Oversikt over relevante kurs innen IT-kurs i Gamle Oslo

Du vil få tilsendt en «Core guidance» bok og sertifiserings-voucher slik at du kan ta sertifiseringstesten for eksempel hjemme eller på jobb. Denne vil være gyldig i ett år. Tid for sertifiseringstest avtales som beskrevet i e-post med voucher. Eksamen overvåkes av en web-basert eksamensvakt.   Eksamen er på engelsk. Eksamensformen er multiple choice - 40 spørsmål skal besvares, og du består med 70% riktige svar (dvs. 28 av 40). Deltakerne har 1 time og 30 minutter til rådighet på eksamen.  Ingen hjelpemidler er tillatt.  Nødvendige forkunnskaper: Bestått ITIL Foundation sertifisering Gjennomført godkjent kurs/e-læring [-]

ITIL® Foundation er et grunnkurs for ITIL® og gir kandidatene kjennskap til sentrale elementer, konsepter og viktig terminologi brukt i ITIL®. Etter fullført kurs og bestått eksamen skal kandidatene ha tilegnet en grunnleggende kunnskap og forståelse av ITIL®. Bestått ITIL® Foundation-kurs er også en forutsetning for å kunne gå videre til neste nivå i ITIL® opplæringen. Dokumenter din kompetanse gjennom ITIL® sertifisering Kurset vil gi en forståelse av ITIL® rammeverk og tilrettelegger rutiner for både bedriften og ansatte. Målet er å lette bruk av ressurser og gi bedre kvalitet, samt synliggjøre rutiner og prosesser som er viktige for din arbeidsplass. Kursets hensikt: Gi en forståelse av ITIL® rammeverk og tilrettelegger rutiner for både bedriften og ansatte. Lette bruk av ressurser og gi bedre kvalitet. Vise rutiner og prosesser som er viktige for din arbeidsplass. Dokumentere din kompetanse gjennom ITIL® sertifisering. ITIL® gir bedriften: Prosessbeskrivelser og dokumentasjon Gode råd for implementering og tilpassing Felles forståelse og språk Konsulenter med kunnskap om ITIL® ITIL® gir resultater: Bedre behandling av brukere og kunder Tydelige roller og ansvar Bedre driftstabilitet og sikkerhet Bedre evne til å lære og utveksle erfaringer Økt kvalitet på leveransen av IT tjenester Profesjonelle IT tjenester PrisKr. 16700,-Prisen inkluderer dokumentasjon og lunsjVariget3 dager - fra kl. 09:00 - 16:00 (siste kursdag 09:00-12.00) Kursene gjennomføres i samarbeid med Sopra Steria. UiS-IKT tilbyr sertifiseringer for kursene ved vårt autoriserte testsenter PeopleCert i forbindelse med kurs som gjennomføres ved Universitetet i Stavanger. ITIL® is a registered trade mark of AXELOS Limited, used under permission of AXELOS Limited. The Swirl logo™ is a trade mark of AXELOS Limited, used under permission of AXELOS Limited. All rights reserved. [-]

Through a combination of presentations, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, systems, and application services. This course also covers deploying practical solutions including securely interconnecting networks, customer-supplied encryption keys, security and access management, quotas and billing, and resource monitoring. Course Objectives This course teaches participants the following skills: Configure VPC networks and virtual machines Administer Identity and Access Management for resources Implement data storage services in Google Cloud Manage and examine billing of Google Cloud resources Monitor resources using Google Cloud services Connect your infrastructure to Google Cloud Configure load balancers and autoscaling for VM instances Automate the deployment of Google Cloud infrastructure services Leverage managed services in Google Cloud All courses will be delivered in partnership with ROI Training, Google Cloud Premier Partner, using a Google Authorized Trainer. Course Outline Module 1: Introduction to Google Cloud -List the different ways of interacting with Google Cloud-Use the Cloud Console and Cloud Shell-Create Cloud Storage buckets-Use the Google Cloud Marketplace to deploy solutions Module 2: Virtual Networks -List the VPC objects in Google Cloud-Differentiate between the different types of VPC networks-Implement VPC networks and firewall rules-Implement Private Google Access and Cloud NAT Module 3: Virtual Machines -Recall the CPU and memory options for virtual machines-Describe the disk options for virtual machines-Explain VM pricing and discounts-Use Compute Engine to create and customize VM instances Module 4: Cloud IAM -Describe the Cloud IAM resource hierarchy-Explain the different types of IAM roles-Recall the different types of IAM members-Implement access control for resources using Cloud IAM Module 5: Data Storage Services -Differentiate between Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Firestore and Cloud Bigtable-Choose a data storage service based on your requirements-Implement data storage services Module 6: Resource Management -Describe the cloud resource manager hierarchy-Recognize how quotas protect Google Cloud customers-Use labels to organize resources-Explain the behavior of budget alerts in Google Cloud-Examine billing data with BigQuery Module 7: Resource Monitoring -Describe the services for monitoring, logging, error reporting, tracing, and debugging-Create charts, alerts, and uptime checks for resources with Cloud Monitoring-Use Cloud Debugger to identify and fix errors Module 8: Interconnecting Networks -Recall the Google Cloud interconnect and peering services available to connect your infrastructure to Google Cloud-Determine which Google Cloud interconnect or peering service to use in specific circumstances-Create and configure VPN gateways-Recall when to use Shared VPC and when to use VPC Network Peering Module 9: Load Balancing and Autoscaling -Recall the various load balancing services-Determine which Google Cloud load balancer to use in specific circumstances-Describe autoscaling behavior-Configure load balancers and autoscaling Module 10: Infrastructure Modernization -Automate the deployment of Google Cloud services using Deployment Manager or Terraform-Outline the Google Cloud Marketplace Module 11: Managed Services Describe the managed services for data processing in Google Cloud [-]

