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How Genius AI Migrated Complex Data Pipelines to Matia in a Few Weeks

Genius AI had its most complex data pipeline running on Matia in 24 hours, then moved pipelines from Postgres and external sources over a few weeks.

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Genius AI's data stack was assembled over 10 years from a mix of vendors, and it was hard to maintain. Brady Allchin, VP of Engineering, wanted the team to deliver a reliable data pipeline to stakeholders inside and outside the company.

24 hours for the most complex pipeline, a few weeks to migrate

"We knew within 24 hours it was the right decision"

Their largest, most complex pipeline was up and running on Matia within 24 hours.

The broader migration took a few weeks. That covered pipelines from internal Postgres stores and external data sources. "

The timeline to migrate to Matia really did surprise me"

Time goes back to the business

Moving to Matia streamlined Genius AI's stack and saved money. It also changed how the data team spends its time. "It now goes to supporting the business instead of keeping our pipelines alive."

Lineage catches upstream changes

Matia's data lineage alerts the team when another part of engineering changes one of their data sources, before it causes a reliability problem for the analytics team. Teams in every department get data on a tight SLA, refreshed and up to date, so they can run the reports they need.

"If you're a data leader and you want to take your team from reactive work to one that's proactive and enabling the business, Matia is the partner that enables you."
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Blog
Product Updates

Matia Adds Sigma to Its Data Catalog

Matia's Catalog now connects to Sigma. Trace any Sigma workbook, element, or column back to the Snowflake tables behind it.

Today, Matia is launching Sigma in the Catalog.

Connect Sigma, and your workbooks, elements, and columns show up in Matia with lineage back to Snowflake. When a number in Sigma looks wrong, you can see where it came from. Before you change a Snowflake column, you can see which workbooks use it.

Sigma joins Metabase, Tableau, Omni, Hex, and Power BI all in one lineage graph in Matia.

What's New

What’s included:

  • Sigma assets: folders, workbooks, reports, data models, datasets, elements, and columns
  • Element lineage: which elements each element pulls from
  • Snowflake lineage: which Snowflake tables each Custom SQL element queries
  • Column-level lineage: Custom SQL and column formulas read, and each column links to the Snowflake columns or element columns it uses
  • Formulas and data types: each column shows its Sigma formula and type

Why It Matters: Lineage No Longer Stops at the Warehouse

Sigma workbooks are built in layers. One element pulls from another. Custom SQL pulls from Snowflake. Column formulas reference columns in other elements.

Without lineage into Sigma, someone traces those layers by hand when a number looks wrong. And when a Snowflake column changes, nobody knows which workbooks break until someone opens one.

Six BI Tools, One Lineage Graph

Most data teams run more than one BI tool. Matia's Catalog covers six, and they all sit in the same lineage graph.

Matia also runs ETL and Observability, so lineage goes past the warehouse:

  • Back to the source: when your data moves through Matia, a Sigma column traces through Snowflake and dbt to the system it came from.
  • Before a change: when Matia detects a schema change in a sync, lineage shows which Sigma workbooks sit downstream.
  • In your AI tools: ask Matia MCP which Sigma workbooks a table change will affect.

What Teams Use It For

No matter where you sit in the data org, you can benefit from Snowflake/Matia lineage capabilities.

  • Data engineers: see which Sigma workbooks use a column before changing it
  • Analytics engineers: show where a number came from without reopening every element.
  • Heads of data: one catalog across all your BI tools

Already on Matia? Connect Sigma from the Catalog.

New to Matia? Book a demo to see lineage across Sigma and your other BI tools.

Product Updates
Case Studies

Sync PlanetScale to Your Data Warehouse with Matia

Teams choose PlanetScale so they don’t have to manage MySQL servers themselves. The data in it still needs to reach the warehouse for reporting, models, and the business tools that depend on it.

Because PlanetScale manages the database for you, syncing changes works differently than it does on a self-managed MySQL server. Standard MySQL change data capture reads the binary log, which you’d normally configure on the server yourself.

PlanetScale streams through VStream, the change stream built into Vitess. To sync incrementally, a connector needs to read from VStream. Without that, keeping the warehouse current means reloading full tables.

Once the data lands in the warehouse, teams need to know whether it’s current, catch schema changes before they break a report, and trace a bad number back to its source. In most data stacks, each of those is a separate tool. In Matia, they’re part of the same platform.

