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Data Engineering 365

@dataengineering365

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Making Data Engineering simple 👋
💯 Scroll and Learn
☁️ Cloud • Big Data • Interview Prep & more
🎯 One concept every day
📚 365 days. One journey. 🚀

POSTS STORIES REELS TAGGED

DAY 7/365 | Star and Snowflake schema
On day 6 we learned what a database is and how it helps applications store and retrieve data. 🗄️

But when a business has millions of orders, simply storing data isn’t enough.

Imagine a pizza company asking:

🍕 Which pizza sells the most?
🏙️ Which city generates the most revenue?
📅 Which month had the highest sales?

This is where Data Warehousing & Schemas come in.

Instead of keeping all the information in one giant table, we organize it into related tables.

⭐ Star Schema keeps things simple and easy to query.

❄️ Snowflake Schema takes normalization further by splitting dimensions into additional related tables.

Same business. Same data. Different ways to organize it for analytics.

And that organization can make a huge difference when you’re working with large amounts of data.

💡 Remember

“Good data is valuable. Well-organized data is powerful.”

Tomorrow: We’ll take this organized data and see how data actually moves from source systems into a data warehouse. 🚀 by @dataengineering365

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a month ago

📌 DAY 6/365 | Networking + Databases

Yesterday, we went behind the scenes of Azure. 🏢☁️

We learned about Data Centers, Regions, Availability Zones, and LRS/ZRS/GRS, and why choosing the right location and redundancy matters.

But now we have another question:

If our servers and data are sitting inside all these different places, how do they actually communicate? 🌐

Imagine you order a pizza through an app. 🍕

You tap “Place Order”.

📱 Your app sends the request
🌐 Networking finds the path
🖥️ Server receives and processes it
🗄️ Database stores your order
🌐 The response travels back
📱 You see “Order Confirmed!”

And all of this happens in seconds.

Think of our previous lessons as a city:

🏢 Data Center = The city
🌍 Region = Where the city is located
🖥️ Server = The workers/buildings doing the work
💾 Storage = The warehouses
🌐 Networking = The roads connecting everything
🗄️ Database = The organized records everyone needs to access

Without networking, all these components would be isolated.

Without databases, applications would have nowhere to organize, store, and retrieve important information.

💡 The bigger picture is starting to form:

Cloud → Data Centers → Regions & Zones → Storage → Servers → Networking → Databases

And now we’re ready to take the next step.

Tomorrow we’ll see something really interesting:

🍕 Imagine you have millions of pizza orders.
How would you organize all that data so analysts can easily answer questions like:

“Which pizza sells the most?”
“Which city generates the most revenue?”

That’s where Star Schema & Snowflake Schema enter the story. ⭐❄️

Tomorrow: Schemas in Data Warehousing. 🚀

#DataEngineering365 #DataEngineering #Azure #AzureDataEngineering #CloudComputing Networking Databases DataWarehouse BigData LearnDataEngineering by @dataengineering365

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a month ago

DAY 5/365 | Storage & Servers

Yesterday, we went behind the scenes of the Cloud. ☁️

We learned that Azure isn’t some magical place in the sky. It runs on physical data centers, organized into regions and availability zones.

We also saw how Azure can protect our data using LRS, ZRS and GRS, and why choosing the right region matters for performance, availability, compliance and disaster recovery.

Now let’s ask two simple questions:

💾 Where does the data live?
🖥️ Who actually does the work?

Imagine you’re using a food delivery app. 🍔

You place an order:

📱 Your phone sends the request
➡️ 🌐 Network carries it
➡️ 🖥️ Server processes your request
➡️ 💾 Storage/Database provides the required data
➡️ 🖥️ Server processes the information
➡️ 📱 Your phone gets the result

All of this happens in seconds.

Think of the data center we learned about yesterday as the building:

🏢 Data Center → The physical home
🖥️ Servers → The workers doing the computing
💾 Storage → Where files and data are kept
🌐 Networking → How everything communicates
🌍 Regions & Zones → Where these resources are physically located
🔄 LRS / ZRS / GRS → How data can be replicated and protected

💡 The big picture is starting to come together:

Cloud → Data Center → Servers + Storage → Networking → Databases → Data Engineering

And tomorrow, we’ll connect another important piece:

🌐 How does everything communicate?
🗄️ Where does structured data actually live?

Next up: Networking & Databases. 🚀

#DataEngineering365 #DataEngineering #Azure #AzureDataEngineering #CloudComputing AzureStorage Servers BigData LearnDataEngineering by @dataengineering365

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a month ago

🏢 DAY 4/365 | Where does your data actually live?

We talked about the Cloud. Then Azure. But there’s one important question:

Where is the Cloud actually running? ☁️

It runs on physical infrastructure inside data centers, and Azure has data centers distributed across different regions and availability zones around the world.

