Snowflake

Data Storage: From Databases to Data Lakes and Data Warehouses

Imagine you own a small bakery. At first, keeping track of sales is easy—you just write everything in a notebook. Every time a customer buys a croissant or a loaf of bread, you note it down. Simple, right?

But as your business grows, problems start to appear:

- It takes too long to find how many croissants you sold last Tuesday.

- Your notebook is filling up fast, and flipping through pages is exhausting.

- You want to compare sales across different months, but that means manually adding up numbers.

You realise you need something better—and that’s when technology comes in. This is the journey from databases to data lakes to data warehouses.

Databases – Solving Small Business Problems

So, you decide to switch from a notebook to a computerised system, like Excel or a simple software tool. You can now store sales in a digital format, making it easier to search, update, and filter.

A database is like this computerised system—it organises data into tables (like an Excel sheet) and allows you to find information quickly using queries (like searching in a spreadsheet).

Relational databases such as MySQL and PostgreSQL solved these problems:

✅ Quick searches—find last Tuesday’s sales in seconds.

✅ Structured data—each sale has a clear structure (Date, Product, Price, Customer).

✅ Easy to update—mistakes can be fixed without crossing out pages.

But as your bakery grows into a chain of 50 stores, databases start struggling. Why?

1. Too much data – Your single database now holds sales records for thousands of customers.

2. Different types of data – You now want to store customer reviews (text), images of cakes, and even security camera footage.

3. Scaling problems – A single database is slowing down as more employees and systems access it.

You need a new solution. That’s where data lakes come in.

Data Lakes – Handling Massive and Unstructured Data

Now, instead of trying to fit everything into a structured database, you get a giant digital storage room.

Think of a data lake as a massive storage space where you can throw in everything—not just structured tables but also:

📊 Sales data (structured like a database)

📷 Photos of your cakes (unstructured images)

📝 Customer reviews (semi-structured text data)

🎥 Security camera footage (videos)

Unlike a database, a data lake does not force data into a strict format. It is like a giant pool where all types of data can be stored, whether structured or not.

This helps with:

✅ Large-scale data storage – You can store billions of records without worrying about format.

✅ Flexibility – You do not have to decide the structure in advance.

But problems arise when you try to use it:

1. Messy data – The data lake becomes a data swamp if not organised properly. Searching for specific sales data from last Tuesday is now harder.

2. Slow processing – Unlike databases, you cannot just run quick queries on a data lake; it takes time to find and process information.

So, you need something fast, structured, and optimised for analysis. That is where data warehouses come in.

Data Warehouses – Turning Data into Insights

Now that you have large amounts of data in your data lake, you need a clean, structured, and fast way to analyse it.

A data warehouse is like a highly organised library where only the most valuable data from your data lake is stored, cleaned, and structured for fast decision-making.

Your bakery chain now has:

📈 Sales trends across different locations.

👥 Customer behaviour insights (for example, which cakes are trending).

📅 Demand forecasting (for example, predicting holiday sales).

Data warehouses are designed for business intelligence. They help companies make decisions based on data. Unlike data lakes, they focus on:

✅ Cleaned and structured data – No mess, only valuable data ready for analysis.

✅ Fast performance – Optimised for running reports and analytics.

✅ Business-friendly use – Built for dashboards and decision-making.

However, traditional data warehouses still had problems, such as:

1. Expensive scaling – Storing and processing large amounts of data was costly.

2. Rigid structure – Changing data formats required a lot of effort.

That is when cloud-based solutions like Snowflake changed the game.

Snowflake – The Future of Data Warehousing

Snowflake is a modern, cloud-native data warehouse that fixes the problems of old data warehouses:

✅ Scalability – It expands or shrinks automatically based on need.

✅ Separation of storage and computing – Store as much data as you want and pay only for what you process.

✅ Handles semi-structured data – Works with both structured and semi-structured data (for example, JSON).

✅ Multi-cloud support – Runs on AWS, Azure, and Google Cloud.

With Snowflake, your bakery chain can:

🔹 Analyse real-time sales instantly.

🔹 Store and process both structured and semi-structured data.

🔹 Scale dynamically without worrying about infrastructure.

Why This Matters

1. Databases are great for storing structured transactional data, such as sales records.

2. Data lakes help store all types of raw data, but they can become messy.

3. Data warehouses clean and structure data for fast analysis.

4. Snowflake, and other modern cloud data warehouses, solve issues related to scalability, performance, and cost.

If you are running a business today, understanding these concepts helps you make smarter data decisions. Whether it is managing a bakery or running a global company, data is the key to growth, and choosing the right system depends on how you want to use it.