Kurs i Gildeskål

Målsetting Hensikten med opplæringen er å gi traverskranførere en god teoretisk grunnopplæring i sikker bruk av traverskraner, slik at uhell og ulykker i forbindelse med løfteoperasjoner unngåsOpplæringen skal gi kandidatene god innsikt i prinsippene for traverskranens konstruksjon, virkemåte, vedlikehold og bruk. Praktisk forståelse for prinsippene for kranens konstruksjon, virkemåte, montering, kjøremåte, løftekapasitet og sikkerhetsutstyr mot overlastArbeidsoppgaver vil kunne kompensere for noen timer. Emneliste Innledning Modul 2.3 Løfteredskap (O-1.1) Krav til kranfører Bruksområder for traverskraner Ulykker med bro- og traverskraner Oppbygging av forskjellige typer bro- og traverskraner Hovedkomponenter på bro- og traverskraner Elektrisk og hydraulisk anlegg Sikkerhetsbrytere (Overlastbryter) Ståltau/blokk Sertifisering/dokumentasjon Kontroll og vedlikehold Arbeidsoppgaver Praktisk bruk av løfteredskap Øvingsoppgaver Kjøreteknikk Eksamen Avslutningsprøve Teori min. 30 spørsmålPraktisk prøve gjennomføres etter at kandidaten har i tråd med fadderavtale dokumentert min. 40 timer praksis Kompetansebevis / sertifikat Et kursbevis vil bli utstedt til hver kandidat som har gjennomført og bestått opplæringen.Kursbeviset vil inneholde informasjon om opplæringssted, kursinnhold, dato for gjennomføring, kandidatens navn og fødselsdato og være signert av daglig leder/kurs koordinator Pris er per deltaker og inkluderer alle kursdager, eksamen samt lunsj i vår kantine. [-]

I samarbeid med Norges bygg- og eiendomsforening og Fabrica Kulturminnetjenester AS.   Kulturminner er verdier for både publikum og eiere, men de kan også være utfordrende å utvikle på en god måte. I dette kurset vil vi formidle hva som skal til for å sikre prosesser som utløser merverdiene. Vi skal gi en innføring i begreper, regler og verdier, samt konsekvenser av fredning og annet formelt vern. Det blir en filmbefaring til Tollboden, Tolldirektoratets hovedkontor i Kvadraturen i Oslo. De har fungert som Tollsted siden 1800-tallet og har vært i bruk helt frem til 2016. Steinpakkhuset er fra 1850, mens Administrasjonsbygningen sto ferdig i 1896. [-]

ENCOR: Implementing and Operating Cisco Enterprise Network Core Technologies [-]

Engelskkurs A2 – 1-til-1 Privatklasser for litt viderekomne Har du allerede grunnleggende kunnskap i engelsk og ønsker å ta språket et steg videre? Vårt A2 privatkurs passer perfekt for deg som vil styrke språkkunnskapene i ditt eget tempo, med personlig oppfølging fra en erfaren lærer. Undervisningen er praktisk, strukturert og gir deg trygghet til å bruke språket mer aktivt i hverdagen. Hvem passer kurset for? Dette kurset passer for: Expats og innvandrere som vil forbedre sine engelskkunnskaper Studenter og jobbsøkere som trenger bedre språklig selvtillit Alle som ønsker å kommunisere bedre på engelsk i dagligliv og arbeid Kursmateriell Du får tilgang til moderne og praktiske læremidler: Hverdagsdialoger og lytteøvelser med høyere vanskelighetsgrad Enkle tekster og situasjoner som krever mer språklig presisjon Grammatikk og vokabular tilpasset A2-nivå Alt hjelper deg med å utvikle ferdigheter innen lesing, lytting, skriving og muntlig kommunikasjon på neste nivå. Kursbevis Etter fullført kurs får du et digitalt kursbevis som dokumenterer at du har nådd A2-nivå i henhold til CEFR. Fleksibel undervisning Vi tilbyr undervisning både fysisk og online – du velger det som passer best. Du kan starte når som helst og lære i ditt eget tempo, med full støtte underveis. Bygg videre på engelsken din – med trygg og effektiv én-til-én undervisning som gir deg resultater! [-]

