Oversikt over relevante kurs innen IT-kurs i Brønnøysund

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 [-]

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. [-]

  Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: Ingen Innleveringer: For å kunne gå opp til eksamen må 8 utvalgte øvingsoppgaver være godkjente. Personlig veileder: ja Vurderingsform: Skriftlig eksamen, individuell, 3 timer. Ansvarlig: Arne Bjørn Mikalsen Eksamensdato: 16.12.13 / 19.05.14         Læremål: KUNNSKAPERKandidaten:- kan gjøre rede for de mest brukte teknologiene for lokalnettverk- kan gjøre rede for teknisk oppbygning av nettverk- kan gjøre rede for ulike nettverkskomponenter, deres virkemåte og bruksområde- kan planlegge og vurdere sikkerhet i lokalnettverk FERDIGHETER:Kandidaten:- kan koble til og konfigurere en datamaskin slik at den fungerer i et nettverk med internettoppkobling- kan opprette brukerkontoer, tildele rettigheter, samt administrere nettverk med en ressursdatabase- kan planlegge, implementere og konfigurere et mindre lokalnettverk GENERELL KOMPETANSE:Kandidaten:- har kompetanse til selvstendig både å formidle og å ta i bruk sine kunnskaper og ferdigheter innen emnets tema i en driftssituasjon- kan i en praktisk driftssituasjon, forklare og gjøre bruk av sin kunnskap både innen hvert enkelt tema i faget og på tvers av temaene- kan kommunisere med andre om nettverksløsninger Innhold:Fysiske medier i bruk i lokalnettverk. Nettverkskomponenter. Design av nettverk (nettverk infrastruktur). Trådløse nettverk, design og sikkerhet. Generelt om forskjellige typer nettverksoperativsystem. Introduksjon til Active Directory og eDirectory. Prinsipper for konfigurasjon, installasjon, drift og sikkerhet og driftsfilosofi i lokalnettverk. Introduksjon til virtualisering. Driftsmodeller: Fjerndrift eller ASP (Application Service Provider)Les mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag Drift av lokalnettverk 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.    [-]

  Kurs med Daniel Webb som lærer deg grunnleggende ferdigheter  i Power BI.   I vår moderne verden er det data overalt – i bilen, ute, hjemme og ikke minst på jobb. Å ha kontroll på dataene er viktigere enn noensinne, og da gjelder det å gjøre informasjon lett tilgjengelig for rett person på rett sted og til rett tid. Power BI er Microsofts Business Intelligence-verktøy, og kan hjelpe deg med hele prosessen fra innhenting av data til ferdig rapport. Power BI er ypperlig for å tilgjengeliggjøre, dele og samarbeide om viktig informasjon med det resultatet at du kan ta faktabaserte valg og beslutninger. Power BI er tett integrert med Microsoft sine andre løsninger, bl.a. Excel, Teams og Power Platform. I dette kurset bruker Daniel Webb Power BI til å lage en salgsrapport basert på data fra en Excel-fil. Du vil lære deg de grunnleggende ferdighetene for å kunne lage dine egne rapporter i Power BI Desktop, samt få en oversikt over hele Power BI-økosystemet og hvordan ting henger sammen.    Leksjoner Introduksjon til kurset Power BI – Introduksjon og oversikt Power BI Desktop Power BI Service Power BI – lisensiering Introduksjon til Get Data Import vs Direct Query vs Live Connection Gjennomgang av ofte brukte data connectors Excel connector, lokale filer og gateways Introduksjon tll Power Query Arbeid med første data Query Import av andre queries Datamodelleringsteori Modellering i praksis DAX og measures Å skrive DAX DAX-eksempler Quick measures Report view i Power BI Desktop Hvilken visual skal du bruke? Slicers og filters Setter ting sammen Workspaces i Power BI Service Workspace-elementer – reports, datasets og dashboards Deling og samarbeid Oppsummering   [-]

httpWeb Security for Developers [-]

