Kurs i Ullern

Kurs i sykefravær [-]

MEDLEMSKURS: Retningslinjer for virksomheten i aksjeloven § 6-12 (Gratis for medlemmer) Nettbasert / Ca. 45 min / "Live" med Styrekonsulent   Ett komprimert og nyttig kurs med mange verdifulle tips og råd, "live" med styrekonsulent – enten via Teams eller Zoom. Mulighet for å stille spørsmål mm. underveis.   Kursinnhold: Styrets ansvar for organisering av selskapet i Aksjeloven § 6-12 Hvorfor styret bør vurdere å etablere retningslinjer Hva menes med «retningslinjer» i lovgivningen? Grunner til at styret bør påse utarbeidelse av retningslinjer Fundament for styring og ledelse/ansvarsbegrensende prosess Hvem utvikler retningslinjene og hva kan de omfatte? Verdien i og for selskapet av gjennomtenkte retningslinjer for drift Retningslinjenes oppdragende og retningsgivende effekt Retningslinjer i praksis – definere og bestemme omfang og områder Eksempler fra andre selskaper og tips og råd   Kursmateriell: Det inngår ikke skybaserte filmapper med materiell og hjelpemidler for øvrig i våre GRATIS medlemskurs, men vi viser til artikler, hjelpemidler, dokumentmaler mm på medlemsportalen under kurset slik at du kan benytte deg av det i ettertid.   NB!  GRATIS for alle medlemmer i Styreforeningen.no Pris for "ikke medlemmer" tilsvarer 12 måneders medlemskap i Styreforeningen.no, regnet fra og med kursdato, for deg som ikke fra tidligere var medlem i Styreforeningen.no   Som medlem får du da full tilgang til alle medlemsfordeler fra og med dagen etter kursdato: alle typer GRATIS kurs og webinarer / medlemspris på alle andre kurs dokumentmalregister, spørsmål og svar basene, artikkelseriene mm avklaringstime hvert halvår, medlemssupport pr telefon/epost anledning til å registrere din CV GRATIS i Styrerekruttering.no ... og alle de øvrige medlemsfordelene i Styreforeningen.no   [-]

C# is a power language. [-]

Test test [-]

Fellesmøte for alle deltakerne i alle styrenettverksgruppene - Faglig rettet digitalt fellesmøte   Tirsdag 15.10.24 kl 17:00 - 19:00 på Teams    Program:   Velkommen og kort om programmet - Bjarne Aamodt Orientering om Styreforeningen og Styresenteret AS - Karl Ibsen Kjønnsbalanse og mangfold i styrerommet - Karl Ibsen og Ingvild Ragna Myhre   Pause   "Anbefalingen" for SMB (Den ideelle foreningen CORPRT/ Tenketanken lanserte 15.02.24 anbefalinger for verdiskapende styrearbeid for SMB) - Ewout Vidar Top "Styrescore" fra Board Ability AS (tidligere Corprt AS) - nytt opplegg for rating av en persons kvalifikasjoner for styreverv, bl.a. basert på bruk av KI, også lansert 15.02.24  - Ewout Vidar Top Avrunding og takk for i dag - Bjarne Aamodt   [-]