  Certificate of Cloud Security Knowledge (CCSK)    CCSK er et bevis på at innehaveren forstår hovedkonseptene i de tre dokumentene CSA bruker som kunnskapsbase for CCSK: CSAs eget rammeverk for beste praksis innen skysikkerhet, «Security Guidance for Critical Areas of Focus in Cloud Computing, v4.0” ENISAs – det europeiske byrået for nettverks- og informasjonssikkerhet – white paper «Cloud Computing: Benefits, Risks and Recommendations for Information Security.» ENISA er EUs ekspertisesenter for internettsikkerhet i Europa. CSA har i en årrekke hatt et tett samarbeid med ENISA. «CSA Cloud Controls Matrix» er et verktøy for å evaluere skytjenester opp mot en lang rekke standarder og rammeverk, for enklere å forstå hvor godt den enkelte skytjeneste er egnet til å understøtte deg og din bedrift i deres forpliktelser. I tillegg til det offisielle materialet, får kursdeltakere fra kraftbransjen tilgang til Berigos egenutviklede materiale som utvider Cloud Controls Matrix til også å omfatte kraftberedskapsforskriften.   [-]

Lean Six Sigma Black Belt Course You will learn to use DMAIC to solve complex problems and lead improvement projects with cross-functional teams. You will learn to use data analysis and visualization for process improvement and product development. The course is a 4-day classroom course.   Learning Objectives: Use the DMAIC method to solve complex problems and lead larger improvement projects involving multiple departments. Utilize data analysis and visualization for process improvement and product development. Make fact-based decisions based on statistics and data analysis. Strengthen your leadership skills to drive improvement efforts within the company. Develop skills to successfully implement change. Coach Green Belt and project team participants.   Target Audience: This course is suitable for those who lead improvement programs, have operational or quality responsibility, work with process and product development, lead complex improvement projects involving multiple departments, and others who are focused on maintaining a holistic approach to improvement work.   Course Content: The course follows the DMAIC structure: Define: Identify and define process- or product-related problems to be improved. Set specific, measurable goals for improvement. Measure: Collect and analyze data to understand the current situation (use of descriptive statistics, histograms & control charts). Evaluate process performance with capability analysis and determine how to improve it. Use control charts for variation analysis to understand and quantify sources of variation. Evaluate measurement systems to ensure accurate measurements (repeatability, reproducibility, stability, sensitivity, and capability). Analyze: Prove root causes with graphical analyses: Pareto Box plot Scatter plot Correlation and regression Hypothesis testing Improve: Use Design of Experiments (DOE) to identify optimal process settings. Apply DOE in product development. Implement improvements. Control: Maintain improvements using control charts and continuous monitoring. Implement control plans to ensure that improvements are sustained.   Tools & Methods: Brainwriting Voice of the Customer (VOC) Requirement Trees Defining KPIs (Key Performance Indicators) Operational Definition Strategic Goal Deployment Process Walk / "Go to Gemba" Problem Statement Specific Goals Project Selection Project Charter Communication Plan Context Diagram High-Level Process Map (SIPOC) Process Variable Mapping Value Stream Analysis Data Analysis Descriptive Statistics Histogram Normal Distribution and Other Distributions Pareto Chart Boxplot SPC & Control Charts Capability Analysis Variation Analysis Measurement System Evaluation Scatterplot, Correlation, and Regression Design of Experiments (DOE) Hypothesis Testing Prioritization Matrix Some of the methods and tools are described at an introductory level, while others are covered in depth.   