Oversikt over relevante kurs innen IT-kurs i Tranås

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

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.   [-]

Dette er et grunnleggende kurs i SQL-programmering. Kurset passer godt for deg som skal jobbe med relasjonelle databaser, som f.eks. Oracle, PostgreSQL, Microsoft SQL-server eller MySQL/MariaDB.   Etter gjennomført kurs vil deltakerne være fortrolige med å opprette databaser og tabeller, sette inn data, endre og slette data og søke etter data i SQL-databaser.    Kursinnhold Introduksjon til relasjonsdatabaser og relasjonsmodellen: normalisering på tredje normalform. Introduksjon til MySQL, PostgreSQL, Oracle Express og tilhørende verktøy Introduksjon til SQL i Big Data (HiveQL, Cassandra QL, Phoenix HBase-klient) Søk i SQL-databaser, bl.a. med bruk av under-spørringer og inner og outer joins. Filtrering, gruppering og sortering av data. Oppretting, endring, kopiering og sletting av databaser og tabeller, Innsetting, oppdatering og sletting av data i tabeller Bruk av indekser og views. Skjema-design med bruk av ulike data-typer, tegnsett og lagringsformater. Introduksjon til MySQL, PostgreSQL og Oracle Express Bruk av bl.a. MySQL Workbench, PhPMyAdmin og Oracle Application Express. Kurset gjennomføres med en kombinasjon av online læringsmidler, gjennomgang av temaer og problemstillinger og praktiske øvelser med ulike typer datasett.    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.      [-]

Through a combination of presentations, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as pods, containers, deployments, and services; as well as networks and application services. This course also covers deploying practical solutions including security and access management, resource management, and resource monitoring. Objectives This course teaches participants the following skills: Understand how software containers work Understand the architecture of Kubernetes Understand the architecture of Google Cloud Platform Understand how pod networking works in Kubernetes Engine Create and manage Kubernetes Engine clusters using the GCP Console and gcloud/kubectl commands Launch, roll back and expose jobs in Kubernetes Manage access control using Kubernetes RBAC and Google Cloud IAM Managing pod security policies and network policies Using Secrets and ConfigMaps to isolate security credentials and configuration artifacts Understand GCP choices for managed storage services Monitor applications running in Kubernetes Engine   Course Outline Module 1: Introduction to Google Cloud Platform Use the Google Cloud Platform Console Use Cloud Shell Define cloud computing Identify GCP’s compute services Understand regions and zones Understand the cloud resource hierarchy Administer your GCP resources Module 2: Containers and Kubernetes in GCP Create a container using Cloud Build Store a container in Container Registry Understand the relationship between Kubernetes and Google Kubernetes Engine (GKE) Understand how to choose among GCP compute platforms Module 3: Kubernetes Architecture Understand the architecture of Kubernetes: pods, namespaces Understand the control-plane components of Kubernetes Create container images using Google Cloud Build Store container images in Google Container Registry Create a Kubernetes Engine cluster Module 4: Kubernetes Operations Work with the kubectl command Inspect the cluster and Pods View a Pod’s console output Sign in to a Pod interactivelty Module 5: Deployment, Jobs, and Scaling Create and use Deployments Create and run Jobs and CronJobs Scale clusters manually and automatically Configure Node and Pod affinity Get software into your cluster with Helm charts and Kubernetes Marketplace Module 6: GKE Networking Create Services to expose applications that are running within Pods Use load balancers to expose Services to external clients Create Ingress resources for HTTP(S) load balancing Leverage container-native load balancing to improve Pod load balancing Define Kubernetes network policies to allow and block traffic to pods Module 7: Persistent Data and Storage Use Secrets to isolate security credentials Use ConfigMaps to isolate configuration artifacts Push out and roll back updates to Secrets and ConfigMaps Configure Persistent Storage Volumes for Kubernetes Pods Use StatefulSets to ensure that claims on persistent storage volumes persist across restarts Module 8: Access Control and Security in Kubernetes and Kubernetes Engine Understand Kubernetes authentication and authorization Define Kubernetes RBAC roles and role bindings for accessing resources in namespaces Define Kubernetes RBAC cluster roles and cluster role bindings for accessing cluster-scoped resources Define Kubernetes pod security policies Understand the structure of GCP IAM Define IAM roles and policies for Kubernetes Engine cluster administration Module 9: Logging and Monitoring Use Stackdriver to monitor and manage availability and performance Locate and inspect Kubernetes logs Create probes for wellness checks on live applications Module 10: Using GCP Managed Storage Services from Kubernetes Applications Understand pros and cons for using a managed storage service versus self-managed containerized storage Enable applications running in GKE to access GCP storage services Understand use cases for Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Bigtable, Cloud Firestore, and BigQuery from within a Kubernetes application [-]