COURSE OVERVIEW You’ll learn how to implement and secure a wireless network infrastructure and use Cisco Identity Service Engine (ISE), Cisco Prime Infrastructure (PI), and Cisco Connect Mobile Experience to monitor and troubleshoot network issues.   The course provides hands-on labs to reinforce concepts including deploying Cisco Prime Infrastructure Release 3.5, Cisco Catalyst 9800 Wireless Controller Release IOS XE Gibraltar 16.10, Cisco Digital Network Architecture (DNA) Center Release 1.2.8, Cisco CMX Release 10.5, Cisco MSE Release 8.0 features and Cisco Identity Services Engine (ISE) Release 2.4.   This course also helps you prepare to take the Implementing Cisco Enterprise Wireless Networks (300-430 ENWLSI) exam, which is part of the new CCNP Enterprise certification. Passing the exam will also provide you with the Cisco Certified Specialist - Enterprise Wireless Implementation certification.   TARGET AUDIENCE Individuals needing to understand how to implement, secure and troubleshoot a Cisco Enterprise Wireless Network.   COURSE OBJECTIVES After completing this course you should be able to: Implement network settings to provide a secure wireless network infrastructure Troubleshoot security issues as it relates to the wireless network infrastructure Implement a secure wireless client and troubleshoot wireless client connectivity issues Implement and troubleshoot QoS in wireless networks Implement and troubleshoot advanced capabilities in wireless network services   COURSE CONTENT Securing and Troubleshooting the Wireless Network Infrastructure Implement Secure Access to the WLCs and Access Points Configure the Network for Access Point 802.1X Authentication Use Cisco DNA Center for Controller and AP Auto Install Implement Cisco Prime Infrastructure Define Network Troubleshooting Techniques Troubleshoot Access Point Join Issues Monitor the Wireless Network Implementing and Troubleshooting Secure Client Connectivity Configure the Cisco WLC for Wireless Client 802.1x Authentication Configure the Wireless Client for 802.1X Authentication Configure a Wireless LAN for FlexConnect Implement Guest Services in the Wireless Network Configure the Cisco WLC for Centralized Web Authentication Configure Central Web Authentication on Cisco ISE Implement BYOD Implement Location-Aware Guest Services Troubleshoot Client Connectivity Describe Issues that Affect Client Performance Monitor Wireless Clients Implementing and Troubleshooting QoS in Wireless Networks Implement QoS in the Wireless Network Configure the Cisco WLC to Support Voice Traffic Optimize Wireless Utilization on the Cisco WLC Implement Cisco AVC in the Wireless Network Implement Multicast Services Implement mDNS Service Implement Cisco Media Stream Troubleshoot QoS Issues in the Wireless Network Troublehoot mDNS Issues Troubleshoot Media Stream Issues Implementing and Troubleshooting Advanced Wireless Network Services Implement Base Location Services on Cisco Prime Infrastructure Implement Hyperlocation in the Wireless Network Implement Detect and Locate Services on Cisco CMX Implement Analytics on Cisco CMX Implement Presence Services on Cisco CMX Monitor and Locate Rogue Devices with Cisco Prime Infrastructure and Cisco CMX Monitor and Detect Wireless Clients with Cisco CMX and Cisco DNA Center Run Analytics on Wireless Clients Troubleshoot Location Accuracy with Cisco Hyperlocation Monitor and Manage RF Interferers on the Cisco WLC Monitor and Manager RF Interferers on Cisco Prime Infrastructure and Cisco CMX Labs Lab Familiarization (Base Learning Lab) Configure Secure Management Access for WLCs and APs Add Network Devices and External Resources to Cisco Prime Infrastructure Capture a Successful AP Authentication Implement AAA Services for Central Mode WLANs Implement AAA Services for FlexConnect Mode WLANs Configure Guest Services in the Wireless Network Configure BYOD in the Wireless Network Capture a Successful Client Authentications Configure QoS in the Wireless Network for Voice and Video Services Configure Cisco AVC in the Wireless Network Capture Successful QoS Traffic Marking in the Wireless Network Configure Detect and Locate Services on the Cisco CMX Identify Wireless Clients and Security Threats [-]

