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

En pivottabell er et kraftig verktøy i Microsoft Excel som gjør at du kan beregne, summere og analysere store mengder data på en rask og effektiv måte. En pivottabell kan brukes til å analysere numeriske data og til å besvare uventede spørsmål om dataen. Kort fortalt, en pivottabell hjelper deg med å ta informerte beslutninger basert på funnene i dataene dine. I dette kurset, ledet av Espen Faugstad, vil du lære alt du trenger å vite for å jobbe med pivottabeller i Microsoft Excel. Kurset vil dekke hva en pivottabell er, hvordan du klargjør data, organiserer data, formaterer data, presenterer data, og mye mer. For å ta dette kurset, bør du ha grunnleggende forståelse av Microsoft Excel. Kurset er strukturert i følgende kapitler: Kapittel 1: Introduksjon Kapittel 2: Grunnleggende Kapittel 3: Viderekommen Kapittel 4: Avslutning Etter å ha fullført kurset vil du være i stand til å bruke pivottabeller til å analysere data, trekke innsikter og ta informerte beslutninger basert på dataene i Excel.   Varighet: 1 time   Om Utdannet.no: Utdannet.no tilbyr noen av landets beste digitale nettkurs. Vår tjeneste fungerer på samme måte som strømmetjenester for musikk eller TV-serier, der våre kunder betaler en fast månedspris for tilgang til alle kursene vi har tilgjengelig. Vi har opplevd betydelig vekst de siste årene, med over 30 000 registrerte brukere og 1,5 millioner videoavspillinger. Vårt mål er å gjøre kompetanseutvikling engasjerende, spennende og tilgjengelig for alle, og vi har støtte fra Innovasjon Norge og Forskningsrådet. [-]

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 [-]

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.   [-]

Ønsker du å jobbe med ulike tegninger i Visio, men føler du ikke mestrer programmet? Vil du i tillegg kunne lage egne maler for å jobbe mer effektivt? Da er ”Visio Grunnleggende” kurset for deg! Kurset kan også spesialtilpasses og holdes bedriftsinternt i deres eller våre lokaler.   Kursinnhold:   Dag 1    Hva er Visio? Få oversikt. Bli kjent med programvinduet og hvordan du kan tilpasse det etter dine behov. Mal. Hvordan er en mal bygd opp og hvordan jobbe med en tegning? Formatering. Lær å formatere og hva formateringsbegrepet betyr. Sjablonger og figurer. Hva er sjablonger og figurer?   Å jobbe effektivt med Visio Bygge opp en tegning. Lær å bygge opp en tegning fra bunnen av. Hurtigtaster. Effektiv bruk av tastatur og mus. Formatering. Bruk formatering for å gjøre tegningene oversiktlige og informasjonen mest mulig tilgjengelig. Ark. Lær å jobbe med flere ark, navngi dem, slette dem, bruke bakgrunner etc. Praktisk oppgaveløsing. Jobb med skreddersydde oppgaver innenfor dagens temaer. Andre Office-programmer. Lær å bruke Visio-tegninger i andre Office-programmer.   Flytskjema og organisasjonskart Koblinger. Lær å koble figurer på en effektiv måte. Oppsett. Hvordan sørge for at figurene står plassert på en nøyaktig og oversiktlig måte? Navigasjon. Bygge opp praktisk navigasjon mellom sidene i en større tegning.   Dag 2    Nettverksdiagram Figurdata. Knytt praktisk informasjon til figurene i tegningen. Rapporter. Hvordan hente ut rapporter fra en tegning?   Prosjektplaner Tidslinje. Illustrere faser i et prosjekt på en oversiktlig måte. Gantt-diagram. Vise prosjektinformasjon på en mer detaljert måte. Utskrift. Få oversikt over de vanligste problemstillingene ved utskrift.   Egne maler Maler. Hva er maler, deres styrke og hvordan kan jeg utnytte dem best mulig i mitt arbeid? Sjablonger. Bygge opp en egen samling med de figurene du skal bruke. Figurer. Lær å lage egne tilpassede figurer. Praktisk oppgaveløsing. Jobb med skreddersydde oppgaver innenfor dagens temaer.   4 gode grunner til å velge KnowledgeGroup 1. Best practice kursinnhold 2. Markedets beste instruktører 3. Små kursgrupper 4. Kvalitets- og startgaranti   [-]

Excelkurs Basis - elæringskurs [-]

