It’s 2024, and you have a mountain of data to organize - and learn from. How do you do it?

Two real-life examples:

• In 2013, UPS upgraded their data warehouse with petabytes of structured data. This powered a project to dynamically optimize delivery routes. They analysed large amounts of logistics data to make rapid real-time route adjustments, ultimately significantly cutting shipping miles and carbon emissions.
• In 2021, Coca-Cola Andina leveraged AWS to build a data lake, consolidating 95% of its disparate business data and integrating analytics, AI, and machine learning. Because data was all in one place, the analytics team spent less time talking to data owners to find what they needed - increasing productivity by 80%. This fostered a culture of data-driven decision-making across the organization, as well as increasing revenue.

These show the two dominant data organization patterns: data warehouses and data lakes. Here are the main differences:

Data types

• Data warehouses are primarily for structured data.
• Data lakes can store any data type: structured, semi-structured or unstructured.

Flexibility

• Data warehouses require setting up a data schema upfront. This streamlines querying, but limits the ability to pivot to new data or use cases that don't fit the original plan.
• Data lakes let you ingest raw data from diverse sources without prior organization - no matter its type or structure - and decide how to use it later. This approach is highly flexible but can increase complexity.

Scaling cost

• Data warehouses are intended for smaller amounts of operational data, and tend to require upfront investment, especially on-prem. Because storage and compute are coupled, costs tend to increase with scale - and past a certain size, large datasets become prohibitively expensive.
• Data lakes tend to be more cost-effective off the bat, especially with pay-as-you-go cloud offerings. As storage is inexpensive and decoupled from compute, they can leverage serverless elasticity to scale up and down automatically, and store operational and archive data in one place.

Use cases

• Data warehouses are great for high-speed querying and reporting on structured datasets - ideal for critical decision-making with tools like PowerBI and Tableau. Generally a smaller group of business professional users.
• Data lakes are best for vast amounts of diverse raw data, from CSV files to multimedia. This breadth allows for exploratory analysis, predictive modeling, statistical analysis, and ML. Variety of users, from analysts to data scientists.

The choice depends on what kind of problems you want to solve. If you're after fast analysis within a predefined structure, data warehouses could be your go-to. On the flipside, data lakes offer flexible insights across a broad range of data, more easily scalable to large datasets.