Ingeniørpakken gir til sammen 2 realfagspoeng og passer for deg som vil bli ingeniør, men som mangler fag eller må forbedre fagene. Har du studiekompetanse, men mangler realfag for å kunne søke opptak ved ingeniørhøyskoler? Fagene du trenger, er matematikk R1 og R2 og fysikk 1. Her kan du prøve Matematikk eller Fysikk (gratis) Gjennomføring NettstudierDu bestemmer hva, hvor og når du vil lære. Her får du varierte leksjoner i form av tekster, video, quiz, podcast, veiledning og oppgaver. Du har alltid kontakt med din personlige lærer hos K2. Målet er å gjøre deg best mulig forberedt til eksamen. Her kan du prøve alle kursene (gratis) Din digitale læringsplattform Den nettbaserte læringsportalen til K2 er tilpasset både mobil, nettbrett og pc. Det gir deg enkelt tilgang til å studere faget på en engasjerende og spennende måte, uansett hvor du er.  Eksamen Som deltaker ved K2 er du privatist og må ta eksamen i fagene for å få karakter. Oppmeldingsfristene er normalt 15. september og 1.februar. Plattform for oppmelding til eksamen kan variere fra fylke til fylke. Se fylkesoversikt her. Husk at betaling av eksamensavgiften skjer ved oppmelding.  Velg Ingeniørpakken og få tilgang til Fysikk 1, Matematikk R1 og Matematikk R2 i 12 måneder. Du velger selv når du vil ta eksamen.  Fysikk 1, eksamen nov/des eller mai/juni Matematikk R1, eksamen nov/des eller mai/juni Matematikk R2, eksamen nov/des eller mai/juni Veien videre Om du har generell studiekompetanse (GENS) og velger å ta fagene fysikk 1 og matematikk R1 og R2 som privatist, da oppfyller du opptakskravene til flere studier, inkludert ingeniørstudier. Se praktisk info for frister og opptak til universitet og høyskole. Gratis veiledning Er du usikker på hva som skal til for å få studiekompetanse, ta gjerne kontakt med oss for gratis veiledning. Vi har veiledere med mange års erfaring som står klare til å hjelpe deg!Ønsker du mer informasjon om kurset velg "Send meg info"-knappen under. Vil du chatte med oss, så klikk på ikonet nederst i høyre hjørne. Lånekassestøtte Utdanningen er godkjent i lånekassen. Søk direkte via lanekassen.no.For mer informasjon se nettsiden vår under Praktisk info. Støtteordninger Er du organisert i en fagforening, kan du i de fleste fagforeningene søke støtte til utdanning. Dersom du er organisert bør du sjekke med din fagforening om muligheter for støtte, frister og hvordan du søker. Forkunnskaper Du må ha fullført grunnskole eller tilsvarende opplæring. Minoritetsspråklige bør ha minimum B1-nivå i norsk muntlig og skriftlig. Dersom du har behov for å lære mer norsk før du starter på utdanning har vi norskkurs på forskjellig nivå (A1-B2). Språkkursene er digitale med personlig oppfølging fra lærer. Se alle kurs her Krav til utstyr Som deltaker på K2 Nettstudier kan du bruke både mobil, nettbrett og PC når du tar kurset.Til eksamen må du ha tilgang til PC. I tillegg trenger du PC-versjonen av Office eller tilsvarende programmer. Se her hva du har tilgang til av nettbaserte ressurser på eksamen. Praktisk info Du finner svar på ofte stilte spørsmål på nettsiden vår under praktisk info.   [-]

Du vil få tilsendt en 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 casebasert med 50 tilknyttede multiple choice-spørsmål, og du består ved 66% korrekte svar (dvs 30 av 50 spørsmål). Deltakerne har 2 timer og 30 minutter til rådighet på eksamen.  Dette er en åpen bok eksamen. Nødvendige forkunnskaper: PRINCE2® Agile Foundation eller PRINCE2® Foundation sertifisering [-]

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 - 40 spørsmål skal besvares, og du består med 70% riktige svar (dvs. 28 av 40). Deltakerne har 1 time og 30 minutter til rådighet på eksamen.  Ingen hjelpemidler er tillatt.  Nødvendige forkunnskaper: Bestått ITIL Foundation sertifisering Gjennomført godkjent kurs/e-læring [-]

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 55% korrekte svar (dvs 28 av 50 spørsmål). Deltakerne har 1 time og 15 minutter til rådighet på eksamen.  Ingen hjelpemidler er tillatt.   Nødvendige Forkunnskaper: Ingen [-]

