Azure Mastery: DP-100| AI-900| DP-900| DP-300| AI Chatbot

Gain a foundational overview of Azure AI fundamentals, covering AI workloads, machine learning on Azure, computer vision, and natural language processing with cognitive services.

Define artificial intelligence, distinguish narrow AI from general AI, and explore machine learning, natural language processing, and perception with examples like self-driving cars and real-time transcriptions.

Discover how a machine learning model powers predictions and patterns, from training and evaluation to deployment in Azure Machine Learning, with practical examples and exam context.

Discover the AI-900 exam requirements for Microsoft Azure AI Fundamentals, covering AI workloads like anomaly detection, computer vision, NLP, with a 700 passing score.

Explore common AI workloads in Azure, including prediction and demand forecasting, supervised learning, anomaly detection, computer vision, natural language processing, knowledge mining, and conversational AI.

Discover Microsoft's six guiding principles for responsible ai—fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability—and how unintended consequences arise in real-world ai deployments.

Promote fairness by examining AI decisions that affect groups, ensure reliability and safety in cases, and safeguard privacy and security in scenarios like emergency rooms, bank loans, and Tesla Autopilot.

Explain inclusiveness, transparency, and accountability in ai systems, promote accessibility for diverse users, and ensure governance and ongoing improvement to prevent bias in high-stakes decisions.

Explore microsoft's official ai principles, view individual principles, and review learn.microsoft.com guidelines for human ai interaction, including printable cards and practical examples.

Discover common machine learning types on Azure, including regression, binary and multi-class classification, and clustering, with examples like movie recommendations, car image recognition, happiness scoring, and spam detection.

Learn core machine learning concepts by defining features as input variables and labels as the target, and explore the Goldilocks effect to find right features with a life expectancy example.

Prepare large datasets and randomly split them into training and validation sets. Evaluate regression and classification models in Azure Machine Learning using metrics like mean squared error and confusion concepts.

Analyze confusion matrix outcomes: true positives, false positives, true negatives, and false negatives. Relate them to false positive rate and true positive rate, and to the area under the curve.

Explore no-code machine learning with Azure Machine Learning by using AutoML and the machine learning designer to prepare data, select features, train models, and evaluate results for production-ready predictions.

Create and configure an Azure machine learning workspace, set up storage, keys, and compute resources, access ml.azure.com designer, and explore automated ML to build no-code models.

Create a dataset from the web and use Azure AutoML to build a no-code model, configure regression with rmse, and deploy the trained model to a web service.

Explore Azure ML designer for no-code model building: drag data onto canvas, view histograms of columns, normalize features, exclude rows or columns, and train a linear regression model with evaluation.

Build a no-code ml model with ml designer, select and clean dataset columns, normalize features, split data into training and testing sets, and train and score a linear regression model.

Explore common computer vision workloads in Azure, including image classification, object detection with coordinate localization, semantic segmentation, optical character recognition, and facial detection/recognition, with options for pre-built or custom models.

Azure cognitive services umbrella, with a single endpoint and key, covering computer vision, custom vision, face service, and form recognizer for object detection, captions, text recognition, and content moderation.

Create and configure an Azure cognitive service, obtain the key and endpoint, and run a pre-built computer vision model in Python to describe images and identify objects and faces.

Create a custom vision resource and project, configure a classification task, upload images, tag negative samples, train the model, and evaluate predictions.

Discover natural language processing workloads in Azure and how they enable conversational AI with Azure Bot services. Explore key phrase extraction, entity recognition, sentiment analysis, speech-to-text, text-to-speech, and translation.

Explore Azure cognitive services for language processing, including text analytics (key phrases, entities, sentiment, language detection) and language understanding (utterances, entities, intents), plus speech and translation features for conversational ai.

Explore how conversational AI uses natural language understanding to respond via web chat, voice, and assistants, with Azure bot tools like Q&A Maker and Bot Framework.

Explore Azure conversational AI services, including Q&A Maker and the Azure Bot Service, to build no code and customized chatbots that leverage a knowledge base and cognitive features.

Conclude the Azure fundamentals course by preparing to schedule your exam on the Microsoft site, and enjoy lifetime access while studying a little more and revisiting videos.

Explore dp-900, Microsoft Azure data fundamentals, a low cost certification. Learn data concepts, relational and non-relational data, and analytics as a stepping stone to data engineer or database administrator roles.

Learn DP-900 exam requirements and where to access the official study guide; cover core data concepts, relational and non-relational Azure data, analytics workloads, data ingestion, and Power BI visualizations.

Describe core data concepts by comparing structured, unstructured, and semi-structured data, with examples from relational databases, Excel, storage blob containers, and xml, json, and yaml formats.

Identify the best data storage options by data type and business needs. Use relational databases for structured data, blob storage for unstructured files, and Cosmos DB for semi-structured workloads.

Describe common data workloads—oltp for day-to-day transactions, olap for analysis with cubes and dimensions, and data warehouses for reporting—along with Azure relational options and analytics services.

Explore the three core data roles: database administrator, data engineer, and data analyst—and how they manage design, data movement, and insights with security, backups, governance, and pipelines.

Learn the features of relational databases, including tables with rows and columns, schemas, primary keys, and indexes, and how views create virtual tables for secure joins.

Describe normalization in relational databases to reduce data redundancy and improve data integrity by using primary keys, referential integrity, and compact data designs.

Learn how normalization structures database design to improve integrity and reduce redundancy. Explore the first, second, and third normal forms, including primary keys, atomic columns, and compound keys.

Connect to an Azure SQL Server, explore sample tables in SQL Server Management Studio, and run select, where, and update queries on a cloud database.

Explore the fundamentals of structured query language, its standards, vendor variations, and the main SQL statement types—DDL, DML, and DCL—alongside common commands like create, alter, drop, and select.

Explore creating and using views in SQL Server Management Studio, turning a query into a five-column view called customer limited, and granting view permissions for security.

Explore creating, altering, and executing stored procedures with input parameters, using select statements to fetch filtered customer data and improve performance.

Learn how database indexes speed searches by sorting data and targeting columns like customer id, email, and first name, and how to create non-clustered indexes for efficient queries.

Explore non-relational databases and NoSQL, explain how they differ from table-based relational databases, and why they optimize for diverse sources and massive web-scale data such as JSON and XML.

Explore Azure relational database options across IaaS, PaaS, and SaaS models. Review SQL Server on a VM, Azure SQL Database, elastic databases with resource pools, Synapse Analytics, and managed instances.

Learn how to provision and deploy an Azure SQL database in the portal, including selecting a server, configuring pricing, networking, and connecting via SQL Server Management Studio.

Explore how ARM templates and Azure Resource Manager translate portal actions into JSON templates deployed via REST API, enabling parameterized deployments and scalable SQL servers and databases.

