Oversikt over relevante kurs innen Kurs i programvare og applikasjoner i Skjetten

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

CCNA: Implementing and Administering Cisco Solutions [-]

Innhold: Bygge opp gode regnearkoppsett med formler, funksjoner og diagrammer. Summere flere regneark. Låse celler. Absolutt celle referanse, parenteser, hvis formler, Pivottabell. Kursholder Marianne Nylund er utdannet systemasvarlig/IKT-rådgiver fra forsvaret,Hun er sertifisert Microsoft-instruktør og har holdtMicrosoft Office-kurs siden 1998. Kursleder er tydelig, pedagogisk og flink til å forklare. Hun engasjerer sine kursdeltakere og gjør det underholdende å delta på våre kurs.Hun er meget tålmodig og tilpasser undervisningen etter hver enkelt deltagers behov, slik at alle skal få et stort utbytte av kursene.   [-]

Oracle GoldenGate 19c: Fundamentals for Oracle [-]

Excel controllere/økonomer kurs vinklet fra en Controllers hverdag med fokus på gode metoder for å arbeide med Excel-lister. Påfyllet kurset gir deg, gjør deg i stand til å jobbe mer effektivt i Excel. Kursets mange eksempler, viser hva du virkelig kan få til i Excel, enten det er å jobbe med formler / funksjoner, lister eller store Excel-modeller. Kurset er utviklet av controllere, med det for øye at du skal kunne angripe dine Excel-utfordringer på en smart og effektiv måte. Kurset kan også spesialtilpasses og holdes bedriftsinternt i deres eller våre lokaler.   Kursinnhold:     Datautveksling/Klargjøring av data Mulige inndatametoder: Importering av tekstfil, webspørringer, innliming av data fra WWW. Importere data med ulikt dataformat: tekst - eller databaseformat Formatproblemer ifm. dataimport: ”feil” formater, fjerne deler av informasjon i en celle, fjerne duplikater, kjapt finne skrivefeil   Data/Konsolider Konsolider data: hvor data ligger i samme regneark Konsolidering data: hvor dataene skal behandles på tvers av regneark. Konsolidering data: hvor data ligger i ulike arbeidsbøker   Tabeller/Listefunksjonalitet Definisjonen av en tabell: Viktig å huske når tabellen skal behandles videre. Jobbe med en liste: få gode råd angående arbeid med lister Sortere tabellen: kjapt og enkelt med egendefinerte sorteringsnøkler Filter med Autofilter: filtrere ut bare de data du ønsker eller slette tomme rader. Filter med Avansert filter: få råd om i hvilke situasjoner det er smart å bruke avansert filter og hvordan du enklest bruker dette filteret. Beregninger i tabeller og lister Beregninger med flere variabler: Funksjonene Dsummer, Dantall og DGjennomsnitt Organiser og beregne data : Ved hjelp av delsammendrag. Lær hvordan resultatet kan kopieres til et annet sted. Oppslag i en liste: Funksjonene Finn.Kolonne og Finn.Rad Lag rapporter ved hjelp av Pivotteknikk: Opplev hvor enkelt det er å lage rapporter ved hjelp av pivotteknikk!   "Hva hvis"-analyser og optimalisering Sensitivitetsanalyse: Målsøking er en måte å regne ”baklengs” på. Sensitivitetsanalyse: Tabeller med 1 og 2 variabler Optimalisering: Ved hjelp av Problemløseren   Samarbeid med andre Deling av arbeidsbøker: ved ønske om å jobbe samtidig i samme arbeidsbok Sporing av endringer: når du ønsker å plukke opp de endringer andre har gjort     4 gode grunner til å velge KnowledgeGroup 1. Best practice kursinnhold 2. Markedets beste instruktører 3. Små kursgrupper 4. Kvalitets- og startgaranti   [-]

Kubernetes for App Developers (LFD459) [-]

DP-3007: Train and deploy a machine learning model with Azure Machine Learning [-]

MB-910: Microsoft Dynamics 365 Fundamentals (CRM) [-]

Check Point Certified Security Administrator (CCSA) R81.20 [-]

GeT Everything NTI tilbyr eLæringkurs av høyeste kvalitet. Uansett hvilket kurs du velger har du tilgang til dem 24/7 i 1 år (365 dager). Med elæringskurs velger du selv ditt tempo i læringen, og om du vil lære gjennom kombinasjon av å se, lese, lytte og teste selv – eller hvorfor ikke en kombinasjon av flere? Det du trenger er en PC, smarttelefon eller nettbrett som har internettilgang   Her er et utvalg av kursene du får tilgang på: Autodesk Adobe Primavera Bluebeam Bentlye Microsof Trimble McNeel NTI  leverer opplæring for å forenkle og effektivisere din arbeidshverdag Vi 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. Global eTraining har over 25 års erfaring med opplæring, og har solgt millioner av elæringskurs i over 150 land.  PC, smarttelefon eller nettbrett som har internettilgang Pål har gjennomført e-læringskurs Pål hadde ikke anledning til å reise bort en uke for å delta på kurs, da var det forlokkende å gjennomføre kurset som e-læringskurs. Kurset var absolutt verdt pengene. Det var en viktig bekreftelse på at jeg har basiskunnskapene. Jeg lærte mange nye viktige kommandoer for å gjøre operasjoner mer effektivt, fikk også en pekepinn på hvor jeg kan bygge på med kunnskap.Les mer her [-]