About Arrow At Arrow, our core service is Business English. This strategic focus allows us to provide unparalleled quality and results for our clients. We have a proven methodology and the ability to measure the results our clients achieve, which most language service providers in Norway do not provide.  Courses With our Business English Training, clients can choose to follow either a general business English curriculum, or have their curriculum specifically focussed on a particular business English sector (e.g. financial, HR, strategy, etc). We offer: One-to-One Training Group Training (6 participants max) Bespoke Courses All course material is developed by Arrow based on the specific needs of the client. New clients will receive a needs assessment at the beginning of their training. This allows us to not only create an effective curriculum but also the parameters by which we measure the results of our clients.  How we’re different Experience
Over the years have provided top-level Business English training for Norwegian clients in diverse industries, in diverse sectors, in a wide area of professional competencies. 
 Our Business English Trainers 
Our trainers have significant experience in Business English instruction. They come from a wide degree of backgrounds, many of whom have worked in the business world before working with us.
 Strategic Focus
Arrow's strategic focus on Business English training allows us to be the premier provider on the market today. This focus allows for a singular purpose by which to provide the highest quality, most effective Business English training in the Norwegian market.
 Proven Methodology
Arrow has formulated a proven methodology that is not only diverse in its approach, but highly engaging, and effective at committing the Business English our clients gain to long-term memory.
 Measurable Results
Finally, we are able to demonstrate measurable results for our Business English clients, which is something that most language training services can offer. After all, if a client is going to take time out of their busy day to learn Business English, there needs to be a measurable return on that time/financial investment. We provide that.  [-]

Dette er studium for deg som har påbegynt yrkesutdanning, og som ønsker generell studiekompetanse. Med Vg3 påbygging kan du bygge på den utdanningen du har, slik at du oppnår generell studiekompetanse, og dermed blir kvalifisert for høyere utdanning ved høyskole og universitet. Velg dette studium dersom du har fullført og bestått Vg1 og Vg2 innen yrkesfaglig retning.    Obligatoriske emner Norsk Vg3, Påbygging til generell studiekompetanse         Historie Vg3 påbygging         Matematikk 2P-Y Vg3 påbygging         Naturfag Vg3 påbygging         Kroppsøving Vg3         Sosialkunnskap Vg3 programfag   [-]

COURSE OVERVIEW  This course covers the process of designing, implementing, and managing core Azure networking infrastructure, Hybrid Networking connections, load balancing traffic, network routing, private access to Azure services, network security and monitoring. Learn how to design and implement a secure, reliable, network infrastructure in Azure and how to establish hybrid connectivity, routing, private access to Azure services, and monitoring in Azure. TARGET AUDIENCE This course is aimed at Network Engineers looking to specialize in Azure networking solutions. An Azure Network engineer designs and implements core Azure networking infrastructure, hybrid networking connections, load balance traffic, network routing, private access to Azure services, network security and monitoring. The azure network engineer will manage networking solutions for optimal performance, resiliency, scale, and security. COURSE CONTENT Module 1: Azure Virtual Networks In this module you will learn how to design and implement fundamental Azure Networking resources such as virtual networks, public and private IPs, DNS, virtual network peering, routing, and Azure Virtual NAT. Azure Virtual Networks Public IP Services Public and Private DNS Cross-VNet connectivity Virtual Network Routing Azure virtual Network NAT Lab 1: Design and implement a Virtual Network in Azure Lab 2: Configure DNS settings in Azure Lab 3: Connect Virtual Networks with Peering After completing module 1, students will be able to: Implement virtual networks Configure public IP services Configure private and public DNS zones Design and implement cross-VNET connectivity Implement virtual network routing Design and implement an Azure Virtual Network NAT   Module 2: Design and Implement Hybrid Networking In this module you will learn how to design and implement hybrid networking solutions such as Site-to-Site VPN connections, Point-to-Site VPN connections, Azure Virtual WAN and Virtual WAN hubs. Site-to-site VPN connection Point-to-Site VP connections Azure Virtual WAN Lab 4: Create and configure a local gateway Create and configure a virtual network gateway Create a Virtual WAN by using Azure Portal Design and implement a site-to-site VPN connection Design and implement a point-to-site VPN connection Design and implement authentication Design and implement Azure Virtual WAN Resources   Module 3: Design and implement Azure ExpressRoute In this module you will learn how to design and implement Azure ExpressRoute, ExpressRoute Global Reach, ExpressRoute FastPath and ExpressRoute Peering options. ExpressRoute ExpressRoute Direct ExpressRoute FastPath ExpressRoute Peering Lab 5: Create and configure ExpressRoute Design and implement Expressroute Design and implement Expressroute Direct Design and implement Expressroute FastPath   Module 4: load balancing non-HTTP(S) traffic in Azure In this module you will learn how to design and implement load balancing solutions for non-HTTP(S) traffic in Azure with Azure Load balancer and Traffic Manager. Content Delivery and Load Blancing Azure Load balancer Azure Traffic Manager Azure Monitor Network Watcher Lab 6: Create and configure a public load balancer to load balance VMs using the Azure portal Lab:7 Create a Traffic Manager Profile using the Azure portal Lab 8: Create, view, and manage metric alerts in Azure Monitor Design and implement Azure Laod Balancers Design and implement Azure Traffic Manager Monitor Networks with Azure Monitor Use Network Watcher   Module 5: Load balancing HTTP(S) traffic in Azure In this module you will learn how to design and implement load balancing solutions for HTTP(S) traffic in Azure with Azure Application gateway and Azure Front Door. Azure Application Gateway Azure Front Door Lab 9: Create a Front Door for a highly available web application using the Azure portal Lab 10: Create and Configure an Application Gateway Design and implement Azure Application Gateway Implement Azure Front Door   Module 6: Design and implement network security In this module you will learn to design and imponent network security solutions such as Azure DDoS, Azure Firewalls, Network Security Groups, and Web Application Firewall. Azure DDoS Protection Azure Firewall Network Security Groups Web Application Firewall on Azure Front Door Lab 11: Create a Virtual Network with DDoS protection plan Lab 12: Deploy and Configure Azure Firewall Lab 13: Create a Web Application Firewall policy on Azure Front Door Configure and monitor an Azure DDoS protection plan implement and manage Azure Firewall Implement network security groups Implement a web application firewall (WAF) on Azure Front Door   Module 7: Design and implement private access to Azure Services In this module you will learn to design and implement private access to Azure Services with Azure Private Link, and virtual network service endpoints. Define Azure Private Link and private endpoints Design and Configure Private Endpoints Integrate a Private Link with DNS and on-premises clients Create, configure, and provide access to Service Endpoints Configure VNET integration for App Service Lab 14: restrict network access to PaaS resources with virtual network service endpoints Lab 15: create an Azure private endpoint Define the difference between Private Link Service and private endpoints Design and configure private endpoints Explain virtual network service endpoints Design and configure access to service endpoints Integrate Private Link with DNS Integrate your App Service with Azure virtual networks   TEST CERTIFICATION This course helps to prepare for exam AZ-700 [-]