Discover how Azure SQL Database enforces security with firewall rules, public network access control, IP whitelisting, TLS version settings, and virtual networks.

Explore relational query tools in Azure, including the built-in query editor, Azure Data Studio, and SQL Server Management Studio, plus the cross-platform SQL command line.

Explore Azure relational database options—SQL on a VM, SQL managed instance, Azure SQL database, and Azure databases for MySQL, PostgreSQL, and MariaDB—covering compatibility and deployment choices.

Explore non-relational data types such as JSON documents, column family databases, key-value tables, graph databases, and time series with Azure Cosmos DB and related services.

Choose databases by scenario rather than a solution, balancing relational and non-relational options, normalization, and data integrity. Cosmos db and IoT hub enable real-time processing, streaming analytics, and scale.

Explore Azure non-relational databases led by Cosmos DB with graph, document, and table models, MongoDB API compatibility, configurable consistency, and global scale with a fast single-digit millisecond service level agreement.

Create an Azure Cosmos DB in the Azure portal, configure settings such as geo-redundancy, public endpoint, virtual network, backup policy, and encryption, and compare it with relational databases.

Explore Cosmos DB basics by creating a database and container, inserting JSON items, and running SQL-like queries to understand dynamic schema, access keys, and API usage.

Learn to deploy and manage Cosmos DB with arm templates, modify resources in a template, and use Azure CLI or PowerShell to implement infrastructure as code.

Explore cosmos db security by configuring networking to restrict public access, enabling selected networks or private access, managing read-write and read-only keys, and regenerating keys to revoke compromised access.

Learn how Cosmos DB enables geo-replication across global regions, creating read-only copies in East US and Northern Europe, and weighing the cost of multi-region replication and read-write setups.

Explore the modern data warehouse architecture, linking data sources, data factory, data lake, Databricks, Synapse Analytics, Analysis Services, and Power BI.

Leverage Azure Data Factory to orchestrate data ingestion and transformation for batch and streaming data, linking external sources to destinations with pipelines, activities, and triggers.

Master Power BI data visualization, covering paginated and interactive reports, plus dashboards. Learn to build paginated reports with Power BI Report Builder, publish to Power BI service, and enable drill-down.

Connect to data sources, build a data model with relational tables and joins in Power BI Desktop, then publish and share your reports through the content workflow.

Schedule your exam on the Microsoft website and review your notes before test day. Finish the course with best wishes for your exam and take care.

Explore DP-300's focus on relational databases and SQL Server administration in Azure, guiding you toward the Microsoft Certified Azure Database Administrator Associate through practical, hands-on exam preparation.

Explore the DP-300 exam requirements for Azure SQL solutions, covering plan and implement data platform resources, secure environments, monitor and automate, and plan high availability and disaster recovery.

Explore an Azure free account with 12 months of popular services, 40+ always free options, and a $200 credit for 30 days while practicing Cosmos DB account.

Explore Azure relational databases, including Azure SQL Database, SQL Server on a VM, and managed instance, plus open source options like MySQL, PostgreSQL, and MariaDB.

Explore deployment of data resources in azure, comparing sql server in a virtual machine across windows and linux options, licensing, and pricing to guide your configuration and migration decisions.

Create an Azure virtual machine with Windows Server 2019, enable RDP, and install SQL Server 2019 evaluation on the VM.

Enable tcp/ip and open port 1433 in Azure and Windows firewall, restart SQL Server, then connect with SQL Server Management Studio to create a dummy database on a 2019 VM.

Deploy SQL server 2019 on Windows Server 2019 via Azure Marketplace, with automatic installation, then connect via SQL Server Management Studio to create a database such as edu cpa db.

Configure automated patching for a SQL server running on an Azure virtual machine, and set a maintenance window of 60 minutes to apply patches automatically.

Create an Azure SQL single database, configure a new server with strong credentials, choose the sample AdventureWorks, and connect via SQL Server Management Studio while adjusting firewall rules.

Learn how to connect to the Azure SQL server, run queries in the query editor or SQL management studio, and view or edit results.

Choose the right Azure relational database by evaluating open source migrations, cloud-native Azure SQL Database, managed instance, or SQL Server on VM, based on your requirements.

Compare the functional requirements and costs of SQL server on VM, SQL managed instance, and Azure SQL database while considering patching, security, expertise, SLA, and migration strategies.

Evaluate scaling requirements for cloud databases by understanding scaling up and down, scale out, read scale out, and global scale out in Azure SQL and SQL Server on VM.

Discover high availability and backup options for Azure SQL Database and SQL managed instance, including always on availability groups, zone-redundant deployments, backups, and disaster recovery.

Explore a multi-layer security model for Azure databases, covering network and access controls, threat protection, auditing, encryption at rest and in transit, and customer managed keys via Key Vault.

Scale Azure SQL Database by adjusting the compute plus storage setting on your database server, increasing resources and DTUs, with pricing updates reflecting your changes in the cloud.

Explore Azure SQL elastic pools that share resources across databases, create pools, move databases into the pool, and scale with core-based or DTU-based pricing and maintenance windows.

Scale a SQL server on a virtual machine by resizing the VM; this is disruptive and causes downtime, though data remains safe on the same disk. Optimize storage and configuration.

Explore Azure SQL replicas, offering read-only and read-write copies across regions, improving performance by distributing reads, and learn about sharding across servers, noting Azure SQL lacks multi-master like Cosmos DB.

Learn to synchronize data between two databases on the same server with SQL Data Sync, create a sync group, set a hub and member databases, and enable automatic two-way synchronization.

Plan and execute cloud migration of on-premises databases to azure with azure database migration service, covering assessment, firewall rules, port 1433, data transfer methods, and offline options.

Plan an upgrade strategy to migrate older on-prem SQL Server to Azure with the data migration assistant, including 32-bit to 64-bit upgrade and lock shipping.

Migrate SQL server data to Azure SQL database using the data migration assistant and Azure Migrate; create a migration project, run an assessment, and review results.

Perform a database migration using Azure database migration service by creating a migration service and project, mapping source and target sql servers, and validating the data transfer.

Learn Microsoft Azure fundamentals with hands-on demos of web apps, service plans, databases, and deployment, and compare PaaS and IaaS using virtual machines.

Understand cloud computing fundamentals and on-premise versus cloud models. Learn how Azure delivers compute, storage, and network as scalable, subscription-based IaaS, PaaS, and SaaS.

Explore Azure cloud architecture, contrasting IaaS, PaaS, and SaaS, and see how Azure provides virtual machines, networks, storage, web apps, and disaster recovery with high availability.

Explain security and identity management in cloud services and how Azure protects access. Describe SaaS, PaaS, and IaaS with examples from Office 365, Salesforce CRM, Google, and Rackspace.

