Data Storage Architecture Simplified - How do businesses store their data for analytics?
There are a ton of good resources on this topic, dissecting the differences between data warehouses and data lakes. This will provide a very high level overview, potentially oversimplifying in some areas. For a technical deep dive, check out the paper linked at the bottom of this page.
Let’s take a look at common ways companies store and use their data. At a fundamental level:
A data warehouse is like an organized library. It’s essentially a database optimized for analytics.
A data lake is like a giant storage room where anything can be dumped. It’s basically a folder or collection of folders full of files on a computer.
A data lakehouse combines the best of both to give you organization and flexibility. This one is the fuzziest and most fluid in terms of definition, it’s a marketing term and can look different ways in practice. Many large organizations already use a raw data → data lake → data warehouse → analytics pipeline, so this term just combines them. The idea is to build an organized section of the data lake that could be called a data warehouse. Hence the house built on a lake.
Here’s a breakdown with simple examples:
Data Warehouse
A data warehouse organizes and stores clean, structured or semi-structured data that can easily be used to create reports and track performance. It is not a transactional or operational database like what an ERP runs on. It’s purpose built for making sense of data as it accumulates over time.
Example:
Imagine your manufacturing company has two software systems:
System 1 tracks how many products you make each day.
System 2 tracks how much you sell and who your customers are.
You decide to combine this data to see how production and sales match up. You organize it so you can answer questions like:
“Which products are our best sellers?”
“Are we making enough of them to meet demand?”
A data warehouse helps you put this information into clear reports or charts. For example:
You create a bar chart that shows your best customers by product.
You compare production numbers to sales numbers to spot gaps.
The data warehouse makes it easy to get reliable, organized insights from your data.
Pros:
Clean and organized data, easy to use for reporting.
Helps answer specific business questions.
Cons:
Can’t store unstructured data like images or machine logs.
Expensive to maintain and grow.
Data Lake
A data lake is where you can dump all your raw data: whether it’s spreadsheets, logs, images, or video, even if you don’t know how you’ll use it yet. This data can be structured, semi-structured, or unstructured.
Example:
Your manufacturing company collects data from machines on the shop floor. This data includes:
Temperature readings
Vibration levels
Error messages when machines break down
At first, you don’t know what to do with this information, so you just store it in a data lake.
Later, a data scientist decides to analyze the machine data. They discover a pattern: Machines with higher-than-normal temperatures and unusual vibrations often break down a few days later.
With this insight, you create a system that alerts you when a machine is likely to fail so you can fix it early. This prevents costly breakdowns and saves time.
Pros:
Can store all types of data, even unstructured data like logs or images.
Great for exploring data to find new insights.
Cons:
Without proper management, the data lake can become a data swamp: messy and hard to use. Think of a disorganized folder on a network drive. That’s a data swamp.
It’s harder to create simple, organized reports directly from a data lake. It takes more engineering work to get the data processed and ready.
Data Lakehouse
A data lakehouse combines the best of a warehouse and a lake. It gives you organized data for reports and analysis while also letting you store raw data for advanced analytics and machine learning.
Example:
Your manufacturing company decides to combine everything into a data lakehouse:
You store unstructured machine sensor data (temperature, vibration) in its raw form.
You store structured, organized sales and production data from your systems.
You analyze all of this data together in one place.
Here’s how this works:
Your sales team creates simple charts to see which products are selling the most.
At the same time, your data scientists look at the machine data to predict failures.
Combining these insights, you discover that certain machines breaking down delays production of your top-selling products.
With this knowledge, you take action:
You use alerts to repair machines before they fail.
You ensure production runs smoothly, and you meet customer demand.
The data lakehouse allows your team to get simple, clear reports while also supporting deeper analysis to solve bigger problems.
Pros:
Combines the organization of a warehouse with the flexibility of a lake.
Lets you analyze both clean, structured data and messy, raw data.
Supports simple reports and deeper analysis in one system.
Cons:
Newer approach, so it may take time to set up properly.
Summary
With the abundance of data, much of it unstructured, businesses looking to implement an analytics architecture should explore the concepts behind a data lakehouse before throwing up an outdated data warehouse or creating a data lake that turns into a data swamp. The advent of AI allows you to combine images, video, Excel files, databases, PDFs, JSON logs, customer inquiries, emails, and more to produce analytical insight to give you a competitive advantage.
The data lakehouse brings everything together, helping companies save time, reduce costs, and get the best insights from all their data.
This post was inspired by this paper by Databricks that introduced the concept of data lakehouses. Check it out for a more technical journey through modern data architecture.
Morph Data Strategies manages the setup of data architecture for you:
Ingestion ELT pipelines to connect to data sources
Data lakehouse architecture to store and combine unstructured and structured data
Business intelligence and data science run on the lakehouse.
To hear more, contact us.
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