Oversikt over relevante kurs innen IT-kurs i Aarhus

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.   [-]

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

Power Pivot - Microsoft Excel [-]

Fleksible kurs for fremtiden Ny 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.   MagiCAD VVS for AutoCAD grunnkurs Her er et utvalg av temaene du vil lære på kurset: Etablering av prosjekt Prosjektering av ventilasjonsanlegg, varmeanlegg, og sanitæranlegg Sammenkobling av systemer gjennom flere tegninger Tekstefunksjoner, snitt, tegninger til utskrift Beregninger, utbalansering, lyd, mengdeberegning Bruk av leverandørspesifike produkter Kollisjonskontroll Automatisk generering av utsparinger Deltakerne skal lære å håndtere tegninger i et prosjekt; arkitekt, VVS-tegninger etc. De skal lære å berike en VVS-modell slik at mest mulig informasjon kan nyttes med hensyn til BIM, 2D-tegninger, strømningstekniske beregninger og lydberegninger. Tilpassete kurs for bedrifter Vi vil at kundene våre skal være best på det de gjør - hele tiden.  Derfor tenker vi langsiktig om kompetanseutvikling og ser regelmessig kunnskapsløft som en naturlig del av en virksomhet. Vårt kurskonsept bygger på et moderne sett av ulike læringsmiljøer, som gjør det enkelt å finne riktig løsning uansett behov. Ta kontakt med oss på telefon 483 12 300, epost: salg@nticad.no eller les mer på www.nticad.no [-]

  Studieår: 2013-2014   Gjennomføring: Høst Antall studiepoeng: 5.0 Forutsetninger: IINI1001 IT Introduksjon eller tilsvarende forhåndskunnskaper. Innleveringer: Et større prosjektarbeid som gjennomføres som gruppearbeid. Personlig veileder: ja Vurderingsform: Vurderingen i faget er basert på prosjektarbeidet. Prosjektene gjennomføres gruppevis. Individuelle karakterer kan gis ved manglende deltakelse eller ved kontraktsbrudd med øvrige medlemmer. Ansvarlig: Thor O. Olsen         Læremål: Etter å ha gjennomført emnet «Lokale informasjonstjenester» skal studenten ha følgende samlete læringsutbytte: KUNNSKAPER:Kandidaten:- kjenner til ulike typer informasjon som brukes i bedrifter og organisasjoner- har kunnskap om hvordan datateknologi og nettløsninger kan brukes i bedriftens forvaltning av informasjon- har kunnskap om moderne former for intern opplæring og oppbevaring og tilgjengelighet av kunnskapskapital FERDIGHETER:Kandidaten:- kan se behov for og være pådriver for små og mellomstore informasjonsløsninger for intern informasjon- kan komme med anbefalinger for bruk av moderne IT-kommunikasjonsløsninger - kan både individuelt og i grupper diskutere og redegjøre for holdninger og standpunkter i forhold til informasjonsforvaltning og ivaretakelse av virksomheters kunnskapskapital GENERELL KOMPETANSE:Kandidaten:- har forståelse for betydningen av aktiv informasjons- og kunnskapsforvaltning.- kan delta i planlegging og gjennomføring av informasjonshåndteringsprosjekter- kan identifisere, planlegge og gjennomføre en selvstendig oppgave i samarbeid med andre Innhold:Intranett og intranett-teknologi; Tjenesteinnhold i lokale informasjonssystemer; Sikkerhet i informasjonstjenester; Bedriftsopplæring;Les mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Dette faget går: Høst 2013    Fag Lokale informasjonstjenester 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.    [-]

