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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     [-]

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 [-]

AZ-400: Designing and Implementing Microsoft DevOps solutions [-]

COURSE OVERVIEW  You will learn to deliver secure remote access to apps and desktops integrating Citrix Virtual Apps and Citrix Desktops with Citrix Gateway.  This course includes an exam. TARGET AUDIENCE Built for IT Professionals working with Citrix ADC and Gateway, with little or no previous Citrix networking experience. Potential students include administrators, engineers, and architects interested in learning how to deploy or manage Citrix ADC or Citrix Gateway environments. COURSE OBJECTIVES  Identify the functionality and capabilities of Citrix ADC and Citrix Gateway Explain basic Citrix ADC and Gateway network architecture Identify the steps and components to secure Citrix ADC Configure Authentication, Authorization, and Auditing Integrate Citrix Gateway with Citrix Virtual Apps, Citrix Virtual Desktops and other Citrix components COURSE CONTENT Module 1: Getting Started Introduction to Citrix ADC Feature and Platform Overview Deployment Options Architectural Overview Setup and Management Module 2: Basic Networking Networking Topology Citrix ADC Components Routing Access Control Lists Module 3: ADC Platforms Citrix ADC MPX Citrix ADC VPX Citrix ADC CPX Citrix ADC SDX Citrix ADC BLX Module 4: High Availability Citrix ADC High Availability High Availability Configuration Managing High Availability In Service Software Upgrade Troubleshooting High Availability Module 5: Load balancing Load Balancing Overview Load Balancing Methods and Monitors Load Balancing Traffic Types Load Balancing Protection Priority Load Balancing Load Balancing Troubleshooting Module 6: SSL Offloading SSL Overview SSL Configuration SSL Offload Troubleshooting SSL Offload SSL Vulnerabilities and Protections Module 7: Security Authentication, Authorization, and Auditing Configuring External Authentication Admin Partitions Module 8: Monitoring and Troubleshooting Citrix ADC Logging Monitoring with SNMP Reporting and Diagnostics AppFlow Functions Citrix Application Delivery Management Troubleshooting Module 9: Citrix Gateway Introduction to Citrix Gateway Advantages and Utilities of Citrix Gateway Citrix Gateway Configuration Common Deployments Module 10: AppExpert Expressions Introduction to AppExpert Policies Default Policies Explore Citrix ADC Gateway Policies Policy Bind Points Using AppExpert with Citrix Gateway Module 11: Authentication, Authorization, and Secure Web Gateway Authentication and Authorization Multi-Factor Authentication nFactor Visualizer SAML authentication Module 12: Managing Client Connections Introduction to Client Connections Session Policies and Profiles Pre and Post Authentication Policies Citrix Gateway Deployment Options Managing User Sessions Module 13: Integration for Citrix Virtual Apps and Desktops Virtual Apps and Desktop Integration Citrix Gateway Integration Citrix Gateway WebFront ICA Proxy Clientless Access and Workspace App Access Fallback SmartControl and SmartAccess for ICA Module 14: Configuring Citrix Gateway Working with Apps on Citrix Gateway RDP Proxy Portal Themes and EULA [-]

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 Kurset passer for deg som har basisferdighetene på plass men som ønsker å lære mer. Kurset passer også for deg som er selvlært og som ønsker å jobbe mer effektivt. Bruk av stiler gir profesjonelle og flotte dokumenter. Lær å lage innholdsfortegnelse, stikkordliste og figurliste automatisk. Profesjonelt sideoppsett med spalter, marger, sidefarger, sidekantlinjer og dokumenttemaer. Auto korrektur, byggeblokker, egenskaper og felt gjør det enklere å gjenbruke tekst. Flere deldokumenter kan samles i et hoved dokument ved hjelp av hoveddokumentvisning. I lange dokumenter kan du ha uliketopp- og bunntekster og selv bestemme side nummerering. For å friske opp et dokument kan du sette inn utklipp, figurer, SmartArt og diagram. Med tekstbokser kan du presentere sitater eller sammendrag fra dokumentet. Tabeller kan brukes til å presentere informasjon på en oversiktlig måte men kan også sorteres og inneholde beregninger. Maler brukes for å sikre at dokumenter av samme type får en ensartet formatering. Felt, innholdskontroller og skjemakontroller kan settes inn for å effektivisere bruken av maler. Med makroer kan du effektivisere avanserte oppgaver som består av serie med handlinger. Med fletting kan du masseprodusere brev, konvolutter, etiketter og e-post. 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 Word erfaring som de gjerne deler med deg! Meld deg på Word-kurs allerede i dag og sikre deg plass! Lær deg: behandling av stiler rask og enkel opprettelse av innholdsfortegnelse sette inn forsider samarbeid om felles dokument spalter beregninger i tabeller innsetting av diagram sett inn bilder og bildetekst grafikk og tegning maler og skjema bruk av makroer integrasjon med Excel og andre programmer [-]

