Oversikt over relevante kurs innen Kurs i programvare og applikasjoner i Strandebarm

AZ-400: Designing and Implementing Microsoft DevOps solutions [-]

Elasticsearch [-]

AZ-700: Designing and Implementing Microsoft Azure Networking Solutions [-]

  Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: Generelt gode IT-kunnskaper. Bør ha kjennskap til bruk av Windows server og fordel med MS SQL server. Kjennskap til AD, RDP og DNS. Innleveringer: 8 obligatoriske øvinger Personlig veileder: ja Vurderingsform: 2 dagers praktisk hjemmeeskamen med både teoretiske og praktiske oppgaver. Ansvarlig: Stein Meisingseth Eksamensdato: 17.12.13 / 20.05.14         Læremål: KUNNSKAPER:Kandidaten skal:- kjenne til fordelen med å ta i bruk Citrix XenApp for en bedrift/organisasjon- kunne gjøre rede for hvordan Citrix XenApp brukes som publiseringsplattform- kunne beskrive Citrix XenApp brukes for å rulle ut applikasjoner- kunne gjøre rede for hvordan XenApp kan tas i bruk som applikasjonsvirtualisering på klientside (streaming) og på serverside (publishing)- kunne oppnå optimal applikasjonsytelse og fleksible leveransemuligheter FERDIGHETER:Kandidaten skal:- kunne installere Citrix XenApp- kunne sette opp administrativ konfigurasjon- kunne sette opp og publisere applikasjoner, innhold og desktops for brukere- kunne konfigurere applikasjoner for streaming til servere og til desktops- kunne vurdere hvilken sikkerhet som kreves- kunne sette opp og konfigurere overvåkning- kunne konfigurere og bruke et administrativt grensesnitt GENERELL KOMPETANSEKandidaten:- har kompetanse til selvstendig både å formidle og å ta i bruk sine kunnskaper og ferdigheter i en bedrift som vil bruke Citrix XenApp- kan i et forklare og gjøre bruk av sin kunnskap for bruk av Citrix XenApp Innhold:- introduksjon til Citrix XenApp - installasjon av Citrix XenApp (6.5) - praktisk bruk - konfigurasjon - bruk av Web Interface - publisere applikasjoner og innhold - streame applikasjoner - sette opp restriksjoner - konfigurere lastbalansering - maksimere brukeropplevelse - bruke av skrivere - sikkerhet - overvåkningLes mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag Citrix XenApp 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.   [-]

Etter den fantastiske suksessen med Premiere Pro-klassen vår for nybegynnere, bestemte vi oss for å lage en oppfølging. Grundig produsert med tanke på en morsom og engasjerende læringsopplevelse. Kursmål Du vil lære alle funksjonene i Adobe Premiere Pro som lar deg gjøre følgende: Ulike redigeringsarbeidsflytteknikker Avanserte innstillinger og alternativer Jobber med snarveier Bildetekster og undertekster Lag egendefinerte forhåndsinnstillinger og maler Overgangsteknikker Stabiliser og reverser bevegelsesklipp Tidsomlegging Maskering og sporing Fargekorrigering og gradering Fikser og forbedrer lyd Arbeid med kodeker og gjengivelsesalternativer [-]

Fleksible kurs for fremtiden Ny kunnskap skal gi umiddelbar effekt, og samtidig være holdbar og bærekraftig på lang sikt. NTI AS har 30 års erfaring innen kurs og kompetanseheving, og utdanner årlig rundt 10.000 personer i Nord Europa innen CAD, BIM, industri, design og konstruksjon.   3ds Max grunnkurs   Lag fotorealistiske presentasjoner av dine designløsninger! Her er et utvalg av temaene du vil lære på kurset: Grunnleggende funksjoner – Transformationer vha. move, rotate og scale Link til og import av DWG- og DXF-filer Lyssetning med standard lys Rendering med Scanline renderen og Mental Ray – Basics Editering av 2D- og 3D-geometri Dette kurset er tilpasset for arkitekter, ingeniører, designere og visualiseringseksperter, og gir en introduksjon til design og visualisering i 3ds MAX. Kurset vil gjøre deg i stand til å arbeide med lys, materialer og kamera i eksisterende 3D CAD/BIM-modeller.   Dette er et populært kurs, meld deg på nå!   Tilpassete kurs for bedrifterVi vil at kundene våre skal være best på det de gjør - hele tiden.  Derfor tenker vi langsiktig om kompetanseutvikling og ser regelmessig kunnskapsløft som en naturlig del av en virksomhet. Vårt kurskonsept bygger på et moderne sett av ulike læringsmiljøer, som gjør det enkelt å finne riktig løsning uansett behov. Ta kontakt med oss på telefon 483 12 300, epost: salg@nticad.no eller les mer på www.nticad.no [-]

Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: Windows server 2008/2012 - god kjennskap om Windows server Innleveringer: Øvinger: 8 av må være godkjent. Personlig veileder: ja Vurderingsform: Eksamen blir arrangert som 2 dagers hjemmeeksamen (start kl 09.00 og innlevering kl 15.00 dagen etter). Hver student får tildelt et virtuelt område. Det skal også leveres en skriftelig begrunnelse for de valg som er foretatt. Hjemmeeksamen, individuell, 2 dager, 0 Ansvarlig: Stein Meisingseth Eksamensdato: 10.12.13 / 13.05.14         Læremål: KUNNSKAPER:Kandidaten:- har innsikt i drift av nettverk basert på Windows Server, programvaredistribusjon og kjenner til hvilke verktøy som kan brukes for administrasjon av virtuelle maskiner og nettverk- kan forklare systemer som kan benyttes til overvåkning og vedlikehold FERDIGHETER:Kandidaten kan:- installere og konfigurere System Center Configuration Manager 2012- automatisere manuelle operasjoner- sikre, oppdatere og overvåke IT-systemer GENERELL KOMPETANSE:Kandidaten har:- perspektiv og kompetanse i å velge riktige og tilpassete driftsløsninger- kompetanse i å formidle driftsterminologi, både muntlig og skriftlig Innhold:- Automatisering og sikring ved hjelp av System Center Cooperation Manager 2012 (SCCM 2012) - Applikasjonsutrulling - Operativ System utrulling - Klient tilstands-monitorering - Programvare oppdateringer - Sikkerhetsbeskyttelse vha Endpoint ProtectionLes mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag Microsoft System Center i overvåkning og drift 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.    [-]

  SketchUp for nybegynnere   Om kurset Vil du lære 3D-modellering fra grunnen av? Dette kurset gir deg en grundig og praktisk introduksjon til SketchUp for desktop, et av verdens mest populære verktøy for arkitekter, designere, ingeniører og kreative fagfolk. Du trenger ingen forkunnskaper – vi starter med det helt grunnleggende og bygger gradvis opp til mer avanserte funksjoner. Instruktør Espen Faugstad tar deg gjennom alt fra organiseringen av brukergrensesnittet til navigasjon med Orbit, Pan og Zoom. Du lærer hvordan ulike visninger og perspektiver påvirker modellene dine, hvordan du tegner i både 2D og 3D, og hvordan du manipulerer figurer med verktøy som Push/Pull og Rotate. Videre ser vi på måling, merking, materialbruk, teksturer og hvordan du organiserer komplekse prosjekter. Kurset avsluttes med hvordan du presenterer ferdige modeller på en profesjonell måte. Dette kurset passer perfekt for deg som vil ha en praktisk, inspirerende og lettforståelig inngang til 3D-modellering.   Hva du lærer Gjennom 57 leksjoner og over 3 timer med innhold får du en komplett innføring i SketchUp. Etter kurset vil du kunne tegne, tilpasse og presentere egne modeller – fra enkle ideer til ferdige prosjekter.   Kursholder Espen Faugstad er en av Norges mest erfarne kursholdere innen digital kompetanse. Som gründer av Utdannet.no har han utviklet mer enn 160 nettkurs og hjulpet titusenvis av deltakere med å mestre alt fra Google Ads og SEO til Adobe-programmer og tekniske verktøy. Med over 1,5 millioner videoavspillinger er han kjent for å gjøre læring både praktisk, engasjerende og tilgjengelig for alle.   I kurset lærer du: Forstå og bruke SketchUp-grensesnittet effektivt Navigere med Orbit, Pan og Zoom Tegne i 2D og 3D med Line, Rectangle og Circle Manipulere figurer med Push/Pull og Rotate Endre visninger med Parallel Projection og Two-Point Perspective Organisere prosjekter og bruke komponenter Måle og merke modeller nøyaktig Bruke materialer og teksturer på modellene Presentere ferdige modeller på en profesjonell måte   [-]

ArchiMate® 3 Training Course Combined Foundation and Practitioner [-]

AZ-900: Introduction to Microsoft Azure [-]

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     [-]

You’ll get hands-on experience with building a Reactive application to stream data leveraging the newly available Reactive data types, Spring WebFlux, Spring Data and Rsocket. Besides that, we will work with some new tools which make dealing with errors and blocking APIs visible. Agenda Introduction Reactive Streams, Publisher/Subscriber types and Reactor types Getting familiar with Project Reactor Using Reactive RabbitMQ with Spring Using Rsocket as a streaming message protocol Using Spring WebFlux Functional configuration API for Spring WebFlux Using Reactive Types with Thymeleaf Using Spring Security Reactive Using Spring Data MongoDB to reactively stream data Using Spring Data R2DBC Using Reactive Redis with Spring What’s next?  Target audience This workshop is targeted at developers and architects who are new to reactive programming and want to learn how to use the Spring ecosystem to get started. Requierments Participants should have some prior knowledge about Java and the Spring Framework/Ecosystem. The workshop will be held in English. In order to participate, you will need: A laptop with at enough spare disk space and RAM Java, Maven and your favourite IDE installed (Eclipse, Spring STS, IntelliJ, Netbeans) Docker for some demos [-]

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

SFWIPF: Fundamentals of Cisco Firewall Threat Defense and Intrusion Prevention [-]

AZ-204: Developing Solutions for Microsoft Azure [-]