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:
- How to store data ? For minutes, years, or temporarily?
- How to decide whether data is needed for analytics or transaction processing?
- How to store based on the companies IT budget and management?
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
- “What is the current problem?”
- “What do you want to achieve?”
- “What is the purpose?”
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.
- Databases are versatile and not expensive
- Easily accessible through SQL
- Manageable data volumes with simple indexing, query optimization, and good architecture and design (pivot table fo example)
- Not that technically impossible lots of resources online exists on how to setup a proper database, with basic roles and minimum security
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:
- Public cloud environments
- Private cloud setups
- On-premise infrastructure in company server rooms
Detailing the 3 main solutions
1. Database
Best for: Transactional processing, real-time operations, structured data
- Use cases: Customer records, order processing, inventory management
- Characteristics: ACID compliance, fast read/write operations, structured schema
- Examples: PostgreSQL, MySQL, Oracle, SQL Server
2. Data Warehouse
Best for: Analytics, reporting, business intelligence
- Use cases: Historical analysis, trend reporting, executive dashboards
- Characteristics: Optimized for complex queries, columnar storage, data modeling
- Examples: Amazon Redshift, Snowflake, Google BigQuery, Azure Synapse
3. Data Lake
Best for: Raw data storage, machine learning, exploratory analysis
- Use cases: IoT data, log files, unstructured data, data science projects
- Characteristics: Schema-on-read, scalable storage, supports multiple data formats
- Examples: Amazon S3, Azure Data Lake, Google Cloud Storage
Making the Right Choice
When choosing between these solutions, more questions needs to be answered:
- Data Volume: How much data do you need to store and process?
- Data Velocity: How fast is data being generated and how quickly do you need to access it?
- Data Variety: What types of data are you working with (structured, semi-structured, unstructured)?
- Use Case: Are you primarily doing transactions, analytics, or both?
- Budget: What are your cost constraints and ROI expectations?
- 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.