#azure data factory v2

[learnomate]

Azure Data Factory Components

Azure Data Factory Components are as below:

Pipelines: The Workflow Container

A Pipeline in Azure Data Factory is a container that holds a set of activities meant to perform a specific task. Think of it as the blueprint for your data movement or transformation logic. Pipelines allow you to define the order of execution, configure dependencies, and reuse logic with parameters. Whether you’re ingesting raw files from a data lake, transforming them using Mapping Data Flows, or loading them into an Azure SQL Database or Synapse, the pipeline coordinates all the steps. As one of the key Azure Data Factory components, the pipeline provides centralized management and monitoring of the entire workflow.

Activities: The Operational Units

Activities are the actual tasks executed within a pipeline. Each activity performs a discrete function like copying data, transforming it, running stored procedures, or triggering notebooks in Databricks. Among the Azure Data Factory components, activities provide the processing logic. They come in multiple types:

Data Movement Activities – Copy Activity

Data Transformation Activities – Mapping Data Flow

Control Activities – If Condition, ForEach

External Activities – HDInsight, Azure ML, Databricks

This modular design allows engineers to handle everything from batch jobs to event-driven ETL pipelines efficiently.

Triggers: Automating Pipeline Execution

Triggers are another core part of the Azure Data Factory components. They define when a pipeline should execute. Triggers enable automation by launching pipelines based on time schedules, events, or manual inputs.

Types of triggers include:

Schedule Trigger – Executes at fixed times

Event-based Trigger – Responds to changes in data, such as a file drop

Manual Trigger – Initiated on-demand through the portal or API

Triggers remove the need for external schedulers and make ADF workflows truly serverless and dynamic.

How These Components Work Together

The synergy between pipelines, activities, and triggers defines the power of ADF. Triggers initiate pipelines, which in turn execute a sequence of activities. This trio of Azure Data Factory components provides a flexible, reusable, and fully managed framework to build complex data workflows across multiple data sources, destinations, and formats.

Conclusion

To summarize, Pipelines, Activities & Triggers are foundational Azure Data Factory components. Together, they form a powerful data orchestration engine that supports modern cloud-based data engineering. Mastering these elements enables engineers to build scalable, fault-tolerant, and automated data solutions. Whether you’re managing daily ingestion processes or building real-time data platforms, a solid understanding of these components is key to unlocking the full potential of Azure Data Factory.

At Learnomate Technologies, we don’t just teach tools, we train you with real-world, hands-on knowledge that sticks. Our Azure Data Engineering training program is designed to help you crack job interviews, build solid projects, and grow confidently in your cloud career.

Want to see how we teach? Hop over to our YouTube channel for bite-sized tutorials, student success stories, and technical deep-dives explained in simple English.

Ready to get certified and hired? Check out our Azure Data Engineering course page for full curriculum details, placement assistance, and batch schedules.

Curious about who’s behind the scenes? I’m Ankush Thavali, founder of Learnomate and your trainer for all things cloud and data. Let’s connect on LinkedIn—I regularly share practical insights, job alerts, and learning tips to keep you ahead of the curve.

And hey, if this article got your curiosity going…

Thanks for reading. Now it’s time to turn this knowledge into action. Happy learning and see you in class or in the next blog!

Happy Vibes!

ANKUSH

[atplblog]