COURSE OVERVIEW  Learn how to implement security principles within an enterprise network and how to overlay network design by using solutions such as SD-Access and SD-WAN. The automation and programmability of Enterprise networks is also incorporated in this course. This course will help you: Configure, troubleshoot, and manage enterprise wired and wireless networks Implement security principles within an enterprise network Earn 64 CE credits toward recertification   Please note that this course is a combination of Instructor-Led and Self-Paced Study - 5 days in the classroom and approx. 3 days of self study. The self-study content will be provided as part of the digital courseware that you receive at the beginning of the course and should be part of your preparation for the exam. Additional lab access will be provided at the end of the class, this will be valid for 60 hours or 90 days whichever is the shorter. It will be possible to complete all but 7 of the labs after the class. TARGET AUDIENCE Network engineers involved in the installation, support and troubleshooting of enterprise networks. COURSE OBJECTIVES After completing this course you should be able to: Illustrate the hierarchical network design model and architecture using the access, distribution, and core layers Compare and contrast the various hardware and software switching mechanisms and operation, while defining the Ternary Content Addressable Memory (TCAM) and Content Addressable Memory (CAM), along with process switching, fast switching, and Cisco Express Forwarding concepts Troubleshoot Layer 2 connectivity using VLANs and trunking Implementation of redundant switched networks using Spanning Tree Protocol Troubleshooting link aggregation using EtherChannel Describe the features, metrics, and path selection concepts of Enhanced Interior Gateway Routing Protocol (EIGRP) Implementation and optimization of Open Shortest Path First (OSPF)v2 and OSPFv3, including adjacencies, packet types, and areas, summarization, and route filtering for IPv4 and IPv6 Implementing External Border Gateway Protocol (EBGP) interdomain routing, path selection, and single and dual-homed networking Implementing network redundancy using protocols including Hot Standby Routing Protocol (HSRP) and Virtual Router Redundancy Protocol (VRRP) Implementing internet connectivity within Enterprise using static and dynamic Network Address Translation (NAT) Describe the virtualization technology of servers, switches, and the various network devices and components Implementing overlay technologies such as Virtual Routing and Forwarding (VRF), Generic Routing Encapsulation (GRE), VPN, and Location Identifier Separation Protocol (LISP) Describe the components and concepts of wireless networking including Radio Frequency (RF) and antenna characteristics, and define the specific wireless standards Describe the various wireless deployment models available, include autonomous Access Point (AP) deployments and cloud-based designs within the centralized Cisco Wireless LAN Controller (WLC) architecture Describe wireless roaming and location services Describe how APs communicate with WLCs to obtain software, configurations, and centralized management Configure and verify Extensible Authentication Protocol (EAP), WebAuth, and Pre-shared Key (PSK) wireless client authentication on a WLC Troubleshoot wireless client connectivity issues using various available tools Troubleshooting Enterprise networks using services such as Network Time Protocol (NTP), Simple Network Management Protocol (SNMP), Cisco Internetwork Operating System (Cisco IOS®) IP Service Level Agreements (SLAs), NetFlow, and Cisco IOS Embedded Event Manager Explain the use of available network analysis and troubleshooting tools, which include show and debug commands, as well as best practices in troubleshooting Configure secure administrative access for Cisco IOS devices using the Command-Line Interface (CLI) access, Role-Based Access Control (RBAC), Access Control List (ACL), and Secure Shell (SSH), and explore device hardening concepts to secure devices from less secure applications, such as Telnet and HTTP Implement scalable administration using Authentication, Authorization, and Accounting (AAA) and the local database, while exploring the features and benefits Describe the enterprise network security architecture, including the purpose and function of VPNs, content security, logging, endpoint security, personal firewalls, and other security features Explain the purpose, function, features, and workflow of Cisco DNA Centre™ Assurance for Intent-Based Networking, for network visibility, proactive monitoring, and application experience Describe the components and features of the Cisco SD-Access solution, including the nodes, fabric control plane, and data plane, while illustrating the purpose and function of the Virtual Extensible LAN (VXLAN) gateways Define the components and features of Cisco SD-WAN solutions, including the orchestration plane, management