IT-kurs

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 [-]

Bli kjent med Revu (Bruksområder, grensesnitt, menyer, verktøy, paneler og profiler) Grunnleggende PDF-håndtering med Revu Markeringsverktøy og måleverktøy Innføring i Tool Chest Innføring i Markeringslisten Innføring i Studio [-]

Leading SAFe® [-]

This course is intended for students, architects, engineers, contractors, managers, and those who wish to make their creative ideas feasible and enhance collaboration with other stakeholders in simple to the most complex projects. Designed to provide you with a comprehensive understanding of the Revit Architecture software and the many opportunities it offers, covering not only the features and techniques of modeling but also providing you with theoretical knowledge merged with the correct design principles applied in the industry within different workflows. From designing a real-built construction, 3D modeling the asset to collaborating with other disciplines and generating plans, sections, facades, and perspectives, you will gain hands-on practical experience, tips, and tricks from our expert instructors. Moreover, the education covers schedules and quantity take-offs, preparing sheets for printing, and how to apply relevant NTI TOOLS to enhance your designs. With our flexible and interactive learning options, you can also progress at your own pace and revisit course materials as needed. This course is a unique combination of theoretical review and practical exercises, ensuring that you gain both the knowledge and skills necessary to succeed in your carrier and your coming projects. Completing the course will provide you with a certificate to formally support your acquired skills.   Objective Finalizing the training, you will be able to confidently sketch and design projects in Revit and understand the BIM processes, setting you on a path towards creating professional-quality designs. Introduction to Building Information Modelling (BIM), the past, the present, the future processes - Understanding the information management processes - Understanding the pillars of BIM and the importance of tools within processes - Creating the 3D digital model of an asset with the right information - Communication, collaboration, and coordination with all discipline models - Using the embedded information for different workflows and analysis - Generation of plans - Schedules and Quantity take-off - Preparing drawings for printing and communication to stakeholders The course is a combination of theoretical examination and practical exercises. It can also be Customized, and personalized ensuring the curriculum suits the needs of individuals, companies, or project teams. [-]

Introduksjon Kurset gir deg en grunnleggende innføring i ITIL® beste praksis rammeverket versjon 4. Kurset gir innføring i basis konseptet, prinsippene, prosessene og funksjonene som er definert som beste praksis. Kurset vil også forberede deg til å gjennomføre ITIL Foundation sertifiseringtest. ForutsetningerIngen forutsetninger for å delta på kurset.Faglige kvalifikasjonKurset leder frem til ITIL Foundation Certificate.(Sertifiseringstest gjennomføres på siste kursdag)KursdokumentasjonOmfattende kursdokumentasjon som inneholder Kurskompendium, ITIL Pocket Guide, Norsk-Engelsk ordliste, 115 testoppgaver.SertifiseringInkludert i kursprisen er ITIL Fondation eksamen EXIN.Eksamen gjennomføres på ettermiddagen dag 3 - den avholdes skriftlig og er på engelsk.Eksamensformen er multiple choice - 40 spørsmål skal besvares, og du består ved 65% korrekte svar (dvs 26 av 40 spørsmål). Deltakerne har 1 time og 15 minutter til rådighet på eksamen. ITIL® is a registered trade mark of AXELOS Limited.   Kursinnhold Service Management as a practise • Innføring i IT Service Management• BESTE PRAKSIS: Forstå hva og hvorfor• Tjenesteorientering The Service Lifecycle Innføring i alle fasene av livssyklusen - målene og forretningsverdien for hver fase.• Service Strategy• Service Design• Service Transition• Service Operations• Continual Service Improvement Service Management processes Hvordan de ulike prosessene spiller inn i livssyklusen. Vi diskuterer mål, omfang, aktiviteter, nøkkeltall og annet: Service Strategy • Strategy Management• Service Portfolio Management• Demand Management• Financial Management Service Design • Design Coordination• Service Catalogue Management• Service Level Management• Supplier Management• Availability Management• Capacity Management• IT Service Continuity Management• Information Security Management Service Transition • Transition planning and support• Change Management• Service Asset and Configuration Management• Release and Deployment Management Service Operations • Event Management• Incident Management• Request Fulfilment• Problem Management Continual Service Operations • Continual Service Improvement model• The seven-step improvement process Continual Service Operations • Continual Service Improvement model• The seven-step improvement process Functions Forstå mål, organisasjonsstruktur, bemanning, nøkkeltall og sammenhenger i ulike funksjoner• Service Desk• Technical Management• Application Management• IT Operations Management (IT Operations Control, Facilities Management) Roles Ansvar og funksjon hos de to nøkkelrollene i Service Management og innblikk i øvrige roller.• RACI modell• Process owner• Service owner Technology and Architecture • Service Management Technology [-]