Price: [price_with_discount] (as of [price_update_date] - Details) [ad_1] Leverage the power of Microsoft Azure Data Factory v2 to build hybrid data solutions Key Features Combine the power of Azure Data Factory v2 and SQL Server Integration Services Design and enhance performance and scalability of a modern ETL hybrid solution Interact with the loaded data in data warehouse and data lake using Power BI Book Description ETL is one of the essential techniques in data processing. Given data is everywhere, ETL will always be the vital process to handle data from different sources. Hands-On Data Warehousing with Azure Data Factory starts with the basic concepts of data warehousing and ETL process. You will learn how Azure Data Factory and SSIS can be used to understand the key components of an ETL solution. You will go through different services offered by Azure that can be used by ADF and SSIS, such as Azure Data Lake Analytics, Machine Learning and Databrick’s Spark with the help of practical examples. You will explore how to design and implement ETL hybrid solutions using different integration services with a step-by-step approach. Once you get to grips with all this, you will use Power BI to interact with data coming from different sources in order to reveal valuable insights. By the end of this book, you will not only learn how to build your own ETL solutions but also address the key challenges that are faced while building them. What you will learn Understand the key components of an ETL solution using Azure Data Factory and Integration Services Design the architecture of a modern ETL hybrid solution Implement ETL solutions for both on-premises and Azure data Improve the performance and scalability of your ETL solution Gain thorough knowledge of new capabilities and features added to Azure Data Factory and Integration Services Who this book is for This book is for you if you are a software professional who develops and implements ETL solutions using Microsoft SQL Server or Azure cloud. It will be an added advantage if you are a software engineer, DW/ETL architect, or ETL developer, and know how to create a new ETL implementation or enhance an existing one with ADF or SSIS. Table of Contents Azure Data Factory Getting Started with Our First Data Factory ADF and SSIS in PaaS Azure Data Lake Machine Learning on the Cloud Sparks with Databrick Power BI reports ASIN ‏ : ‎ B07DGJSPYK Publisher ‏ : ‎ Packt Publishing; 1st edition (31 May 2018) Language ‏ : ‎ English File size ‏ : ‎ 32536 KB Text-to-Speech ‏ : ‎ Enabled Screen Reader ‏ : ‎ Supported Enhanced typesetting ‏ : ‎ Enabled X-Ray ‏ : ‎ Not Enabled Word Wise ‏ : ‎ Not Enabled Print length ‏ : ‎ 371 pages [ad_2]

[fromdevcom]

Data is everywhere! Consider any industry, be it healthcare, finance, or education; there is a lot of information to be stored. Storing data can be done efficiently in the cloud using data storage services like Azure Blob store, Azure SQL Database, etc., or you can prefer keeping it on-premises. Whatever may be the case, a considerable amount of unstructured data is stored every day. Also, some enterprises will ingest data across both cloud & on-premises where there might be a need to combine data from both these sources to perform better analytics. What is Azure Data Factory? In the above situations, it becomes more important to transform and move data across different datastores, and this is when Azure Data Factory comes into play! Data Factory is a cloud-based ETL (Extract-Transform-load) and data integration service that allows you to automate data movement between various data stores and perform data transforming by creating pipelines. Where can I use it? Data Factory helps in the same way as any other traditional ETL tool, which helps extract raw data from one/multiple sources to transform & load them to any destination like a Data warehouse. But Data Factory differs from other ETL tools by performing these tasks without any code to be written. Now, don’t you agree that it is a solution that can perfectly fit in if you are looking to transform all your unstructured data into a structured one? Before getting into the concepts, here is a quick recap of Data Factory’s history The version we are using has improved and developed in numerous ways compared to the first version made generally available in 2015. Back then, you have to build a workflow only in Visual Studio. But the version 2 (public preview in 2017) was released to overshadow all the challenges in v1. With Data Factory v2, build code-free ETL processes where you can also leverage 90+ built-in connectors to acquire data from any data store of your choice. Top-level Concepts Now imagine, if you are moving a CSV file from a Blob Storage to a customer table in SQL database, then all the below-mentioned concepts will get involved, Here are the six essential components that you must know, Pipeline A pipeline is a logical grouping of activities that performs a unit of work. For example, a pipeline performs a series of tasks like ingesting data from Blob Storage, transforming it into meaningful data, and then writing it into the SQL Database. It involves mapping the activities in a sequential. So, you can automate the ETL process by creating any number of pipelines for a particular Data Factory. Activities These are the actions that get performed on the data in a pipeline. It includes three activities – Data Movement (Copy), Data transformation & control flow activities. But copying & transforming are the two core activities of Azure Data Factory. So here, copy data activity will get the CSV file from a Blob and loads it into the database, during which you can also convert the file formats. And transformation is mainly done with the help of a capability called Data Flows, which allows developing data transformation logic without using code. Datasets The datasets show what kind of data you are pulling from the data stores. So, it simply points to the data used in the activity as an input or output. Linked Services Linked Services helps to connect other resources with Data Factory, acting like connection strings. When considering the above example, Linked Service will serve as a definition of connectivity to the Blob storage & performs authorization. Similarly, the target (SQL Database) will also have a separate Linked Service. Triggers Triggers are to execute your pipelines without any manual intervention (i.e.,) it determines when a pipeline should get completed. There are three essential triggers in Data Factory, Schedule Trigger: A trigger that invokes a pipeline on a set schedule. You can specify both the date and time on which the trigger should initiate the pipeline run.