plane, control plane, and data plane Describe the concepts, purpose, and features of multicast protocols, including Internet Group Management Protocol (IGMP) v2/v3, Protocol-Independent Multicast (PIM) dense mode/sparse mode, and rendezvous points Describe the concepts and features of Quality of Service (QoS), and describe the need within the enterprise network Explain basic Python components and conditionals with script writing and analysis Describe network programmability protocols such as Network Configuration Protocol (NETCONF) and RESTCONF Describe APIs in Cisco DNA Centre and vManage COURSE CONTENT Examining Cisco Enterprise Network Architecture Cisco Enterprise Architecture Model Campus LAN Design Fundamentals Traditional Multilayer Campus Layer Design Campus Distribution Layer Design   Understanding Cisco Switching Paths Layer 2 Switch Operation Control and Data Plane Cisco Switching Mechanisms Implementing Campus LAN Connectivity Revisiting VLANs Trunking with 802.1Q Inter-VLAN Routing Building Redundant Switched Topology Spanning-Tree Protocol Overview Spanning-Tree Protocol Operation Spanning-Tree Protocols Types and Features Multiple Spanning Tree Protocol PortFast and BPDU Implementing Layer 2 Port Aggregation (Self-Study) Need for EtherChannel EtherChannel Mode Interactions Layer 2 EtherChannel Configuration Guidelines EtherChannel Load-Balancing Options Troubleshoot EtherChannel Issues Understanding EIGRP EIGRP Features EIGRP Reliable Transport Establishing EIGRP Neighbour Adjacency EIGRP Metrics EIGRP Path Selection Explore EIGRP Path Selection Explore EIGRP Load Balancing and Sharing EIGRP for IPv6 Compare EIGRP and OSPF Routing Protocols Implementing OSPF Describe OSPF The OSPF Process OSPF Neighbour Adjacencies Building a Link-State Database OSPF LSA Types Compare Single-Area and Multi-Area OSPF OSPF Area Structure OSPF Network Types Optimizing OSPF OSPF Cost OSPF Route Summarization Benefits OSPF Route Filtering Tools Compare OSPFv2 and OSPFv3 Exploring EBGP Interdomain Routing with BGP BGP Operations Types of BGP Neighbour Relationships BGP Path Selection BGP Path Attributes Implementing Network Redundancy Need for Default Gateway Redundancy Define FHRP HSRP Advanced Features Cisco Switch High Availability Features Implementing NAT Define Network Address Translation NAT Address Types Explore NAT Implementations NAT Virtual Interface Introducing Virtualisation Protocols and Techniques Server Virtualisation Need for Network Virtualisation Path Isolation Overview Introducing VRF Introducing Generic Routing Encapsulation Introducing Virtualisation Protocols and Techniques Server Virualization Need for Network Virtualisation Path Isolation Overview Introducing VRF Introducing Generic Routing Encapsulation Understanding Virtual Private Networks and Interfaces Site-to-Site VPN Technologies IPSec VPN Overview IPSec: IKE IPsec Modes IPsec VPN Types Cisco IOS VTI Understanding Wireless Principles Explain RF Principles Describe Watts and Decibels Describe Antenna Characteristics Describe IEEE Wireless Standards Identify Wireless Component Roles Examining Wireless Deployment Options Wireless Deployment Overview Describe Autonomous AP Deployment Describe Centralized Cisco WLC Deployment Describe FlexConnect Deployment Cloud Deployment and Its Effect on Enterprise Networks Describe the Cloud-Managed Meraki Solution Cisco Catalyst 9800 Series Controller Deployment Options Describe Cisco Mobility Express Understanding Wireless Roaming and Location Services Wireless Roaming Overview Mobility Groups and Domains Wireless Roaming Types Describe Location Services Examining Wireless AP Operation Universal AP Priming Explore the Controller Discovery Process Describe AP Failover Explain High Availability Explore AP Modes Understanding Wireless Client Authentication Authentication Methods Pre-Shared Key (PSK) Authentication 802.1X User Authentication Overview PKI and 802.1X Certificate Based Authentication Introduction to Extensible Authentication Protocol EAP-Transport Layer Security (EAP-TLS) Protected Extensible Authentication Protocol EAP-FAST Guest Access with Web Auth Troubleshooting Wireless Client Connectivity Wireless Troubleshooting Tools Overview Spectrum Analysis Wi-Fi Scanning Packet Analysis Cisco AireOS GUI and CLI Tools Cisco Wireless Config Analyzer Express Common Wireless Client Connectivity Issues Overview Client to AP Connectivity WLAN Configuration Infrastructure Configuration Introducing Multicast Protocols (Self-study) Multicast Overview Internet Group Management Protocol Multicast Distribution Trees IP Multicasting Routing Rendezvous Point Introducing QoS (Self-study) Understand the Impact of User Applications on the Network Need for Quality of Service (QoS) Describe QoS Mechanisms Define and Interpret a QoS Policy Implementing Network Services Understanding Network Time Protocol Logging Services Simple Network Management Protocol Introducing NetFlow Flexible NetFlow Understanding Cisco IOS Embedded Event Manager Using Network