Data Lakes to Data Malls: Organizing Big Data

Data lakes store raw data from many sources in many formats. They work well for experiments and archival work. Business teams, however, often need clean, well-defined data for dashboards and decisions. A data mall turns a lake into domain-focused, curated slices. Each mall offers consistent definitions, governed access, and ready-to-use datasets designed for areas like sales, marketing, or finance.

Moving from lake to mall adds governance, cataloging, and a semantic layer. The goal is faster, trusted data for daily decisions and recurring reports. A simple catalog helps people find the right data quickly, while a semantic layer translates business terms into the actual fields you store.

Practical steps

  • Define a small set of core domains (for example, Sales, Marketing, Finance) and appoint data owners.
  • Create a lightweight catalog that describes datasets, fields, lineage, and the responsible team.
  • Map business terms to data fields with a semantic layer so analysts see familiar names.
  • Enforce access controls, data quality rules, and audit trails to keep data safe and reliable.
  • Start with 3–5 curated datasets or “marts” and grow thoughtfully as needs emerge.

A practical example

A retailer uses a SALES_MART with fields like customer_id, order_id, date, amount, region, and product_category. MARKETING_MART adds campaign_id, channel, and impressions. Both marts pull from the same CRM and ERP feeds, but each serves different questions and dashboards. This shared source, plus clear definitions and keys, reduces confusion and speeds reporting.

Common pitfalls

  • Building too many marts and adding overhead without clear business value.
  • Vague data ownership or missing lineage, which undermines trust.
  • Inconsistent definitions across marts, which breaks comparability.

With data marts, you create clarity from data chaos. Teams move faster, dashboards stay consistent, and data teams can scale to serve more domains.

Key Takeaways

  • Data lakes can be organized into data marts to serve business domains.
  • A data catalog, governance, and a semantic layer improve access and trust.
  • Start small with 3–5 core domains and iterate.