Oversikt over relevante kurs innen IT-kurs i Longyearbyen

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 [-]

Fleksible kurs for fremtidenNy kunnskap skal gi umiddelbar effekt, og samtidig være holdbar og bærekraftig på lang sikt. NTI AS har 30 års erfaring innen kurs og kompetanseheving, og utdanner årlig rundt 10.000 personer i Nord Europa innen CAD, BIM, industri, design og konstruksjon. Inventor grunnkurs Her er et utvalg av temaene du vil lære på kurset: Generelt Part-modellering (3D-komponenter) Samlinger Skjelettmodellering på basisnivå Tegninger i 2D Autodesk Inventor 3D CAD programvare brukes til produktdesign, rendering og simuleringer. Løsningen er viktig når smarte ideer skal bli til produksjonsklar design, og for å utvikle fremtidens produkter og tjenester. Inventor tilfører større kvalitet til utviklingsprosesser med smarte funksjoner som optimaliserer, gjør det enkelt å «se» modellen, og simulere hvordan konseptet/prototypen vil fungere i bruk.   Dette er et populært kurs, meld deg på nå!   Tilpassete kurs for bedrifterVi vil at kundene våre skal være best på det de gjør - hele tiden.  Derfor tenker vi langsiktig om kompetanseutvikling og ser regelmessig kunnskapsløft som en naturlig del av en virksomhet. Vårt kurskonsept bygger på et moderne sett av ulike læringsmiljøer, som gjør det enkelt å finne riktig løsning uansett behov. Ta kontakt med oss på telefon 483 12 300, epost: salg@nticad.no eller les mer på www.nticad.no [-]

  Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: Du må ha god kjennskap til Windows 2008 server og oppsett av AD og DNS og installasjon av SQL 2008 server. For deg som ikke har kjennskap til Windows 2008 server og/eller som heller vil bruke mer tid på SharePoint kan vi tilby en variant der du får ferdig oppsatte servere og SharePoint installert. Innleveringer: 8 obligatoriske øvinger. Personlig veileder: ja Vurderingsform: 2 dagers praktisk hjemmeeksamen med både teoretiske og praktiske oppgaver. Ansvarlig: Stein Meisingseth Eksamensdato: 19.12.13 / 15.05.14         Læremål: KUNNSKAPER:Kandidaten må:- kjenne til bruken av SharePoint i forskjellige situasjoner i en bedrift/organisasjon- kunne gjøre rede for hvordan SharePoint brukes i samskriving- kunne beskrive hvordan ulike roller i en organisasjon kan bruke SharePoint- ha kjennskap til hva det vil si å ha ansvaret for bruken av SharePoint i en bedrift/organisasjon FERDIGHETER:Kandidaten må:- kunne opprette webområder for deling av informasjon med andre- kunne sette opp administrasjon av dokumenter- kunne sette opp publisering av rapporter- kunne sette opp sidestruktur og brukerrettigheter, sideoppsett og tillatelser- kunne opprette infrastruktur for webområder- kunne sette opp muligheter for søking- kunne konfigurere systemet for bruk fra Internett- kunne konfigurerer Office Web Apps- kunne sette opp muligheter for å benytte arbeidsflyt GENERELL KOMPETANSE:Kandidaten:- har kompetanse til selvstendig både å formidle og å ta i bruk sine kunnskaper og ferdigheter i en bedrift som vil bruke SharePoint- kan i et praktisk prosjekt forklare og gjøre bruk av sin kunnskap for bruk av SharePoint Innhold:Hva er SharePoint. Roller. Tilgang til SharePoint. Opprette og bruke Team Sites. Navigering. Lister og Libraries. Home page. Opprette og bruke webpart. Opprette og bruke SharePoint Sites and Site Collections. Opprette og bruke maler. Sikkerhet i SharePoint. Opprette og bruke Wiki. Dokumentdeling. Søking.Les mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag SharePoint 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.    [-]

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.   [-]