Analysis Tools Troubleshooting Concepts Network Troubleshooting Procedures: Overview Network Troubleshooting Procedures: Case Study Basic Hardware Diagnostics Filtered Show Commands Cisco IOS IP SLAs Switched Port Analyzer(SPAN) Overview Remote SPAN (RSPAN) Encapsulated Remote Switched Port Analyzer(ERSAPN) Cisco Packet Capture Tools Overview Implementing Infrastructure Security ACL Overview ACL Wildcard Masking Types of ACLs Configure Numbered Access Lists Use ACLs to Filter Network Traffic Apply ACLs to Interfaces Configured Named Access Lists Control Plane Overview Control Plane Policing Implementing Secure Access Control Securing Device Access AAA Framework Overview Benefits of AAA Usage Authentication Options RADIUS and TACACS+ Enabling AAA and Configuring a Local User for Fallback Configuring RADIUS for Console and VTY Access Configuring TACACS+ for Console and VTY Access Configure Authorization and Accounting Understanding Enterprise Network Security Architecture (Self-study) Explore Threatscape Cisco Intrusion Prevention Systems Virtual Private Networks Content Security Logging Endpoint Security Personal Firewalls Antivirus and Antispyware Centralized Endpoint Policy Enforcement Cisco AMP for Endpoints Firewall Concepts TrustSec MACsec Identity Management 802.1X for Wired and Wireless Endpoint Authentication MAC Authentication Bypass Web Authentication Exploring Automation and Assurance Using Cisco DNA Centre (Self-study) Need for Digital Transformation Cisco Digital Network Architecture Cisco Intent-Based Networking Cisco DNA Centre Automation Overview Cisco DNA Centre Platform Overview Cisco DNA Centre Design Cisco DNA Centre Inventory Overview Cisco DNA Centre Configuration and Management Overview Onboarding of Network Devices Using Cisco DNA Centre Cisco DNA Centre Software Image Management Overview Cisco DNA Assurance Key Features and Use Cases Cisco DNA Centre Assurance Implementation Workflow Examining the Cisco SD-Access Solution (Self-study) Need for Cisco SD-Access Cisco SD Access Overview Cisco SD-Access Fabric Components Cisco SD-Access Fabric Control Plane Based on LISP Cisco SD-Access Fabric Control Plane Based on VXLAN Cisco SD-Access Fabric Control Plane Based on Cisco TrustSec Role of Cisco ISE and Cisco DNA Centre in SD-Access Cisco SD-Access Wireless Integration Traditional Campus Interoperating with Cisco SD-Access Understanding the Working Principles of the Cisco SD-WAN Solution (Self-study) Need for Software Defined Networking for WAN Cisco SD-WAN Components and Functions Cisco SD-WAN Orchestration Plane Cisco SD-WAN Management Plane- vManage Cisco SD-WAN Control Plane - vSmart Cisco SD-WAN Data Plane - WAN Edge Cisco SD-WAN Programmatic APIs Cisco SD-WAN Automation and Analytics Cisco SD-WAN Terminology Cisco IOS XE and IOS XE SD-WAN Software Flexible Controller Deployment Options Cisco SD-WAN Security Understanding the Basics of Python Programming Describe Python Concepts String Data Types Numbers Data Types Boolean Data Types Script Writing and Execution Analyse Code Introducing Network Programmability Protocols Configuration Management Evolution of Device Management and Programmability Data Encoding Formats Understanding JSON Model Driven Programmability Stack Introduction to YANG Types of YANG Models Understanding NETCONF Explain NETCONF and YANG REST Understanding RESTCONF Protocol Introducing APIs in Cisco DNA Centre and vManage (Self-study) Application Programming Interfaces REST API Response Codes and Results REST API Security Cisco DNA Centre APIs Cisco SD-WAN REST API Overview Labs Lab 1: Investigate the CAM Lab 2: Analyse Cisco Express Forwarding Lab 3: Troubleshoot VLAN and Trunk Issues Lab 4: Tuning STP and Configuring RSTP Lab 5: Configure Multiple Spanning Tree Protocol Lab 6: Troubleshoot EtherChannel Lab 7: Implementing Multiarea OSPF Lab 8: Implement OSPF Tuning Lab 9: Apply OSPF Optimization Lab 10: Implement OSPFv3 Lab 11: Configure and Verify Single-Homed EBGP Lab 12: Implementing HSRP Lab 13: Configure VRRP Lab 14: Implement NAT Lab 15: Configure and Verify VRF Lab 16: Configure and Verify a GRE Tunnel Lab 17: Configure Static VTI Point-to-Point Tunnels Lab 18: Configure Wireless Client Authentication in a Centralized Deployment (No Extended Access) Lab 19: Troubleshoot Wireless Client Connectivity Issues (No Extended Access) Lab 20: Configure Syslog Lab 21: Configure and Verify Flexible NetFlow Lab 22: Configuring Cisco IOS Embedded Event Manager (EEM) Lab 23: Troubleshoot Connectivity and Analyse Traffic with Ping, Traceroute and Debug Lab 24: Configure and Verify Cisco IP SLA's Lab 25: Configure Standard and Extended ACLs Lab 26: Configure Control Plane Policing Lab 27: Implement Local and Server-Based AAA (No Extended Access) Lab 28: Writing and Troubleshooting Python Scripts (No Extended Access) Lab 29: Explore JSON Objects and Scripts in Python (No Extended Access) Lab 30: Use NETCONF via SSH (No Extended Access) Lab 31: Use RESTCONF with Cisco IOS XE Software (No Extended Access) [-]