Jira Service Management Essentials (Cloud) [-]

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     [-]

Data genereres i stadig større mengder - av mennesker, av sensorer og av innebygde dataenheter. Mottak, behandling og analyse av store datamengder krever distribuerte teknologier og lagringsformater. Big Data er blitt et fellesbegrep på disse teknologiene og dataene de behandler. Det er i dag forretningskritisk innenfor flere og flere bransjer å kunne håndtere Big Data. Men hvor skal man begynne? Kursinnhold Hvordan defineres Big Data? Hvilke problemstillinger kan løses med Big Data Hvilke Big Data teknologier finnes og hvilke bør vi satse på? Hva er hovedutfordringene med å ta i bruk Big Data? Kurset gjennomføres som en serie foredrag med rom for spørsmål og utdypninger innen hvert emne. De mest brukte teknologiene innen Big Data lagring, datahåndtering og analyse blir gjennomgått og vurdert, inkludert Hadoop, Spark, Hive, HBase, Cassandra, Kafka, MongoDB og en rekke andre. [-]

Through a combination of presentations, demos, and hands-on labs, participants learn how to use GCP services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications. Objectives This course teaches participants the following skills: Use best practices for application development Choose the appropriate data storage option for application data Implement federated identity management Develop loosely coupled application components or microservices Integrate application components and data sources Debug, trace, and monitor applications Perform repeatable deployments with containers and deployment services Choose the appropriate application runtime environment; use Google Container Engine as a runtime environment and later switch to a no-ops solution with Google App Engine Flex All courses will be delivered in partnership with ROI Training, Google Cloud Premier Partner, using a Google Authorized Trainer. Course Outline Module 1: Best Practices for Application Development -Code and environment management-Design and development of secure, scalable, reliable, loosely coupled application components and microservices-Continuous integration and delivery-Re-architecting applications for the cloud Module 2: Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK -How to set up and use Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK-Lab: Set up Google Client Libraries, Google Cloud SDK, and Firebase SDK on a Linux instance and set up application credentials Module 3: Overview of Data Storage Options -Overview of options to store application data-Use cases for Google Cloud Storage, Google Cloud Datastore, Cloud Bigtable, Google Cloud SQL, and Cloud Spanner Module 4: Best Practices for Using Cloud Datastore -Best practices related to the following:-Queries-Built-in and composite indexes-Inserting and deleting data (batch operations)-Transactions-Error handling-Bulk-loading data into Cloud Datastore by using Google Cloud Dataflow-Lab: Store application data in Cloud Datastore Module 5: Performing Operations on Buckets and Objects -Operations that can be performed on buckets and objects-Consistency model-Error handling Module 6: Best Practices for Using Cloud Storage -Naming buckets for static websites and other uses-Naming objects (from an access distribution perspective)-Performance considerations-Setting up and debugging a CORS configuration on a bucket-Lab: Store files in Cloud Storage Module 7: Handling Authentication and Authorization -Cloud Identity and Access Management (IAM) roles and service accounts-User authentication by using Firebase Authentication-User authentication and authorization by using Cloud Identity-Aware Proxy-Lab: Authenticate users by using Firebase Authentication Module 8: Using Google Cloud Pub/Sub to Integrate Components of Your Application -Topics, publishers, and subscribers-Pull and push subscriptions-Use cases for Cloud Pub/Sub-Lab: Develop a backend service to process messages in a message queue Module 9: Adding Intelligence to Your Application -Overview of pre-trained machine learning APIs such as Cloud Vision API and Cloud Natural Language Processing API Module 10: Using Cloud Functions for Event-Driven Processing -Key concepts such as triggers, background functions, HTTP functions-Use cases-Developing and deploying functions-Logging, error reporting, and monitoring Module 11: Managing APIs with Google Cloud Endpoints -Open API deployment configuration-Lab: Deploy an API for your application Module 12: Deploying an Application by Using Google Cloud Build, Google Cloud Container Registry, and Google Cloud Deployment Manager -Creating and storing container images-Repeatable deployments with deployment configuration and templates-Lab: Use Deployment Manager to deploy a web application into Google App Engine flexible environment test and production environments Module 13: Execution Environments for Your Application -Considerations for choosing an execution environment for your application or service:-Google Compute Engine-Kubernetes Engine-App Engine flexible environment-Cloud Functions-Cloud Dataflow-Lab: Deploying your application on App Engine flexible environment Module 14: Debugging, Monitoring, and Tuning Performance by Using Google Stackdriver -Stackdriver Debugger-Stackdriver Error Reporting-Lab: Debugging an application error by using Stackdriver Debugger and Error Reporting-Stackdriver Logging-Key concepts related to Stackdriver Trace and Stackdriver Monitoring.-Lab: Use Stackdriver Monitoring and Stackdriver Trace to trace a request across services, observe, and optimize performance [-]