The students will also explore how to design data security including data access, data policies and standards. They will also design Azure data solutions which includes the optimization, availability and disaster recovery of big data, batch processing and streaming data solutions. Agenda Module 1: Data Platform Architecture Considerations. -Core Principles of Creating Architectures-Design with Security in Mind-Performance and Scalability-Design for availability and recoverability-Design for efficiency and operations-Case Study Module 2: Azure Batch Processing Reference Architectures. -Lambda architectures from a Batch Mode Perspective-Design an Enterprise BI solution in Azure-Automate enterprise BI solutions in Azure-Architect an Enterprise-grade Conversational Bot in Azure Module 3: Azure Real-Time Reference Architectures. -Lambda architectures for a Real-Time Perspective-Lambda architectures for a Real-Time Perspective-Design a stream processing pipeline with Azure Databricks-Create an Azure IoT reference architecture Module 4: Data Platform Security Design Considerations. -Defense in Depth Security Approach-Network Level Protection-Identity Protection-Encryption Usage-Advanced Threat Protection Module 5: Designing for Resiliency and Scale. -Design Backup and Restore strategies-Optimize Network Performance-Design for Optimized Storage and Database Performance-Design for Optimized Storage and Database Performance-Incorporate Disaster Recovery into Architectures-Design Backup and Restore strategies Module 6: Design for Efficiency and Operations. -Maximizing the Efficiency of your Cloud Environment-Use Monitoring and Analytics to Gain Operational Insights-Use Automation to Reduce Effort and Error [-]

Kurset er for deg som allerede kan bruke Microsoft Word, men som vil jobbe mer hensiktsmessig med tekstbehandling. På kurset vil vi vise deg hvordan du kan jobbe smart og effektivt når du lager dokumenter, slik at du sparer tid i din arbeidshverdag og samtidig ender opp med mer elegante og flotte dokumenter og rapporter. Mål for kursetEtter endt kurs skal du kunne bruke verktøyet på en smart og effektiv måte. ForkunnskaperNoe kjennskap til Word. MålgruppeDette kurset er for deg som vet litt om Word og som ønsker å lære effektiv bruk av verktøyet. UndervisningsformKlasseromsundervisning med maks 12 deltakere. Deltakere må ha med egen datamaskin med relevant programvare. Pris Klasseromsundervisning 6 timer: 3900 kroner inkludert lunsj.  Ansatte ved UiS har egne betalingsbetingelser.   Varighet1 dag fra 09:00 til 15:00 Emner Merketeknikker Navigering og snarveier Disposisjonsvisning Tekst eller tegnformatering Avsnittsformatering Side og inndelingsformatering Stiler – bruke, endre og definere Temaer Innholdsfortegnelse Kryssreferanser, noter Bilde SmartArt Forside Diagram – integrasjon med Excel Hurtigdeler Språkverktøy Topp og bunntekster i inndelinger Åpne et eldre dokument i Word Oppdatere/oppgradere et dokument Hva er .docx? Fletting i Word til word, epost, printer Sette utskriftsopsjoner Skrive ut utvalg og inndelinger [-]

Trenger du å bygge opp store og avanserte regneark? Ønsker du å lage rapporter og beregninger på store tallgrunnlag? Vil du finne ut hvordan du kan effektivisere arbeidet ditt i Excel? Ønsker du å lære de første stegene mot automatiserte rapporter? Kurset kan også spesialtilpasses og holdes bedriftsinternt i deres eller våre lokaler.   Kursinnhold:   Dag 1    Generelt om regneark Om regneark og infrastruktur Bruke tastatur og hurtigtaster effektiv Absolutte referanser og definerte navn   Funksjoner Mer om funksjoner, hvis, antall.hvis, summer.hvis.sett Lær om "må ha funksjonen" Finn.rad [Vlookup] Andre funksjoner for spesielle oppgaver   Avansert formatering Spesiell formatering – dato, tekst og egendefinert Betinget formatering og cellestiler   Dag 2    Lister og tabeller Viktige regler og råd Bruk av autofilter og sortering Tabellfunksjonalitet Validering ved inntasting Beregninger av store datamengder via gode funksjoner   Pivottabell Hva er pivottabell og hvordan lage raske og enkle rapporter Utvidede muligheter i Pivot som grupperinger, vis verdier som og slicer   Dag 3   Metoder for dataimport Direkte import fra database   Innføring til makro Spille inn /registrere makro Ord/uttrykk og VBA editor   Datavask Slette tommer rader, fylle tomme celler Bruk av funksjoner for å klargjøre datagrunnlag Identifisere og håndtere avvik i grunnlag   Alternative temaer (hvis tid) Tips til diagrammer Hva hvis analyse Konsolidering   4 gode grunner til å velge KnowledgeGroup 1. Best practice kursinnhold 2. Markedets beste instruktører 3. Små kursgrupper 4. Kvalitets- og startgaranti   [-]

Objectives Describe specialized data classifications on Azure Identify Azure data protection mechanisms Implement Azure data encryption methods Secure Internet protocols and how to implement them on Azure Describe Azure security services and features Agenda Module 1: Identity and Access -Configure Azure Active Directory for Azure workloads and subscriptions-Configure Azure AD Privileged Identity Management-Configure security for an Azure subscription Module 2: Platform Protection -Understand cloud security-Build a network-Secure network-Implement host security-Implement platform security-Implement subscription security Module 3: Security Operations -Configure security services-Configure security policies by using Azure Security Center-Manage security alerts-Respond to and remediate security issues-Create security baselines Module 4: Data and applications -Configure security policies to manage data-Configure security for data infrastructure-Configure encryption for data at rest-Understand application security-Implement security for application lifecycle-Secure applications-Configure and manage Azure Key Vault       [-]

TOGAF® EA Training Foundation [-]