Explore cloud computing basics, virtualization, and software as a service, including how hypervisors enable multiple virtual machines on shared hardware and abstract infrastructure.

Explore cloud computing drawbacks like low internet speed and latency. Learn mitigations such as redundant internet, express route, offline desktop/mobile apps with synchronization, and StorSimple on-prem storage with data centers.

Explore the Azure portal, managing subscriptions and pricing, and learn how credits, data transfer, storage transactions, and virtual machines affect cloud costs.

Explore Azure app services and virtual machines, selecting free, basic, standard, or premium tiers to host web applications, with consumption-based bandwidth charges and scalable infrastructure options.

Explore Azure mobile services and the differences between virtual machines, application services, and cloud services, including data management, connectivity options (virtual network, endpoint, express route), and trade-offs like scalability.

Explore Azure's shared development and testing environments, with load balancing, auto scale, and backups, and learn on-prem migration and production deployment options for scalable, highly available applications.

Explore the Azure portal to access pricing, documentation, and core services. Create, deploy, and monitor web apps with IaaS compute and app services, configure endpoints, default documents, and web jobs.

Learn to monitor and scale a web application in Azure, configure backups and linked resources, publish via file transfer protocol, and explore app service plans, pricing, and the marketplace.

Discover azure virtual network and hybrid cloud concepts with on premise and cloud resources. Experience high availability, auto scaling, and load balancing with CapEx and OpEx considerations.

Configure an Azure cloud network by selecting a dns server and establishing a site-to-site vpn to an on-premise network, while defining address spaces and department subnets.

Learn to create and connect an IBM Cloud virtual network to on-premises networks using gateways, VPN, and dynamic routing, while configuring DNS and local area networks.

Learn how Azure virtual machines fit into the paas and iaas models, configure compute, storage, and network, and deploy and access web applications in a cloud environment.

Create a virtual machine in cloud app dot net, set a dns name, choose an operating system image and hardware, select data center, attach a disk, and access from anywhere.

Create and manage virtual machine images from ready-made or custom disks to deploy machines efficiently. Monitor CPU, disk I/O, and network traffic while configuring autoscaling and availability sets.

Learn to access and connect to cloud virtual machines via remote desktop, choose endpoints and availability sets, including standalone and load-balanced options, all in minutes at zero cost.

Connect to a Windows Server VM via an RDP file, then attach a storage account to persist data and learn local vs geo redundant options.

Add a 500 GB disk to an Azure virtual machine, initialize and format it as the 500 GB F drive with NTFS, and compare cloud storage with physical disks.

Configure virtual networks and local area networks in Azure by defining private IP ranges, creating gateways, and establishing site-to-site VPN connections to link on-premises networks.

Configure vpn networking, reserve ip addresses, and access control lists for Azure vm networking to enable site-to-site connectivity, internal name resolution, endpoints, and load balancing.

Plan and deploy Azure virtual machines for workloads, configure VM networking, and manage images and disks, including specialized and generalized images and uploading VHD between storage accounts.

Perform configuration management in Azure by using cloud configuration manager to read configuration from the platform on which application runs, such as web.config, app.config, and service configuration cfg files.

Create and configure a cloud virtual network, assign IP ranges and subnets, set optional DNS servers for on-premises resolution, and enable point-to-site or site-to-site connectivity.

Explore subnetting to create private network subnets for office locations like Bangalore, Ahmedabad, Chennai, and Noida, with defined IP ranges and gateways for cloud and on-premise connectivity.

Explore how a gateway assigns the router’s IP, configure subnets in a virtual Azure network, and allocate IP ranges for departments like HR and production.

Explore how to scale machines in a virtual network by managing IP address ranges, avoiding overlaps, and configuring local area networks, gateways, and corporate bridge settings.

Learn to create a SharePoint server in Azure by selecting an image, configuring authentication, cloud services, storage, and availability sets.

Demonstrates provisioning and deploying a SharePoint server 2013 as an Azure virtual machine, configuring rdp access, and loading apps like BizTalk Server, SQL Server, and Dynamics via iaas gallery options.

Learn to create and configure Windows and Linux virtual machines on Azure, deploying IaaS infrastructure and enabling DNS naming, domains, and replication.

Deploy and configure a Linux virtual machine in Azure IaaS. Create and manage endpoints to access services via public and private ports using RDP and PowerShell.

Explore how public and private ports enable external access to Azure virtual machines through endpoints, load balancing, and standard versus basic configurations for scalable, high-availability deployments.

deploy windows and linux machines using portal, configure cloud service and endpoint, attach a 25 gb disk, install the operating system, and access via rdp through cloud app dot net.

Learn to configure and manage Azure virtual machines, including public and private IPs, availability sets, and autoscale, to ensure security, high availability, and scalable performance.

Explore deploying and managing Azure web apps and web jobs via the portal, linking storage and databases, configuring scaling and backups, and monitoring traffic with traffic manager.

Learn to create and deploy a web app in Azure, choose hosting plans from free to standard, and configure autoscale and scaling by CPU for high availability.

Set up and scale a web site by configuring runtimes, 32/64 bit options, domain binding, authentication, deployment slots, and web jobs, with debugging and monitoring capabilities.

Deploy and manage Azure web apps and web jobs, configure scaling, link resources like SQL databases and storage, enable backups, monitoring, and Azure Traffic Manager.

Understand Azure IaaS and PaaS and deploy web applications on Windows or Linux servers in Southeast Asia. Explore app service plans, IIS, autoscale, load endpoints, and Active Directory integration.

Learn scalability on a public platform by increasing machine instances to handle load, monitor cpu usage and http errors, and configure scheduled scaling for weekdays and weekends.

Create a web application by linking a database and resources, configure a new SQL database server, connect it to your app, and set monitoring alerts for deadlocks and failed connections.

Configure automated backup and export for your SQL database, set seven-day backups with 30-day retention, enable auditing, dynamic data masking, and geo-replication for secure, scalable web apps and web jobs.

Connect Azure to on-site networks by converting physical machines to virtual machines, configuring storage and internal networking, and deploying mobile and web apps via Azure mobile services and SQL databases.

Explore Azure cloud services as PaaS based solutions and virtual machine bundled offerings, including web roles and worker roles, with deployment, endpoints, load balancing, and autoscale for high availability.

Create and scale virtual machines by provisioning new instances, assigning them to a cloud service, and configuring an availability set to optimize resources in a data center.

Explore storage networking concepts, including local and geo redundant storage, raid levels, blob storage, and monitoring of read and write requests.

Explore how a virtual network in Azure extends your on-premise network into the cloud, enabling site-to-site and point-to-site connectivity, dns integration, and scalable cloud services.

Explore how azure virtual network addresses assign IP addresses to virtual machines, define subnets, connect to on-premise networks with gateways and VPN, and enable DNS name resolution across networks.