Jira Project Administration (Cloud) [-]

Jobb mer effektivt i Word [-]

AI-3004: Build an Azure AI Vision solution with Azure AI services [-]

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     [-]

COURSE OVERVIEW This course does not provide an Azure pass or time in the classroom for students to do any hands-on activities. TARGET AUDIENCE  This course is suitable for program managers and technical sales, with a general IT background. These students want to learn about our offerings, see how components are implemented, and ask questions about products and features. COURSE OBJECTIVES   Discuss the basics of cloud computing and Azure, and how to get started with Azure's subscriptions and accounts. Describe the advantages of using cloud computing services, learning to differentiate between the categories and types of cloud computing, and how to examine the various concepts, resources, and terminology that are necessary to work with Azure architecture. Outline the core services available with Microsoft Azure. Discuss the core solutions that encompass a wide array of tools and services from Microsoft Azure. Describe the general security and network security features, and how you can use the various Azure services to help ensure that your cloud resources are safe, secure, and trusted. Discuss the identity, governance, privacy, and compliance features, and how Azure can help you secure access to cloud resources, what it means to build a cloud governance strategy, and how Azure adheres to common regulatory and compliance standards. Discuss the factors that influence cost, tools you can use to help estimate and manage your cloud spend, and how Azure's service-level agreements (SLAs) can impact your application design decisions. COURSE CONTENT Module 1: Cloud Concepts In this module, you'll take an entry level end-to-end look at Azure and its capabilities, which will provide you with a solid foundation for completing the available modules for Azure Fundamentals. Introduction to Azure fundamentals Fundamental Azure concepts After completing this module, students will be able to: Understand the benefits of cloud computing in Azure and how it can save you time and money. Explain concepts such as high availability, scalability, elasticity, agility, and disaster recovery. Module 2: Core Azure Services In this module, you learn about core Azure services like Azure database, Azure compute, Azure storage, and Azure Networking. Core Azure architectural components Core Azure workload products Azure networking services Azure storage services Azure database services After completing this module, students will be able to: Describe core Azure architecture components such as subscriptions, management groups, and resources. Summarize geographic distribution concepts such as Azure regions, region pairs, and availability zones. Understand the services available in Azure including compute, network, storage, and databases. Identify virtualization services such as Azure VMs, Azure Container Instances, and Azure Kubernetes. Compare Azure's database services such as Azure Cosmos DB, Azure SQL, and Azure Database for MySQL. Examine Azure networking resources such as Virtual Networks, VPN Gateways, and Azure ExpressRoute. Summarize Azure storage services such Azure Blob Storage, Azure Disk Storage, and Azure File Storage. Module 3: Core Solutions In this module, you'll learn about AI machine learning, Azure DevOps, monitoring fundamentals, management fundamentals, serverless computing fundamentals. and IoT fundamentals. Choose the best Azure IoT service Choose the best AI service Choose the best Azure serverless technology Choose the best tools with DevOps and GitHub Choose the best management tools Choose the best Azure monitoring service After completing this module, students will be able to: Choose the correct Azure AI service to address different kinds of business challenges. Choose the best software development process tools and services for a given business scenario. Choose the correct cloud monitoring service to address different kinds of business challenges. Choose the correct Azure management tool to address different kinds of technical