Oversikt over relevante kurs innen IT-kurs i Vest-Agder

Kurset fokuserer på integrering av forskjellige verdistrømmer og aktiviteter for å lage, levere og støtte IT-aktiverte produkter og tjenester, samtidig som den dekker støtte for praksis, metoder og verktøy. Kurset gir kandidatene forståelse for tjenestekvalitet og forbedringsmetoder. E-læringskurset inneholder 18 timer med undervisning, og er delt inn i 8 moduler. Les mer om ITIL® 4 på AXELOS sine websider. Inkluderer: Tilgang til ITIL® 4 Specialist: Create, Deliver and Support e-læring (engelsk) i 12 måneder. ITIL® 4 Specialist: Create, Deliver and Support online voucher til sertifiseringstest.   ITIL®/PRINCE2®/MSP®/MoP® are registered trademarks of AXELOS Limited, used under permission of AXELOS Limited. All rights reserved. [-]

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 [-]

Kursinnhold: De ulike begrepene som blir brukt i Google Analytics Segmentering av brukere i statistikken Hvordan lese relevant statistikk Hva du kan bruke tallene til videre i din markedsføring Hvordan nettsidene dine fungerer og hvor konverteringene kommer fra [-]

UTDANNINGSMÅLDu vil lære avansert bruk av programmet, og skal kunne utføre tilpassninger og oppbygning av egne objekter. Du lærer også om håndtering av prosjekter og utarbeidelse av rapporter. KURSINNHOLD: Tags Families Group Tabeller - dør og vinduslister DWG import - export Terreng /kart Prosjektfaser Worksharing - flere arkitekter i et prosjekt Legend Filter [-]

The students will also explore how to implement data security including authentication, authorization, data policies and standards. They will also define and implement data solution monitoring for both the data storage and data processing activities. Finally, they will manage and troubleshoot Azure data solutions which includes the optimization and disaster recovery of big data, batch processing and streaming data solutions. Agenda Module 1: Azure for the Data Engineer -Explain the evolving world of data-Survey the services in the Azure Data Platform-Identify the tasks that are performed by a Data Engineer-Describe the use cases for the cloud in a Case Study Module 2: Working with Data Storage. -Choose a data storage approach in Azure-Create an Azure Storage Account-Explain Azure Data Lake storage-Upload data into Azure Data Lake Module 3: Enabling Team Based Data Science with Azure Databricks. -Explain Azure Databricks and Machine Learning Platforms-Describe the Team Data Science Process-Provision Azure Databricks and workspaces-Perform data preparation tasks Module 4: Building Globally Distributed Databases with Cosmos DB. -Create an Azure Cosmos DB database built to scale-Insert and query data in your Azure Cosmos DB database-Provision a .NET Core app for Cosmos DB in Visual Studio Code-Distribute your data globally with Azure Cosmos DB Module 5: Working with Relational Data Stores in the Cloud. -SQL Database and SQL Data Warehouse-Provision an Azure SQL database to store data-Provision and load data into Azure SQL Data Warehouse Module 6: Performing Real-Time Analytics with Stream Analytics. Module 7: Orchestrating Data Movement with Azure Data Factory. -Explain how Azure Data Factory works-Create Linked Services and datasets-Create pipelines and activities-Azure Data Factory pipeline execution and triggers Module 8: Securing Azure Data Platforms. -Configuring Network Security-Configuring Authentication-Configuring Authorization-Auditing Security Module 9: Monitoring and Troubleshooting Data Storage and Processing. -Data Engineering troubleshooting approach-Azure Monitoring Capabilities-Troubleshoot common data issues-Troubleshoot common data processing issues Module 10: Integrating and Optimizing Data Platforms. -Integrating data platforms-Optimizing data stores-Optimize streaming data-Manage disaster recovery [-]