Learn how to configure and scale web jobs and web applications, back up data, manage application service plans, and monitoring of virtual machines and cloud services.

Learn provisioning a virtual machine and the difference between infrastructure as a service and platform as a service, with web, mobile services, and SQL database storage.

Learn to monitor a physical box in azure, tracking cpu, disk, and network metrics, review endpoints, ports, and load balancing, and compare basic and standard virtual machine sizes.

Create and connect virtual machines to a network; capture snapshots and images; manage Windows and Linux VMs with PowerShell cmdlets for Azure virtual machines.

Explore Azure components and virtual machines, including endpoints, compute, storage, and network and application services, and learn to manage images, disks, and billing across subscriptions.

Explore Azure Active Directory, its differences from on-premises Active Directory, and how to use directory sync, single sign-on, and custom domains to manage identities across cloud and on-premises.

Learn to use Azure AD reports for auditing sign-ins, license usage, anomalous activity, and external provisioning, and explore premium reports with mobility tools such as Intune and Azure Rights Management.

Create and manage an Azure SQL database quickly, configure server settings, enable auditing and replication, monitor security and performance, and connect with Visual Studio Online and Visual Studio Code tools.

Explore recovery services as Azure backup vaults for data and on-prem resources. Register and protect virtual machines, install the agent, and apply policy-driven backups across Azure and on-prem environments.

Provision Azure remote desktop services by creating a remote application collection, adding application images and templates, and publishing programs for access from anywhere via cloud or hybrid deployments.

Publish and manage remote desktop services in Azure by creating a remote application collection, configuring user access, and distributing access via a client link for any device.

Discover StorSimple, an on-prem storage device that backs up unused data to cloud for availability, and learn to manage Azure blob storage with StorSimple manager and media services.

Explore how content delivery networks and streaming endpoints reduce latency by distributing video data across global CDN nodes, with encoding workflows, content protection, and programmatic delivery via Azure media services.

Explore migrating on-premise workloads to the Azure cloud using the Microsoft Assessment and Planning Toolkit for readiness and inventory, and the Microsoft Migration Accelerator for site moves and VM readiness.

Assess on-premises readiness for migration to Azure by evaluating VM capacity, storage, and network usage; map toolkit discovery guides desktop and Office 365 readiness for cloud migration.

Explore large-scale HPC computing with Azure Batch and Azure Site Recovery for VMware and physical servers, plus premium SSD storage and HD insight.

Learn to view Azure billing, inspect subscription costs, and use the pricing calculator to estimate charges, including data transfer, storage, and compute hours, with emphasis on the preview portal.

Plan and execute on-premises to Azure cloud migration by assessing virtual machine readiness, inventorying Windows systems and Active Directory, and estimating capacity and licensing with migration tools.

Learn to plan migrating on-premise workloads to Azure using data center migration and cost estimation tools, and to implement streaming analytics with inputs, outputs, and recovery services.

Analyze website log files with the Azure website log analysis solution to gain insights from errors, referrers, and page hits, and explore sensor data analysis and storage analytics.

Explore Azure machine learning workflows with HDInsight clusters to perform sensor data and log analysis, build movie recommendations with Mahout, and manage data in Azure blob storage via studio.

Learn Azure content delivery network endpoints and propagation, with https and custom domains, then explore BizTalk services for cloud to on-premise enterprise integration with SAP, Oracle, and SQL Server.

Traffic Manager routes user traffic to similar cloud services across data centers using an intelligent DNS policy, while Azure automation uses runbooks and assets to automate backups, patches, and checks.

Create and run Azure automation runbooks to stop virtual machines on a schedule, manage jobs and backups, and explore HDInsight clusters for big data processing with Hive queries.

Create an HD Insight cluster to store and analyze big data across multiple machines, enabling scalable analytics and near real time stream processing with Azure Stream Analytics.

Explore Azure Cognitive Services and learn to provision, deploy, and use vision, speech, search, language, and knowledge features to build smart applications.

Create an Azure free account, verify email and phone, and access 12-month services such as virtual machines, sql database, blob storage, cosmos db, app service, anomaly detector, and custom vision.

Explore the vision API within Azure Cognitive Services, enabling computer vision analysis of images and videos with tags, descriptions, confidence scores, object detection, color metrics, and OCR-based data extraction.

Learn to create your first Azure cognitive service for computer vision, study REST APIs with Postman, and explore documentation, sample apps, and SDKs for C, Node.js, Python, Java, and Go.

Explore hands-on computer vision with Azure cognitive service: obtain subscription keys, configure endpoints, and run post requests to analyze images, generate captions, and identify objects and landmarks.

Explore Microsoft Azure face API capabilities, including face detection, age, gender, emotions, and landmarks, then use face verification, grouping, identification, and similarity tools to manage and search image sets.

Hands-on with the Azure Face API: detect faces, extract face IDs, landmarks, and attributes, then compare images and run Postman API calls to explore find similar, identify, and verify.

Explore Azure Custom Vision to train a tailored image classifier for your unique use cases, using your own images from different categories, training, testing, and deploying through Azure REST APIs.

Create a custom vision service to classify mango leaf and neem leaf images, train with quick training, test predictions, publish a prediction URL, and call the API via Postman.

Explore Azure cognitive service video indexing to extract metadata from audio and video for tagging and search, including spoken words and written text, plus video form recognizer and ink recognizer.

Perform a hands-on exercise with the Vision Indexer and Azure Video Indexer to upload media, index videos, and view insights like labels, scenes, captions, and searchable queries.

Launch the form recognizer in the Azure portal and create a free-trial service in East US. Use the 2.1 api with postman to analyze documents and extract text.

Explore cognitive speech service, enabling speech-to-text, text-to-speech, speech translation, and speaker verification, with four major offerings and cautions on production use.

Explore how the speech to text service converts audio into transcripts for downstream processing. Leverage use cases like powering voice assistants, translation, and storing call center conversations for future investigation.

Learn to set up an Azure speech service and perform speech-to-text via rest api and Postman, authenticating with a key, sending wav audio, and extracting transcription.

Explore text to speech in Azure by converting text into lifelike voice files with dozens of voices and accents, and customize via xml in speech studio.

Access the Azure speech service in the portal, fetch authentication tokens, list voices in East US, and perform text-to-speech with an XML request for a 16kHz audio output.

Learn how azure speech translation converts speech to multiple languages using generic and custom translators, via sdks, with no rest api, and prepare for hands-on implementation.

Apply a hands-on approach to speech translation by configuring Azure's speech translation service with Java, Maven dependencies, and SDKs, then translate microphone audio into multiple languages.

Explore the speech recognition service to identify speakers and enable speaker ID with custom voices. Learn how REST API access requires Azure approvals and fill requests for educational use.

