Oversikt over relevante kurs innen IT-kurs i Vestfold

Kurset fokuserer på integrering av forskjellige verdistrømmer og aktiviteter for å lage, levere og støtte IT-aktiverte produkter og tjenester, samtidig som den dekker støtte for praksis, metoder og verktøy. Kurset gir kandidatene forståelse for tjenestekvalitet og forbedringsmetoder. E-læringskurset inneholder 18 timer med undervisning, og er delt inn i 8 moduler. Les mer om ITIL® 4 på AXELOS sine websider. Inkluderer: Tilgang til ITIL® 4 Specialist: Create, Deliver and Support e-læring (engelsk) i 12 måneder. ITIL® 4 Specialist: Create, Deliver and Support online voucher til sertifiseringstest.   ITIL®/PRINCE2®/MSP®/MoP® are registered trademarks of AXELOS Limited, used under permission of AXELOS Limited. All rights reserved. [-]

Kurset fokuserer på konvertering av etterspørsel til verdi via IT-relaterte tjenester. Modulen dekker sentrale emner som SLA-design, styring av flere leverandører, kommunikasjon, relasjonsstyring, CX- og UX-design, kartlegging av kunder og mer. E-læringskurset inneholder 18 timer med undervisning, og er delt inn i 8 moduler.   Les mer om ITIL® 4 på  AXELOS sine websider. [-]

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 - 50 spørsmål skal besvares, og du består ved 50% korrekte svar (dvs 25 av 50 spørsmål). Deltakerne har 40 minutter til rådighet på eksamen.  Ingen hjelpemidler er tillatt.   Nødvendige forkunnskaper: Ingen [-]

Modulen dekker bruk og effekt av Lean og agile arbeidsmåter, og hvordan dette kan utnyttes til fordel for organisasjonen. Kurset vil gi kandidatene en praktisk og strategisk metode for å planlegge og levere kontinuerlig forbedring med nødvendig smidighet.  E-læringskurset inneholder 18 timer med undervisning, og er delt inn i 12 moduler. Les mer om ITIL® 4 på AXELOS sine websider [-]

Man lærer å lage en konsistent presentasjon som gir et profesjonelt uttrykk. Lysbildeoverganger og animasjoner i lysbildene blir også gjennomgått, i tillegg til tips om hvordan man lager og gjennomfører en god presentasjon. Kursinnhold: Brukergrensesnittet Opprette nye presentasjoner (og fra mal) Valg av og endring av lysbildeoppsett Sette inn ulike typer objekter i lysbildene Lysbildeutforming og tema Lysbildeoverganger Lysbildeanimasjoner Konfigurering av lysbildekjøring Visninger Utskrift av presentasjon Lagring av presentasjon Tips for gjennomføring og innhold [-]

UTDANNINGSMÅLKursdeltakerne skal etter endt kurs kunne bruke AutoCAD til å: Opprette nye tegninger Gjøre forandringer i eksisterende tegninger Bruke og forstå de vanligste tegne- og editeringskommandoer Målsette og påføre tekst i en tegning Bruke og forstå lagoppbyggingen i AutoCAD Symbol- og blokkhåndtering Enkel layout/plotting Lagre tegninger KURSINNHOLD: Introduksjon av AutoCAD 2-dimensjonal tegning Zoom og skjermbehandlingsteknikk Redigeringsfunksjoner Målsettingsfunksjoner Laghåndtering Symbol- og blokkhåndtering Hente symboler og parametere (målsettings- og tekststiler, osv.) fra eksisterende tegninger Layout/plotting av tegninger Lagrings- og avslutningsrutiner [-]