I dette kurset vil Espen Faugstad lære deg hvordan du oppretter, tilpasser og bruker stiler i Adobe InDesign. Dette er teknikker som er velkjente blant profesjonelle InDesign-brukere og kan gjøre deg til en mer effektiv bruker av programmet, samtidig som de sparer deg for verdifull tid. Kurset er delt inn i flere kapitler som dekker ulike aspekter av stilarbeid i InDesign: Kapittel 1: Introduksjon Kapittel 2: Grunnleggende Kapittel 3: Viderekommen Kapittel 4: Avansert Kapittel 5: List styles Kapittel 6: Object styles Kapittel 7: Table styles Kapittel 8: Avslutning I Kapittel 1 lærer du å tilpasse arbeidsområdet i InDesign for optimalt arbeid med stiler. Kapittel 2 gir deg en forståelse av forskjellen mellom "Character Styles" og "Paragraph Styles" samt hvordan du oppretter, tilpasser og bruker tekststiler. I Kapittel 3 lærer du å importere tekststiler fra InDesign-dokumenter og Word-dokumenter, samt hvordan du kopierer tekst og stiler og bruker "Quick Apply". Kapittel 4 tar deg med inn i avanserte tekststiler, inkludert "Nested Stiles," "New Line Style" og "GREP Style." I Kapittel 5 lærer du å arbeide med "List styles," inkludert oppretting av punktliste, nummerliste og flernivåsliste. Kapittel 6 fokuserer på "Object Styles," og Kapittel 7 dekker "Table Styles" og "Cell Styles." Dette kurset gir deg den nødvendige kunnskapen og ferdighetene til å jobbe effektivt med stiler i Adobe InDesign, noe som er avgjørende for å produsere profesjonelt layoutet innhold.   Varighet: 2 timer og 58 minutter   Om Utdannet.no: Utdannet.no tilbyr noen av landets beste digitale nettkurs. Vår tjeneste fungerer på samme måte som strømmetjenester for musikk eller TV-serier, der våre kunder betaler en fast månedspris for tilgang til alle kursene vi har tilgjengelig. Vi har opplevd betydelig vekst de siste årene, med over 30 000 registrerte brukere og 1,5 millioner videoavspillinger. Vårt mål er å gjøre kompetanseutvikling engasjerende, spennende og tilgjengelig for alle, og vi har støtte fra Innovasjon Norge og Forskningsrådet. [-]

Objectives This course teaches participants the following skills: Identify the purpose and value of each of the Google Cloud Platform products and services Interact with Google Cloud Platform services Describe ways in which customers have used Google Cloud Platform Choose among and use application deployment environments on Google Cloud Platform: Google App Engine, Google Kubernetes Engine, and Google Compute Engine Choose among and use Google Cloud Platform storage options: Google Cloud Storage, Google Cloud SQL, Google Cloud Bigtable, and Google Cloud Datastore Make basic use of BigQuery, Google’s managed data warehouse for analytics Make basic use of Cloud Deployment Manager, Google’s tool for creating and managing cloud resources through templates Make basic use of Google Stackdriver, Google’s monitoring, logging, and diagnostics system All courses will be delivered in partnership with ROI Training, Google Cloud Premier Partner, using a Google Authorized Trainer. Course Outline Module 1: Introducing Google Cloud Platform -Explain the advantages of Google Cloud Platform-Define the components of Google's network infrastructure, including: Points of presence, data centers, regions, and zones-Understand the difference between Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) Module 2: Getting Started with Google Cloud Platform -Identify the purpose of projects on Google Cloud Platform-Understand the purpose of and use cases for Identity and Access Management-List the methods of interacting with Google Cloud Platform-Lab: Getting Started with Google Cloud Platform Module 3: Virtual Machines and Networks in the Cloud -Identify the purpose of and use cases for Google Compute Engine.-Understand the various Google Cloud Platform networking and operational tools and services.-Lab: Compute Engine Module 4: Storage in the Cloud -Understand the purpose of and use cases for: Google Cloud Storage, Google Cloud SQL, Google Cloud Bigtable, and Google Cloud Datastore.-Learn how to choose between the various storage options on Google Cloud Platform.-Lab: Cloud Storage and Cloud SQL Module 5: Containers in the Cloud -Define the concept of a container and identify uses for containers.-Identify the purpose of and use cases for Google Kubernetes Engine and Kubernetes.-Lab: Kubernetes Engine Module 6: Applications in the Cloud -Understand the purpose of and use cases for Google App Engine.-Contrast the App Engine Standard environment with the App Engine Flexible environment.-Understand the purpose of and use cases for Google Cloud Endpoints.-Lab: App Engine Module 7: Developing, Deploying, and Monitoring in the Cloud -Understand options for software developers to host their source code.-Understand the purpose of template-based creation and management of resources.-Understand the purpose of integrated monitoring, alerting, and debugging.-Lab: Deployment Manager and Stackdriver Module 8: Big Data and Machine Learning in the Cloud -Understand the purpose of and use cases for the products and services in the Google Cloud big data and machine learning platforms.-Lab: BigQuery [-]

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     [-]

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