Kurset tar for seg de mest sentrale problemstillingene knyttet til sikkerhet rundt bruken av datautstyr som datamaskiner, smarttelefoner og nettbrett.    Kurset vil gi brukeren kunnskap om ulike «feller» man kan gå i samt nyttige og praktiske tips og veiledninger til hvordan man unngår at data kommer på avveie eller ødelegges permanent.   Kurset inneholder 50 opplæringsvideoer. Mens andre  kurs fokuserer på å bruke IT-verktøy effektivt, vil dette kurset gi deg innsikt i å bruke IT trygt og sikkert.   Kurset passer for databrukere i alle typer bedrifter og organisasjoner.   Innhold i kurset • Datamaskinen • Passord • Ute av kontoret • Minnepinner • Sikkerhetskopi • E-post • Internett • Ettertest Krav til forkunnskaper Grunnleggende datakunnskaper Kursbevis Etter endt opplæring vil man kunne ta en ettertest for å måle sin nye kunnskap. Ved bestått test så vil man få tilgang til et kursbevis   Nettbasert  Timetall: 6  Kursstart Info: Når som helst - Hele året !  Klokkeslett: 00:00 - 24:00  Påmeldingsfrist:  Pris: kr 1.175,- inkl. mva.  Nettbasert - Web     Kontaktperson: Frode Ingebrigtsen    Status: Åpent for påmelding Gå til påmelding [-]

Delta på webinar å lær hvordan man bygger en prosjektplan for å få god kontroll med gjennomføringen! Webinaret varer i 2 timer og består av to økter à 45 min. Etter hver økt er det 10 min spørsmålsrunde. Mellom øktene er det 10 min pause. Webinaret kan også spesialtilpasses og holdes bedriftsinternt kun for din bedrift.   Kursinnhold:   Tabeller og felt i Project Forskjellige typer felt. Forskjeller mellom felt i aktiviteter og ressurser Legge til og fjerne felt Opprette og tilpasse felt Endre eksisterende tabeller Opprette nye tabeller   Forskjellige visninger Sortering Filtrering - Innebygde filetere. Definere nye filtre Gruppering. Benytte grupper til bedre oversikt og kontroll   3 gode grunner til å velge KnowledgeGroup 1. Best practice kursinnhold 2. Markedets beste instruktører 3. Gratis support   [-]

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 [-]

Sliter du med å importere data og lage rapporter/analysegrunnlag i Excel? Et riktig grunnlag er avgjørende for en god rapport! Se hvor enkelt man lager rapporter og analyserer data med Pivottabeller. Webinaret varer i 2 timer og består av to økter à 45 min. Etter hver økt er det 10 min spørsmålsrunde. Mellom øktene er det 10 min pause. Webinaret kan også spesialtilpasses og holdes bedriftsinternt kun for din bedrift.   Kursinnhold:   Import av tekstfil med problemer   Bruk veiviser for å importere tekstfil Fiks ulike problemstilling allerede i veiviser   Kort repetisjon av prinsipper om å arbeide med lister   Datavask - opprydding i grunnlaget Lær ulike metoder for å rydde opp i grunnlaget   Tabellfunksjonalitet Formater liste som tabell Bli kjent med fordeler å jobbe mot tabell Bli kjent med strukturere cellereferanser   Pivotteknikk [-]

Blended Virtual Course The course is a hybrid of virtual training and self-study which will be a mixture of teaching using Microsoft Teams for short bursts at the beginning of the day, then setting work for the rest of the day and then coming back at the end of the day for another on-line session for any questions before setting homework in the form of practice exams for the evening. You do not have to install Microsoft Teams , you will receive a link and can access the course using the web browser.  Remote proctored examTake your exam from any location. Read about iSQI remote proctored exam here Requirements for the exam: The exam will be using Google Chrome and there is a plug-in that needs to be installed  You will need a laptop/PC with a camera and a microphone  A current ID with a picture  This 3-day course is aimed at anyone wishing to attain the ISTQB Advanced Test Automation Engineer qualification. This qualification builds upon the Foundation syllabus and provides essential skills for all those involved in test automation and who want to develop further their expertise in one or more specific areas. Bouvet sine kursdeltakeres testresultater vs ISTQB gjennomsnitt A Test Automation Engineer is one who has broad knowledge of testing in general, and an in-depth understanding in the special area of test automation. An in-depth understanding is defined as having sufficient knowledge of test automation theory and practice to be able to influence the direction that an organization and/or project takes when designing, developing and maintaining test automation solutions for functional tests. The modules offered at the Advanced Level Specialist cover a wide range of testing topics.   The course is highly practical addressing the following areas: Introduction and objectives for Test Automation This section provides an introduction to test automation explaining the objectives, advantages, disadvantages and limitations of test automation as well as technical success factors of a test automation project. Preparing for Test Automation Understanding the type of system is vital for determining the most appropriate automation solution and also how we can design systems and testing for more effective automation. This section also looks at how we can evaluate for the most appropriate tools. The generic Test Automation architecture A test automation engineer has the role of designing, developing, implementing, and maintaining test automation solutions. As each solution is developed, similar tasks need to be done, similar questions need to be answered, and similar issues need to be addressed and prioritized. These reoccurring concepts, steps, and approaches in automating testing become the basis of the generic test automation architecture, and this will be discussed in detail during this section Deployment risks and contingencies This section looks at the various risks associated with the deployment of test tools and how to avoid test automation failure. Test Automation reporting and metrics Providing information to stakeholders for them to make informed decisions about the quality of the software is a vital part of testing and this section looks at the various metrics that can be used to monitor test automation and what information should be supplied to the stakeholder and how it should be presented. Transitioning manual testing to an automated environment This section looks at the various criteria to apply to determine the suitability for automation and understanding the factors for transitioning from manual to automation testing Verifying the Test Automation solution To have justified confidence in the information we supply to the stakeholders regarding test automation we must have justified confidence in the test environment and test automation solution supporting the information Continuous improvement This section looks ahead and how we can improve the automation solution making it more effective and efficient The Exam The ISTQB Advanced Test Automation Engineer exam is a 1 hour 30 minute, 40 question multiple-choice exam totaling 75 points. The pass mark is 65% (49 out of 75). It is a pre-requisite that attendees hold the ISTQB Foundation Level certificate. [-]

