A data lake is a centralized repository that stores large volumes of data in its original format —structured, semi-structured, and unstructured— to leverage later for analytics, reporting, and artificial intelligence. The core idea is simple and powerful: first you centralize all your data, and then you decide how to use it.
What is a data lake?
In a traditional database, you define the structure before storing the information: fixed tables, columns, and data types. A data lake reverses that order. It takes data as it arrives —transaction records, application logs, files, images, device data— and keeps it in its original format, without forcing you to model it up front.
This difference is known as schema-on-read versus schema-on-write. In a data lake, structure is applied at query time, not at storage time. That gives enormous flexibility: you can store information today whose use you don’t yet know, and later explore it to answer questions you hadn’t even considered when you captured it.
The result is a single place where all the organization’s data lives, ready to feed business dashboards, machine learning models, and artificial intelligence use cases.
What is a data lake for?
A data lake solves a very common problem: data lives scattered across many systems that don’t talk to each other. Consolidating it in one place enables several uses:
- Analytics and business intelligence: combine sales, operations, and customer data into dashboards that show a complete picture of the business.
- Data science and machine learning: train models on varied historical data that would otherwise be fragmented.
- Artificial intelligence: give AI models access to rich, up-to-date corporate information for cases such as assistants, recommendations, and automation.
- Data in its raw form: keep unstructured information —text, images, audio— that traditional databases handle with difficulty.
Data lake vs data warehouse
This is the most frequent comparison, and the short answer is that they don’t compete: they complement each other.
A data lake stores raw data in its original format and applies structure when you query it. It is ideal for exploration, data science, and AI workloads, where flexibility matters more than having everything perfectly modeled in advance.
A data warehouse stores already transformed, modeled, and curated data, optimized for fast and consistent queries. It is the right tool for business reporting and dashboards where definitions must be stable and reliable.
| Data lake | Data warehouse | |
|---|---|---|
| Data type | Raw, in its original format | Modeled and curated |
| Schema | At query time (schema-on-read) | At storage time (schema-on-write) |
| Best for | Exploration, data science, AI | Reporting and business dashboards |
| Flexibility | High | Structured |
Many companies run both: the data lake centralizes everything and serves as the foundation, and from there it feeds data warehouses for the reporting cases that require already modeled data. It is a mature and widely adopted pattern.
How to build a data lake on AWS
On AWS, the data lake relies on managed services that remove the need to administer infrastructure:
- Storage: Amazon S3 is the foundation, with durable, low-cost object storage that scales virtually without limit. Learn more on our Amazon S3 page.
- Catalog and transformation: AWS Glue discovers, catalogs, and transforms the data to make it analysis-ready, all serverless.
- Query: services like Amazon Athena let you query data directly over S3 with SQL, and Amazon Redshift covers large-scale analytics.
- Governance: AWS Lake Formation centralizes permissions and access control across the entire dataset.
The key is not just stitching services together, but designing ingestion, modeling, and governance so the data lake stays reliable and useful as it grows. A data lake with no governance ends up as a “data swamp” —a marsh of data that is hard to find and trust— and avoiding that is precisely part of the engineering work.
Best practices for a reliable data lake
- Define a clear zone structure (raw, processed, and curated data) so you don’t mix everything at the same level.
- Catalog data from the start so teams can discover and understand it.
- Apply governance and permissions by domain, so each team accesses only what it should.
- Automate ingestion and transformation pipelines to keep data fresh without manual work.
At Caleidos we build data platforms on AWS as part of our Data Engineering practice: we design the data lake, the pipelines that feed it, and the governance that keeps it reliable, ready for analytics and artificial intelligence. You can find production cases in our case studies.
Frequently asked questions
What is a data lake? A centralized repository that stores data in its original format —structured, semi-structured, and unstructured— to use for analytics, reporting, and AI, without defining the schema before storing it.
How does it differ from a data warehouse? The data lake stores raw data and applies structure at query time (schema-on-read); the data warehouse stores already modeled and curated data (schema-on-write). They are used in a complementary way.
What is it for? To centralize data from many sources and enable advanced analytics, machine learning, and artificial intelligence over all that information.
Which AWS services are used? Amazon S3 for storage, AWS Glue to catalog and transform, and Athena or Redshift to query, with Lake Formation for governance.
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