Acrobat Pro DC er et kraftig verktøy som gir deg muligheten til å opprette, redigere og signere PDF-dokumenter. PDF, som står for Portable Document Format, er en standard for å presentere og dele dokumenter uavhengig av programvare, maskinvare og operativsystem. Med Acrobat Pro DC kan du arbeide med tekst, bilder, videoer, koblinger, knapper og skjemaer i PDF-format. PDF-formatet ble introdusert i 1991 av Dr. John Warnock, medgrunnleggeren av Adobe, med målet om å gjøre det enkelt for alle å samle, dele og skrive ut dokumenter fra hvilket som helst program. I dag foretrekkes PDF-formatet av bedrifter over hele verden. I dette kurset, ledet av Espen Faugstad hos Utdannet.no, vil du lære å utnytte Adobe Acrobat Pro DC til fulle. Kurset vil ta deg gjennom programmets organisasjon, verktøy og paneler. Du vil lære å opprette, søke, redigere og organisere PDF-dokumenter. I tillegg vil du bli kjent med elektronisk signering, passordbeskyttelse, skjemaoppretting og kryptering av PDF-dokumenter.   Innhold: Kapittel 1: Organisering og Verktøy Kapittel 2: Opprette PDF Kapittel 3: Søke og Erstatte Kapittel 4: Redigere PDF Kapittel 5: Organisere Sider Kapittel 6: Kommentarer Kapittel 7: Skjema og Signatur Kapittel 8: Beskyttelse og Kryptering Kapittel 9: Lagre PDF Kapittel 10: Avslutning   Varighet: 2 timer og 23 minutter   Om Utdannet.no: Utdannet.no tilbyr noen av landets beste digitale nettkurs. Vår tjeneste fungerer som strømmetjenester for musikk eller TV-serier, der kundene våre betaler en fast månedspris for tilgang til alle kursene vi har produsert. Plattformen har hatt betydelig vekst de siste årene, med over 30 000 registrerte brukere og 1,5 millioner videoavspillinger. Vårt mål er å gjøre kompetanseutvikling morsomt, spennende og tilgjengelig for alle, og vi har støtte fra Innovasjon Norge og Forskningsrådet. [-]

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 [-]

ISO/IEC 27701 Foundation [-]

C# 12 Development and .NET 8 [-]

COURSE OVERVIEW ou will learn how to configure and manage Cisco Nexus 9000 Series Switches in ACI mode, how to connect the Cisco ACI fabric to external networks and services, and fundamentals of Virtual Machine Manager (VMM) integration. You will gain hands-on practice implementing key capabilities such as fabric discovery, policies, connectivity, VMM integration, and more. This course is based on ACI Software v5.2 release.   This course helps you prepare to take the exam, Implementing Cisco Application Centric Infrastructure(300-620 DCACI), which leads to CCNP® Data Center and Cisco Certified Specialist – Data Center ACI Implementation certifications. TARGET AUDIENCE Individuals who need to understand how to configure and manage a data center network environment with the Cisco Nexus 9000 Switch operating in ACI Mode.   COURSE OBJECTIVES After completing this course, you should be able to: Describe Cisco ACI Fabric Infrastructure and basic Cisco ACI concepts Describe Cisco ACI policy model logical constructs Describe Cisco ACI basic packet forwarding Describe external network connectivity Describe VMM Integration Describe Layer 4 to Layer 7 integrations Explain Cisco ACI management features COURSE CONTENT Introducing Cisco ACI Fabric Infrastructure and Basic Concepts What Is Cisco ACI? Cisco ACI Topology and Hardware Cisco ACI Object Model Faults, Event Record, and Audit Log Cisco ACI Fabric Discovery Cisco ACI Access Policies Describing Cisco ACI Policy Model Logical Constructs Cisco ACI Logical Constructs Tenant Virtual Routing and Forwarding Bridge Domain Endpoint Group Application Profile Tenant Components Review Adding Bare-Metal Servers to Endpoint Groups Contracts Describing Cisco ACI Basic Packet Forwarding Endpoint Learning Basic Bridge Domain Configuration **** Introducing External Network Connectivity Cisco ACI External Connectivity Options External Layer 2 Network Connectivity External Layer 3 Network Connectivity Introducing VMM Integration VMware vCenter VDS Integration Resolution Immediacy in VMM Alternative VMM Integrations Describing Layer 4 to Layer 7 Integrations Service Appliance Insertion Without ACI L4-L7 Service Graph Service Appliance Insertion via ACI L4-L7 Service Graph Service Graph Configuration Workflow Service Graph PBR Introduction Explaining Cisco ACI Management Out-of-Band Management In-Band Management Syslog Simple Network Management Protocol Configuration Backup Authentication, Authorization, and Accounting Role-Based Access Control Cisco ACI Upgrade Collect Tech Support Labs Validate Fabric Discovery Configure Network Time Protocol (NTP) Create Access Policies and Virtual Port Channel (vPC) Enable Layer 2 Connectivity in the Same Endpoint Group (EPG) Enable Inter-EPG Layer 2 Connectivity Enable Inter-EPG Layer 3 Connectivity Compare Traffic Forwarding Methods in a Bridge Domain Configure External Layer 2 (L2Out) Connection Configure External Layer 3 (L3Out) Connection Integrate Application Policy Infrastructure Controller (APIC) With VMware vCenter Using VMware Distributed Virtual Switch (DVS) TEST CERTIFICATION Recommended as preparation for the following exams: 300-620 DCACI - Implementing Cisco Application Centric Infrastructure [-]