Instructor: The course instructor Sissel Pedersen Lundeby is an IASSC (International Association for Six Sigma Certification) accredited instructor (the only one in Norway as of August 2024): "This accreditation publicly 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."  Sissel holds a master's degree in chemical engineering from NTNU and has more than 19 years of experience in production and environmental technology. Her Lean Six Sigma training began in 2002, at an American company, where she became Black Belt certified in 2004. In 2017, she was also Black Belt certified through IASSC. Sissel has extensive experience in using Lean Six Sigma for improvements and focuses on achieving measurable results. The courses use practical, recognizable examples and present Lean Six Sigma in a simple and understandable way.    Feedback: "Inspiring, professionally skilled, makes a theoretical subject accessible to everyone." Espen Fjeld, Commercial Director at Berendsen "Highly competent and clear delivery. Fun and builds trust." Jon Sørensen, Production Manager at Berendsen "10/10, good at reaching everyone." Erlend Stene, Sales Manager at Berendsen "Clear and well-presented. Good at checking understanding and listening." Morten Bodding, Production Manager at Berendsen "Made a difference, engaged and skilled." Course Participant from EWOS "You are inspiring, positive, and skilled in your field." Course Participant from EWOS "I was very impressed with Sissel's Lean Six Sigma knowledge. She makes it easy to identify improvements and achieve results." Daryl Powell, Lean Manager, Kongsberg Maritime Subsea   Diploma and Certification: Diploma: To receive a diploma for successful completion, you must have attended at least 3 out of 4 course days. Certification: To become certified as a Lean Six Sigma Black Belt, the following requirements must be met: Attendance at a minimum of 3 out of the 4 course days. Completion of a Green Belt course with a certificate of completion. Completion of a Six Sigma improvement project within 12 months after the course ends. Participation in mandatory coaching throughout the Black Belt project. Mandatory coaching ensures that your project is executed in line with best practices and the Lean Six Sigma methodology. Through six 1-hour sessions with a senior consultant, you will receive support and guidance to: Select a project that is realistic and value-adding. Execute each phase of the project according to best practices. Gain confidence and competence in using Lean Six Sigma tools through practical application. The coaching sessions include: 1 hour for project selection. 1 hour for review after each DMAIC phase. It is sufficient to have a Green Belt certificate of completion. Participants do not need to complete both a Green Belt project and a Black Belt project to become Black Belt certified. The focus is on the successful completion and approval of the Black Belt project. Coaching fee: 19,950 NOK (includes six 1-hour sessions and project approval). This is an investment in your success, and our experience shows that participants who receive coaching achieve better results and execute their projects more effectively. [-]

DO180: Red Hat OpenShift Administration I: Operating a Production Cluster [-]

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.   AutoCAD plant 3D grunnkurs  Her er et utvalg av temaene du vil lære på kurset: Prosjektoppsetning og Modullinjer/net Design av stålkonstruksjoner Utstyr (opprettelse av utstyr og import av utstyr bl.a. fra Inventor) Rørdesign i 3D-modellen Redigering av stål, utstyr og rørtrekk Opprettelse av arrangementstegninger og rørisometritegninger  Uttrekk av mengdedata i listeform Kurset  gir  en innføring i systemets oppbygging med rørdesign i sentrum. Videre gjennomgås de enkelte modulene i henhold til følgende arbeidsflyt: P&ID. Integrert i løsningen er velkjente AutoCAD P&ID og vi tar utgangspunkt i et enkelt flytdiagram som representerer det skjematiske designet for minifabrikken vi skal modellere Stål/Struktur. 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   [-]

PRINCE2® 7 Practitioner  all english [-]