Filer i SharePoint lagres i bibliotek. Her tar vi en grundig gjennomgang av bibliotek og tilpasningsmuligheter for disse, som versjonering, maler og Office-integrasjon. Webinaret varer i 2 timer og består av to økter à 45 min. Etter hver økt er det 10 min spørsmålsrunde. Mellom øktene er det 10 min pause. Webinaret kan også spesialtilpasses og holdes bedriftsinternt kun for din bedrift.   Kursinnhold:   Om bibliotek Møt biblioteksmalene i SharePoint Opplasting, nedlasting Office-programmene og bibliotek Områdepapirkurv   Tilpasse bibliotek Endre Office-mal for et bibliotek Tilpass kolonner og metadata   Tips til bibliotek Bruke kolonner i Word Bibliotek i Windows Utforsker   Utvidet om bibliotek Gjennomgang av versjonering Bli kjent med godkjenning Arkivering og Send til   Veien videre Introduksjon til innholdstyper Introduksjon til dokumentsenter og innholds-sortering 3 gode grunner til å delta 1. Møt SharePoint sine bibliotek-apper og lær måter å åpne og lagre i bibliotek og håndtere innholdet 2. Forstå mer om versjonering, godkjenning og arkivering 3. Bli kjent med dokumentsenter og innholds-sortering   [-]

The course will cover general cloud computing concepts as well as general cloud computing models and services such as Public, Private and Hybrid cloud and Infrastructure-as-a-Service (IaaS), Platform-as-a-Service(PaaS) and Software-as-a-Service (SaaS). It will also cover some core Azure services and solutions, as well as key Azure pillar services concerning security, privacy, compliance and trust. It will finally cover pricing and support services available.   Agenda Module 1: Cloud Concepts -Learning Objectives-Why Cloud Services?-Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS)-Public, Private, and Hybrid cloud models Module 2: Core Azure Services -Core Azure architectural components-Core Azure Services and Products-Azure Solutions-Azure management tools Module 3: Security, Privacy, Compliance and Trust -Securing network connectivity in Azure-Core Azure Identity services-Security tools and features-Azure governance methodologies-Monitoring and Reporting in Azure-Privacy, Compliance and Data Protection standards in Azure Module 4: Azure Pricing and Support -Azure subscriptions-Planning and managing costs-Support options available with Azure-Service lifecycle in Azure [-]

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. After completing this course, students will be able to: 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 prerequisites Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions.Recommended prerequisites:M-DP900 - Microsoft Azure Data FundamentalsM-AZ900 - Microsoft Azure Fundamentals Agenda 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. 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. 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. 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). 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Module 16: Build reports using Power BI integration with Azure Synapase 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. 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. [-]

Excelkurs Basis - elæringskurs [-]

