Data Hub vs Data Lake: My Hands-On Take

I’ve built both. I got burned by both. And yes, I still use both. Here’s what actually happened when I put a data lake and a data hub to work on real teams. For an expanded breakdown of the differences, check out my standalone explainer on Data Hub vs Data Lake: My Hands-On Take.

First, quick picture talk

  • Data lake: a big, cheap store for raw data. Think S3 or Azure Data Lake. Files go in fast. You read and shape them later.
  • Data hub: a clean station where trusted data gets shared. It sets rules, checks names, and sends data to many apps. Think Kafka plus MDM, or Snowflake with strong models and APIs.

If you’d like an additional industry-focused perspective, TechTarget’s overview does a solid job of contrasting data hubs and data lakes at a high level.

Simple? Kind of. But the feel is different when you live with them day to day.

My retail story: the lake that fed our models

At a mid-size retail shop, we built an AWS data lake. We used S3 for storage. AWS Glue crawled the files. Athena ran fast SQL. Databricks ran our Spark jobs. We also added Delta Lake so we could update data safely.

What went in?

  • Click logs from our site (CloudFront logs and app events)
  • Store sales files (CSV from shops each night)
  • Product data from MySQL (moved with AWS DMS)

What did it do well?

  • Our ML team trained models in hours, not days. Big win.
  • We ran ad-hoc checks on two years of logs. No heavy load on our core DB.
  • Costs stayed low when data sat still.

Where it hurt?

  • File names got messy. We had “final_final_v3.csv” everywhere. Not proud.
  • Lots of tiny files. Athena slowed down. So we had to compact them.
  • People misread columns. One analyst used UTC. One used local time. Oof.

Fixes that helped:

  • Delta Lake tables with simple folder rules
  • Partitions by date, not by every little thing
  • A short “what this table is” note in a shared sheet (later we used a catalog)

You know what? The lake felt like a big garage. Great space. But it gets cluttered unless you clean as you go. I chronicled the gritty details of that build in an in-depth post, I Built a Data Lake for Big Data—Here’s My Honest Take.

My health data story: the hub that kept us honest

At a hospital network, we needed one truth for patients and doctors. Many apps. Many forms. Lots of risk. We built a hub.

Core pieces:

  • Kafka for real-time events
  • Debezium for change data capture from source DBs
  • Informatica MDM for “golden” records (IDs, names, merges)
  • An API layer to share clean data with apps
  • Collibra for terms and who owns what

What it did well:

  • New apps could plug in fast and get the same patient ID. No more “John A vs John Allen” chaos.
  • Access rules were tight. We could mask fields by role.
  • Audits were calm. We could show who changed what and when.

Where it hurt:

  • Adding a new field took time. Reviews, tests, docs. Slower, but safer.
  • Real-time streams need care. One bad event schema can break a lot.
  • Merges are hard. People change names. Addresses change. We had edge cases.

Still, the hub felt like a clean train station. Schedules. Signs. Safe lines. Less wild, more trust.

That experience has weird parallels outside analytics circles too. If you’ve ever tried to manage a local classifieds board without it devolving into spam and duplicate posts, you’ll recognise how disciplined governance keeps things usable. A quick look at the structured layout of the Citrus Heights Backpage listings illustrates how consistent categories, required fields, and active moderation preserve searchability and trust—takeaways you can directly apply when designing data quality rules for your own hub.

A lean startup twist: both, but light

At a startup, we did a simple version of both:

  • Fivetran pulled data into Snowflake.
  • dbt made clean, shared tables (our mini hub).
  • Raw files also lived in S3 as a small lake.
  • Mode and Hex sat on top for charts and quick tests.

This mix worked. When a marketer asked, “Can I see trial users by week?” we had a clean table in Snowflake. When the data science lead asked, “Can I scan raw events?” the S3 bucket had it.

So which one should you use?

Here’s the thing: the choice depends on your need that day.

Use a data lake when:

  • You have lots of raw stuff (logs, images, wide tables).
  • You want low-cost storage.
  • You explore new ideas, or train models.
  • You don’t know all questions yet.

Use a data hub when:

  • Many apps need the same clean data.
  • You need rules, names, and IDs set in one place.
  • You have privacy needs and fine access control.
  • You want a “single source of truth.”

Sometimes you start with a lake. Then, as teams grow, you add a hub on top of trusted parts. That’s common. I’ve done that more than once. For a deeper dive into setting up lightweight governance without slowing teams down, I found the practical guides on BaseNow refreshingly clear.

Real trade-offs I felt in my bones

  • Speed to add new data:

    • Lake: fast to land, slower to trust.
    • Hub: slower to add, faster to share with confidence.

  • Cost:

    • Lake: storage is cheap; compute costs can spike on messy queries.
    • Hub: tools and people cost more; waste goes down.

  • Risk:

    • Lake: easy to turn into a swamp if you skip rules.
    • Hub: can become a bottleneck if the team blocks every change.

  • Users:

    • Lake: great for data scientists and power analysts.
    • Hub: great for app teams, BI, and cross-team work.

My simple rules that keep me sane

  • Name things plain and short. Date first. No cute folder names.
  • Write a one-line purpose for every main table.
  • Add a freshness check. Even a tiny one.
  • Pick 10 core fields and make them perfect. Don’t chase 200 fields.
  • Set owners. One tech owner. One business owner. Real names.
  • For streams, use a schema registry. Do not skip this.

A quick, honest note on “lakehouse”

Yes, I’ve used Databricks with Delta tables like a “lakehouse.” It blends both worlds a bit. It helped us keep data cleaner in the lake. But it didn’t replace the hub need when many apps wanted strict, shared IDs and contracts. For a broader context, IBM’s comparison of data warehouses, lakes, and lakehouses is a handy reference.

If you’re weighing even newer patterns like data mesh or data fabric, I shared my field notes in I Tried Data Lake, Data Mesh, and Data Fabric—Here’s My Real Take.

My bottom line

  • The lake helps you learn fast and train well.
  • The hub helps you share clean data with less fear.
  • Together, they sing.

If I were starting tomorrow?

  • Week 1: land raw data in S3 or ADLS. Keep it neat.
  • Month 1: model key tables in Snowflake or Databricks. Add tests in dbt.
  • Month 2: set a small hub flow for your “golden” stuff (customers, products). Add simple APIs or Kafka topics.
  • Ongoing: write short notes, fix names, and keep owners real.

It’s not magic. It’s chores. But the work pays off. And when someone asks, “Can I trust this number?” you can say, calmly, “Yes.” Anyone promising a one-click “just bang” fix for data quality is selling wishful thinking—though, if you’re curious what a literal one-click gratification pitch looks like, take a peek at JustBang where you’ll see how bold promises are packaged for instant consumption.