Lightroom CC er et råflott bilderedigeringsverktøy for fotoentusiaster. Lightroom CC inneholder alt du trenger for å organisere, redigere, lagre og dele bildene dine på tvers av enheter - dette være seg datamaskin, nettbrett eller mobil. Det betyr at du kan redigere et bilde på datamaskinen og fortsette på mobilen. Bildene synkroniseres nemlig i skyen. I dette kurset kommer Espen Faugstad til å guide deg gjennom programmet fra A til Å. Du kommer til å lære å importere og organisere, redigere ved hjelp av enkle og avanserte verktøy, og eksportere og dele. Du kommer også til å lære hvordan den skybaserte lagringsplassen kommer til å påvirke, og ikke minst, forbedre din digitale arbeidsflyt.   Innhold: Kapittel 1: Introduksjon Kapittel 2: Importere Kapittel 3: Organisere Kapittel 4: Redigere (enkel) Kapittel 5: Beskjære Kapittel 6: Redigere (avansert) Kapittel 7: Eksportere Kapittel 8: Avslutning   Varighet: 2 timer og 16 minutter.   Hørt om Netflix? Vi er som dem, bare at vi lager nettkurs. Utdannet.no AS er en norsk startup som utvikler nettkurs i datateknologi, kreative fagfelt og grunnleggende forretningsferdigheter. Med støtte fra Innovasjon Norge og Forskningsrådet utvikler vi nestegenerasjons kursplattform, med mål om å gjøre kompetanseutvikling moro, spennende og tilgjengelig for alle. Med over 1 million videovisninger, 20.000 registrerte medlemmer og en gjennomsnittlig årlig vekst på 45 % er vi godt i gang med å befeste vår posisjon i det norske markedet. Vi har kunder fra bedrifter som: Adresseavisen, Coca-Cola, Helsedirektoratet, IKEA, Joblearn, NAV, Nordea, NorgesGruppen, NRK, Oslo kommune, Securitas, Telenor og Utdanningsforbundet.   [-]

Mange av prinsippene for stor dokumenter kan med fordel også anvendes på de aller fleste dokumenter, inklusive brev og notater. Her går vi gjennom alt du trenger for å skrive bøker eller avhandlinger og også det du trenger for å skrive invitasjoner til nyttårsfest eller barnebursdag. Kursinnhold Fletting til brev, konvolutter, etiketter og e-post Bruk av stiler Bilder og bildetekster innholdsfortegnelse, stikkordliste og figurliste Spalter, marger, sidefarger, sidekantlinjer og dokumenttemaer. Deldokumenter kan samles i et hoved dokument  Topp- og bunntekster og side nummerering. Utklipp, figurer, SmartArt og diagram. Tekstbokser Tabeller: Formatering, sortering og beregninger. Maler med Felt, innholdskontroller og skjemakontroller Med makroer kan du effektivisere avanserte oppgaver som består av serie med handlinger. Det er fordelaktig å ha to skjermer - en til å følge kurset og en til å gjøre det kursholder demonstrerer. Kurset gjennomføres i sanntid med nettundervisning via Teams. Det blir mulighet for å stille spørsmål, ha diskusjoner, demonstrasjoner og øvelser. Du vil motta en invitasjon til Teams fra kursholder. [-]

MD-100: Windows Client [-]

