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

Du vil få tilsendt en «Core guidance» bok og sertifiserings-voucher slik at du kan ta sertifiseringstesten for eksempel hjemme eller på jobb. Denne vil være gyldig i ett år. Tid for sertifiseringstest avtales som beskrevet i e-post med voucher. Eksamen overvåkes av en web-basert eksamensvakt.   Eksamen er på engelsk. Eksamensformen er multiple choice - 50 spørsmål skal besvares, og du består ved 50% korrekte svar (dvs 25 av 50 spørsmål). Deltakerne har 40 minutter til rådighet på eksamen.  Ingen hjelpemidler er tillatt.   Nødvendige forkunnskaper: Ingen [-]

Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: Windows server 2008/2012 - god kjennskap om Windows server Innleveringer: Øvinger: 8 av må være godkjent. Personlig veileder: ja Vurderingsform: Eksamen blir arrangert som 2 dagers hjemmeeksamen (start kl 09.00 og innlevering kl 15.00 dagen etter). Hver student får tildelt et virtuelt område. Det skal også leveres en skriftelig begrunnelse for de valg som er foretatt. Hjemmeeksamen, individuell, 2 dager, 0 Ansvarlig: Stein Meisingseth Eksamensdato: 10.12.13 / 13.05.14         Læremål: KUNNSKAPER:Kandidaten:- har innsikt i drift av nettverk basert på Windows Server, programvaredistribusjon og kjenner til hvilke verktøy som kan brukes for administrasjon av virtuelle maskiner og nettverk- kan forklare systemer som kan benyttes til overvåkning og vedlikehold FERDIGHETER:Kandidaten kan:- installere og konfigurere System Center Configuration Manager 2012- automatisere manuelle operasjoner- sikre, oppdatere og overvåke IT-systemer GENERELL KOMPETANSE:Kandidaten har:- perspektiv og kompetanse i å velge riktige og tilpassete driftsløsninger- kompetanse i å formidle driftsterminologi, både muntlig og skriftlig Innhold:- Automatisering og sikring ved hjelp av System Center Cooperation Manager 2012 (SCCM 2012) - Applikasjonsutrulling - Operativ System utrulling - Klient tilstands-monitorering - Programvare oppdateringer - Sikkerhetsbeskyttelse vha Endpoint ProtectionLes mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag Microsoft System Center i overvåkning og drift 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.    [-]

Kom i gang med Power BI Desktop [-]

Due to the Coronavirus the course instructor is not able to come to Oslo. As an alternative we offer this course as a Blended Virtual Course.   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    The courseThis course provides essential skills for all mobile application testers. A mobile application is a software application designed to run on mobile devices such as smartphones and tablet computers. Mobile applications are becoming part of our every day lives and many organisations are developing applications to run on mobile devices to complement their desktop applications. This course will give the mobile application tester the knowledge skills needed to test mobile applications and to understand the differences and similarities in testing mobile applications to conventional applications. This course is highly practical - providing the participant with different mobile applications to test using a variety of techniques and tactics in order to find those mobile application bugs.The pre-requisite in receiving this qualification is that you have have attained the ISTQB Foundation.   The examThe Foundation Level Mobile Application Testing exam is comprised of 40 multiple-choice questions, with a pass mark grade of 65% to be completed within 60 minutes. Participants that take the exam not in their spoken language, will receive additional 25% time, for a total of 75 minutes. [-]

Utvid din Excel-kunnskap til et ekspertnivå med "Excel: Ekspert", et dyptgående kurs ledet av Espen Faugstad hos Utdannet.no. Dette kurset er ideelt for de som allerede har en solid forståelse av Excel gjennom tidligere kurs og ønsker å utvikle avanserte ferdigheter for å håndtere komplekse dataanalyser og problemstillinger. Kurset vil dekke avanserte teknikker og funksjoner i Excel, inkludert ulike variasjoner av HVIS-funksjonen, FINN.RAD, FINN.KOLONNE, tekstbehandlingsfunksjoner som SØK og DELTEKST, samt dato- og tidsfunksjoner. Du vil også lære om avanserte oppslagsfunksjoner, matematiske formler og statistiske analyser ved hjelp av Excel. I tillegg til å lære om avanserte formler, vil kurset veilede deg gjennom bruk av matrisefunksjoner og feilsøking i Excel. Ved kursets slutt vil du ha en omfattende forståelse av Excel på et ekspertnivå, noe som gjør deg i stand til å utføre sofistikerte dataanalyser og rapporteringer.   Innhold: Kapittel 1: Introduksjon Kapittel 2: Formelhåndtering Kapittel 3: HVIS Kapittel 4: GJØR.HVIS Kapittel 5: FINN Kapittel 6: Tekst Kapittel 7: Dato Kapittel 8: Oppslag Kapittel 9: Matematikk Kapittel 10: Statistikk Kapittel 11: Matrise Kapittel 12: Diverse Kapittel 13: Avslutning   Varighet: 4 timer   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. [-]