Tumbling Window Trigger: A trigger that operates on a periodic interval. Event-based Trigger: This trigger executes whenever an event occurs. For instance, when a file is uploaded or deleted in Blob Storage, the trigger will respond to that event. The triggers and pipelines will have a many-to-many relationship where multiple Triggers can kick off a single pipeline, or a single Trigger can also kick off various pipelines. But as an exception, Tumbling Window Triggers alone will have one-to-many relation with the pipelines. Integration Runtime Integration Runtime is the compute infrastructure used by the Data Factory to provide the following data integration capabilities (Data Flow, Data Movement, Activity dispatch, and SSIS package execution) across different networks. It has three types: Azure integration runtime: This is preferred when you want to copy and transform data between data stores in the cloud. Self-hosted integration runtime: Utilize this when you want to execute using On-premises data stores. Azure-SSIS integration runtime: It helps to execute SSIS packages through Data Factory. I hope that you have understood what a Data Factory is and how it works. We are now moving into its monitoring and managing aspects. Monitoring Azure Data Factory The Azure Monitor supports monitoring your pipeline runs, trigger runs, integration runtimes, and other various metrics. It has an interactive Dashboard where you will view the statistics of all the runs involved in your Data Factory. In addition, you get to create alerts on the metrics to get notified whenever something goes wrong in the Data Factory. Azure Monitor does offer all the necessities for monitoring and alerting, but the real-time business demands much more than that! Also, with the Azure Portal, it becomes difficult to manage when there are Data Factories with different pipelines, data sources across various Subscriptions, Regions, and Tenants. So, here is a third-party tool, Serverless360 that can help your operations and support team manage and monitor the Data Factory much more efficiently. Capabilities of Serverless360 Serverless360 will serve as a complete support tool for managing and monitoring your Azure resources. It helps to achieve application-level grouping and extensive monitoring features that are not available in the Azure portal. A glimpse of what Serverless360 can offer: An interactive and vivid dashboard for visualizing complex data metrics. Group all the siloed Azure resources using business application feature to achieve application-level monitoring. Get one consolidated monitoring report to know the status of all your Azure resources. Monitor the health status and get a report at regular intervals, say every 2 hours. Configure Threshold monitoring rules and get alerted whenever your resource is not in the expected state, and it can automatically correct it and bring it back to the active state. Monitor the resources on various metrics like canceled activity runs, failed pipeline runs, succeeded trigger runs, etc., without any additional cost. Conclusion In this blog, I gave an overview of one of the essential ETL Tools (Azure Data Factory) and its core features that you should be aware of if you plan to use it for your business. Along with that, I have also mentioned a third-party Azure support tool capable of reducing the pressure in managing and monitoring your resources. I hope you had a great time reading this article!

[idestrainings1]

Informatica Training - IDESTRAININGS

Fundamentals

Informatica training is a way to learn how to use the Informatica products. It covers the following topics:

What is Informatica?