Objectives This course teaches participants the following skills: Design and build data processing systems on Google Cloud Platform Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow Derive business insights from extremely large datasets using Google BigQuery Train, evaluate, and predict using machine learning models using Tensorflow and Cloud ML Leverage unstructured data using Spark and ML APIs on Cloud Dataproc Enable instant insights from streaming data   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 Data Engineering -Explore the role of a data engineer-Analyze data engineering challenges-Intro to BigQuery-Data Lakes and Data Warehouses-Demo: Federated Queries with BigQuery-Transactional Databases vs Data Warehouses-Website Demo: Finding PII in your dataset with DLP API-Partner effectively with other data teams-Manage data access and governance-Build production-ready pipelines-Review GCP customer case study-Lab: Analyzing Data with BigQuery Module 2: Building a Data Lake -Introduction to Data Lakes-Data Storage and ETL options on GCP-Building a Data Lake using Cloud Storage-Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions-Securing Cloud Storage-Storing All Sorts of Data Types-Video Demo: Running federated queries on Parquet and ORC files in BigQuery-Cloud SQL as a relational Data Lake-Lab: Loading Taxi Data into Cloud SQL Module 3: Building a Data Warehouse -The modern data warehouse-Intro to BigQuery-Demo: Query TB+ of data in seconds-Getting Started-Loading Data-Video Demo: Querying Cloud SQL from BigQuery-Lab: Loading Data into BigQuery-Exploring Schemas-Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA-Schema Design-Nested and Repeated Fields-Demo: Nested and repeated fields in BigQuery-Lab: Working with JSON and Array data in BigQuery-Optimizing with Partitioning and Clustering-Demo: Partitioned and Clustered Tables in BigQuery-Preview: Transforming Batch and Streaming Data Module 4: Introduction to Building Batch Data Pipelines -EL, ELT, ETL-Quality considerations-How to carry out operations in BigQuery-Demo: ELT to improve data quality in BigQuery-Shortcomings-ETL to solve data quality issues Module 5: Executing Spark on Cloud Dataproc -The Hadoop ecosystem-Running Hadoop on Cloud Dataproc-GCS instead of HDFS-Optimizing Dataproc-Lab: Running Apache Spark jobs on Cloud Dataproc Module 6: Serverless Data Processing with Cloud Dataflow -Cloud Dataflow-Why customers value Dataflow-Dataflow Pipelines-Lab: A Simple Dataflow Pipeline (Python/Java)-Lab: MapReduce in Dataflow (Python/Java)-Lab: Side Inputs (Python/Java)-Dataflow Templates-Dataflow SQL Module 7: Manage Data Pipelines with Cloud Data Fusion and Cloud Composer -Building Batch Data Pipelines visually with Cloud Data Fusion-Components-UI Overview-Building a Pipeline-Exploring Data using Wrangler-Lab: Building and executing a pipeline graph in Cloud Data Fusion-Orchestrating work between GCP services with Cloud Composer-Apache Airflow Environment-DAGs and Operators-Workflow Scheduling-Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, -Cloud Storage, and BigQuery-Monitoring and Logging-Lab: An Introduction to Cloud Composer Module 8: Introduction to Processing Streaming Data Processing Streaming Data Module 9: Serverless Messaging with Cloud Pub/Sub -Cloud Pub/Sub-Lab: Publish Streaming Data into Pub/Sub Module 10: Cloud Dataflow Streaming Features -Cloud Dataflow Streaming Features-Lab: Streaming Data Pipelines Module 11: High-Throughput BigQuery and Bigtable Streaming Features -BigQuery Streaming Features-Lab: Streaming Analytics and Dashboards-Cloud Bigtable-Lab: Streaming Data Pipelines into Bigtable Module 12: Advanced BigQuery Functionality and Performance -Analytic Window Functions-Using With Clauses-GIS Functions-Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz-Performance Considerations-Lab: Optimizing your BigQuery Queries for Performance-Optional Lab: Creating Date-Partitioned Tables in BigQuery Module 13: Introduction to Analytics and AI -What is AI?-From Ad-hoc Data Analysis to Data Driven Decisions-Options for ML models on GCP Module 14: Prebuilt ML model APIs for Unstructured Data -Unstructured Data is Hard-ML APIs for Enriching Data-Lab: Using the Natural Language API to Classify Unstructured Text Module 15: Big Data Analytics with Cloud AI Platform Notebooks -What’s a Notebook-BigQuery Magic and Ties to Pandas-Lab: BigQuery in Jupyter Labs on AI Platform Module 16: Production ML Pipelines with Kubeflow -Ways to do ML on GCP-Kubeflow-AI Hub-Lab: Running AI models on Kubeflow Module 17: Custom Model building with SQL in BigQuery ML -BigQuery ML for Quick Model Building-Demo: Train a model with BigQuery ML to predict NYC taxi fares-Supported Models-Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML-Lab Option 2: Movie Recommendations in BigQuery ML Module 18: Custom Model building with Cloud AutoML -Why Auto ML?-Auto ML Vision-Auto ML NLP-Auto ML Tables [-]