COURSE OVERVIEW The Implementing and Configuring Cisco Identity Services Engine course shows you how to deploy and use Cisco Identity Services Engine (ISE) v2.4, an identity and access control policy platform that simplifies the delivery of consistent, highly secure access control across wired, wireless and VPN connections. This hands-on course provides you with the knowledge and skills required to implement and use Cisco ISE, including policy enforcement, profiling services, web authentication and guest access services, BYOD, endpoint compliance services, and TACACS+ device administration. Through expert instruction and hands-on practice, you will learn how to use Cisco ISE to gain visibility into what is happening in your network, streamline security policy management and contribute to operational efficiency.   Delegates will be expected to work in groups and share lab equipment, If you are attending virtually you may also be required to work in virtual breakout rooms. Extended hours may also be required to cover all of the content included in this class.   TARGET AUDIENCE Individuals involved in the deployment and maintenance of the Cisco ISE platform. COURSE OBJECTIVES After completing this course you should be able to: Describe Cisco ISE deployments, including core deployment components and how they interact to create a cohesive security architecture. Describe the advantages of such a deployment and how each Cisco ISE capability contributes to these advantages. Describe concepts and configure components related to 802.1X and MAC Authentication Bypass (MAB) authentication, identity management, and certificate services. Describe how Cisco ISE policy sets are used to implement authentication and authorization, and how to leverage this capability to meet the needs of your organization. Describe third-party network access devices (NADs), Cisco TrustSec®, and Easy Connect. Describe and configure web authentication, processes, operation, and guest services, including guest access components and various guest access scenarios. Describe and configure Cisco ISE profiling services, and understand how to monitor these services to enhance your situational awareness about network-connected endpoints. Describe best practices for deploying this profiler service in your specific environment. Describe BYOD challenges, solutions, processes, and portals. Configure a BYOD solution, and describe the relationship between BYOD processes and their related configuration components. Describe and configure various certificates related to a BYOD solution. Describe the value of the My Devices portal and how to configure this portal. Describe endpoint compliance, compliance components, posture agents, posture deployment and licensing, and the posture service in Cisco ISE. Describe and configure TACACS+ device administration using Cisco ISE, including command sets, profiles, and policy sets. Understand the role of TACACS+ within the authentication, authentication, and accounting (AAA) framework and the differences between the RADIUS and TACACS+ protocols. Migrate TACACS+ functionality from Cisco Secure Access Control System (ACS) to Cisco ISE, using a migration tool. COURSE CONTENT Introducing Cisco ISE Architecture and Deployment Using Cisco ISE as a Network Access Policy Engine Cisco ISE Use Cases Describing Cisco ISE Fucntions Cisco ISE Deployment Models Context Visibility Cisco ISE Policy Enforcement Using 802.1X for Wired and Wireless Access Using MAC Authentication Bypass for Wired and Wireless Access Introducing Identity Management Configuring Certificate Services Introducing Cisco ISE Policy Implementing Third-Party Network Access Device Support Introducing Cisco TrustSec TrustSec Configuration Easy Connect Web Authentication and Guest Services Introducing Web Access with Cisco ISE Introducing Guest Access Components Configuring Guest Access Services Configure Sponsor and Guest Portals Cisco ISE Profiler Introducing Cisco ISE Profiler Profiling Deployment and Best Practices Cisco ISE BYOD Introducing the Cisco ISE BYOD Process Describing BYOD Flow Configuring the My Devices Portal Configuring Certificates in BYOD Scenarios Cisco ISE Endpoint Compliance Services Introducing Endpoint Compliance Services Configuring Client Posture Services and Provisioning Working with Network Access Devices Cisco ISE TACACS+ Device Administration Configure TACACS+ Device Administration Guidelines and Best Practices Migrating from Cisco ACS to Cisco ISE Labs Lab 1: Access the SISE Lab and Install ISE 2.4 Lab 2 : Configure Initial Cisco ISE Setup, Gui Familiarization and System Certificate Usage  Lab 3: Integrate Cisco ISE with Active Directory Lab 4: Configure Cisco ISE Policy Lab 5: Configure Access Policy for Easy Connect Lab 6: Configure Guest Access Lab 7: Configure Guest Access Operations Lab 8: Create Guest Reports Lab 9: Configure Profiling Lab 10: Customize the Cisco ISE Profiling Configuration Lab 11: Create Cisco ISE Profiling Reports Lab 12: Configure BYOD Lab 13: Blacklisting a Device Lab 14: Configure Cisco ISE Compliance Services Lab 15: Configure Client Provisioning Lab 16: Configure Posture Policies Lab 17: Test and Monitor Compliance Based Access Lab 18: Test Compliance Policy Lab 19: Configure Cisco ISE for Basic Device Administration Lab 20: Configure TACACS+ Command Authorization   [-]

Achieve More med MS Outlook (tidl. FTG) [-]

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     [-]

Kom i gang med Power BI [-]

  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.   Navisworks grunnkurs   Her er et utvalg av temaene du vil lære på kurset: forstå hvordan tverrfaglige modeller settes sammen analysere modellen gjennom visualisering og navigering håndtering av objekter sette inn målsetting legg inn snitt finne informasjon på objektene Navisworks håndterer et stort antall filformater og det er viktig å forstå hvordan tverrfaglige modeller settes sammen slik at dette muligjør analyse av modellen gjennom visualisering, navigering, håndtering av objekter, sette inn målsetting, legge inn snitt og finne informasjon på objektene.   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 [-]