needs. Choose the right serverless computing technology for your business scenario. Choose the best Azure IoT service for a given business scenario. Module 4: General security and networking features In this module, you will learn how to protect yourself against security threats, and secure your networks with Azure. Security Tools and Features Secure Network Connectivity After completing this module, students will be able to: Strengthen your security posture and protect against threats by using Microsoft Defender for Cloud. Collect and act on security data from many different sources by using Microsoft Sentinel. Manage dedicated physical servers to host your Azure VMs for Windows and Linux. Identify the layers that make up a defense in depth strategy. Explain how Azure Firewall enables you to control what traffic is allowed on the network. Configure network security groups to filter network traffic to and from Azure resources. Explain how Azure DDoS Protection helps protect your Azure resources from DDoS attacks. Module 5: Identity, Governance, Privacy, and Compliance In this module, you will learn about Azure identity services, how to build a cloud governance strategy, and privacy, compliance and data protection standards on Azure. Core Azure identity services Azure Governance Methodologies Privacy, Compliance, and Data Protection standards After completing this module, students will be able to: Explain the difference between authentication and authorization. Describe how Azure Active Directory provides identity and access management. Explain the role single sign-on (SSO), multifactor authentication, and Conditional Access play. Make organizational decisions about your cloud environment by using the CAF for Azure. Define who can access cloud resources by using Azure role-based access control. Apply a resource lock to prevent accidental deletion of your Azure resources. Apply tags to your Azure resources to help describe their purpose. Control and audit how your resources are created by using Azure Policy. Enable governance at scale across multiple Azure subscriptions by using Azure Blueprints. Explain the types of compliance offerings that are available on Azure. Gain insight into regulatory standards and compliance on Azure. Explain Azure capabilities that are specific to government agencies. Module 6: Azure Pricing and Lifecycle In this module, you will learn how to plan and manage Azure costs, and how to choose the right Azure services though SLAs and service lifecycle. Planning and Cost Management Azure Service Level Agreements (SLAs) and Lifecycle After completing this module, students will be able to: Use the Total Cost of Ownership Calculator. Describe the different ways you can purchase Azure products and services. Use the Pricing calculator to estimate the monthly cost of running your cloud workloads. Define the major factors that affect total cost and apply recommended practices to minimize cost. Describe what a service-level agreement (SLA) is and why SLAs are important. Identify factors, such as the service tier you choose, that can affect an SLA. Combine SLAs to compute a composite SLA. Describe the service lifecycle in Azure. TEST CERTIFICATION This course helps to prepare for exam AZ-900. FOLLOW ON COURSES M-AZ104, Microsoft Azure Administrator M-AZ204, Developing Solutions for Microsoft Azure M-AZ303, Microsoft Azure Architect Technologies M-DP200, Implementing an Azure Data Solution (DP-200) [-]

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

Web Development QuickStart [-]

MasterClass: Hacking and Securing Windows Infrastructure with Paula Januszkiewicz [-]

I kurset ”Måling og mengdebereging” vil du lære hvordan Revu brukes til å kalibrere og måle på PDF-tegninger, samt hvordan du kan opprette, spare og dele tilpassede markeringsverktøy. Disse kan så brukes til effektiv beregning av mengder og priser på alt fra vegg- og gulvarealer, til prising av utstyr på en riggplan. Å lære å bruke Revu til måling og mengdeberegning vil bl.a. gi følgende fordeler: Stor tidsbesparelse Større nøyaktighet og mindre feil Bedre dokumentasjon av mengdeberegningen Oppnå optimal utnyttelse av Bluebeam Revu i prosjektene [-]