Lær å formidle informasjon visuelt ved bruk av diagrammer og en annen grafikk i Excel 2013/2016. Få mer kontroll over Excel modellene ved å forstå betydning av formatering. Webinaret kan også spesialtilpasses og holdes bedriftsinternt kun for din bedrift.   Kursinnhold:   Innføring til formatering og utsende Tekst og tall formatering Kopiere og fjerne format Diverse muligheter med diagrammer Sparkline grafikk Betinget formatering Formater som tabell Egendefinert formatering Dato og klokkeslett Vise og skjule innhold fra en celle Håndtere store tall (hele tusen, hele million) [-]

Microsoft 365 er abonnement på Office-programmer og andre produktivitetstjenester som du får tilgang til via Internett (skytjenester). Tidligere var dette kjent under navnet Office 365, men Microsoft har nå endret navnet til Microsoft 365. Fordelen er at du alltid har tilgang til Office-programmer og dine dokumenter, og kan dele dem med andre fra hvilken som helst enhet. Dette betyr at du har tilgang på dine dokumenter og verktøy via PC, mobil eller nettbrett. På kurset vil du lære om samhandling, kommunikasjon og tilgjengelighet. Om Microsoft 365Microsoft 365 er abonnement på Office-programmer og andre produktivitetstjenester som du får tilgang til via Internett (skytjenester). I abonnementet har du tilgang på en rekke applikasjoner, slik som Word, Excel, PowerPoint, Teams og lagring i OneDrive, samt mange andre nyttige verktøy. Fordelen er at du alltid har tilgang til Office-programmer og dine dokumenter, og kan dele dem med andre fra hvilken som helst enhet. Vi viser deg hvordan du kan jobbe effektivt med ditt Microsoft 365 abonnement. Pris: 1200 kroner Ansatte ved UiS har egne prisbetingelser.   Etter at du har meldt deg på webinaret, vil du få tilsendt praktisk informasjon om pålogging. Webinarene gjennomføres fra din PC eller nettbrett.    [-]

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 [-]

• Final Cut grensesnitt & funksjoner oversikt som: Fordeler av “magnetic timeline”, “connected clips & secondary storyline”, lyd og “roles”• Final Cut keyboard shortcuts• Import og organisasjon av videofiler i “library” med “keywords”• Klipp av en videoreportasje med innklippsbilder, intervju, voiceover og logo/ grafikk• Sync av ekstern lyd• Flerkameraklipping med “Multicam”• Fargekorrigering• Lydmiks og lydforbedring• Enkle “Film looks” effekter og justering av effekter• 2D og 3D tekst, legge på navn og tittel, enkel keyframeing & animasjon av logo og grafikk• Eksport Dag 2: Fordypning i FCPX og Motion 5 for å bygge et sett av animasjoner og grafikk for lynrask produksjon av et TV-program / YouTube video-serie • Avanserte video- og grafikk-komposisjoner med flere lag• Triks til å overkomme begrensningene i “magnetic timeline”• Anonymisering av ansikter og nummerskilt• Motion: Tilpassning av FCPX “Transitions” og “Titles” i Motion 5 for å skape egne design på en enkel måte• Motion 5: 2D animasjoner og tekst tracking• Motion 5: Enkle 3D animasjoner og kamera• Motion 5: Keyframes og Behaviors• Motion 5: Vi kombinerer alt vi lærer om Motion 5 og skaper grafiske elementer for et TV-program / YouTube video-serie som logo-intro-animasjon, lower-third, custom transitions/logo stinger.• Motion 5: Publisering til FCPX for lynrask produksjon i framtiden [-]

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     [-]