Vil du lære å tegne illustrasjoner og logoer til bruk i alle medier? Illustrator tegner vektorgrafikk som kan forstørres ubegrenset, uten å tape kvalitet og kan derfor brukes overalt. Adobe Illustrator er verktøyet for illustratører og grafiske designere, men også et program for deg som vil lage litt enklere illustrasjoner til internett, Power Point og Word. På kurset lærer du å ta utgangspunkt i enkle basisformer og kombinere dem til kompliserte figurer, slik at det blir det lett for alle å tegne. Hvorfor ta dette kurset: Du får en grundig innføring i programmet Du vil lære konkrete tegne- og designoppgaver Du vil lære å redigere/endre Illustrator-filer du mottar Du vil lære å lage illustrasjoner og logoer Du vil lære å lage grafikk for bruk på internett, lesebrett eller mobil Du vil lære effektive arbeidsmetoder Du får kontroll på tegninger med mange elementer og lag Du vil lære om fargebruk og klargjøring av filer for trykk og nett Dette lærer du: Arbeidsmiljøet i programmet Tegning med tegneverktøyene og ved å kombinere enkle grunnformer Redigering og transformering av objekter Innsetting av tekst og bilder Tekstbearbeiding Lage bannerannonser Bruk av farger og forløpninger Lag og gjennomsiktighet [-]

Kursinnhold Hva slags data kan brukes som grunnlag for et diagram Stolpediagram Sektordiagram Kombinert diagram Formatering av diagrammer Tips og triks Smarte løsninger Sparkline Hurtiganalyse Bruk av Excels diagrammer i andre Office-programmer [-]

ITIL® 4 Specialist: Monitor, Support and Fulfil [-]

Ønsker du kontroll på ressursbruken din? Planlegg med ressurser og få en oversikt over hvor mange ressurser du trenger til enhver tid. Du kan også få en veldig god oversikt over økonomien i prosjektet.  Webinaret varer i 2 timer og består av to økter à 45 min. Etter hver økt er det 10 min spørsmålsrunde. Mellom øktene er det 10 min pause.  Webinaret kan også spesialtilpasses og holdes bedriftsinternt kun for din bedrift.   Kursinnhold:   Hvilke typer ressurser har man tilgang på i Project Arbeidsressurser. Hvordan definere og bruke disse. Forskjell mellom generiske og personlige ressurser Materiellkostnader, hvordan benytte seg av dette i Project Hvordan sette opp kostnader   Ressursallokering i prosjektet Legge til, fjerne og endre ressurser Forskjellen mellom innsatsdreven og ikke innsatsdreven aktivitet Håndtere overallokeringer - hva skjer og hvordan få ressursplanlegging på plass   3 gode grunner til å delta 1. Få en oversikt over ressursbruk 2. Planlegg for bedre ressursbruk 3. Du får kontroll på utgiftene i prosjektet ditt   [-]

