Oversikt over relevante kurs innen IT-kurs i Kungsbacka

Kurset fokuserer på konvertering av etterspørsel til verdi via IT-relaterte tjenester. Modulen dekker sentrale emner som SLA-design, styring av flere leverandører, kommunikasjon, relasjonsstyring, CX- og UX-design, kartlegging av kunder og mer. E-læringskurset inneholder 18 timer med undervisning, og er delt inn i 8 moduler. Les mer om ITIL® 4 på  AXELOS sine websider. Du vil motta en e-post med tilgang til e-læringen, sertifiseringsvoucher og digital bok fra Peoplecert. Du avtaler tid for sertifiseringen som beskrevet i e-posten fra Peoplecert. [-]

AZ-1008: Administer Active Directory Domain Services [-]

ITIL® 4 Foundation-kurset er en introduksjon til ITIL® 4. Kurset lar kandidater se på IT-tjenestestyring gjennom en ende-til-ende driftsmodell, som inkluderer oppretting, levering og kontinuerlig forbedring av IT-relaterte produkter og tjenester. E-læringskurset inneholder 12 timer med undervisning, og er delt inn i 8 moduler. Les mer om ITIL® 4 på AXELOS sine websider. Inkluderer: Tilgang til ITIL® 4 Foundation e-læring (engelsk) i 12 måneder. ITIL® Foundation online voucher til sertifiseringstest + digital ITIL Foundation bok Du vil motta en e-post med tilgang til e-læringen, sertifiseringsvoucher og digital bok fra Peoplecert. Du avtaler tid for sertifiseringen som beskrevet i e-posten fra Peoplecert. Sertifiseringen består av: 40 spørsmål Multiple Choice 60 minutter + 15 minutter til rådighet dersom du ikke har engelsk som morsmål For å bestå må du ha minimum 26 riktige (65%) Ingen hjelpemidler tillatt ITIL®/PRINCE2®/MSP®/MoP® are registered trademarks of AXELOS Limited, used under permission of AXELOS Limited. All rights reserved. [-]

Modulen dekker bruk og effekt av Lean og agile arbeidsmåter, og hvordan dette kan utnyttes til fordel for organisasjonen. Kurset vil gi kandidatene en praktisk og strategisk metode for å planlegge og levere kontinuerlig forbedring med nødvendig smidighet.  E-læringskurset inneholder 18 timer med undervisning, og er delt inn i 12 moduler. Les mer om ITIL® 4 på AXELOS sine websider Du vil motta en e-post med tilgang til e-læringen, sertifiseringsvoucher og digital bok fra Peoplecert. Du avtaler tid for sertifiseringen som beskrevet i e-posten fra Peoplecert.   [-]

  Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: Grunnleggende erfaring med objektorientert programmering er en fordel. Innleveringer: Øvinger: 6 av 8 må være godkjent. Større eller mindre øvinger tilsvarende 8 øvinger hvor 6 må være godkjent før endelig karakter settes. Personlig veileder: ja Vurderingsform: Karakter settes basert på et prosjekt som gjennomføres individuelt. Prosjektet gjennomføres mot slutten av emnet. Ansvarlig: Atle Nes         Læremål: KUNNSKAPERKandidaten:- kjenner til grensesnittet og egenskaper ved Windows Phone- kjenner til ulike programmeringsspråk som kan benyttes ved utvikling av applikasjoner på Windows Phone- kan forklare hvordan en Windows Phone applikasjon publiseres på Marketplace FERDIGHETER:Kandidaten:- kan installere nødvendig programvare på egen datamaskin for å komme i gang med applikasjonsutvikling for Windows Phone- kan utvikle enkle mobilapplikasjoner basert på C# eller VB og XAML (Silverlight)- kan deploye en Windows Phone applikasjon til egen telefon eller til emulator- kan bestemme layout og orientering- kan legge til ulike kontrollere og håndtere hendelser- kan legge til multimedia-elementer- kan utnytte telefonens egenskaper ved hjelp av Windows Phone SDK GENERELL KOMPETANSE:Studenten får en grunnleggende innføring i utvikling av applikasjoner for mobiltelefoner med Windows Phone Innhold:Introduksjon til Windows Phone, live tiles og panorama view, installasjon av nødvendig programvare, Hello World, deployment av applikasjoner på telefonen eller emulator, XAML, layout og orientering, touch og navigasjon, ulike kontrollere og hendelser, multimedia (bilder, lyd og video), Windows Phone SDK, utnyttelse av telefonens egenskaper (GPS, akselerometer, kontaktliste, kamera), publisering av applikasjoner på Marketplace.Les mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag Applikasjonsutvikling for Windows Phone 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.   [-]

SFWIPF: Fundamentals of Cisco Firewall Threat Defense and Intrusion Prevention [-]

Excel for Økonomer - elæringskurs [-]

Dette kurset gir deg en grunnleggende innføring i to-dimensjonal Datamaskin Assistert Konstruksjon (DAK). Du får et grunnlag for videre studier, og kompetanse som gjør tegnearbeidet både utfordrende og interessant. Du lærer å bli fortrolig med å bruke denne type hjelpemiddel til tegnearbeid, teknisk tegning og revidering av tegninger.   Studentlisens for AutoCAD og Revit Structure/Architecture er inkludert. Kurset er på norsk, men AutoCAD-programmet er på engelsk. Programvaren er gratis. Du lærer å bruke de grunnleggende kommandoene slik at du kan utføre enklere tegnearbeid. Du blir fortrolig med å bruke denne type hjelpemiddel til tegnearbeid, teknisk tegning og revidering av tegninger. Du lærer å jobbe rasjonelt og å velge enkle løsninger. Bruk av flere lag med ulike farger gir god visualisering og bedre lesing av tegningene. Målsetting og teksting er viktig, og må utføres tydelig og på en riktig måte. Flater fylles med skravur og elementer kan lagres separat for senere bruk i andre tegninger. Kurset gir deg inngående informasjon gjennom studieveiledningen om hvordan du skal bruke de enkelte kommandoene. Det stilles krav til 100 % nøyaktighet, noe du oppnår når du jobber riktig. Du får øvelser med tegneoppgaver innen bygg, elektro, elkraft og maskin.   [-]

