DP-900 Azure Cosmos DB

 DP-900 Azure Cosmos DB

Step 1 : Go to the Azure Portal and search for Azure Cosmos DB.


Step 2 : Click on Create to create a new database.


Step 3 : Select the API in which you want to create your database.



Step 4 : Add details to create a Database.


Capacity Mode :
Provisioned Throughput : 
- Request Units/Second are predefined and billed hourly.
- Used when your traffic is Predictable.

Serverless : 
- Billed only for consumed Requested Units.
- Used when your traffic is unpredictable.


Multi-region writes :
Disable : 
- Normally, only one region can accept writes.

Enable : 
- With multi-region writes every selected region can access read and write.
- It automatically replicates data across all regions and resolves conflicts if two regions write at the same time.
- For higher availability, disaster recovery and low latency.

Geo- redundancy :
- Multi-region write and geo-redundancy both protect data from disaster, but geo-redundancy has one more advantage, which is that if you are unable to access data from your region, it automatically falls back to another region to get a copy of the data.








Step 5 : After adding all details, click Review + Create.


Step 6 : Check your details before clicking Create.



Step 7 : After deploying your database, click Go to resource.



Step 8 : From the sidebar, go to the Quick start page. 


Step 9 : Click Create Sample Application.

- By clicking on this button Azure portal will automatically create a container and a database.
- No SQL databases are created on containers.
- You can add and remove containers according to your requirements.



Step 10 : Go to the Replicate data globally page from the settings section in the sidebar.

- Here you can add and remove regions to replicate your data globally.
- Click the save button after adding or removing regions.








Step 11 : Search for the data explorer in the sidebar.




Step 12 : Click on New Items to add data. And add the data, after adding the data, click the save button.


Step 13 : After saving data, you can see there are some other details that are automatically added to the data.

- id : unique identifier for document.
- _self : eternal link that lets a document refer to itself.
- _etag : Tracks changes. It updates automatically as soon as the document is modified.
              Used for version control or concurrency checks.
- attachments : To store links to any files or attachments related to documents.


































































Comments

Post a Comment

Popular posts from this blog

AI-900-3,4

Image
Module : 3 Get Started with Machine Learning in Azure Machine learning drives today’s AI by turning data into insights for predictions and recommendations. Creating an ML solution requires key decisions that affect cost, speed, and quality. Using Microsoft Azure, the process follows six main steps: Define the Problem: Decide what to predict and how to measure success. Get the Data: Find and access reliable data sources. Prepare the Data: Clean, explore, and format the data for modeling. Train the Model: Choose algorithms and adjust parameters. Integrate the Model: Deploy it to generate predictions. Monitor the Model: Track performance and retrain as needed. Machine learning is an ongoing process. Models evolve as data changes. Step 1: Define the Problem The first step in building a machine learning (ML) solution is to define the problem. You need to clearly understand what you want the model to predict and how you will measure success. Key Considerations Model output: What result shoul...
Read more

AI-900 12,13

Module : 12   Overview of Information Extraction * Information extraction is a process that uses AI to pull structured data from unstructured or semi-structured documents, like receipts, invoices, forms, and scanned files. It combines computer vision, OCR, and machine learning or generative AI. * Key Steps in an Information Extraction Pipeline 1. Text Detection & Extraction (OCR) - It uses computer vision to find text areas in an image. - The system extracts raw text from scanned or image-based documents. 2. Value Identification & Mapping It maps the OCR text to specific data fields using: - Machine learning - Rule-based mapping - Generative AI (in newer solutions) Example From a scanned receipt, the system extracts fields such as: - Vendor - Date - Subtotal - Tax - Total * Choosing the Right Approach When designing an information extraction solution, consider: Document Characteristics - Layout consistency - Fixed templates lead to simple, rule-based extraction - Many forma...
Read more

AI-900 10,11

Image
Module : 10  Introduction to Computer Vision Concepts  Computer Vision Tasks & Techniques   * What is Computer Vision? Computer vision uses AI techniques to process and understand visual input such as: - Images   - Videos   - Live camera streams   - It enables machines to "see" and interpret visual information.   1. Image Classification   - This predicts a single label for an entire image.   - The model is trained using many labeled images.   - For example, a smart grocery checkout can identify an apple, orange, or banana from a single item placed on a scale.   - Use case: Recognizing the main subject in an image.   2. Object Detection   - This identifies multiple objects in a single image.   It returns:   - Object labels   - Bounding box coordinates   - For example, a checkout system can detect all fruits placed together....
Read more