Course Description This course then focuses on enterprise application to cloud concerns including planning and executing a migration, building the business case, managing application dependencies, selecting a proof of concept, and serverless/managed services. A series of instructor-led demonstrations and hands-on activities provide students with practical, hands-on experience. Learning Objectives Learn what technologies enable cloud computing Understand the definition and characteristics of cloud computing Compare service models: IaaS, PaaS, SaaS, Serverless Develop the business case for a cloud migration Plan a successful cloud migration Decipher the risks of both development and security with cloud computing Analyze the costs of using cloud computing and an approach to calculating them Objection handling when dealing with projects situations around risk All courses will be delivered in partnership with ROI Training, Google Cloud Premier Partner, using a Google Authorized Trainer. Course Outline Unit 1: Enabling Technologies -Networking-Virtualization-Overview of Virtualization-Hypervisors and Containers-Security and Virtualization-Multi-tenancy Unit 2: Cloud Computing Concepts -Cloud Definition-Characteristics of Clouds-Cloud Service and Deployment Models-Public Cloud Products and Services Unit 3: Cloud Service Models -Comparing Services Offered by Google Cloud Platform (GCP), Amazon Web Services (AWS), and Azure-Compute Services-Storage Services-Kubernetes Services-Serverless and Managed Services-Big Data and Machine Learning Unit 4: Building a Business Case for the Cloud -Economic and Financial-Understand the Cloud Cost Model-Calculating the Cost of a Cloud Solution-Transform Capital Expenditures to Operating Expenditures-Agility-Lower Risk of Adopting and Evaluating New Technology-Reduce Time to Market-Quickly React as Markets and Requirements Change-Risk Mitigation-High Quality Infrastructure-Reduce Downtime-Cloud SLAs-Leveraging Hybrid and Multi-Cloud Solutions-Staff Utilization-Eliminate Mundane Operational Tasks-Harness Monitoring and Logging-Onboarding Applications and Users Unit 5: Migrating to the Public Cloud -Phases in a Successful Migration-Assessment-Proof of Concept-Data Migration-Application Migration-Employ Cloud Native Services-Cloud Native Development-Selecting Workloads-Backup / Disaster Recovery-Packaged Enterprise Software-Custom Applications-Open-Source Applications Unit 6: Security and the Cloud -Cloud-based Security Issues-Shared Responsibility Model-Security Auditing in the Cloud-Compliance with Regulatory Constraints [-]

Kursinnhold Import av .csv Import av tekstfiler (.txt) Import fra internett Transformering av data Rette opp feil Lage beregnede kolonner Regelmessig import Analyse av store datamengder   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.   [-]

Etter gjennomført kurs skal kursdeltagerne bla. kunne bruke Inventor til å:• Lage modeller• Generere tegninger ut i fra modell• Lage sammenstillinger• Utføre de vanligste tegne- og editeringsfunksjoner• Målsette og påføre tekstKursinnhold:• Grunnleggende begrep og arbeidsmetoder • Parametrisk part-modellering• Arbeide med skisser• Features• Arbeide med sammenstillinger• 2D-layout - oppsett og innstillinger• Generere 2D-tegninger fra modell• Målsetting og innsetting av stykklister [-]

Er innboksen din et stort kaos? Bruker du mye tid på e-post? Vi viser deg hvordan du kan jobbe smart med innkommende og utgående kommunikasjon. 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:   Muligheter rundt e-post Alternativer og innstillinger for e-post Følge opp sendt e-post Automatisk håndtering av e-post ved hjelp av Hurtigtrinn og Regle   Søk og sortering Effektiv bruk av søk Søkemapper for hyppige og «komplekse» søk Visningsinnstillinger for å få fokus på det viktigste   Gjenbruk Lage maler for faste e-poster Opprette hurtigdeler for å kunne sette inn relevant innhold Bruk av distribusjonslister (grupper)   3 gode grunner til å delta 1. Se hvilke muligheter som er tilgjengelig knyttet til e-post 2. Du lærer å automatisere prosessering av e-post med regler og hurtigtrinn 3. Få tips til å bruke søk og søkemapper på en effektiv måte   [-]

Bli en ekspert i å bruke WordPress, verdens mest populære publiseringssystem, med "WordPress: Komplett", et grundig kurs ledet av Espen Faugstad hos Utdannet.no. WordPress er en favoritt blant små og store bedrifter over hele verden og står bak en betydelig andel av nettsider på internett. Dette kurset er designet for å gi deg en omfattende forståelse av WordPress, fra grunnleggende installasjon til avansert tilpasning og administrasjon. Du vil starte med å lære hvordan du kjøper webhotell og domenenavn, og hvordan du laster opp og installerer WordPress ved hjelp av FTP-klienten FileZilla. Kurset dekker også tilpasning av utseendet på din WordPress-nettside, inkludert temaer og utvidelser, samt bruk av det innovative redigeringsverktøyet Gutenberg for å skape innhold. Du vil lære å opprette sider og blogginnlegg, administrere utvidelser og brukere, oversette temaer og utvidelser, og håndtere viktige innstillinger. Videre vil kurset guide deg gjennom hvordan du sikrer og oppdaterer din WordPress-nettside for å holde den trygg og funksjonell. Etter å ha fullført kurset, vil du ha ferdighetene og kunnskapen som trengs for å lage og vedlikeholde en profesjonell WordPress-nettside.   Innhold: Kapittel 1: Introduksjon Kapittel 2: Installere WordPress Kapittel 3: Brukergrensesnitt Kapittel 4: Utseende Kapittel 5: Visuell redigering Kapittel 6: Sider Kapittel 7: Innlegg Kapittel 8: Utvidelser Kapittel 9: Brukere Kapittel 10: Oversette Kapittel 11: Innstillinger Kapittel 12: Sikkerhet Kapittel 13: Avslutning   Varighet: 3 timer og 46 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. [-]