What is ETL?

What is PowerCenter?

Introduction

Informatica is a data integration software and services company. Informatica provides solutions for data preparation, data quality, master data management, and analytics. The company's products are used in the telecommunications, financial services, healthcare, insurance and manufacturing industries.

The ETL (Extract Transform Load) process is used to extract data from multiple sources such as databases or files; transform that information into a standard format; load it into another database or file; then store it there permanently so that it can be accessed by other applications.

Workflow Monitor, Workflow Manager and Workflow Tasks are all part of PowerCenter. They are used to monitor jobs, manage jobs, and control task execution respectively. Designer Tool allows you to create your own transformations using Embedded Transformer Language (ETL).

PowerCenter Basics

PowerCenter is the ETL tool of the Informatica family. It's used to extract data from various sources and load it into various targets. You can create complex data transformation and data migration processes using PowerCenter. For example, you can implement a business process that loads customer master data into an enterprise knowledge base (EKB) or loads transactional data directly into analytics applications such as Tableau Server and Pentaho BI Server without loading it first in a separate staging area.

PowerCenter can also be used to create data integration solutions that integrate on-premises systems with cloud-based services by mapping both internal and external schemas, migrating data between on-premises systems and cloud databases from popular vendors such as Amazon Redshift, Google BigQuery, Snowflake Computing, Azure Data Factory Catalogs, Microsoft Dynamics 365 for Finance & Operations (formerly NAV), Salesforce Marketing Cloud Einstein Analytics Platform with Outbound Hubs (OMH), Facebook Graph API v2 and more

Designer Tool

The Informatica Designer Tool is used to create and edit jobs, mappings and transformations. It is used to design the transformations and mappings that are used to extract data from the source and load data into the target.

The informatica designer tool can be accessed through a web browser or by standalone client software.

Workflow Monitor

Workflow Monitor is the next generation tool for monitoring and managing workflows. It’s a web-based application that allows users to monitor and manage workflows using dashboards, reports, alerts, as well as additional functionality such as:

A dashboard view of all workflows in your organization

The ability to set up alerting for workflow issues

Access to an integrated repository of knowledge articles related to your organization’s business processes (more on this later)

Workflow Manager

Workflow Manager is a tool to manage the workflow of a process. It is used to create, edit and schedule workflows. Workflow Manager helps with the following tasks:

Create, edit and schedule workflows

Create new jobs for different business processes such as sending an email or completing data loads

Workflow Tasks

Workflow tasks are used to automate business processes. They help you create, maintain and execute automated workflows for your data.

Create - Use this task to create new records in the database or to create empty files in a directory on the file system

Modify - This task helps modify existing records in the database and add new values to fields/properties of objects

Delete - This task deletes records from the table based on their criteria and works with any object type (relational tables, file systems etc.)

Enrich your knowledge on the most popular ETL tool, Informatica. This course will help you in mastering the concepts of the sources and targets, mappings and extractions.

Informatica is a popular ETL tool used for extracting, transforming and loading data from one database to another. This course will help you in mastering the concepts of the sources and targets, mappings and extractions. You will also learn how to use external databases such as Oracle, MS SQL Server etc. along with Informatica PowerCenter 10.5

Conclusion

We hope that you enjoyed the contents of this course, and it would help you to gain knowledge on Informatica, which is very important in the field of Business Intelligence.