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     [-]

  Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: Studenten må kunne installere programvare, og programmere. Vanlige programmeringsspråk for opensource er C, C++, java, python, m.fl. Innleveringer:   Vurderingsform: Skriftlig eksamen, individuell, 3 timer, teller 60 %. Prosjektoppgave teller 40 %. Ansvarlig: Helge Hafting Eksamensdato: 02.12.13 / 05.05.14         Læremål: KUNNSKAPER:Kandidaten:- kan forklare bakgrunnen for open source, og forskjellen på de vanligste åpne lisensene.- kan beskrive bruk av flerspråklig programvare FERDIGHETER:Kandidaten:- kan bruke vanlige programmeringsverktøy GENERELL KOMPETANSE:Kandidaten:- kan delta i prosjekter som utvikler åpen programvare videre Innhold:Historien og filosofien bak åpen kildekode. Copyright, lisenser fri programvare. Praktisk bruk av verktøyer som make, diff, patch og versjonskontrollsystemer. Internasjonalisering, flerspråklig programvare. Prosjektarbeid med deltakelse i opensource-prosjekt.Les mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag Opensource-utvikling 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.    [-]

Synes du det er uoversiktlig å samarbeide om dokumenter med andre? Vi lærer deg de viktigste funksjonene og metodene for vellykket dokument-samhandling og ferdigstilling av dokumenter. 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:   Samarbeidsfunksjoner Spor endringer for å se hvem som har gjort hva Bruk av merknader Sammenligne dokumenter Passordbeskytte dokumenter   Filer i SharePoint, OneDrive for Business og OneDrive Samtidigredigering Versjonering   Før publisering Fjerne skjulte data Fjerne personlig informasjon   3 gode grunner til å velge KnowledgeGroup 1. Best practice kursinnhold 2. Markedets beste instruktører 3. Gratis support [-]