Kursinstruktør Terje Berg-Hansen Terje Berg-Hansen har bred erfaring fra prosjektledelse, utvikling og drift med små og store databaser, både SQL- og NoSQL-baserte. I tillegg til å undervise i etablerte teknologier leder han også Oslo Hadoop User Group, og er levende interessert i nye teknologier, distribuerte databaser og Big Data Science.    Kursinnhold XML er en etablert standard for plattformuavhengig lagring og utveksling av data, der innhold og presentasjon bearbeides separat. XSL er en nøkkelteknologi innenfor utvikling og nyttiggjørelse av XML. Viktige hoveddeler innenfor XSL er XSLT, XSL-FO og XPath. Kurset gir deltakerne en innføring i XSL . Vi ser på hvilke muligheter vi har for bearbeiding av XML-data, og hvordan vi kan gjøre data tilgjengelig for presentasjon.   Du får en gjennomgang i: Introduksjon til XML, XSL og XSLT. Introduksjon til XPath og XQuery. Bruk av XSLT-maler og Xpath-uttrykk for å søke etter data i XML-dokumenter. Transformering av XML-dokumenter til xml, html og tekstdokumenter. Introduksjon til XSL-FO og produksjon av svg- og pdf-dokumenter Design og formatering av ouput fra XSLT-transformasjoner Sortering, gruppering og kombinering av XML-dokumenter Bruk av XSLT-verktøy til transformering og søk.   Målsetting Etter endt kurs skal kursdeltakerne blant annet vite hvordan man filtrerer, sorterer og transformerer XML-data, samt hvilke muligheter man har for å trekke inn annet innhold/data for presentasjon.   Gjennomføring Kurset gjennomføres med en kombinasjon av online læremidler, gjennomgang av temaer og problemstillinger og praktiske øvelser. Det er ingen avsluttende eksamen, men det er øvelsesoppgaver til hovedtemaene som gjennomgås.   [-]

GDPR - Certified Data Protection Officer [-]

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

AZ-104: Microsoft Azure Administrator [-]

Brukergrensesnittet. Opprette profiler med tilpasset oppsett. Verktøy for digital dokumentbehandling, slik som å sette sammen PDF’er, opprette hyperkoblinger, påføre digitale signaturer og stempler. Redigere innhold i PDF-filer Automatisk sammenligning Markeringsverktøy for bruk under designgjennomgang, etc. Bruk av Tool Chest til å spare symboler og tilpassede verktøy for enkel gjenbruk Bruk av markeringslisten til å sette status, kommentere, filtrere og rapportere Kalibrering og måleverktøy. Intro til mengdeberegning Intro til skybasert samarbeid med Studio Projects og Sessions   På kurset lærer du alle de viktigste funksjonene i Revu, noe som gir deg et godt overblikk og utgangspunkt for å jobbe videre med programmet. Du blir i stand til å digitalisere og effektivisere en rekke manuelle arbeidsprosesser, med tidsbesparelse og bedre kvalitet som resultat.   [-]

ITIL® 4 Foundation - 2 days course for ITIL experienced [-]

NTI leverer opplæring for å forenkle og effektivisere din arbeidshverdag Årlig utdanner over 8.000 personer seg i ulike CAD- og BIM-løsninger hos NTI.Vi har mer enn 70 forskjellige kurs innen fagområdene CAD/BIM-, Industri, Prosess, Plant og Infrastruktur- og dokumenthåndtering, og i snitt har våre 100 konsulenter og instruktører mer enn 10 års erfaring med opplæring og konsulenttjenester. Hvordan få riktig oppsett av koordinater i prosjekt? Dette er et tema NTI merker stor pågang rundt til support, og henvendelsene kommer fra disipliner som byggteknikk, VVS og elektro i tillegg til arkitekt. Det er ofte arkitekten som setter opp koordinatene i Revit. Hvis utgangspunktet er feil, påvirkes dette i alle andre disipliner også. Spesielt der det er krav til at utvekslingsformatet er IFC. På dette online-kurset vil du lære: Forskjellen mellom de ulike koordinatsystemene Hva er et lokalt nullpunkt Sette opp reelle koordinater (Survey) «Best Practice» i oppsett av koordinater fra start Samhandling ved utveksling av filer og koordinater Behandle flere koordinatsystemer i samme prosjekt IFC export/import i forhold til delte koordinater Det kan gå noe tid mellom hver gang du setter opp koordinater, og det er lett å glemme prosessen. Etter gjennomført kurs, får du en «step by step» dokumentasjon, som kan benyttes som oppslagsverk senere.  Kurs på dine betingelser!Ditt firma har kanskje investert i ny CAD-programvare, oppgradert til ny versjon, oppdatert til ny programvare eller dere trenger rett og slett oppfriskning. Da er det på tide å investere i kompetanse for dine ansatte! Kontakt vår kurskoordinator Wenche, telefon 21 40 27 89 eller epost. [-]