Learn how to use Azure's speech recognition rest api to create voice profiles and perform text dependent and independent verification, using West US regional endpoints and subscription keys.

Explore Azure cognitive service language APIs to analyze sentiment, translate languages, understand natural language, recognize entities, and create efficient question-and-answer makers for apps.

Explore sentiment analysis using azure language services to extract sentiment, key phrases, language, and named entities, with hands-on practice in the language studio and REST APIs.

Discover how the Azure translator service detects language, translates text via REST APIs, and supports custom translation models for manuals and websites.

Discover the understanding service (also called Louis) that extracts content from text, trains customizable models, and deploys them via Luis portals to power voice assistants.

Develop a Python-based app to create, train, and publish a LUIS model in Azure Cognitive Services, then pass text to extract contextual insights.

Poll training status for multiple models with a while loop using app id and version id, then publish a public version and set up prediction endpoints with runtime credentials.

Define ml entities and phrase lists for a pizza order, assemble model objects and labeled examples, then train and publish the model for testing with a prediction.

Learn how to create and train a Luis application for a pizza ordering bot, configure keys and endpoints in West US, run a Python app, and publish via REST APIs.

Discover Azure cognitive service's entity recognition in the language service, identifying and categorizing entities in unstructured text such as people, places, organizations, quantities, date ranges, and events.

Develop a Java Spring Boot app that performs entity recognition using Azure AI Text Analytics, authenticates with key and endpoint, and prints entity details like category, subcategory, score, and offsets.

Create and deploy an Azure language service for entity recognition, retrieve keys and endpoints, and validate results from a Spring Boot application using Azure Cognitive Service.

Explore QnA Maker to build a knowledge base from unstructured data and retrieve answers via a RESTful API, with meaning-based matching.

Build a QnA maker application using Python by installing dependencies, configuring Azure credentials, creating a client, submitting questions, and printing confidence-based answers.

Learn how to create and deploy a custom question answering service in the Azure portal, build a knowledge base from text, extract endpoints and keys, and test QnA responses.

Explore cognitive decision service and how it helps applications make quick decisions, focusing on content moderator, anomaly detector, and Personalizer.

Use content moderator to filter profanity and text, detect offensive images and adult video content, auto-correct misspellings, identify PII, and perform OCR on images for text moderation.

Create an Azure cognitive moderator image moderation app in a spring boot project, configure JSON and Jackson dependencies, and implement evaluation data for image moderation, OCR, and face detection.

Implement an image moderation workflow with face detection and text detection using OCR, perform racy content checks, accumulate results, and write JSON results to a file with error handling.

Design and implement a text moderation workflow that reads input text, calls a moderation service, and writes results to a text moderation results json, with error handling and endpoint configuration.

Learn to run an Azure cognitive service moderation pipeline, performing image and text moderation with ocr and face detection, and review stored outputs like emails and numbers.

Learn how anomaly detector monitors time series data to identify abnormalities, using stream mode for live data and batch mode for full datasets, with Azure-based security examples.

Build the first anomaly detector app in Python using a Jupyter notebook, import Azure AI anomaly detector, set up dependencies and subscription keys, and plot time series data with Bokeh.

Create an Azure anomaly detector client, supply subscription keys and endpoint, then build and plot anomaly graphs using sample data with expected values, margins, and timestamps.

Plot a time series graph with lower bounds, boundary colors, and labeled boundaries using a JSON data source to visualize anomalies against expected values. Future sessions cover normality detection.

Create an anomaly detection function for a time series using sample data, defining sensitivity, skip points, granularity, upper and lower margins to identify negative and positive anomalies.

Trigger the anomaly application to plot graphs from daily data, adjust skip points and sensitivity, and observe how anomalies are detected in the series.

The decision personalizer analyzes real-time user behavior to tailor experiences, predicting optimal ad placements, product recommendations, and article highlights. A Python-based meal recommender demonstrates how prior meals guide future choices.

Create a Python-based Azure Cognitive Services Personalizer app that suggests meals using ranked actions like pizza, pasta, ice cream, and salad, driven by time of day and nutrition.

In the azure mastery course, this lecture builds a decision personalizer application that collects user meal preferences, handles input errors, generates an event id, and ranks actions with context.

See how the Azure Personalizer service uses user inputs and nutrition facts to generate tailored recommendations, such as a salad based on meal choices, and guide advertisement and article suggestions.

Explore cognitive web search services in Azure by leveraging Bing through a RESTful API to bring the power of the search engine into your applications.

Discover cognitive web search services, such as web search, spell check, auto suggestion, image and video search, entity and visual search, with market, region, safe search, and data freshness controls.

Create a Java 8 web search cognitive application that uses rest api to access Azure services. Configure pom.xml, add json jars, subscription keys and endpoints, encode queries, and process responses.

Continue building a cognitive websearch app by creating a search results class, parsing and pretty-printing JSON responses, cleaning code, handling exceptions, and iterating headers to display results.

Set up Azure cognitive web search by configuring Bing Search keys and endpoints, then trigger app to fetch web, entities, videos, YouTube results, and ranking via v7.0 search endpoint.

Learn to build an AI-based chatbot using Azure Cognitive Services, focusing on the QnA service, connect it to a Java application via REST APIs, and train it for faster responses.

Create and train an Azure QnA knowledge base using a QnA chat bot service, publish it with language options, and access its endpoints and URIs for integration.

Create a Spring starter project to build a Java application. Define a Question model and a chat controller using RestTemplate with JSON content and authorization headers.

Resolve application issues by building a rest template to call a q&a service, manage Maven dependencies and Spring Boot setup, and test with Postman on localhost 8080.

Develop and test a Java chat bot by training QnA models from a knowledge base, publishing services, and validating responses with Spring Boot and Postman.

Learn to clean up your Azure setup by deleting knowledge bases, resource groups, and services to avoid charges, and verify the application status with no services running.

Explore cloud computing fundamentals and Microsoft Azure, learn cloud types, access options, and an overview of Azure services. It prepares you for more advanced certification topics.

Explore the five fundamental cloud principles—on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured services—to enable scalable, pay as you go cloud deployments.

Trace the history of cloud computing from its AWS origins to Azure's beta and general availability, highlighting milestones like S3, EC2, and Google App Engine.

Explain the three cloud service models—IaaS, PaaS, and SaaS—along with on-premises versus cloud deployment and the shift from capital expenditure to operational expenditure, with examples like Office 365 and Gmail.

Explore public, private, hybrid, and community cloud deployment models and see how organizations deliver IaaS, PaaS, and SaaS via Azure and other providers.

Understand why Microsoft Azure stands out among public clouds by comparing Gartner magic quadrants, Azure's enterprise focus, and AWS leadership in cloud computing.