Excel Videregående [-]

Fleksible kurs for fremtidenNy kunnskap skal gi umiddelbar effekt, og samtidig være holdbar og bærekraftig på lang sikt. NTI AS har 30 års erfaring innen kurs og kompetanseheving, og utdanner årlig rundt 10.000 personer i Nord Europa innen CAD, BIM, industri, design og konstruksjon. Fusion 360 Her er et utvalg av temaene du vil lære på kurset: Fusion 360 Dashboard - Data og prosjekthåndtering Formgivning - konseptuelt design og parametrisk Samlinger og 2D-tegninger, inkludert stykklister  Dataimport - håndtering av data fra andre CAD-plattformer Kurset for deg som er industriell designer, arbeider med produktdesign eller annen kompleks forgivning. Kurset har fokus på oppbygning av modeller med kompliserte overflater. Du vil få kunnskap til å skape og korrigere avanserte og komplekse 3D-modeller, og samtidig lage avanserte 3D-modeller på bakgrunn av konseptuelle skisser, vedlikeholde og dokumentere dem i forskjellige varianter.    Dette er et populært kurs, meld deg på nå! Tilpassete kurs for bedrifterVi vil at kundene våre skal være best på det de gjør - hele tiden.  Derfor tenker vi langsiktig om kompetanseutvikling og ser regelmessig kunnskapsløft som en naturlig del av en virksomhet. Vårt kurskonsept bygger på et moderne sett av ulike læringsmiljøer, som gjør det enkelt å finne riktig løsning uansett behov. Ta kontakt med oss på telefon 483 12 300, epost: salg@nticad.no eller les mer på www.nticad.no [-]

This workshop will teach you Spring Framework basics and dives into Spring Boot and Spring Cloud to create Microservices.Introduction     Design goals and principles     IOC and dependency injection     Spring Ecosystem Spring Framework     Spring Beans     Java Configuration, Annotation Based Configuration     Dependency injection, beans and properties     Bean Lifecycle     Property Sources, Environment abstraction Spring Boot     Starters, AutoConfiguration, Properties, Actuators     Devtools, LiveReload, debugging     Testing, Test-Properties     Packaging, Logging, YAML, Profiles     Actuator, Monitoring     Data Access with JPA     Restservices with Spring MVC and Spring Data Rest     Security     Custom Spring Boot Starters Microservices     Twelve-factor Apps     Overview of Microservices with Spring Boot / Spring Cloud     Orchestrating a Microservice system with Spring Cloud Netflix stack After the workshop, the participants will be able to independently create web applications using the technologies and frameworks used in the workshop. [-]

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     [-]

AI-050: Develop Generative AI Solutions with Azure OpenAI Service [-]

Sopra Steria Akademiet er en del av Sopra Steria, og tilbyr kurs og opplæring innen: IT Service Management Prosjekt- og programstyring It-styring og kontroll Våre instruktører jobber til daglig som rådgivere innen disse områdene i Sopra Steria.  ITIL® 4 er det mest utbredte og anerkjente rammeverket for IT Service Management (ITSM) i verden, og ITIL® 4 Foundation er et introduksjonskurs til rammeverket. ITIL® 4 Foundation-sertifiseringen er designet som en introduksjon til ITIL® 4 og gjør det mulig for kandidater å se på IT-tjenestestyring gjennom en ende-til-ende driftsmodell for oppretting, levering og kontinuerlig forbedring av teknisk aktiverte produkter og tjenester.Kurset avsluttes med en sertifiseringstest den siste dagen. Kurset varer i 3 dager. Vi stiller med erfarne norske instruktører. Kursmateriell og eksamen er på engelsk. Eksamen varer i 75 minutter. Eksamen på slutten av siste kursdag om ikke annet er avtalt. Det bør beregnes 6 timer til selvstudium. Du kan lese mere om ITIL her ITIL®/PRINCE2®/MSP®/MoP® are registered trademarks of AXELOS Limited, used under permission of AXELOS Limited. All rights reserved. [-]

