Data Lake

In depth

Data lakes emerged to meet the needs of modern analytics. Traditional data warehouses require you to define a structure before you load data. Data lakes use schema-on-read, which means you decide how to organize data when you query it — not when you store it.

Under the hood, a data lake relies on object storage or distributed file systems. You can ingest logs, images, audio files, and database exports side by side. This flexibility supports experimentation and machine learning projects without requiring upfront data modelling.

Without governance, a data lake can get messy. A metadata catalog, access controls, and clear data ownership are required to keep data trustworthy and reliable. Unmanaged lakes quickly become "data swamps" — full of data no one can find or trust.

Pro tip

Tag and catalogue your data as you ingest it. Retroactively applying metadata to thousands of files is slow and error-prone. Building the habit at ingestion keeps your lake navigable as it grows.

Why Data Lakes matter

Data lakes are a foundational component in modern analytics and AI. They give teams the flexibility to store everything now and extract value later — without committing to a schema that might not fit tomorrow's questions.

Here's what that flexibility enables:

• Unlock new insights by combining data from diverse sources, such as logs, CRM data, and video files, in one place.

• Scale storage cost-effectively on platforms like Amazon S3 or services like Google Cloud Storage.

• Support AI and machine learning by preserving raw, unprocessed data that models can train on directly.

Data Lake - In practice

At a growing e-commerce retailer, raw sales transactions, clickstream logs, and customer reviews all land in a data lake. Data engineers apply transformations and perform quality checks. Then, analysts use tools like PowerMetrics to build dashboards and explore metrics on top of the clean data.

The data lake handles the volume and variety. Downstream tools handle the meaning.

Data Lakes and PowerMetrics

With Klipfolio PowerMetrics, you can retrieve data from data warehouses and from services with APIs. You can then model the data, define consistent metrics, and make trustworthy answers available to your team — in dashboards, AI, and the flow of work.