Explore how Microsoft Azure serves as a public cloud platform offering IaaS, PaaS, and SaaS across global regions and data centers with pay-per-use pricing.

Explore the evolution from Azure service management (ASM) to Azure Resource Manager (ARM), and learn how resource groups, JSON templates, tags, REST API, and RBAC simplify deployments.

Register for a free Azure account to access $200 in credits, verify your Microsoft account, phone, and credit card, and practice Azure demos hands-on.

Log in to the Azure portal, explore the ARM and ASM portals, navigate the dashboard, services, subscriptions, and billing, and learn quick access with favorites and Cloud Shell.

Explore the classic ASM portal, compare it with the ARM portal, manage subscriptions and credits, and grasp RBAC changes and the move to ARM architecture.

Compare the classic versus modern Azure portals and explain how enterprises manage subscriptions through a hierarchical model. Learn how enterprise agreements, department delegation, and on-demand pricing shape governance and billing.

Learn how azure accounts and subscriptions are organized, distinguish microsoft and organizational accounts, and master roles of account administrator, service administrator, and co-administrators across asm and alm portals.

Explore the tools to access Azure, including Azure CLI, Azure PowerShell, and Azure Storage Emulator. Learn where to download SDKs and how these tools manage storage, subscriptions, and cross-platform access.

Install the Azure PowerShell module and the Azure command line interface via offline MSI or web-based installer, selecting a stable version like 1.5.1.

Install the Azure CLI on Windows using the web-based platform installer after Azure PowerShell, and explore the cross-platform command line tools available via the web platform installer.

Learn to use the Azure CLI from command prompt or PowerShell, prefixing commands with Azure, log in, manage subscriptions, and switch the current subscription before deployment.

Explore Azure PowerShell, navigate ASM and ARM modules, log in and manage subscriptions, and run account and subscription commands to switch default subscriptions.

Learn to use Azure PowerShell with the ARM module, including logging in with Add Azure account, logging in to the ARM module, and running Get Azure ARM subscription.

Discover Azure virtual machines as an infrastructure as a service offering, configure Windows and Linux VMs, and use Azure CLI and PowerShell to manage subscriptions, connect, and attach disks.

Azure virtual machines provide infrastructure as a service, with Microsoft managing the underlying infrastructure, offering Linux, Windows, Oracle, IBM, and SAP options, scalable across data centers, pay as you go.

Learn Azure virtual machines and their three states—running, stopped, and stopped and deallocated—and how each state influences compute and storage billing.

Azure virtual machines are billed per minute, including the operating system, while you pay licensing for any third-party software you install; Windows VMs tend to cost more.

Explore Azure VM sizes by comparing basic and standard tiers, from A to N series, and learn how scaling, memory, and graphic processing unit options vary across configurations.

Explain azure vm deployment options by contrasting arm (resource manager) and asm (classic), and show how portal.azure.com supports both as Microsoft shifts to resource manager.

Explore the classic Azure virtual machine architecture, where a cloud service hosts VMs with shared public IPs and private IPs, supporting web or worker roles, plus load balancer and endpoints.

Explore the resource manager deployment architecture, grouping resources into a resource group with compute, storage, and network providers to support virtual machines, and leverage ARM templates for infrastructure as code.

Examine Azure VM pricing tiers, compare basic and standard sizes across D series and DS series, and learn data disks, IOPs, load balancing, auto scaling, and premium SSD options.

Create a Windows virtual machine in the Azure portal using Resource Manager. Choose a Windows Server image, set a resource group, subscription, location, and login credentials.

Learn to create a Windows virtual machine in Azure, configuring storage accounts, virtual networks, subnets, disk types (premium vs standard) and managed disks, dynamic public IP addresses, and security groups.

Connect to a Windows virtual machine in Azure via rdp, download the rdp file, and sign in to access the vm and its public ip.

Create a Linux virtual machine on Azure using Ubuntu Server and Resource Manager, configure storage, virtual network, subnet, dynamic public IP, and access options with password or SSH key.

Connect to a Linux virtual machine via SSH from Windows using PuTTY, log in as the user, and inspect the Ubuntu 16.04 LTS server disks with fdisk.

Explore Azure virtual machine disks, including OS disks, temporary disks, and data disks, with standard and premium storage options, and unmanaged versus managed disks and their size limits per VM.

Learn how to attach a data disk to a Windows VM, initialize and format it in disk management, and understand OS, temporary, and data disks.

Attach a data disk to a Linux VM, verify disks via SSH, identify /dev/sda1 and /dev/sdc, create a 10 GB primary partition with fdisk, and detach when finished.

Discover how to achieve 24/7 Azure VM availability by using availability sets across fault and update domains, with two or more instances and a 99.95% sla for internet facing roles.

Explore how fault domains, update domains, and availability sets provide high availability and redundancy in Azure data centers, regions, geographies, and cloud environments.

Learn how availability sets provide fault and update domain separation for web and SQL servers, ensuring uptime with load balancers and traffic manager across single and multi-region deployments.

Create an availability set to group virtual machines across fault and update domains, reducing single points of failure. Learn to name, configure location, and ensure new VMs join at creation.

Learn how to create an availability set in Azure, configure fault domains and update domains to protect apps from single points of failure, and join VMs at creation time.

Configure an isolated Azure virtual network with subnets, traffic controls, and load balancing. Choose private IP ranges up to /29 and DNS options.

Discover Azure virtual network capabilities: subnets, static IPs, DNS, front end access, load balancing, Traffic Manager, and secure connectivity via VPN gateways or ExpressRoute to enable enterprise data center connectivity.

Explore the components of Azure virtual networks, including address space and subnets, private IP prefixes, DNS options, reserved addresses, and how to connect on-premises networks via VPN or express route.

Choose a non overlapping private IP prefix for your Azure virtual network. Assign the first VM the next available address after reserving five for network, DNS, and DHCP.

Master network security groups to segment networks, enforce inbound and outbound rules by allow or deny, and associate with subnets or virtual machines within a virtual network.

Learn how Azure load balancers distribute internet traffic across web servers, application servers, and SQL servers, using internet facing and internal types to support scalable, multi-tier architectures and availability sets.

Plan and implement multi-tier Azure architectures using Traffic Manager for cross-region routing, internet facing and internal load balancers, and Application Gateway for URL based routing to web and database servers.

Explore Azure hybrid connectivity between on-premises data centers and Azure resources, including internet access, point-to-site and site-to-site VPN, ExpressRoute, and VNet-to-VNet connectivity.

Explore Azure Traffic Manager, a DNS-based load balancer that distributes traffic across regions using performance, round robin, and failover algorithms, with health probes for high availability.

Create a virtual network with the resource manager, set the address space, East US region, resource group, and DNS service, then add subnets 10.1.0.0/24 and 10.2.0.0/24.