COURSE OVERVIEW . With extensive hands-on-labs, the class enables administrators and engineers to effectively manage data in an ever-changing technical and business environment, bringing tangible benefit to businesses in the digital world. This course is based on Veeam Availability Suite v11. TARGET AUDIENCE This course is suitable for anyone responsible for configuring, managing or supporting a Veeam Availability Suite v11 environment. COURSE OBJECTIVES After completing this course, attendees should be able to: Describe Veeam Availability Suite components usage scenarios and relevance to your environment. Effectively manage data availability in on-site, off-site, cloud and hybrid environments. Ensure both Recovery Time Objectives (RTOs) and Recovery Point Objectives (RPOs) are met. Configure Veeam Availability Suite to ensure data is protected effectively. Adapt with an organization’s evolving technical and business data protection needs. Ensure recovery is possible, effective, efficient, secure and compliant with business requirements. Provide visibility of the business data assets, reports and dashboards to monitor performance and risks. COURSE CONTENT Introduction Describe RTOs and RPOs, what they mean for your business, how to manage and monitor performance against them The 3-2-1 Rule and its importance in formulating a successful backup strategy Identify key Veeam Availability Suite components and describe their usage scenarios and deployment types Building Backup Capabilities Backup methods, the appropriate use cases and impact on underlying file systems Create, modify, optimize and delete backup jobs, including Agents and NAS Backup jobs. Explore different tools and methods to maximize environment performance Ensure efficiency by being able to select appropriate transport modes while being aware of the impact of various backup functions on the infrastructure Building Replication Capabilities Identify and describe the options available for replication and impacts of using them Create and modify replication jobs, outline considerations to ensure success Introduce the new Continuous Data Protection (CDP) policy Secondary Backups Simple vs. advanced backup copy jobs, how to create and modify them using best practices to ensure efficient recovery Discuss using tapes for backups Advanced Repository Capabilities Ensure repository scalability using a capability such as SOBR on-premises and off-site including integration with cloud storage Ensure compatibility with existing deduplication appliances Introduce the new hardened repository Protecting Data in the Cloud Review how Veeam can protect the data of a cloud native application Review how Veeam Cloud Connect enables you to take advantage of cloud services built on Veeam Review how Veeam can be used to protect your Office 365 data Restoring from Backup Ensure you have the confidence to use the correct restore tool at the right time for restoring VMs, bare metal and individual content such as files and folders Utilize Secure Restore to prevent the restoration of malware Describe how to use Staged Restore to comply with things like General Data Protection Regulation (GDPR) before releasing restores to production Identify, describe and utilize the different explores and instant recovery tools and features Recovery from Replica Identify and describe in detail, failover features and the appropriate usage Develop, prepare and test failover plans to ensure recovery Disaster recovery from replica to meet a variety of real-world recovery needs Testing Backup and Replication Testing backups and replicas to ensure you can recover, what you need, when you need to Configure and setup virtual sandbox environments based on backup, replicas and storage snapshots Veeam Backup Enterprise Manager and Veeam ONE Introduce the concept of monitoring your virtual, physical and cloud environments with Veeam Backup Enterprise Manager and Veeam ONE™ Configuration Backup Locate, migrate or restore backup configuration   TEST CERTIFICATION Completion of this course satisfies the prerequisite for taking the Veeam Certified Engineer (VMCE) 2021 exam. [-]