Throughout four years of teaching my High-Performance Java Persistence course, I came to realize that there is so much Java developers can learn about the latest SQL features introduced by Oracle, SQL Server, PostgreSQL, or MySQL.This training spans over the course of 2 days and covers the Top 4 relational database systems: Oracle, SQL Server, PostgreSQL, and MySQL.From execution plans to the best way to paginate data, this training explains lesser-known techniques such as LATERAL JOIN, CROSS APPLY, as well as Derived Tables, Common Table Expressions, recursive queries, and the amazing Window Functions, PIVOT, or UPSERT statements.Last but not least, we are going to learn that, not only modern databases support JSON column types, but you can combine JSON structures with the traditional relational ones, therefore getting the best of both worlds.All examples are inspired by real-life scenarios, and they come in a GitHub repository for which attendees have exclusive and unlimited time access.At the end of these two days of training, the attendees will be better prepared to solve various data-intensive tasks using all these awesome SQL features that have been over the past 20 years.Agenda  Day 1Introduction - 1h 30m    - Beyond SQL:92    - SQL Parsing    - SQL Operation Order    - TOP-N queries    - OFFSET pagination    - Keyset PaginationSubqueries - 1h 15m    - EXISTS and NOT EXISTS    - IN and NOT IN    - ANY and ALL    - INSERT with subqueries    - Aggregation with subqueries   Joins - 1h 15m    - CROSS JOIN    - INNER and LEFT/RIGHT OUTER JOIN    - FULL OUTER JOIN    - NATURAL JOIN    - LATERAL JOIN and CROSS APPLYDay 2Window Functions - 1h 30m    - Analytics queries and window frame processing    - ROW_NUMBER, RANK, and DENSE_RANK    - FIRST_VALUE, LAST_VALUE, LEAD and LAG    - CUME_DIST and PERCENT_RANK    - PERCENTILE_DISC and PERCENTILE_CONTDerived Tables, CTE, Hierarchical Queries - 1h 30m    - Derived Tables    - CTE (Common Table Expressions)    - Recursive CTE    - Hierarchical queries   PIVOT, UNPIVOT, FILTER, and CASE - 1h    - CASE Expressions    - PostgreSQL FILTER Expressions    - PIVOT    - UNPIVOTDay 3UPSERT and MERGE - 30m- MERGE statements- UPSERT statements   JSON processing - 1h 30m    - Schemaless data structures and JSON    - JSON queries    - EAV Model   Transactions and Concurrency Control - 2h    - ACID, 2PL, MVCC    - Isolation Levels and anomalies    - Pessimistic and optimistic locking    - SKIP_LOCKED, NOWAIT [-]

Dette kurset tilbys som bedriftsinternt kurs   Kursinstruktør Lloyd Roden Loyd har over 30 års er faring fra IT-bransjen. Han har jobbet som utvikler, ledet en uavhengig test gruppe innenfor et programvarehus og har jobbet 10 år i  UK-baserte Grove Consultants som konsulent/partner. I 2011 startet han eget konsulentselskap med software testing som spesialfelt. Lloyd har holdt foredrag på konferanser som STAREAST, STARWEST, Eurostar, AsiaSTAR, Software Test Automation, Test Kongressen, og Unicom m.fl.   Lloyd Rodens verdier:"Jeg ønsker at arbeidet som jeg gjør, enten det er i form av rådgivning eller opplæring, må være relevant, praktisk og må gjøre en forskjell for den enkelte samt organisasjonen. Det er viktig for meg at deltakerne på mine kurs forbedrer sine ferdigheter i softwaretesting, og at dette til slutt vil gjenspeile seg i den forbedrede kvaliteten på produktene som leveres av organisasjonen."   Kursinnhold WorkshopThis workshop briefly explains the principles of Agile and how this differs from traditional lifecycle models. This workshop will explain the fundamentals of Scrum and how testing can effectively contribute to this software development approach. Learn the core components of Scrum and how testing can be involved in reviewing the product-backlog and how to be actively involved in the day-to-day sprints. This workshop also discusses how testing and test management need to adapt for maximum effectiveness within a Scrum project, how Exploratory Testing is the best approach to use and why collaboration is the key to the project's success. This workshop will be a practical session showing how scrum boards work, how to use planning poker and how to create burn-down charts. Learn how to use automation powerfully within an Agile project and how this would differ from the traditional lifecycle.    [-]