til fordel for Power Pivot? Fordeler og ulemper med Pivot og Power Pivot. Lage Power Pivot-tabell med data fra flere forskjellige datasett. [-]

Her er et utvalg av temaene du vil lære på kurset: Oppsett av nytt sprinklerprosjekt i Revit Prosjektering av sprinkleranlegg Behandling av rørtyper, systemer etc Lage egne produkter for sprinklerhoder og andre produkter Hydrauliske beregninger IFC-eksport Oppsett av tegninger [-]