[bhavaniv]

Azure Data Factory Overview-Visualpath

What is Azure Data Factory? Azure Data Factory is Azure's cloud ETL provider for scale-out server less records integration and records transformation. It gives a code-loose UI for intuitive authoring and single-pane-of-glass tracking and management. You also can carry and shift current SSIS programs to Azure and run them with complete compatibility in ADF. Why Azure Data Factory? While you may use SSIS to reap maximum of the records integration desires for on-premises records, transferring records to/from the cloud gives some challenges: Job scheduling and orchestration. A SQL Server Agent offering, that's the maximum famous to cause records integration obligations isn't always to be had at the cloud. Although, there are some different alternatives, like SQL Agent offerings on SQL VM, Azure scheduler, and Azure Automation, for records motion obligations, task scheduling capabilities protected in ADF appears to be best. Furthermore, ADF lets in construct occasion primarily based totally records flows and dependencies. For example, records flows may be configured to begin while documents are deposited right into a sure folder. Security. ADF robotically encrypts records in-transit among on-premises and cloud sources. Azure data factory training in Hyderabad Scalability. ADF is designed to address large records volumes, way to its integrated parallelism and time-cutting capabilities and permit you to pass many gigabytes of records into the cloud in a be counted of some hours. Continuous integration and delivery. ADF integration with GitHub lets in you to develop, construct and robotically installation into Azure. Furthermore, the whole ADF configuration can be downloaded as an Azure ARM template and used to installation ADF in different environments (Test, QA and Production). For folks that are professional with Power Shell, ADF lets in you to create and installation all of its additives, the use of Power Shell. Minimal coding required. ADF configuration is primarily based totally on JSON documents and a brand new interface coming with ADF v2 lets in developing additives from the Azure Portal interactively, without a good deal coding (that's one cause why I love Microsoft technologies!). Azure Data Factory - Main Concepts Connectors or Linked Services. Linked offerings comprise configuration settings to sure statistics sources. This can also additionally consist of server/database call, report folder, credentials, etc. Depending on the character of the job, every statistics glide can also additionally have one or greater related offerings. Datasets. Datasets additionally comprise statistics supply configuration settings, however on a greater granular level. Datasets can comprise a desk call or report call, structure, etc. Each dataset refers to a sure related carrier and that related carrier determines the listing of viable dataset properties. Linked offerings and datasets are much like SSIS's statistics supply/vacation spot additives, like OLE DB Source, OLE DB Destination, besides SSIS supply/vacation spot additives comprises all of the connection precise records in a unmarried entity. Azure data factory training Activities. Activities constitute actions, those can be statistics motion, variations or manage glide actions. Activity configurations comprise settings like database query, saved method call, parameters, script location, etc. An interest can take 0 or greater enter datasets and convey one or greater output datasets. Although, ADF sports can be as compared to SSIS Data Flow Task additives (like Aggregate, Script component, etc.), SSIS has many additives for which ADF has no suit yet. Pipelines. Pipelines are logical groupings of sports. A statistics manufacturing facility will have one or greater pipelines and every pipeline might comprise one or greater sports. Using pipelines makes it an awful lot simpler to time table and reveal a couple of logically associated sports. Triggers. Triggers constitute scheduling configuration for pipelines and that they comprise configuration settings, like start/cease date, execution frequency, etc. Triggers aren't obligatory components of ADF implementation; they're required best in case you want pipelines to be done automatically, on a pre-described time table. Integration runtime. The Integration Runtime (IR) is the compute infrastructure utilized by ADF to offer statistics motion, compute abilities throughout special community environments. The essential runtime sorts are: Azure IR. Azure integration runtime affords a completely managed, server less compute in Azure and that is the carrier in the back of statistics motion sports in cloud. Azure data factory online training in Hyderabad Self-hosted IR. This carrier manages replica sports among a cloud statistics shops and a statistics shop in a personal community, in addition to transformation sports, like HDInsght Pig, Hive, and Spark. Azure-SSIS IR. SSIS IR is needed to natively execute SSIS packages For More Information Click Here Contact Us +91-9989971070

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[100% OFF] Azure Data Factory V2: Guide to Cloud Data Engineers(DP 200) What you Will learn ? Student will learn how to utilize Azure Data Factory ,a cloud based integration service to design and develop different pipelines to move data from on premise to Azure Cloud and inter cloud sources.

