If you’ve ever worked with old systems, you know the pain: data scattered across different sources, different formats, and no single source of truth. The goal is simple, bring it all together into one clean data lake. The journey usually has two parts:

Step 1: Old Data Migration

This is the one-time lift. You take a dump of existing data (JSONs, SQL files, whatever’s lying around), map it to a new schema, clean it up, and migrate. Think of it as spring cleaning—tedious but straightforward. Depending on how messy things are, this can take anywhere from 3 to 8 weeks.

Step 2: Ongoing Data Feeds

Now comes the trickier part—keeping the lake fresh every day. There are two main ways to do this:

• APIs: Set up APIs to serve the right data, pull it every 12–24 hours, and feed it into the lake. The upside? Clean integration. The downside? APIs need ongoing maintenance. Any backend change means updating the API.

• AI Agents: These act like virtual interns. They log in, mimic a real user, grab the data snapshots, and push them into your database. Fast to set up (under a week with the right skills), but they’re more of a workaround than a permanent fix.

Streamlining the ETL pipelines and creating a single-source-of-truth datalake is super essential for any company as they optimise, scale, and adapt to the latest technological changes.

Toystack has done this time and again with clients across industries—helping them unify messy data, streamline their ETL pipelines, and build reliable data lakes.

If you’re dealing with fragmented systems, reach out to us for a FREE Tech Audit of your data warehouse and data feeds.