Create a VNet and subnet, configure a network security group with inbound RDP and HTTP rules, then deploy a Windows VM and install IIS.

Azure storage is a cloud-based, pay-for-what-you-use service with blob, disk, file, queue, and table types, accessible via multiple clients and tools on Windows and Linux.

Explore Azure blob storage for unstructured data, using storage accounts, containers, and URLs to store block, append, and page blobs, including security policies and page blobs for virtual hard disks.

Explore Azure disk storage for virtual machines, comparing premium ssd and standard hdd options with iops, throughput, and latency, offering persistent, durable storage replicated across the data center.

Explore azure file storage, its file shares, and hybrid access for on-premises and Azure VMs, supported by rest APIs, SMB protocols, and multi-client access.

Explore how Azure Table Storage provides a scalable NoSQL key-value store for semi-structured data with a flexible schema. Access is fast and consistent across updates, supporting enterprise-scale applications.

Azure queue storage provides a simple, cost-effective queuing service that buffers and stores messages between applications until the receiver is ready, enabling resilience and elastic scaling under heavy load.

Create a general-purpose storage account and use blobs and containers, with files, tables, and queues; follow naming conventions and choose replication options like LRS, ZRS, GRS, or RA-GRS.

Azure web apps as a platform as a service and learn to create, deploy, and scale web apps using App Service plans and slots.

Explore Azure Data Lake architecture as a cloud storage for all data forms and sizes, and learn how HDInsight, Data Lake Store, and Data Lake Analytics enable big data queries.

Azure Data Lake combines data lake store and data lake analytics, offering U-SQL, Spark, and Hive analytics, and stores unstructured, semi-structured, and structured data.

Log in to portal.azure.com, create a resource group for this project, and set up a data lake analytics account linked to a data lake store in East US 2.

Navigate Azure data lake services by accessing the Data Lake Analytics and Data Lake Store, explore data with Data Explorer, and use the u-sql catalog to manage databases, tables, and views.

Upload a sample space-separated file to the data lake store, place it in a vendor folder, and skip header rows marked with hash while mapping date, vendor, customer, and amount.

Understand usql, a hybrid of SQL and C-sharp, and its extract syntax. Create a usql job to read data from the Azure Data Lake and output it as text.

Define analytic units as compute resource in Azure Data Lake, where one unit equals two CPU cores and six GB RAM, and 1 to 250 units affect cost and performance.

Reuse the script to filter out commented rows in the sales data using a C# substring. See the filter run in Data Lake Analytics and validate the output.

Explore the four stages of a u-sql job in azure data lake analytics: preparing, queuing, running, and done, including compilation, resource-based queuing, and possible failures at each stage.

Discover U-SQL, a big data query language for Azure Data Lake Analytics that blends SQL with C#. Use built-in extractors and outputters like text, csv, and tsv with case sensitivity.

Apply schema on read to map a space-separated row into date, salesman, customer, and amount using dotnet data types, contrasting with schema on write in data lake workflows.

Upload data, handle file overwrites, and resubmit jobs to ensure data appears in the sales folder. Verify status, access results dot CSV, and view formatted table output.

aggregate the data by date using a group by, compute the total amount per day, and export the summarized results to a csv file.

Diagnose a sql script syntax error caused by a lowercase group by, then correct keyword capitalization, add explicit aliases, and apply order by for data output, ensuring the job succeeds.

Learn how to ingest multiple files into Azure Data Lake analytics using a wildcard to read all txt files, summarize by date, and output a consolidated results file.

Create and manage a database in the u-sql catalog inside azure data lake analytics, transforming data with tables, views, and stored procedures for better performance in data lake store.

Explore data distribution strategies—range, hash, direct hash, and round robin—and learn how to apply hash-based distribution on a column, and manage tables and schemas in the Azure data lake catalog.

Explore data distribution strategies, including range, hash, direct hash, and round-robin, and learn how hash-based distribution routes rows by column values to distribution buckets.

Learn to insert data into the sales by month table in a data lake, compare data lake querying with SQL, and run an insert into select workflow from text files.

Name and manage data jobs, create tables, and implement a two-step process to query data and export to CSV in outputs folder, linking data lake store with the SQL catalog.

Create a view from the indi.sales_by_month table to summarize date and total amount, using a create view statement; a view stores a saved sql query and returns data when queried.

Learn how table valued functions differ from scalar functions, create a tvf in Azure Data Lake, and return a table from a view with date-based filters.

Explore creating and querying a table valued function (tvf) with parameters year and day set to 2010 and 01, using a view as data source and debugging common errors.

Create a store procedure to extract date, salesman, customer, and amount from a file and insert the data into the sales by month table; then debug common syntax errors.

Run a stored procedure to append data from the data lake into a sql table, resolving caps lock and semicolon errors, and verify results by querying sales by month table.

Learn to use inline C# functions in U-SQL scripts, filtering with string contains in the where clause, and exporting results to CSV.

Move from the Azure portal to Visual Studio to submit, track, and monitor jobs using the Azure .NET SDK and Azure Data Lake and Stream Analytics tools.

Install Visual Studio, the .NET SDK, and Azure Data Lake tools to create a U-SQL project and work entirely within Visual Studio, exploring Solution Explorer and Cloud Explorer.

Navigate and manage data lake store and data lake analytics in your Azure account, preview files, explore databases and jobs, and review resources and diagnostics to optimize data processing.

Submit a u sql job from Visual Studio, implement a code-behind function to apply a 15% discount, and output two columns to csv while monitoring the job in Azure portal.

Explore Azure Data Lake Analytics workflow: submit SQL jobs from Visual Studio, manage parallelism as the analytic unit, monitor progress and costs, and understand assemblies and code behind references.

Create a separate U-sql class library project to host a custom function, deploy it to Azure, and reuse it across multiple jobs via the deployed assembly.

Register the assembly, submit the job, and monitor the status in Azure Portal and Data Explorer, view outputs, and enable cross-team reuse of custom functions.

Use the Azure Data Lake job comparison tool to compare similar jobs with different analytic units, assess time and cost differences, and filter by author or name for optimization.

Uncover how an SQL engine builds a job graph by breaking a job into vertices and stages, then executes parallel vertices as super vertices to optimize extraction, aggregation, and output.

Explore how a job is partitioned into stages and vertices, enabling parallel reading and processing with analytic units, while advancing through stages via a critical path.

Learn how a submitted job is executed by a SQL engine that breaks tasks into stages and vertices, with input and output data, read/write counts, and status colors illustrating progress.

Use a heat map to identify bottlenecks across stages by color-coded read, write, and compute time, highlighting slow stages. Visual Studio provides richer legends and throughput metrics beyond Azure portal.

Explore vertex execution view to analyze job graphs, load profiles, and use vertex selection and the critical path to understand running times and debug performance.