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.   Dynamo for Revit Her er et utvalg av temaene du vil lære på kurset: Intro til brukerflate og grunnleggende funksjoner Dynamo – Revit-interaksjon Parametrisk/Regelbasert Design Geometri i Dynamo Plassering av Revit-elementer Datauttrekk Opprettelse av Analytisk modell Skrive i Revit-parametre/nummerering Tilpasning av Revit-elementer Import og behandling av ekstern geometri Kjenner du til Grasshopper for Rhino og ønsker å komme videre med komplekse geometrier? I så fall er Dynamo en mulighet. Her kan regelbasert design settes opp med direkte integrasjon til Revit. Med Dynamo for Revit åpnes en verden med en hittil usett parametrisk tilgang til prosjektene. Med Dynamo som visuelt programmeringsverktøy kobles egne algoritmer sammen med Revits parametriske database, uansett om fokuset er formgivning, designoptimering, fabrikasjon eller automatisering. Dette, sammen med toveiskommunikasjonen mellom Dynamo og Revit, gjør kombinasjonen både sterk og unik.   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 [-]

TOEFL for Studier Dersom du planlegger å studere i utlandet, er TOEFL en av de mest anerkjente engelsktestene, spesielt blant universiteter i USA og Canada. De fleste utdanningsinstitusjoner krever en poengsum mellom 80 og 100 på en skala fra 0 til 120. Bachelorprogrammer godtar vanligvis poengsummer mellom 80 og 90, mens masterprogrammer ofte krever 100 eller høyere for opptak. TOEFL for Immigrasjon Selv om TOEFL ikke er like utbredt som IELTS innen immigrasjon, godtas testen fortsatt for enkelte visum- og oppholdsprogrammer – særlig i sammenheng med akademisk migrasjon, som for eksempel studentvisum. TOEFL for Arbeid TOEFL er også relevant for yrker som krever høyt nivå i engelsk, spesielt innen akademia og undervisning. Mange institusjoner, særlig forskningsmiljøer og universiteter, etterspør poengsummer mellom 90 og 100. Hvorfor velge TOEFL-forberedelseskurs ved NLS Norsk Språk Skole? Hos NLS Norsk Språk Skole tilbyr vi et profesjonelt utviklet TOEFL-forberedelseskurs som hjelper deg å nå dine mål, enten de er akademiske, profesjonelle eller immigrasjonsrelaterte. Målrettet trening i alle testdeler Kurset gir grundig forberedelse i alle fire delene av TOEFL-eksamenen: leseforståelse, skriftlig fremstilling, lytteforståelse og muntlig kommunikasjon. Skreddersydde strategier for suksess Våre erfarne lærere gir deg effektive strategier for å styrke din akademiske engelskkunnskap og møte TOEFLs krav med trygghet. Prøveeksamener og profesjonell tilbakemelding Du får tilgang til realistiske prøveeksamener og mottar detaljert tilbakemelding fra våre TOEFL-spesialister, slik at du kan måle fremgang og justere studieteknikken. Eksamen og kursbevis Etter fullført kurs mottar du et offisielt kursbevis fra NLS Norsk Språk Skole som dokumenterer antall undervisningstimer og hvilket engelsknivå du har oppnådd. Ta steget mot dine akademiske og profesjonelle mål – meld deg på vårt TOEFL-forberedelseskurs i dag! [-]

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     [-]

Microsoft 365 Enterprise Administrator Expert (MCE) - Boot Camp [-]

TOGAF® EA Course Combined [-]

MålsettingKurset gir en generell og relativt omfattende innføring i petroleumsregelverket, og hvordan det skal forstås og brukes EmnelisteInnledning Struktur av regelverket Ansvar og rollefordeling Arbeidstakermedvirkning Pliktsubjekt Påse plikt Rammeforskriften Styringsforskriften Innretningsforskriften Aktivitetsforskriften Teknisk og operasjonell forskrift Norsok R-003 AvslutningsprøveKompetansebevis / sertifikatEt kursbevis vil bli utstedt til hver kandidat som har gjennomført og bestått opplæringen.Kursbeviset vil inneholde informasjon om opplæringssted, kursinnhold, dato for gjennomføring, kandidatens navn og fødselsdato og være signert av daglig leder/kurs koordinator [-]