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: Faget «Datakommunikasjon» eller tilsvarende grunnleggende fag. (TCP/IP forutsettes kjent). Faget «Nettverksteknologi» Innleveringer: Øvinger: 8 av 12 må være godkjent. Øvingene må dekke en bred del av pensum. Vurderingsform: Skriftlig, individuell, 3 timer, Ansvarlig: Helge Hafting Eksamensdato: 04.12.13 / 07.05.14         Læremål: KUNNSKAPER:Kandidaten:- kan forklare en del protokollbaserte farer/angrep i kablede og trådløse nett- kan gjøre rede for mottiltak mot angrepene over- kan gjøre rede for andre farer og mottiltak, som fysiske sikringstiltak og «social engineering»- kan gjøre rede for og planlegge bruk av vanlige sikringstiltak som IDS, IPS, VPN og proxyer FERDIGHETER:Kandidaten kan:- sette i drift et VPN- installere brannmur- Observere nettverkstrafikk med pakkesniffer GENERELL KOMPETANSE:Kandidaten:- kan granske sikkerheten i et nettverk, og velge passende tiltak.Innhold:Generell nettverkssikkerhet. Hvordan planlegge, organisere og sette sikkerhet i små og store nettverk. Brannmurer, VPN, IDS/IPS. Sikkerhet rundt epost, trådløse nett og rutere. En del vanlige angrep, og mottiltak.Les mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag Nettverkssikkerhet 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.    [-]

Dette kurset tilbys som bedriftsinternt kurs   Maskinlæring handler om sette datamaskiner i stand til å lære fra og utvikle atferd basert på data. Det vil si at en datamaskin kan løse en oppgave den ikke er eksplisitt programmert for å håndtere. I stedet er den i stand til å automatisk lære gjenkjenning av komplekse mønstre i data og gjøre beslutninger basert på dette disse. Maskinlæring gir store muligheter, men mange bedrifter har problemer med å ta teknologien i bruk. Nøyaktig hvilke oppgaver kan maskinlæring utføre, og hvordan kommer man i gang? Dette kurset gir oversikt over mulighetene som ligger i maskinlæring, og hvordan i tillegg til kunnskap om hvordan teknologien kan løse oppgaver og skape resultater i praksis. Hva er maskinlæring, datavitenskap og kunstig intelligens og hvordan det er relatert til statistikk og dataanalyse? Hvordan å utvinne kunnskap fra dataene dine? Hva betyr Big data og hvordan analyseres det? Hvor og hvordan skal du bruke maskinlæring til dine daglige forretningsproblemer? Hvordan bruke datamønstre til å ta avgjørelser og spådommer med eksempler fra den virkelige verden? Hvilke typer forretningsproblemer kan en maskinen lære å håndtere Muligheter som maskinlæring gir din bedrift Hva er de teoretiske aspekter på metoder innen maskinlæring? Hvilke ML-metoder som er relevante for ulike problemstillinger innen dataanalyse? Hvordan evaluere styrker og svakheter mellom disse algoritmene og velge den beste? Anvendt data science og konkrete kunde eksempler i praksis   Målsetning Kurset gir kunnskap om hvordan maskinlæring kan løse et bestemt problem og hvilke metoder som egner seg i en gitt situasjon. Du blir i stand til å kan skaffe deg innsikt i data, og vil kunne identifisere egenskapene som representerer dem best. Du kjenner de viktigste maskinlæringsalgoritmene og hvilke metoder som evaluerer ytelsen deres best. Dette gir grunnlag for kontinuerlig forbedring av løsninger basert på maskinlæring.   [-]

Dette kurset er innrettet mot dem som jobber med økonomisk oppfølging i bedriften. Vi går inn på prosessene fra innhenting av data, bearbeidelse av dataene, sammendrag og analyse av dataene, og til sist rapportering av dataene til bedriftens beslutningstagere. Vi bruker en god del tid på Pivot og Power Pivot her, men vi går ikke fullt så langt som i spesialkurset om Pivottabeller. Kurset forutsetter at man er godt kjent i Excel, og vant til å jobbe med litt kompliserte problemstillinger i Excel. Kontroll/gjennomgang av en del sentral funksjonalitet – bl.a. absolutte, relative og blandede referanser. Sammendrag av data fra flere ark i samme eller flere arbeidsbøker, bl.a. gjennomgående summering og tabulering v.hj.a. INDIREKTE-funksjonen. Betingende sammendrag v.hj.a. matriseformler og funksjoner Sentrale funksjoner, bl.a. HVIS, HVISFEIL, FINN.RAD, FINN.KOLONNE, ANTALL.HVIS, etc. Sammendrag av data med Pivottabell Power Pivot Formler Rapportering av data Statiske rapporter Rapporter med interaktivitet, forskjellige teknikker Visualisering av tallene Dashboard Aktuelle teknikker for å lage dashboards Avstemming av to eller flere lister mot hverandre, f.ex. bank Lister – verktøy i Excel som er aktuelle når vi jobber med lister Makroer/VBA – introduksjon til automatisering [-]

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     [-]

Docker and Kubernetes Development [-]

Pivottabeller og rapportering i Excel [-]