You’ll get hands-on experience with building a Reactive application to stream data leveraging the newly available Reactive data types, Spring WebFlux, Spring Data and Rsocket. Besides that, we will work with some new tools which make dealing with errors and blocking APIs visible. Agenda Introduction Reactive Streams, Publisher/Subscriber types and Reactor types Getting familiar with Project Reactor Using Reactive RabbitMQ with Spring Using Rsocket as a streaming message protocol Using Spring WebFlux Functional configuration API for Spring WebFlux Using Reactive Types with Thymeleaf Using Spring Security Reactive Using Spring Data MongoDB to reactively stream data Using Spring Data R2DBC Using Reactive Redis with Spring What’s next?  Target audience This workshop is targeted at developers and architects who are new to reactive programming and want to learn how to use the Spring ecosystem to get started. Requierments Participants should have some prior knowledge about Java and the Spring Framework/Ecosystem. The workshop will be held in English. In order to participate, you will need: A laptop with at enough spare disk space and RAM Java, Maven and your favourite IDE installed (Eclipse, Spring STS, IntelliJ, Netbeans) Docker for some demos [-]

Vil du lære å tegne illustrasjoner og logoer til bruk i alle medier? Illustrator tegner vektorgrafikk som kan forstørres ubegrenset, uten å tape kvalitet og kan derfor brukes overalt. Adobe Illustrator er verktøyet for illustratører og grafiske designere, men også et program for deg som vil lage litt enklere illustrasjoner til internett, Power Point og Word. På kurset lærer du å ta utgangspunkt i enkle basisformer og kombinere dem til kompliserte figurer, slik at det blir det lett for alle å tegne. Hvorfor ta dette kurset: Du får en grundig innføring i programmet Du vil lære konkrete tegne- og designoppgaver Du vil lære å redigere/endre Illustrator-filer du mottar Du vil lære å lage illustrasjoner og logoer Du vil lære å lage grafikk for bruk på internett, lesebrett eller mobil Du vil lære effektive arbeidsmetoder Du får kontroll på tegninger med mange elementer og lag Du vil lære om fargebruk og klargjøring av filer for trykk og nett Dette lærer du: Arbeidsmiljøet i programmet Tegning med tegneverktøyene og ved å kombinere enkle grunnformer Redigering og transformering av objekter Innsetting av tekst og bilder Tekstbearbeiding Lage bannerannonser Bruk av farger og forløpninger Lag og gjennomsiktighet [-]