Kursbeskrivelse Kurset lærer deg å utnytte de mer avanserte mulighetene i Access. Avanserte utvalg fra databasen, automatikk i skjermbilder, egne menyer og automatisk oppstart av databaseapplikasjonen, er noen av de mange spennende temaene som blir belyst i kurset. Etter dette kurset vil du kunne lage robuste og avanserte applikasjoner som både du og brukerne vil synes er enklere og mer intuitiv. Du vil aktivt kunne senke brukerterskelen på dine applikasjoner. Definer dine egne inn datamasker slik at du reduserer muligheten for feil ved inntasting av data. Definer faste relasjoner som sikrer at dine data henger sammen; at enhver ordre har en kunde og en vare. Lag avanserte spørringer med egendefinerte beregninger. Grupper data og beregne summer og totaler. Lagskjemaer som fungerer som menyer ogbygg et meny system ved hjelp av veivisere og makro kommandoer. Bruk informasjon i skjemaet for å velge data du vil presentere. Sett inn kontroller i skjema og få dem til å gjøre det du ønsker ved å endre deres egenskaper. Lag skjemaer med delskjema og skjemaer med oppslag i andre skjemaer. Se hvordan du kan få flotte utskrifter med egendefinerte beregninger ved hjelp av avanserte rapportegenskaper. Lag ditt eget meny system med Access sin egen innebygde metode. Lær hvordan du kan sørge for at din egendefinerte hovedmeny åpnes når databasen åpnes. Forstå hvordan Access kan tilpasses databasen. I tillegg får du en rekke tips og triks du kan bruke i din arbeidsdag.  Alt du lærer får du repetert gjennom aktiv oppgaveløsning slik at du husker det du har lært når du kommer tilbake på jobb. Kursdokumentasjon, lunsj og pausemat er selvsagt inkludert! Kursholderne har mer enn 20 års Access erfaring som de gjerne deler med deg! Meld deg på Access-kurs allerede i dag og sikre deg plass!   Kursinnhold Tabellens utformingsvisning Egendefinerte formaterEgendefinerte inndatamaskerBruk av indeks Relasjoner Definere faste relasjoner mellom tabelleneReferanseintegritet Avanserte spørringer Egenskaper for spørringerMengdefunksjonerBeregne totaler for alle postene/grupper med posterLage kalkulerte feltVillkårsuttrykk med områdemengdefunksjonerDelspørringerHente villkår fra skjemaerArbeide med tomme felt (nullverdier og tomme strenger)KrysstabellspørringerSQL-spørringer Avanserte skjemaer Opprette et delskjemaEndre kontrolltype for eksisterende kontrollKombinasjonsboks med flere kolonnerKalenderkontrollAlternativkontroll Avanserte rapporter Spesielle egenskaperGrupperingsintervaller og datatyperGruppering og summering Makroer Makroer og hendelserEn sammenligning med programmeringsmuligheten [-]

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 etablert standard for plattformuavhengig lagring og utveksling av data, der innhold og presentasjon bearbeides separat. XSL er en nøkkelteknologi innenfor utvikling og nyttiggjørelse av XML. Viktige hoveddeler innenfor XSL er XSLT, XSL-FO og XPath. Kurset gir deltakerne en innføring i XSL . Vi ser på hvilke muligheter vi har for bearbeiding av XML-data, og hvordan vi kan gjøre data tilgjengelig for presentasjon.   Du får en gjennomgang i: Introduksjon til XML, XSL og XSLT. Introduksjon til XPath og XQuery. Bruk av XSLT-maler og Xpath-uttrykk for å søke etter data i XML-dokumenter. Transformering av XML-dokumenter til xml, html og tekstdokumenter. Introduksjon til XSL-FO og produksjon av svg- og pdf-dokumenter Design og formatering av ouput fra XSLT-transformasjoner Sortering, gruppering og kombinering av XML-dokumenter Bruk av XSLT-verktøy til transformering og søk.   Målsetting Etter endt kurs skal kursdeltakerne blant annet vite hvordan man filtrerer, sorterer og transformerer XML-data, samt hvilke muligheter man har for å trekke inn annet innhold/data for presentasjon.   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.   [-]

COURSE OVERVIEW The course includes hands-on experience configuring, managing, and monitoring a firewall in a lab environment TARGET AUDIENCE This course is aimed at Security Engineers, Security Administrators, Security Operations Specialists, Security Analysts, and Support Staff. COURSE OBJECTIVES After you complete this course, you will be able to: Configure and manage the essential features of Palo Alto Networks next-generation firewalls Configure and manage Security and NAT policies to enable approved traffic to and from zones Configure and manage Threat Prevention strategies to block traffic from known and unknown IP addresses, domains, and URLs Monitor network traffic using the interactive web interface and firewall reports COURSE CONTENT 1 - Palo Alto Networks Portfolio and Architecture 2 - Configuring Initial Firewall Settings 3 - Managing Firewall Configurations 4 - Managing Firewall Administrator Accounts 5 - Connecting the Firewall to Production Networks with Security Zones 6 - Creating and Managing Security Policy Rules 7 - Creating and Managing NAT Policy Rules 8 - Controlling Application Usage with App-ID 9 - Blocking Known Threats Using Security Profiles 10 - Blocking Inappropriate Web Traffic with URL Filtering 11 - Blocking Unknown Threats with Wildfire 12 - Controlling Access to Network Resources with User-ID 13 - Using Decryption to Block Threats in Encrypted Traffic 14 - Locating Valuable Information Using Logs and Reports 15 - What's Next in Your Training and Certification Journey Supplemental Materials Securing Endpoints with GlobalProtect Providing Firewall Redundancy with High Availability Connecting Remotes Sites using VPNs Blocking Common Attacks Using Zone Protection   FURTHER INFORMATION Level: Introductory Duration: 5 days Format: Lecture and hands-on labs Platform support: Palo Alto Networks next-generation firewalls running PAN-OS® operating system version 11.0     [-]

