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Database vs Data Warehouse vs Data Lake | Key Differences and Best Use Cases

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5 min read time

Introduction

In the world of data and digitalization, storage is always an up-to-date topic. From floppy disks to CDs, USB sticks, hard drives, SSDs, and NVMe drives, the evolution of storing information, whether programs, pictures, music, or videos,the evolution of storing data is linked to the current technology.

The choice of storage solution depends on your specific needs and the era you’re in (no more floppy disk unfortunately)

But this data storage is only linked to individuals, storing private or critical information, but plug-in the device to access it. As a company, the need is quite different…

In the business world (for data field), storage systems have evolved accordingly, and now companies have (too much) choices that brings new challenges:

Those questions belong to the technical team and engineering department, but the technical solution must solve business problems. To make the right choice, we need to understand the company’s specific needs and objectives.

Ask questions

Those are always the most obvious questions, yet sometimes the one we forget: What are the company’s goals for using their data?

Without knowing why the company needs a specific proper storage, then it’s impossible to bring a proper solution.

If the answer is “We don’t know yet, but we’ll figure it out later…” then you can be almost certain that a database will be chosen, that’s the default choice. As simple as it sounds, a simple database give opportunities to the business to store in a shared place, important information accross the entity.

If the company knows exactly what they want to achieve—for example, if analytics is a priority—a data warehouse might be more appropriate.

Data warehouses focus primarily on analytics and query performance, often complementing rather than replacing traditional databases when the business starts to deal with more and more data.

Available Solutions

No One-Size-Fits-All Approach

Nothing is black or white. Even combinations of databases, data warehouses, and data lakes will have their challenges. The best solution isn’t necessarily the most “trendy,” “beautiful,” or “used by other companies.”

The best solution is the one that solves the specific problems identified in the “Ask questions” section.

Currently, the market offers numerous choices. Data solutions can be deployed in:

Detailing the 3 main solutions

1. Database

Best for: Transactional processing, real-time operations, structured data

2. Data Warehouse

Best for: Analytics, reporting, business intelligence

3. Data Lake

Best for: Raw data storage, machine learning, exploratory analysis

Making the Right Choice

When choosing between these solutions, more questions needs to be answered:

  1. Data Volume: How much data do you need to store and process?
  2. Data Velocity: How fast is data being generated and how quickly do you need to access it?
  3. Data Variety: What types of data are you working with (structured, semi-structured, unstructured)?
  4. Use Case: Are you primarily doing transactions, analytics, or both?
  5. Budget: What are your cost constraints and ROI expectations?
  6. Team Expertise: What skills does your team have?

Conclusion

The choice between a database, data warehouse, or data lake (or a combination) should be driven by the specific business requirements from the company, not by industry trends.

First start by clearly defining the data goals, then select the solution that solve the problem considering a balance between technical capabilities and budget constraints.

As a reminder : the most expensive or complex solution isn’t always the best one.

Focus on solving your actual business problems rather than building infrastructure for problems you might have in the future (because they might never happens)


Thanks for reading this article. If you have comments feel free to reach me by mail or fill up the form directly and I’ll personally answer you.



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