Python Programming [-]

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     [-]

Agenda Module 1: Creating Azure App Service Web Apps -Azure App Service core concepts-Creating an Azure App Service Web App-Configuring and Monitoring App Service apps-Scaling App Service apps-Azure App Service staging environments Module 2: Implement Azure functions -Azure Functions overview-Developing Azure Functions-Implement Durable Functions Module 3: Develop solutions that use blob storage -Azure Blob storage core concepts-Managing the Azure Blob storage lifecycle-Working with Azure Blob storage Module 4: Develop solutions that use Cosmos DB storage -Azure Cosmos DB overview-Azure Cosmos DB data structure-Working with Azure Cosmos DB resources and data Module 5: Implement IaaS solutions -Provisioning VMs in Azure-Create and deploy ARM templates-Create container images for solutions-Publish a container image to Azure Container Registry-Create and run container images in Azure Container Instances Module 6: Implement user authentication and authorization -Microsoft Identity Platform v2.0-Authentication using the Microsoft Authentication Library-Using Microsoft Graph-Authorizing data operations in Azure Storage Module 7: Implement secure cloud solutions -Manage keys, secrets, and certificates by using the KeyVault API-Implement Managed Identities for Azure resources-Secure app configuration data by using Azure App Configuration Module 8: Implement API Management -API Management overview-Defining policies for APIs-Securing your APIs Module 9: Develop App Service Logic Apps -Azure Logic Apps overview-Creating custom connectors for Logic Apps Module 10: Develop event-based solutions -Implement solutions that use Azure Event Grid-Implement solutions that use Azure Event Hubs-Implement solutions that use Azure Notification Hubs Module 11: Develop message-based solutions -Implement solutions that use Azure Service Bus-Implement solutions that use Azure Queue Storage queues Module 12: Monitor and optimize Azure solutions -Overview of monitoring in Azure-Instrument an app for monitoring-Analyzing and troubleshooting apps-Implement code that handles transient faults Module 13: Integrate caching and content delivery within solutions -Develop for Azure Cache for Redis-Develop for storage on CDNs [-]

React: Hooks, Concurrency, Performance, Maintainability & Tests [-]

Bruker du mye tid i Excel på å få gjort enkle arbeidsoppgaver? Kommer det til stadighet prosent og dato i celler hvor du vil ha vanlige tall? Blir en formel ødelagt når du flytter den? Er det vanskelig å lage det diagrammet du ønsker? Blir ikke utskriftene dine slik du ønsker? Dette er vanlige problemstillinger mange sliter med og som blir borte etter endt kurs! På kun 2 dager vil du mestre de vanligste formler og funksjoner du trenger i din arbeidsdag. Du lærer gode rutiner og hurtigtastene du trenger for å kunne arbeide raskt og effektivt. Du vil kunne bygge alt fra enkle til mer avanserte modeller og vil føle deg trygg på at modellen din virker og gir rett resultat. Du vil også få en rekke tips og triks du kan bruke i din arbeidsdag.  Alt du lærer får du repetert gjennom aktiv oppgaveløsning slik at du husker det du har lært når du kommer tilbake på jobb. Kursdokumentasjon, lunsj og pausemat er selvsagt inkludert!   Kursholderne har mer enn 20 års Excel erfaring som de gjerne deler med deg!   Kurset passer for deg med liten erfaring og som ønsker å lære Excel fra grunnen av. Kurset passer også for deg som er selvlært og som ønsker å jobbe mer effektivt. Meld deg på Excel-kurs allerede i dag og sikre deg plass!   Krav til forkunnskaper Grunnleggende kunnskaper i Windows.   Kursinnhold Redigering Merking Sletting Angre muligheter Flytting og kopiering Innsetting og sletting Formler Bruk av formler Autofyll Cellereferanser Formatering Hva er formatering? Kolonnebredde og radhøyde Tallformatering Skriftformatering Justering av celleinnhold Kantlinjer og fyllfarger Betinget formatering Funksjoner Bruk av funksjoner Summering Minst, størst, antall og gjennomsnitt Hvis-funksjonen Betinget summering Diagram Utforming av diagram Diagramtyper Flere regneark Arbeid med regneark Innsetting og sletting av regneark Flytting og kopiering av regneark Referering til andre regneark Enkle formler på tvers av ark Vindus håndtering Lister og tabeller Sortering Tabeller Filtrering Deling og frysing av vindu   [-]

