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.

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 and accelerates experimentation and machine learning projects.

Without governance, a data lake can get messy. A metadata catalog, access controls, and clear data ownership are required to ensure trustworthy, reliable data.

Pro tip

Tag and catalogue your data as you ingest it. Proper metadata management prevents your lake from turning into a “data swamp” of lost information.

Why Data Lakes matter

Data lakes are an important component in modern analytics and AI. They help teams:

• Unlock new insights by combining data from diverse sources, such as logs, CRM data, and video files.
• Scale storage cost-effectively on a platform, like Amazon S3, or a service, like Google Cloud Storage.

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 real-time dashboards on top of the clean data.

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 and define and build metrics for self-serve analytics.