  An Azure IoT Developer is responsible for implementing and then maintaining the cloud and edge portions of an Azure IoT solution. In addition to configuring and maintaining devices by using Azure IoT services and other Microsoft tools, the IoT Developer also sets up the physical devices and is responsible for maintaining the devices throughout the life cycle. The IoT Developer implements designs for IoT solutions, including device topology, connectivity, debugging and security. For Edge device scenarios, the IoT Developer also deploys compute/containers and configures device networking, which could include various edge gateway implementations. The IoT Developer implements designs for solutions to manage data pipelines, including monitoring and data transformation as it relates to IoT. The IoT Developer works with data engineers and other stakeholders to ensure successful business integration. IoT Developers should have a good understanding of Azure services, including data storage options, data analysis, data processing, and the Azure IoT PaaS versus SaaS options. After completing this course, students will be able to: Create, configure, and manage an Azure IoT hub. Provision devices by using IoT Hub and DPS, including provisioning at scale. Establish secure 2-way communication between devices and IoT Hub. Implement message processing by using IoT Hub routing and Azure Stream Analytics. Configure the connection to Time Series Insights and support business integration requirements. Implement IoT Edge scenarios using marketplace modules and various edge gateway patterns. Implement IoT Edge scenarios that require developing and deploying custom modules and containers. Implement device management using device twins and direct methods. Implement solution monitoring, logging, and diagnostics testing. Recognize and address security concerns and implement Azure Security Center for IoT. Build an IoT Solution by using Azure IoT Central and recongize SaaS opportunities for IoT. Course prerequisites IoT Developers should have basic programming skills in at least one Azure-supported language, including C#, Node.js, C, Python, or Java. Software development experience is a prerequisite for this course, but no specific software language is required, and the experience does not need to be at a professional level. Data Processing Experience: General understanding of data storage and data processing is a recommended but not required.  Cloud Solution Awareness: Students should have a basic understanding of PaaS, SaaS, and IaaS implementations. Microsoft Azure Fundamentals (M-AZ-900T00/M-AZ900), or equivalent skills, is recommended.  This course helps to prepare for exam AZ-220.   Agenda Module 1: Introduction to IoT and Azure IoT Services -Business Opportunities for IoT-Introduction to IoT Solution Architecture-IoT Hardware and Cloud Services Module 2: Devices and Device Communication -IoT Hub and Devices-IoT Developer Tools-Device Configuration and Communication Module 3: Device Provisioning at Scale -Device Provisioning Service Terms and Concepts-Configure and Manage the Device Provisioning Service-Device Provisioning Tasks Module 4: Message Processing and Analytics -Messages and Message Processing-Data Storage Options-Azure Stream Analytics Module 5: Insights and Business Integration -Business Integration for IoT Solutions-Data Visualization with Time Series Insights-Data Visualization with Power BI Module 6: Azure IoT Edge Deployment Process -Introduction to Azure IoT Edge-Edge Deployment Process-Edge Gateway Devices Module 7: Azure IoT Edge Modules and Containers -Develop Custom Edge Modules-Offline and Local Storage Module 8: Device Management -Introduction to IoT Device Management-Manage IoT and IoT Edge Devices-Device Management at Scale Module 9: Solution Testing, Diagnostics, and Logging -Monitoring and Logging-Troubleshooting Module 10: Azure Security Center and IoT Security Considerations -Security Fundamentals for IoT Solutions-Introduction to Azure Security Center for IoT-Enhance Protection with Azure Security Center for IoT Agents Module 11: Build an IoT Solution with IoT Central -Introduction to IoT Central-Create and Manage Device Templates-Manage Devices in Azure IoT Central [-]

Stadig flere bedrifter har behov for folk med kunnskaper om data og databehandling. Kan du dokumentere at du har slik kunnskap? Mål med kurset: Deltakeren skal tilegne seg kunnskaper om de mest benyttede Windows programmene og etter avsluttet kurs kunne jobbe smartere og mer effektivt. Krav til forkunnskaper: Et innføringskurs i data eller noe erfaring med data fra før er nødvendig. Kurset MS Office 2016 - 4 Moduler tar for seg de mest brukte Windows programmene: Excel Word PowerPoint Windows 10 Gjennomføring:  Gjennomføres på Internett, interaktivt. Kurset inneholder tekst, bilder, videofremvisninger, små tester og en mengde oppgaver. Enkelt og fleksibelt. Kursdeltageren har on-line tilgang i 12 måneder fra man starter å ta kurset.  Dette gjør at man har tilstrekkelig tid til å tilegne seg nødvendige kunnskaper. [-]

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     [-]