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Azure Data Factory V2: Guide to Cloud Data Engineers(DP 200)

[courseunity]

Azure Data Factory V2: Guide to Cloud Data Engineers(DP 200)

[shirivo]

ConocimientosDataPlatform += 1

¿De qué tratará? Este webinar es el primero de varios eventos virtuales donde se tratarán tópicos de la plataforma de datos que provee Microsoft en su nube: Azure. Entre estos están: Azure Data Factory v2, Azure Data Lake v2, Azure Synapse Analytics y otros recursos asociados a las arquitecturas de datos. Va dirigido a todos aquellos inmersos en el mundo de la tecnología y que quieren estar a la…

[takepara]

なるほどですね

(全 30 件)

1. 自己肯定感が低いと、褒められても、それを素直に受け入れることができない。

2. Azure Data Factory のドキュメント

3. ADF v2 の基本���なパイプラインの構築方法 at SE の雑記

4. 素直さの本質は「従順である」ではなく「無駄な反発をしない」こと。

5. ゲーム「楽譜で音ゲー」を Unity で作った話

6. Kubernetes のコンテキストの切り替えとかいろいろ

7. Kubernetes で高可用性のため、SQL Server のコンテナーを構成します。Configure a SQL Server container in Kubernetes for high availability

8. 時間に依存したテストへの取り組み

9. PSIRT Framework のご紹介

10. Performance implications of default struct equality in C#

11. 「リフォーム Rails app」というトークをしてきました

12. 強いインフラが組織を創る / #jtf2018

13. 最高のITエンジニアリングを支える守りと攻めの「設計技術」と「SRE」

14. ９市町障害「業者コケると…どうしようもない」

15. pipe2excel - CSVを文字列として安全にExcelで開く

16. 昔ながらの腹筋運動は無意味。専門家が勧める腹筋に効くエクササイズ

17. 【論文紹介】YOLOの論文を読んだので要点をまとめてみた

18. 卵落としの謎が解けるかな？―ヨッシー・エルラン

19. “ストレスホ��モン”のコルチゾール値を汗から測定する技術をスタンフォード大が開発

20. Pipe Dreams, part 3

21. Pipe Dreams, part 2

22. Pipe Dreams, part 1

23. Google、「Cloud Build」発表。ビルド／テスト／デプロイの実行をマネージドサービスで提供、1日あたり120時間まで無料。GitHubと提携も

24. Spyra One: The next generation of water guns.

25. 家のデッドスペース、コレでスッキリ＆有効活用です

26. コンビニがおにぎりを2つ多く仕入れる理由――顧客に“響く”データ活用のコツ

27. Netflix、本当にテレビ界を制する──エミー賞112部門ノミネートが告げた新時代の到来｜WIRED.jp

28. ZOZO定期便の真の狙い——サブスクリプションは単なる定額制サービスではない

29. テック大手の無料ランチが廃止に、なぜ？

30. Lepton - JPEGをロスレスで約22%軽減

[nishantrana]

Use Azure Data Factory V2 to load data into Dynamics 365

Use Azure Data Factory V2 to load data into Dynamics 365

Let us take a simple example where we will set up an Azure Data Factory instance and use Copy data activity to move data from the Azure SQL database to Dynamics 365.

Login to Azure Portal.

https://portal.azure.com

Search for Data factories

Create a new data factory instance

Once the deployment is successful, click on Go to resource

Inside the data factory click on Author & Monitor

Click on A…

[chanchalsinghal]

Free Course: Azure Data Factory V2: Guide to Cloud Data Engineers(DP 200)

Free Course: Azure Data Factory V2: Guide to Cloud Data Engineers(DP 200) on freshers1stop.in

Free Certification course:Azure Data Factory V2: Guide to Cloud Data Engineers(DP 200).