Agenda Module 1: Implement VMs for Windows and Linux -Select Virtual Machine Size-Configure High Availability-Implement Azure Dedicated Hosts-Deploy and Configure Scale Sets-Configure Azure Disk Encryption Module 2: Automate Deployment and Configuration of Resources -Azure Resource Manager Templates-Save a Template for a VM-Evaluate Location of New Resources-Configure a Virtual Hard Disk Template-Deploy from a Template-Create and Execute an Automation Runbook Module 3: Implement Virtual Networking -Virtual Network Peering-Implement VNet Peering Module 4: Implement Load Balancing and Network Security -Implement Azure Load Balancer-Implement an Application Gateway-Understand Web Application Firewall-Implement Azure Firewall-Implement Azure Front Door-Implementing Azure Traffice Manager-Implement Network Security Groups and Application Security Grou-Implement Azure Bastion Module 5: Implement Storage Accounts -Storage Accounts-Blob Storage-Storage Security-Managing Storage-Accessing Blobs and Queues using AAD-Configure Azure Storage Firewalls and Virtual Networks Module 6: Implement Azure Active Directory -Overview of Azure Active Directory-Users and Groups-Domains and Custom Domains-Azure AD Identity Protection-Implement Conditional Access-Configure Fraud Alerts for MFA-Implement Bypass Options-Configure Trusted IPs-Configure Guest Users in Azure AD-Manage Multiple Directori Module 7: Implement and Manage Azure Governance -Create Management Groups, Subscriptions, and Resource Groups-Overview of Role-Based Access Control (RBAC)-Role-Based Access Control (RBAC) Roles-Azure AD Access Reviews-Implement and Configure an Azure Policy-Azure Blueprints Module 8: Implement and Manage Hybrid Identities -Install and Configure Azure AD Connect-Configure Password Sync and Password Writeback-Configure Azure AD Connect Health Module 9: Manage Workloads in Azure -Migrate Workloads using Azure Migrate-VMware - Agentless Migration-VMware - Agent-Based Migration-Implement Azure Backup-Azure to Azure Site Recovery-Implement Azure Update Management Module 10: Implement Cloud Infrastructure Monitoring -Azure Infrastructure Security Monitoring-Azure Monitor-Azure Workbooks-Azure Alerts-Log Analytics-Network Watcher-Azure Service Health-Monitor Azure Costs-Azure Application Insights-Unified Monitoring in Azure Module 11: Manage Security for Applications -Azure Key Vault-Azure Managed Identity Module 12: Implement an Application Infrastructure -Create and Configure Azure App Service-Create an App Service Web App for Containers-Create and Configure an App Service Plan-Configure Networking for an App Service-Create and Manage Deployment Slots-Implement Logic Apps-Implement Azure Functions Module 13: Implement Container-Based Applications -Azure Container Instances-Configure Azure Kubernetes Service Module 14: Implement NoSQL Databases -Configure Storage Account Tables-Select Appropriate CosmosDB APIs Module 15: Implement Azure SQL Databases -Configure Azure SQL Database Settings-Implement Azure SQL Database Managed Instances-High-Availability and Azure SQL Database [-]

Through a combination of presentations, demos, and hands-on labs, participants get an overview of the Google Cloud platform and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud Platform. Learning Objectives This course teaches participants the following skills: Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform Employ BigQuery and Cloud Datalab to carry out interactive data analysis Train and use a neural network using TensorFlow Employ ML APIs Choose between different data processing products on the Google Cloud Platform Course Outline Module 1: Introducing Google Cloud Platform -Google Platform Fundamentals Overview-Google Cloud Platform Big Data Products Module 2: Compute and Storage Fundamentals -CPUs on demand (Compute Engine)-A global filesystem (Cloud Storage)-CloudShell-Lab: Set up an Ingest-Transform-Publish data processing pipeline Module 3: Data Analytics on the Cloud -Stepping-stones to the cloud-CloudSQL: your SQL database on the cloud-Lab: Importing data into CloudSQL and running queries-Spark on Dataproc-Lab: Machine Learning Recommendations with Spark on Dataproc Module 4: Scaling Data Analysis -Fast random access-Datalab-BigQuery-Lab: Build machine learning dataset Module 5: Machine Learning -Machine Learning with TensorFlow-Lab: Carry out ML with TensorFlow-Pre-built models for common needs-Lab: Employ ML APIs Module 6: Data Processing Architectures -Message-oriented architectures with Pub/Sub-Creating pipelines with Dataflow-Reference architecture for real-time and batch data processing Module 7: Summary -Why GCP?-Where to go from here-Additional Resources [-]

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   [-]

SDWFND: Cisco SD WAN Operation and Deployment [-]

SISE: Implementing and Configuring Cisco Identity Services Engine [-]

Er du på jakt etter mer avansert funksjonalitet på forsidene dine? På dette webinaret lærer du mer om å sette inn innhold fra andre kilder og å sy sammen komponentene på siden. Webinaret varer i 2 timer og består av to økter à 45 min. Etter hver økt er det 10 min spørsmålsrunde. Mellom øktene er det 10 min pause. Webinaret kan også spesialtilpasses og holdes bedriftsinternt kun for din bedrift.   Kursinnhold:   Sider og sideoppsett Bli kjent med Webdel-sider og oppsett Hvordan legge til skriptsnutter og elementer fra andre nettsider   Bygg inn innhold Legg inn embed-kode Forberede og presentere en PowerPoint-presentasjon på forsiden ved hjelp av Office Web Apps/Office Online   Forsider og dashboards Forberede og presentere en Excel-bok på forsiden med Excel Services Forberede og presentere en Visio-tegning som forsidemeny med Visio Services   Dynamiske sider Målgrupper Koble sammen webdeler og la innhold i en webdel påvirke innholdet i en annen   3 gode grunner til å velge KnowledgeGroup 1. Best practice kursinnhold 2. Markedets beste instruktører 3. Gratis support [-]