But why?

Imagine you’re building an e-commerce application 🌍

Your customers are in the US, Europe and Asia.

You could put everything in one location. But then:

📍 One location → Higher latency for users far away
🔥 One data center goes down → Your application could be affected
🌪️ A regional disaster occurs → You need another location for recovery
⚖️ Government regulations → Certain data may need to remain within a specific geographic boundary
🏢 Company policies → Your organization may require data to stay in approved regions

This is where Regions + Availability Zones + Storage Redundancy come together.

Think of it like this:

Data Center 🏢
→ Where the physical infrastructure lives

Region 🌍
→ A geographic area containing one or more data centers

Availability Zone 🛡️
→ Physically separate locations within a region designed to provide isolation from certain failures

LRS / ZRS / GRS 🔄
→ Different ways Azure can replicate and protect your data

So when choosing where your data lives, it’s not simply:

“Which location is closest?”

You also need to consider performance, availability, disaster recovery, compliance, data residency, company policies and cost.

💡 The big picture:

Cloud → Azure → Data Centers → Regions → Zones → Storage Redundancy

Once you understand this foundation, Azure Data Engineering starts making a lot more sense. 🚀

Tomorrow: Let’s finally talk about the building blocks inside these data centers: Servers & Storage.

#DataEngineering365 #DataEngineering #Azure #AzureDataEngineering #CloudComputing AzureStorage DataCenter BigData CloudArchitecture LearnDataEngineering by @dataengineering365

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a month ago

☁️ Azure, AWS & GCP all solve the same core problem: providing cloud infrastructure and services.
They differ in their strengths, ecosystems, and the use cases they’re best suited for.

💡 There’s no single “best” cloud. Understanding the differences helps you choose the right tool for the job. 🚀

Same destination. Different routes. ☁️
Azure, AWS and GCP offer similar core capabilities, but each has its own strengths and ecosystem.

As a Data Engineer, knowing the cloud matters more than knowing just one cloud. 🚀

☁️ Think of the Cloud as the foundation.
Azure, AWS and GCP provide the infrastructure and services, while tools like Databricks can run across multiple cloud platforms.

For example, you can use Azure Databricks, AWS Databricks, or Databricks on Google Cloud. Databricks isn’t owned by Azure, AWS, or GCP.

💡 Cloud providers provide the infrastructure. Other platforms and tools build on top of it.

Same goal. Different layers. 🚀 by @dataengineering365

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a month ago

☁️ DAY 2/365 | What is Microsoft Azure?

So, we know what the Cloud is. But where does Azure fit into the picture?

Microsoft Azure is Microsoft’s cloud computing platform that provides a huge range of services to build, run, store, process, and analyze applications and data without having to own and manage the physical infrastructure yourself.

Why is Azure important? 👇

🔹 Scalable: Increase or decrease resources as needed
🔹 Cost-effective: Pay for the resources you use
🔹 Secure: Enterprise-grade security and compliance
🔹 Global: Infrastructure available across the world
🔹 Wide range of services: Compute, storage, databases, analytics, AI, networking & more

For a Data Engineer, Azure provides the building blocks to ingest, store, transform, process, and analyze data at scale.

💡 Remember:
Cloud = the concept
Azure = Microsoft’s cloud platform

And this is just the beginning. 🚀

Tomorrow → Azure vs AWS vs GCP

#DataEngineering365 #DataEngineering #MicrosoftAzure #AzureDataEngineering

Microsoft Azure is a cloud computing platform that provides on-demand computing resources and managed services over the internet.

Instead of a company purchasing and maintaining physical servers, storage, networking equipment, and other infrastructure, Azure allows them to use these capabilities as cloud services.

For Data Engineering, some important Azure services include:

* Azure Data Lake Storage → Store large amounts of data
* Azure Data Factory → Data integration and orchestration
* Azure Databricks → Data processing and analytics
* Azure Synapse Analytics → Analytics and data warehousing
* Azure SQL Database → Managed relational database

Simple mental model:

Data Sources → Azure → Store → Process → Transform → Analyze → Insights

That mental model will become increasingly important as we move through the 365-day journey. by @dataengineering365

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a month ago

☁️ **DAY 1/365 | What is the Cloud?**

Before the Cloud, companies had to buy, maintain, and manage their own servers, storage, networking, electricity, and cooling.

The Cloud changed that. Instead of owning all the hardware, you can use computing resources over the internet whenever you need them.

💡 **In simple words:**
Cloud = Computing resources delivered over the internet.

And this is where our Data Engineering journey begins. 🚀

**Tomorrow: What is Azure?**

#DataEngineering365 #DataEngineering #CloudComputing #Azure #BigData #LearnDataEngineering #DataEngineer #TechEducation by @dataengineering365

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a month ago