Zara  Bryant

Zara Bryant


Deploying to Azure | Beginner's Series to: Django

A website isn’t complete until it’s on the Internet, for other people to see. In this video, we will deploy the application onto Microsoft Azure’s free App Service tier from VS Code.

Useful Links:

#azure #django

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Deploying to Azure | Beginner's Series to: Django
Ahebwe  Oscar

Ahebwe Oscar


Django admin full Customization step by step

Welcome to my blog , hey everyone in this article you learn how to customize the Django app and view in the article you will know how to register  and unregister  models from the admin view how to add filtering how to add a custom input field, and a button that triggers an action on all objects and even how to change the look of your app and page using the Django suit package let’s get started.


Custom Titles of Django Admin

Exclude in Django Admin

Fields in Django Admin

#django #create super user django #customize django admin dashboard #django admin #django admin custom field display #django admin customization #django admin full customization #django admin interface #django admin register all models #django customization

Aisu  Joesph

Aisu Joesph


Azure Series #2: Single Server Deployment (Output)

No organization that is on the growth path or intending to have a more customer base and new entry into the market will restrict its infrastructure and design for one Database option. There are two levels of Database selection

  • a.  **The needs assessment **
  • **b. Selecting the kind of database **
  • c. Selection of Queues for communication
  • d. Selecting the technology player

Options to choose from:

  1. Transactional Databases:
    • Azure selection — Data Factory, Redis, CosmosDB, Azure SQL, Postgres SQL, MySQL, MariaDB, SQL Database, Maria DB, Managed Server
  2. Data warehousing:
    • Azure selection — CosmosDB
    • Delta Lake — Data Brick’s Lakehouse Architecture.
  3. Non-Relational Database:
  4. _- _Azure selection — CosmosDB
  5. Data Lake:
    • Azure Data Lake
    • Delta Lake — Data Bricks.
  6. Big Data and Analytics:
    • Data Bricks
    • Azure — HDInsights, Azure Synapse Analytics, Event Hubs, Data Lake Storage gen1, Azure Data Explorer Clusters, Data Factories, Azure Data Bricks, Analytics Services, Stream Analytics, Website UI, Cognitive Search, PowerBI, Queries, Reports.
  7. Machine Learning:
    • Azure — Azure Synapse Analytics, Machine Learning, Genomics accounts, Bot Services, Machine Learning Studio, Cognitive Services, Bonsai.

Key Data platform services would like to highlight

  • 1. Azure Data Factory (ADF)
  • 2. Azure Synapse Analytics
  • 3. Azure Stream Analytics
  • 4. Azure Databricks
  • 5. Azure Cognitive Services
  • 6. Azure Data Lake Storage
  • 7. Azure HDInsight
  • 8. Azure CosmosDB
  • 9. Azure SQL Database

#azure-databricks #azure #microsoft-azure-analytics #azure-data-factory #azure series

Ahebwe  Oscar

Ahebwe Oscar


How model queries work in Django

How model queries work in Django

Welcome to my blog, hey everyone in this article we are going to be working with queries in Django so for any web app that you build your going to want to write a query so you can retrieve information from your database so in this article I’ll be showing you all the different ways that you can write queries and it should cover about 90% of the cases that you’ll have when you’re writing your code the other 10% depend on your specific use case you may have to get more complicated but for the most part what I cover in this article should be able to help you so let’s start with the model that I have I’ve already created it.

**Read More : **How to make Chatbot in Python.

Read More : Django Admin Full Customization step by step

let’s just get into this diagram that I made so in here:

django queries aboutDescribe each parameter in Django querset

we’re making a simple query for the myModel table so we want to pull out all the information in the database so we have this variable which is gonna hold a return value and we have our myModel models so this is simply the myModel model name so whatever you named your model just make sure you specify that and we’re gonna access the objects attribute once we get that object’s attribute we can simply use the all method and this will return all the information in the database so we’re gonna start with all and then we will go into getting single items filtering that data and go to our command prompt.