Verden er i endring, det snakkes om den fjerde industrielle revolusjon og stadig flere ledere etterspør «radikal digital innovasjon». Men hva betyr det? Hvor skal vi begynne? Og hvordan kan vi sikre en plass med på toget inn i fremtiden, når vi lever i en virkelighet der teknologi utvikler seg eksponentielt og selskaper logaritmisk? Mange mener Design Thinking er svaret på det. Designtenking er et tankesett og en brukerorientert tilnærming til innovasjon. Metoden kombinerer designernes iterative tilnærming til tjeneste- og produktutvikling, med økonomenes analytiske og strategiske metoder for forretningsutvikling. Resultatet blir løsninger som har større sannsynlighet for å svare på brukerbehovene, er lønnsomme og i tråd med forretningsstrategi. Bli med på to dagers intensivt kurs i Design Thinking, lær å lage knallgode kundeopplevelser. Mål og gjennomføring Kurset er en blanding av praktisk workshop og foredrag med fokus på kundeopplevelse og de enorme digitale mulighetene vi har i dag. Med en «fail fast, fail cheap» tilnærming skal vi få kjenne på kroppen hva det betyr å ikke forelske seg i første idé, samarbeide på tvers av fagdisipliner og ikke minst ALLTID ha brukeren i fokus. Vi vil jobbe med å kartlegge kundens brukerbehov og jobber, og videre designe verdiforslag, tar valg, utforske gode forretningsmodell, teste, evaluerer og ikke minst LÆRE. Målet er at du skal forlate kurset med en verktøykasse du kan bruke på din egen arbeidsplass. Kurset inneholder: Forståelse av dagens digitale landskap Strategisk arbeid med innovasjon Design Thinking: teori og praktiske verktøy for design av verdifulle kundeopplevelser Kursleder: André Nordal Sylte, fagleder kundekonsept i DNB. Han jobber med å utforske og spesifisere prioriterte kundesegmenters viktigste behov, og designe verdiforslag til disse. Han har tidligere jobbet i Deloitte og Creuna. André er veldig kunnskapsrik og inspirerende, vi lover deg to meget lærerike og innholdsrike kvelder. Tid: 25. – 26. november kl. 17 – 21, matservering fra kl. 1630.   [-]

Informasjon i SharePoint er lagret i lister. Bli kjent med SharePoint liste-apper og hvordan du tilpasser eller lager egne med krav til utfylling, oppslag i andre lister, beregninger og validering av innfylte verdier. Webinaret varer i 2 timer og består av to økter à 45 min. Etter hver økt er det 10 min spørsmålsrunde. Mellom øktene er det 10 min pause. Webinaret kan også spesialtilpasses og holdes bedriftsinternt kun for din bedrift.   Kursinnhold:   Om Lister Bruk av listemalene i SharePoint Lag liste av Excel-bok Lag liste fra bunn av i SharePoint   Bruke lister Kolonnetyper og eksempler på bruk Opprette en liste og listeinnstillinger Krav om utfylling   Utvide lister Valider verdier bruker fyller inn Logg-kolonne Beregningskolonner og formler Presenter informasjon med visninger   Avanserte temaer Visnings-stiler Innføring i områdekolonner Veien videre til innholdstyper   3 gode grunner til å delta 1. Møt SharePoint sine liste-apper og lær å lage liste fra bunn eller fra eksisterende Excel-bok 2. Lær om kolonner som beregner eller sjekker inntastede verdier 3. Presenter informasjonen med visninger    [-]