Learn Azure cloud based data integration tool Data Factory with hands on practical sessions to clear DP 200 and DP 201. After, you enroll for this , you can access it for lifetime. Just, you need to subscribe “Azure Data Factory V2: Guide to Cloud Data Engineers(DP 200)” course. 

August 18, 2020.

Requ…

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Free 100% Off Udemy Courses & Coupons: 18th August 2020

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UDEMY Azure Data Factory V2: Guide to Cloud Data Engineers(DP 200) for FREE

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[brondra]

Ignite 2018

afinita - pri load balanceru - stejny klient chodi na stejny server, možná i jako geo u cache apod? retence=napr u zaloh jak stare odmazavat webjob= WebJob is a feature of Azure App Service that enables you to run a program or script in the same context as a web app, background process, long running cqrs = Command Query Responsibility Segregation - zvláštní api/model na write a read, opak je crud webhook = obrácené api, zavolá clienta pokud se něco změní (dostane eventu)

docker - muze byt vice kontejneru v jedné appce, docker-compose.yml azure má nějaké container registry - tam se nahraje image (napr website) a ten se pak deployne někam např. app service, ale jiné image nemusí container registry - není veřejné narozdíl do dockeru, někdo jiný z teamu může si stáhnout

azure functions 2 - v GA

xamarin - ui je vlastne access k native api, ale v csharp projekty - shared, a special pro ios a android

ML.NET - je to framework pro machine learning

hosting SPA na azure storage static website - teoreticky hodně výhod, cachování, levné hosting

devops - automate everything you can

sponge - learn constantly multi-talented - few thinks amazing, rest good konverzace, teaching, presenting, positivity, control share everything

powershell - future, object based CLI to MS tech powerShell ISE - editor - uz je ve windows, ale ted VS code cmdlets - mini commands, hlavni cast, .net classy [console]::beep() function neco {  params{[int] Seconds} } pipeline - chain processing - output je input pro dalsi atd dir neco | Select-object modules - funkce dohromady, k tomu manifest

web single sign-on = fedaration - nekdo jiny se zaruci ze ja neco muzu a ze jsem to ja federation=trust data jsou na jednom miste SAML - security assertion markup language, jen web, složité API Security - header Authorization Basic (username, heslo zakodovane) OAuth2 - misto toho tokeny (vstupenka) openid connect - id token, access token - všechny platformy, code flow doporučené, jiné implicit flow? fido - fast identity online - abstrakce uh wtf, private public key pair per origin - nejde phising ldap. kerberos - jak to zapada?

httprepl - cli swagger

asm.js - polyfil pro web assembly, web assembly je native kod v browseru - napr .net = blazzor

svet bez hesel windows hello - windows login - face nebo fingerprint ms authenticator  - mobilni apka - matchnu vygenerovany kod FIDO2 - novy security standart - mam u sebe privatni klic, server posle neco (nonce), to zakryptuju privatnim pošlu zpátky - přes veřejný rozšifruje a má potvrzené, pak to samé s tokenem

cosmos db transakce - jen pouzitim stored procedure, single partition default index na vše, jde omezit při vytváření kolekce change feed - log of changes, in order trik jak dostat rychle document count - meta info o kolekci a naparsovat key-value cosmos - vyhody globalni distribuce, eventy, multimodel, pro big data asi

AKS - container - appka, orchestrator - komunikace mezi kontejnery, správa kontejnerů, healthchecks, updates AKS - orchestrator - nejčastější orchestrátor, standart, extensible, self healing představa něco jako cli nebo klient - řeknu jaké kontejnery, počet apod uvnitř se to nějak zařídí - api server, workers atd.. - je to managed kubernetes v azure, customer se stará jen o to co nasadit a kdy - ci/cd aks = azure kubernetes service

dev spaces - share aks cluster for dev (ne ci/cd), realne dependency (bez mock jiných service apod) extension do VS, pracuju lokalne, sync do azure, využíví namespace v aks (každý má svojí verzi service) - normalne frontned.com, já mám ondra.frontend.com a svojí api, pokud se zeptá na url tak se koukne jestli běží lokálně, když ne tak se zeptá team verze respektive je to celé v azure, ale je tam moje verze aplikace

