Integrate Azure services in a solution Flashcards
Overview
1) Creating data-centric web applications
2) Working with Big Data and the Internet of Things
3) Building enterprise mobile applications
4) Creating media applications
5) Managing related services
Creating data-centric web applications ingredients
1) automatically abstract schemas out of data sets to re-enable structured queries
2) use full-text indexes
3) Content Delivery Networks
Map between Azure services and application features
1) Search and query ->DocumentDB, Azure search
2) cache -> Azure cache, CDN
3) Recommendation - >Azure search, azure machine learning
Azure DocumentDB features
1) NoSQL database for JSON documents
2) It automatically indexes all documents for relational and hierarchical queries.
3) DocumentDB is optimized for frequent writes
Azure Search
1) Instead of trying to manually locate the correct page to view, users can easily acquire the most relevant data by performing simple searches
2) autosuggestion, autocompletion, faceted searches, results highlighting, spell corrections, and geospatial searches
Azure CDN
1) caches static compute contents and Blobs such as images, stylesheets, and script files on its physical nodes across the globe
2) faster response time by serving contents from locations closest to the end user, and second, it removes a large portion of content serving workloads from the service servers
Big Data pipeline comprises
1) data producers,
2) data collectors,
3) data ingestors,
4) data transformation,
5) long-term storage,
6) analysis, and presentation and action
Data ingestors
Data ingestors take data into the cloud at great scale. They are the starting points of the data processing pipeline in the cloud.
Event Hubs description
Event Hubs facilitats efficient data ingress at scale. An event hub can connect to more than one million data producers, with over 1 Gbps aggregated throughput. You can send data to Event Hubs through either HTTP or AMQP.
Event Hubs provides such scale by
1) create multiple partitions that work in parallel
2) can directly address a partition by its partition ID.
3) it can use hash-based distribution or random distribution
Event Hubs consuming side
1) Consumer Groups are different views of the same data stream
2) You can receive data by using either .NET API or a generic AMQP 1.0 client.
3) Supporting AMQP enables great interoperability with existing queue clients of other platforms.
Stream Analytics
1) SQL-like language for you to easily filter, project, aggregate, and join data streams by using familiar SQL syntax
2) advanced scenarios such as pattern detections with a few lines of SQL code
Azure Machine Learning
1) classification, ranking, clustering, and prediction scenarios
2) web-based drag-and-drop UI for you to build and test models, with minimum coding required
3) rich set of best-in-class machine learning algorithms out of the box. And, you can extend it by using R
Building enterprise mobile applications - ingredients
1) Azure Active Directory, Azure AD Authentication Library (ADAL), SSO
2) Azure Application Proxy and Service Bus Relay - to access on site services
3) Azure Notification Hub - push notifications
4) Azure Mobile Services - turn-key solution
Security and compliance
1) Workplace join
2) Key Vault
3) Azure Rights management