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 [-]

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

I dette kurset kombinerer vi teori med praksis. Gjennom relevante oppgaver får du forståelse og ferdigheter til å trekke ut data og gjøre analyser av hva som skjer på ditt nettsted. Du vil lære hvordan du kan måle effekt av endringer i løsningen, design og markedsføringstiltak. Google Analytics gir deg det datagrunnlaget du trenger for å lage rapporter og analyser for en faktabasert forståelse av hvordan den digitale løsningen fungerer.  Etter kurset vil du kunne hente ut data og lage analyserapporter som gir innsikt og støtte til din markedsføring og kommunikasjon, samt en god utvikling og forbedring av nettstedet. Noen av temaene som dekkes i kurset er: Hva er webanalyse og hvordan fungerer Google Analytics Sentrale begreper De viktigste rapportene Eventtracking / brukeradferd Hva må du vite om oppsett KPIer og måling - hva er viktig å måle Hvordan bruke GA sammen med andre relevante verktøy som Google Data Studio, Google Tag Manager, Google Search Console [-]

Kurs i hypotesetesting I forbedringsarbeid og problemløsning tester vi hypoteser for å bestemme rotårsaker og årsakssammenhenger. Dette kurset lærer deg å utforme og teste hypoteser. Du får svar på spørsmål som: Er det signifikante forskjeller i gjennomsnitt eller variasjon? Har endringen vi har gjort medført en signifikant forbedring?   Kurset er for deg som vil: utforme hypotese basert på egne teorier om rotårsak eller årsakssammenhenger bestemme om datasett har signifikante forskjelliger i gjennomsnitt eller variasjon avgjøre om forbedringsarbeid har gitt signifikante forskjeller forstå årsakssammenhenger ved hjelp av statistikk   Du lærer følgende: Bruk av statistisk hypotesetesting Praktisk og statistisk signifikans Statistikk og sannsynlighet Utforme hypotese Velge Hypotesetest (type data, fordeling, statistikk av interesse, # populasjoner) Trekke konklusjon basert på p-verdi Type I og type II feil Vurdering av datautvalg og prøveantall Bruke av p-verdi Vi bruker praktiske eksempler og øvelser i undervisningen.     Kursholder Kursholder Sissel Pedersen Lundeby er IASSC (International association for Six Sigma certification) akkreditert kursholder (eneste i Norge per januar 2022): "This accreditation publically reflects that you have met the standards established by IASSC such that those who participate in a training program led by you can expect to receive an acceptable level of knowledge transfer consistent with the Lean Six Sigma belt Bodies of Knowledge as established by IASSC."  Hypotesetesting er et av verktøyene som benyttes innen Lean Six Sigma, og Sissel har bred erfaring med anvendelse av dette verktøyet.  Sissel er utdannet sivilingeniør i kjemiteknikk fra NTNU, og har mer enn 20 års erfaring innen produksjon og miljøteknologi. Hennes Lean Six Sigma opplæring startet i 2002, hos et amerikansk firma, hvor hun ble Black Belt sertifisert. I 2017 ble hun også Black Belt sertifisert gjennom IASSC. Sissel har svært god erfaring med å bruke Lean Six Sigma til forbedringer, og fokuserer på å skape målbare resultater. Kursene bruker praktiske, gjenkjennelige eksempler, og formidler Lean Six Sigma på en enkel, forståelig måte.      Tilbakemeldinger "Inspirerende, faglig dyktig, folkeliggjør et teoretisk fagområde" Espen Fjeld, Kommersiell direktør hos Berendsen "Faglig meget dyktig og klar fremføring. Morsom og skaper tillit" Jon Sørensen, Produksjonsleder hos Berendsen "10/10 flink til å nå alle" Erlend Stene, Salgsleder hos Berendsen "Tydelig og bra presentert. God til å kontrollspørre og lytte (sjekke forståelse)" Morten Bodding, Produksjonsleder hos Berendsen "Utgjorde en forskjell, engasjert og dyktig" Kursdeltager fra EWOS "Du er inspirerende, positiv og dyktig i faget" Kursdeltager fra EWOS "Jeg var veldig imponert over Sissels Lean Six Sigma kunnskap. Hun gjør det enkelt å identifisere forbedringer og skape resultater" Daryl Powell, Lean Manager, Kongsberg Maritime Subsea   Praktisk informasjon Kurset arrangeres på forespørsel fra bedrifter. Åpne kurs arrangeres ihht kurskalenderen. Kurset består av et nettmøte på 2 timer. [-]

Microsoft 365: Bli en digital pådriver [-]