Explore the concept of job efficiency, examining degree of parallelism and analytic units to identify under allocation, over allocation, and how iterative graphs guide optimal resource use.

Explore the diagnostics tab to interpret warnings like data skew and resource usage, and use the usage modeler to choose optimal analytic units to boost job efficiency.

Compare Azure Data Lake, Data Lake Store, and data lake analytics access methods beyond the portal and Visual Studio using Azure PowerShell and Azure CLI, highlighting cross-platform capabilities and trade-offs.

Create an Azure Data Lake Store with PowerShell, register Data Lake Store provider, create a resource group in East US 2, and manage data via upload, rename, download, and delete.

Create and verify a Data Lake Store account via PowerShell and the Azure portal, confirming resource group creation, account deployment, encryption state, and provisioning status.

Create a root directory and a sales folder in the Azure Data Lake Store, verify via Data Explorer and PowerShell by listing child items at the root.

Learn to upload data to a data lake store with PowerShell, then rename, download, and delete files using import, move, export, and remove commands.

Learn to submit jobs to a data lake analytics account using PowerShell, craft inline u-sql scripts, and monitor progress to generate CSV outputs.

Learn to use azure cli 2.0 to manage data lake store and data lake analytics, log in with az and device login, and create a resource group.

Create and organize an Azure Data Lake Store account using az data lake store commands, set the resource group, ensure lowercase naming, and build a folder structure for data uploads.

Upload data to Azure Data Lake Store with the Azure CLI, specifying the account, source-path, and destination-path, then list files and prepare for rename, download, or delete.

Rename, download, and delete files in a Data Lake Store account using move, download, and delete commands, with source and destination paths and recursive folder deletion.

Learn to delete a data lake store account using the Azure CLI, including providing the account name and key, and monitor the deletion across resource groups and portals.

Explore Azure data lake pricing through the pricing calculator, comparing data lake store and data lake analytics costs, pay-as-you-go vs monthly commitments, and support options.

Learn how data lake store and analytics work with U-sql to run jobs, build a data lake catalog of databases and tables, and monitor with Azure CLI and Visual Studio.

Explore the architecture of Azure Data Factory, including data factory, pipelines, datasets, linked services, and activities, and how input and output datasets enable data movement and migration.

Create a copy wizard in Azure Data Factory to move data from blob storage to Azure SQL database, setting up source and destination and deploying an ADF instance.

Create and manage a free subscription by building a new resource group, deploying storage accounts, and using automation templates to replicate environments, while exploring Azure Storage Explorer for local access.

Create a blob service in an Azure storage account and learn replication options, including read access geo redundant storage, and container access levels: private, blob, or container.

Explore Azure blob storage properties, including block blob, append blob, and page blob, their sizes and use cases, plus the entity tag (eTag) caching mechanism for web resources.

Create a destination by provisioning an Azure SQL server and database, configure endpoints and firewall rules, connect via SQL Azure, and prepare for an ADF instance.

Learn how to create an Azure Data Factory in Microsoft Azure, upload a sample csv to blob storage, and navigate the deployment and basic setup steps.

Create a schema in sql azure from a csv in blob storage, using query editor or visual studio deployment, then configure adf to pull data from source to target.

Create a copy data activity in Azure Data Factory to move data from blob storage to SQL Server Testdb using a copy pipeline with source, destination, and scheduling options.

Learn to configure an Azure Data Factory copy activity moving data from blob storage to a SQL Azure table, with schema mapping, upsert, and fault-tolerant settings.

Explore the azure data factory resource explorer, view pipelines and copy activity details, and understand how JSON inputs define sources, sinks, datasets, and mappings for blob to SQL.

learn how to author and deploy azure data factory pipelines, moving data from blob storage to azure sql database using data sets, linked services, and pipelines.

Create and deploy azure data factory datasets for blob storage and azure sql, linking them to the services, then build a copy pipeline to move data from source to target.

Identify and fix a failed pipeline by examining the error, adjusting the source dataset and blob path, redeploying, and re-running to confirm the pipeline succeeds.

Learn to schedule data ingestion with Azure Data Factory by scanning daily folders in a year-month-day hierarchy and moving data to a destination using pipelines.

Azure Data Factory scheduling monitors daily blob storage groceries files and loads them into a SQL table, capturing name, category, and price with an identity column.

Learn how to upload files to Azure Blob storage, configure linked services and input/output datasets in Azure Data Factory, and verify daily groceries data in SQL Azure.

Create and deploy a groceries pipeline in Azure Data Factory, replacing the template with a custom copy activity from blob storage to SQL sink, including dataset mappings and scheduling.

Explore how to monitor and troubleshoot Azure Data Factory pipelines, inspect consumed and produced datasets, read slice status, and diagnose failures due to missing blobs.

Understand the scheduling architecture for daily data slices in blob storage processed by ADF. Learn how oldest first versus newest first and retrospective processing handle validation and data availability.

Explore how an Azure Data Factory pipeline uses partition by and runtime variables like slice start to populate year, month, day, enabling dynamic, daily folder paths.

Explore Azure Data Factory diagram views to analyze a groceries pipeline, showing activities, input and output data sets, and ETL-style data flow.

Monitor and manage pipelines in Azure Data Factory using the monitor and manage tool, explore diagrams, resource explorer, activity windows, and concurrency, data sets, and linked services.

Learn how to recover a failed data slice by uploading the file to azure blob storage, manually running the slice, and validating results in the sql database.

Pause or stop a failing Azure Data Factory pipeline, fix issues in the underlying data source, and resume execution using the monitoring and manage tools.

Create an azure data factory pipeline that transforms and aggregates blob storage data with azure data lake analytics, storing results in azure data lake store and loading into sql azure.

Create Azure data lake analytics and data lake store, linking to blob storage and SQL Azure, while configuring pay as you go pricing and encryption.

Explore data lake analytics and data lake store, focusing on the job and data explorer options, submitting sql jobs, and linking adls to analytics, then upload files to blob storage.

Upload a grocery sales data set to blob storage, extract with a space-delimited sql script, aggregate by date, and output a summary csv for the target sql table.

Build an Azure Data Factory pipeline with two activities: summarize blob data into a data lake store, then copy data from the data lake store to SQL Azure.

Define and reuse three data sets to move data from Azure blob storage through Azure Data Lake Store to SQL Azure, via Azure Data Factory transformations and a SQL job.

Create a sql azure data set and json schema named groceries destination, map columns date entered, total number of sales, and total items, and configure data lake analytics data sources.

Learn to build an end-to-end Azure Data Factory pipeline that summarizes blob data in Azure Data Lake Analytics and copies results into SQL Azure, with datasets, linked services, and mappings.

Diagnose a missing input error in a data pipeline, fix the json by adding the name, deploy, and verify the groceries pipeline runs green after removing the small data validation.