kubernets - master (api server) - jeden Node - vice, VMs, v nem pods - containers, mají unikátní ip, networking - basic pro dev, advanced pro live nody a pody jsou interní věc, ven přes services helm - něco jako worker co se o to stará? jako docker-compose - vic imagu, help je pro aks??, arm template pro aks (skrpit jak postavit prostředí)

event notification patern - objednávky do fronty, ostatní systémy zpracují, co nejvíce info v eventě event sourcing - ukládat změny - inserted, updated, updated, updated, místo get update save, jde také udělat přes event, materialized view - spočítání stavu podle těch event, jdě dělat jednou za čas event grid - event routing

azure function - zip, z něj to spustí (vyhneme se problemu při update file by file), přes proměnné, nyní default ve 2.0 je startup, kde je možné připravit DI a funkce pak přes konstruktor už se dá kombinovat s kontejnery, aks atd durable functions - složitější věci s návaznostmi funkci, long running, local state, code-only, orchestartor function - vola activity function, má vnitrni stav, probudi se dela do prvni aktivity, tu spusti, spi, probudi se checkne jestli dobehla, pokracuje dal logic apps - design workflow, visual azure function runtime jde teoreticky hostovat na aks? v devops pro non .net jazyky potreba instalovat zvlast extension v2 - vice lang, .net core - bezi vsude, binding jako extension key vault - v 2008 ani preview funkce hosting - consuption = shared, app service - dedicated microservice=1 function app, jeden jazyk, jeden scale api management = gateway - na microservices, jde rozdělat na ruzné service azure storage tiery - premium (big data), hot (aplikace, levne transakce, drahy store), cold (backup, levny store, drahe transakce), archive (dlouhodobý archiv) - ruzné ceny/rychlosti, soft delete - po dobu retence je možnost obnovit smazané, data lifecycle management - automaticky presouvat data mezi tiery, konfigurace json

hybric cloud - integrace mezi on premise a cloudem - azure stack - azure které běží on premise někde use case: potřebujeme hodně rychle/jsme offline, vyhovění zákonům, model

místo new HttpClient, raději services.AddHttpCLient(addretry, addcircuitbreaker apod) a pak přes konstruktor, používá factory, používání Polly (retry apod..) - pro get, pro post - davat do queue

people led, technology enpowered

service fabric 3 varianty - standalone (on prem), azure (clustery vm na azure), mesh (serverless), nějaká json konfigurace zase, umí autoscale (trigger a mechanism json konfig), spíš hodně interní věc - běží na tom věci v azure, předchudce aks, jednoduší, proprietární, stateful, autoscale apod..

důležitá věc microservices - vlastní svoje data, nemají sdílenou db principy: async publish/subscr komunikace, healt checks, resilient (retry, circuit breaker), api gateway, orchestrator (scaleout, dev) architektura - pres api gateway na ruzne microservice (i ms mezi sebou) - ocelot orchestrator - kubernetes - dostane cluster VMs a ty si managuje helm = package manager pro kubernetes, dela deploy, helm chart = popis jak deploynout standartni

key valut - central pro všechny secrets, scalable security, aplikace musí mít MSI (nějaké identity - přes to se povolí přístup)

Application Insights - kusto language, azure monitor search in (kolekce) "neco" where neco >= ago(30d) sumarize makelist(eventId) by Computer - vraci Comuter a k tomu list eventId, nebo makeset umí funkce nějak let fce=(){...}; join kind=inner ... bla let promenna - datatable napr hodně data - evaluate autocluster_v2() - uděla grupy cca, podobně evaluate basket(0.01) pin to dashboard, vedle set alert

ai oriented architecture: program logic + ai, trend dostat tam ai nejak

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