Kurs i cybersikkerhet med Carsten Maartmann-Moe. Du lærer om cyberkriminelle,  og de største risikoene for angrep. Cybersikkerhet eller IT-sikkerhet er i nyhetene nesten hver dag, men hva er fakta og hva er fiksjon? Kan du bli hacket når som helst? Er alle ute etter din virksomhet? Hva er de største risikoene, og hvordan kan man effektivt redusere dem? I dette kurset lærer du hvordan cyberkriminelle opererer, hva de største risikoene er, og hvordan nyanser og kompleksitet påvirker hvilke sikringstiltak som fungerer. Vi vil også lære hvordan den undergrunnsøkonomien i cyberverden fungerer, slik at du kan ta gode beslutninger for å beskytte din egen og din virksomhets informasjon.    Være i stand til å forklare hva cyberkriminalitet er, og hvorfor cybersikkerhet er viktig Være i stand til å forstå hvorfor cybersikkerhet er komplekst Være i stand til å analysere hvilke risikoer som du og din virksomhet står ovenfor Være i stand til å finne og beskytte din mest verdifulle informasjon Være i stand til å håndtere sikkerhetsbrudd på en betryggende måte Morgendagens trusler   Introduksjon til kurset Hvordan er det å bli hacket Hvem er hackerene? God cybersikkerhet – en forutsetning for vellykket digitalisering Hvordan ser cyberspace ut? Å jobbe strukturert med cybersikkerhet 4 myter om cybersikkerhet Personvern og cybersikkerhet Hva er risiko, og hvordan måler du den? Vurdere og redusere risiko – tips og triks Hvordan kommunisere med ledelsen om risiko? Digitalisering og tilpasning til din virksomhet Effektive sikkerhetstiltak for din virksomhet Effektive sikkerhetstiltak for deg som person Rammeverk og verktøy Fremtidige utfordringer Hjelp jeg har blitt hacket Hvem som kan hjelpe Oppsummering og videre læring [-]

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.    [-]

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     [-]

ISO/IEC 27701 Foundation [-]

COURSE OVERVIEW This course is comprised of the following Microsoft Official Curriculum modules: MS-500T01 Managing Microsoft 365 Identity and Access, MS-500T02 Implementing Microsoft 365 Threat Protection, MS-500T03 Implementing Microsoft 365 Information Protection and MS-500T04 Administering Microsoft 365 Built-in Compliance.   MS-500T01 Managing Microsoft 365 Identity and Access Help protect against credential compromise with identity and access management. In this course you will learn how to secure user access to your organization’s resources. Specifically, this course covers user password protection, multi-factor authentication, how to enable Azure Identity Protection, how to configure Active Directory federation services, how to setup and use Azure AD Connect, and introduces you to Conditional Access. You will also learn about solutions for managing external access to your Microsoft 365 system.   MS500T02 Implementing Microsoft 365 Threat Protection Threat protection helps stop damaging attacks with integrated and automated security. In this course you will learn about threat protection technologies that help protect your Microsoft 365 environment. Specifically, you will learn about threat vectors and Microsoft’s security solutions for them. You will learn about Secure Score, Exchange Online protection, Azure Advanced Threat Protection, Windows Defender Advanced Threat Protection, and how to use Microsoft 365 Threat Intelligence. It also discusses securing mobile devices and applications. The goal of this course is to help you configure your Microsoft 365 deployment to achieve your desired security posture.   MS500T03 Implementing Microsoft 365 Information Protection Information protection is the concept of locating and classifying data anywhere it lives. In this course you will learn about information protection technologies that help secure your Microsoft 365 environment. Specifically, this course discusses information rights managed content, message encryption, as well as labels, policies and rules that support data loss prevention and information protection. Lastly, the course explains the deployment of Microsoft Cloud App Security.   MS500T04 Administering Microsoft 365 Built-in Compliance Internal policies and external requirements for data retention and investigation may be necessary for your organization. In this course you will learn about archiving and retention in Microsoft 365 as well as data governance and how to conduct content searches and investigations. Specifically, this course covers data retention policies and tags, in-place records management for SharePoint, email retention, and how to conduct content searches that support eDiscovery investigations. The course also helps your organization prepare for Global Data Protection Regulation (GDPR).   Virtual Learning   This interactive training can be taken from any location, your office or home and is delivered by a trainer. This training does not have any delegates in the class with the instructor, since all delegates are virtually connected. Virtual delegates do not travel to this course, Global Knowledge will send you all the information needed before the start of the course and you can test the logins. TARGET AUDIENCE This course is for the Microsoft 365 security administrator role. This role collaborates with the Microsoft 365 Enterprise Administrator, business stakeholders and other workload administrators to plan and implement security strategies and ensures that the solutions comply with the policies and regulations of the organization. COURSE CONTENT Module 1: User and Group Security This module explains how to manage user accounts and groups in Microsoft 365. It introduces you to Privileged Identity Management in Azure AD as well as Identity Protection. The module sets the foundation for the remainder of the course.   Module 2: Identity Synchronization This module explains concepts related to synchronizing identities. Specifically, it focuses on Azure AD Connect and managing directory synchronization to ensure the right people are connecting to your Microsoft 365 system.   Module 3: Federated Identities This module is all about Active Directory Federation Services (AD FS). Specifically, you will learn how to plan and manage AD FS to achieve the level of access you want to provide users from other directories.   Module 4: Access Management This module describes Conditional Access for Microsoft 365 and how it can be used to control access to resources in your organization. The module also explains Role Based Access Control (RBAC) and solutions for external access.   Module 5: Security in Microsoft 365 This module starts by explaining the various cyber-attack threats that exist. It then introduces you to the Microsoft solutions to thwart those threats. The module finishes with an explanation of Microsoft Secure Score and how it can be used to evaluate and report your organizations security posture.   Module 6: Advanced Threat Protection This module explains the various threat protection technologies and services available in Microsoft 365. Specifically, the module covers message protection through Exchange Online Protection, Azure Advanced Threat Protection and Windows Defender Advanced Threat Protection.   Module 7: Threat Intelligence This module explains Microsoft Threat Intelligence which provides you with the tools to evaluate and address cyber threats. You will learn how to use the Security Dashboard in the Microsoft 365 Security and Compliance Center. It also explains and configures Microsoft Advanced Threat Analytics.   Module 8: Mobility This module is all about securing mobile devices and applications. You will learn about Mobile Device Management and how it works with Intune. You will also learn about how Intune and Azure AD can be used to secure mobile applications.   Module 9: Information Protection This module explains information rights management in Exchange and SharePoint. It also describes encryption technologies used to secure messages. The module introduces how to implement Azure Information Protection and Windows Information Protection.   Module 10: Data Loss Prevention This module is all about data loss prevention in Microsoft 365. You will learn about how to create policies, edit rules, and customize user notifications.   Module 11: Cloud Application Security This module is all about cloud app security for Microsoft 365. The module will explain cloud discovery, app connectors, policies, and alerts.     [-]

  Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: IINI1003 Databaser eller tilsvarende forhåndskunnskaper Innleveringer: Tilsvarende 8 obligatoriske øvinger må være godkjent før endelig karakter settes. Personlig veileder: ja Vurderingsform: Individuell netteksamen, 2 timer. Ansvarlig: Tore Mallaug Eksamensdato: 13.12.13 / 16.05.14         Læremål: KUNNSKAPERKandidaten:- kjenner sentrale begreper innen flerbrukerproblematikk og databaseadministrasjon, og kan gjøre rede for disse- forstår hvordan innebygd funksjonalitet i relasjonsdatabasesystem kan utnyttes i en klient/tjener-arkitektur- vet hvordan ulike typer data kan lagres og representeres i et databasesystem; tekst, XML og temporale data.- kan gjøre rede for hvordan NoSQL-løsninger er et alternativ til relasjonsdatabaser i Web-løsninger FERDIGHETERKandidaten:- kan administrere en flerbrukerdatabase med SQL-kommandoer i et valgt databaseverktøy- lager sin egen (mest mulig normaliserte) relasjonsdatabase med nøkler og referanseintegritet som ikke bare lagrer strukturelle data, men også tekst og semi-strukturelle data (XML)- kan utnytte databaseverktøyet funksjonalitet til utvidet bruk av SQL i en klient/tjener-sammenheng for å støtte opp rundt applikasjoner mot databasen- kan utnytte databaseverktøyet til å lagre temporale data- kan utføre SQL-spørringer mot ulike typer data i en database GENERELL KOMPETANSEKandidaten:- viser en bevisst holdning til lagring og representasjon av ulike typer data i et informasjonssystem- viser en bevisst holdning til databasedesign for å unngå unødvendig dobbeltlagring av data i en database Innhold:Installering og bruk av valgt databaseverktøy (MySQL). Flerbrukerproblematikk og databaseadministrasjon (DBA) i SQL. Bruk av SQL og innebygd funksjonalitet i databaseverktøyet (bruk av funksjoner/prosedyrer og triggere). Utnytte databaseverktøyet i en klient/tjener -arkitektur. Se på forholdet database - applikasjon. Lagring av ulike typer data; tekst, XML (semi-strukturelle data), dato/tid (temporale data). Enkel bruk av NoSQL-løsning. MySQL blir brukt i eksempler, men noen utfyllende eksempler i Oracle kan forekomme i lærestoffet.Les mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag Databaser 2 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.    [-]

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