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     [-]

Kursinstruktør Terje Berg-Hansen Terje Berg-Hansen har bred erfaring fra prosjektledelse, utvikling og drift med små og store databaser, både SQL- og NoSQL-baserte. I tillegg til å undervise i etablerte teknologier leder han også Oslo Hadoop User Group, og er levende interessert i nye teknologier, distribuerte databaser og Big Data Science.    Kursinnhold PHP er et kraftig skriptspråk som brukes til å lage dynamiske og interaktive websider. PHP brukes bl.a av Facebook, Wikipedia og Wordpress, og er et effektivt alternativ til f.eks. Ruby on Rails, Django, Microsoft ASP/.net og Java EE. MySQL er verdens mest populære open source databasesystem og brukes ofte sammen med PHP i dynamiske løsninger.   Agenda Installasjon av PHP og MySQL. MySQL/relasjonsdatabaser Datatyper Oppbygging av en database Relasjoner SELECT, INSERT INTO, UPDATE, DELETE, CREATE, ALTER TABLE Administrasjon av databaser med PhpMyAdmin, MySQL Workbench og via kommandolinjen PHP-programmering Variabler og datatyper Kontrollstrukturer og løkker Funksjoner Sende/motta verdier mellom sider med POST og GET Cookies og sessions Bruk av include og require Sette inn, oppdatere, slette og søke etter data i MySQL-databaser med PHP Dataobjects (PDO) Utvikling etter MVC-oppsettet (Model, View, Controller). Kursoppgave: Lage et enkelt CMS-system for publisering av data på web   Læremateriell PHP & MYSQL : From novice to ninja fra Sitepoint, samt online kursmateriell på norsk.   [-]

Kursinstruktør Terje Berg-Hansen Terje Berg-Hansen har bred erfaring fra prosjektledelse, utvikling og drift med små og store databaser, både SQL- og NoSQL-baserte. I tillegg til å undervise i etablerte teknologier leder han også Oslo Hadoop User Group, og er levende interessert i nye teknologier, distribuerte databaser og Big Data Science.    Kursinnhold XML er en moden standard for å utveksle informasjon mellom applikasjoner. Med XML og relaterte standarder som XSL(T) og XQuery er det mulig å utvikle distribuerte nettbaserte tjenester for utveksling av data i et standardisert format.    Målsetting Deltakerne vil etter kurset ha en grunnleggende forståelse av og kjennskap til hvorfor og hvordan XML kan anvendes for å oppnå en bedre utveksling og deling av strukturert og ustrukturert informasjon.   Forkunnskaper Grunnleggende kunnskaper om internett, HTML og CSS er en fordel, men ikke nødvendig for å ta dette kurset.   Kursinnhold Introduksjon Introduksjon til XML og XML-relaterte teknologier, som XPath, XQuery og XSL XML-verktøy Editorer og verktøy for validering, søk og endring av XML Grunnleggende XML XML struktur og syntaks. Gjennomgang av målene for XML. Lage og utforme XML dokumenter Navnerom (namespaces) Oppretting og bruk av navnerom for å skille elementer og funksjoner med samme navn. Validering av  XML Gjennomgang av teknologier som Document Type Definitions (DTD's) og XML Schemas for å kontrollere og styre struktur og data i XML filer Presentasjon av XML Bruk av html og CSS til å presentere XML data Søking i XML Søk i XML-dokumenter med XPath . Introduksjon til XSL(T) Kort om XSL og XSL Transformations. Bruk av XSLT til å formatere, sortere, filtrere og konvertere XML Data   Gjennomføring Kurset gjennomføres med en kombinasjon av online læremidler, gjennomgang av temaer og problemstillinger og praktiske øvelser. Det er ingen avsluttende eksamen, men det er øvelsesoppgaver til hovedtemaene som gjennomgås.   [-]