Here and we’ll actually start making our queries from here to do this let’s just go ahead and run** Python shell** and I am in my project file so make sure you’re in there when you start and what this does is it gives us an interactive shell to actually start working with our data so this is a lot like the Python shell but because we did it allows us to do things a Django way and actually query our database now open up the command prompt and let’s go ahead and start making our first queries.

#django #django model queries #django orm #django queries #django query #model django query #model query #query with django

Azure Series #2: Single Server Deployment (Input)

In the previous article, we discussed the Gateway to your single server deployment (example: webserver). In this section, we shall continue with Input and Core Infrastructure.

Input for single-server deployment

When you talk about Data for your organization, it covers all three things, “People, Process, and Technology”. More details for the “Streaming and Sourcing Layer” can be found in a separate section (will update the link soon).

**_People: The Who. _**Producers and Consumers of data.

**_Process: The How. _**How the data is curated and put to use.

**_Technology: The What: _**What technologies are used to fetch, process, pass on and store.

Data: While People, Process and Technology is the golden triangle, if you think about it, the very reason the entire state-of-the-art ecosystem exists is merely to get the raw data to a usable form.

1. Data catalog

Any great state-of-art ecosystem is a waste if the data in need for consumers cannot be discovered and from the Producers side, if data cannot be documented/tagged properly that makes it useable for the consumers or end-users. Azure Data Catalog helps to bridge this gap of making the data correctly discoverable by fixing the traditional problems for both consumers and producers and also helps organizations to get the best value out of their existing information assets.

2. Streaming

While we will discuss more as part of the sourcing section, we shall cover the basics of streaming.

1/ Queue Storage

2/ Service Bus

3/ Event Hubs

4/ Event Grid

#azure-interview #azure-event-grid #azure-event-hub #azure #azure-service-bus

Azure Series #2: Single Server Deployment (Core Infrastructure — II)

4. Virtual Machines

Virtual machines are the computers you can hire on the Azure cloud. You can choose any OS including but not limited to Windows, Linux image, etc. You can choose the CPU, Memory, Network Interface Card (NIC), and Storage based on the workloads that you want to migrate or create new workloads in your virtual machines. A firm can have as many Virtual machines as you may see in the server rooms across regions in the organizations. If you want to deploy advanced solutions such as Big data or voluminous data processing, then you can choose an optimal server configuration. Virtual Machine is part of Virtual Network and Subnet. Whether or not you want to expose the VM publicly depends on which tier of the architecture your VM is going to sit.

Why use Virtual Machine:

Virtual Machines are nothing different from the Application Servers, Database Servers, Web Servers, Phone servers, etc. that are part of your primary and secondary site on-premise. Instead of these servers managed and maintained by the Infra team, the servers will be deployed by the cloud management team but will be managed by the Infra team of your organization and you shall deploy workloads on it. As per the shared security model, whatever that goes into the Virtual Machine, you are responsible to maintain, manage upgrades and ensure security. Whatever is deployed by Aure such as Linux VM, Windows VM, etc., upgrades and next versions will be automatic and downtime will be managed by Azure and will be notified to you. If you are procuring VM and deploying Database on it, then it becomes your responsibility to upgrade the database as it is not managed by Azure. Knowing this difference between unmanaged and managed is critical.

Types of Virtual Machine:

  • **Linux Virtual Machine: **Azure endorsed distribution has Linux on Azure includes SUSE, Redhat, Debian, etc., and Docker, Jenkins from the marketplace.
  • Windows Virtual Machine
  • SQL Server on Virtual Machine
  • **Data Science Virtual Machine **(Discussed in a later section)
  • **Virtual Machine Scaling Sets **(Discussed as part of Multi Scaleable server deployment)
  • Azure Bastion

These Virtual Machines are available in various sizes. We can logically group these sizes based on the key characteristics of the VM

  • Based on CPU: General Purpose or Compute Optimized with very good computing power. Balanced memory to CPU ratio. Advanced options include GPU (special machines) or HPC (for Big data or ML).
  • Based on Storage: Storage optimized with high disk throughput and space.
  • Based on Memory: Memory-optimized with high memory to CPU ratio.

#azure-batch #azure-interview #azure #azure