Kursinstruktør   Geir Johan Gylseth Geir Johan Gylseth er utdannet ved Universitetet i Oslo med hovedvekt på Informatikk og har over 30 års erfaring som instruktør. Geir sin styrke ligger innenfor MS Office. Han har lang erfaring med skreddersøm av kurs, kursmanualer og oppgaver. Geir er en entusiastisk og dyktig instruktør som får meget gode evalueringer. Kursinstruktør   Jonny Austad Jonny Austad er utdannet som Adjunkt og har jobbet som lærer og instruktør siden 1989. Han har dessuten jobbet mye med support og drifting av nettverk og vet som oftest hva som er vanlige problemer ute i bedriftene. Han var den første Datakort-læreren i landet (høsten 1997), og har Office-pakken med spesielt Excel som sitt hjertebarn. Jonny er en meget hyggelig og utadvendt person som elsker å undervise med smarte løsninger på problemer samt vise smarte tips og triks i de ulike programmene. Kursinnhold Bruker du mye tid i PowerPoint på å få gjort enkle arbeidsoppgaver? Er det vanskelig å velge riktige farger og bakgrunner? Ønsker du å få fremhevet budskapet ditt men vet ikke hvordan? Er det vanskelig å lage diagrammer og tabeller? Drukner budskapet ditt i animasjoner og overgangseffekter? Bekymrer du deg for det tekniske når du skal holde en presentasjon? Dette er vanlige problemstillinger mange sliter med og som blir borte etter endt kurs! Kurset passer for deg med liten erfaring og som ønsker å lære PowerPoint fra grunnen av. Kurset passer også for deg som er selvlært og som ønsker å jobbe mer effektivt. På kun 1 dag vil du mestre de vanligste arbeidsoppgavene i PowerPoint. Du lærer gode rutinerog hurtigtastene du trenger for å kunne arbeide raskt og effektivt. Du vil kunne lage alt fra enkle til mer avansertepresentasjoner og vil føle deg trygg på at det er du som kontrollerer PowerPoint og ikke omvendt! Du lærer også hvordan du skal bruke PowerPoint når du skal holde en presentasjon. I tillegg får du en rekke tips og triks du kan bruke i din arbeidsdag.  Alt du lærer får du repetert gjennom aktiv oppgaveløsning slik at du husker det du har lært når du kommer tilbake på jobb. Kursdokumentasjon, lunsj og pausemat er selvsagt inkludert! Kursholderne har mer enn 20 års PowerPoint erfaring som de gjerne deler med deg! Meld deg på PowerPoint-kurs allerede i dag og sikre deg plass! Bli kjent med PowerPoint: Oppstart Åpning Visninger Navigering Lagring og lukking Utforming:Utformingsprosessen Nye presentasjoner Nye lysbilder TemaTekst: Bruk av tekst i presentasjoner Innskriving og redigering Maler Punktlister og nummererte lister Angremuligheter Topptekst og bunntekst Bilder og objekter: Bruk av bilder Utklipp Bilder fra fil Import av objekter Tegning: Koblingslinjer Formatering av objekter WordArt SmartArt Diagram: Utforming av diagram Diagramtyper Organisasjonskart: Utforming av organisasjonskart Formatering av organisasjonskart Tabeller:Utforming av tabeller Merking Innsetting og sletting Rad/høyde og kolonnebredde Formatering av tabeller Utskrift: Utskriftsformat Forhåndsvisning og utskrift Eksport av lysbilder til Word Lysbildeframvisning: Animasjoner Lysbildesortering Overgangseffekter Lysbildeframvisning Framvisning uavhengig av PowerPoint     [-]

Digital markedsføring   Dette er kurs for deg som skal jobbe med salg og markedsføring på nett. I løpet av 5 kursdager  vil du få god digital kompetanse, lære hva som er godt innhold og tilrettelegge dette for deling på nett. Du skal lære å engasjere kundene dine, lage godt innhold, optimalisere nettsidene for søk på nett, samt bruke google analytics for analyse av trafikken på nettstedet ditt. Etter kurset skal du være i stand til å planlegge og gjenomføre digital markedsføring, kartlegge og optimalisere underveis, og få relevant økt trafikk og konvertering på dine nettsider. Pris kr. 16200,- kurs er fra kl. 09 - 15. Kurs start 10. mai, digital markedsføring: Digital strategi, 10. mai Sosiale medier og innholdsmarkedsføring, 11. mai Skriv gode tekster og nettsider, 1. juni Google Analytics, 2. juni SEO – Søkemotoroptimalisering, 3. juni       [-]

Power Pivot - Microsoft Excel [-]

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     [-]