Why we built it

This connector started with a customer. A team running PlanetScale as its primary database wanted to move to Matia, and syncing that database to their warehouse was the one thing in the way. It wasn’t on our roadmap for the quarter, but we like to build quickly when our customers are asking. We added it, built incremental sync on VStream, and now it’s shipped.

We built the connector ourselves, and we support it ourselves. If a PlanetScale sync needs attention, you talk to the same team that runs the rest of your pipeline, and they respond in minutes.

What we built

Matia now supports PlanetScale as an ETL source, along with self-hosted Vitess. The connector reads row changes directly from VStream, so incremental syncs work on PlanetScale the way change data capture works on MySQL. The connector supports:

  • Full refresh, including views and tables without primary keys
  • Incremental sync through VStream
  • Column hashing for sensitive columns
  • Automatic exclusion of system schemas and Vitess internal schemas like _vt

Why it matters

Setup doesn’t require any changes to your production database. You create a read-only password in PlanetScale, and that one password covers both the initial sync and every incremental sync after it.

Incremental syncs load only the rows that changed, so you aren’t reloading full tables to keep the warehouse current. Our post on CDC vs. ETL explains when each method fits.

Because Matia brings ETL, Reverse ETL, Observability, and Catalog together in one platform, the pipeline that syncs your PlanetScale data is the same one that monitors it, traces it, and sends it to the tools your teams use:

  • Observability monitors the sync for schema changes and data quality issues, with alerts in Slack.
  • The Catalog traces lineage from your PlanetScale tables through the warehouse and out to business tools, down to the column.
  • If you send that data back out with Reverse ETL, a failed check can stop the sync before bad data reaches those tools, when configured to do so.

When a number in a report looks wrong, you can trace it back to the PlanetScale table it came from, in the same place you set up the sync.

That context also reaches the AI tools your team already uses through Matia MCP. Your AI assistant can look up a PlanetScale table, trace its lineage, check why a sync failed, and see what a schema change affects downstream. It works under each person’s existing Matia roles, so the assistant can only see what that person can see.

Every source you connect adds to that record. Run history, monitors, and lineage for PlanetScale sit next to the rest of your data. When a number looks wrong, your team or an AI assistant can trace it back to the source and fix it in one place.

How to set it up

In the PlanetScale console, create a read-only password on the branch you want to sync, usually your production branch.

In Matia, add a MySQL connection and choose Vitess as the server type. Enter the host, username, and password from PlanetScale, keep the default VStream port of 443, and set the keyspace to your PlanetScale database name.

Each connection syncs one keyspace, so a sharded database with several keyspaces needs a connection per keyspace. SSH tunneling isn’t available yet for PlanetScale or Vitess, so connect directly.

For self-hosted Vitess, point Matia at your VTGate gRPC port and keep full row images enabled, which is the Vitess default. The setup guide covers both.

Get access

The PlanetScale connector is available now for Matia customers. See the MySQL connector docs to get started. If you’re new to Matia, book a demo and we’ll walk through it with you. If you have questions during setup, our support team responds in minutes.

Blog
Product Updates

Matia Lineage Now Runs Through Your Snowflake Transformations

Matia now traces lineage through transformations you run inside Snowflake, including stored procedures and dynamic tables. Through Matia MCP, Cortex Code (CoCo) and Cortex Agents can use it too.

It's rolling out now for Snowflake Enterprise edition and above.

Snowflake transformations in Matia lineage

A lot of data teams transform inside Snowflake. A stored procedure merges yesterday's orders into a reporting table. A dynamic table keeps a daily revenue rollup fresh. Until now, those steps were a gap in Matia's lineage graph. You could see the table a procedure wrote to, but not how the data got there.

Matia now reads lineage directly from Snowflake Horizon, with no SQL parsing on our side. Each transformation shows up as its own node between the tables it reads and writes. Column-level edges run through it, so you can follow a single field from source to output. Views and dynamic tables get their own badges, which makes a transformation output easy to tell apart from a plain table.

Impact analysis runs through these nodes too. Change a source table and Matia shows what sits downstream, including everything the procedure feeds.

Check impact before CoCo changes a transformation

MCP is an open standard, so Matia MCP works in any client that supports it. CoCo and Cortex Agents both do.

Say an engineer asks CoCo to rewrite the procedure that builds fct_orders. Before it touches the code, CoCo can ask Matia what depends on that table. Matia comes back with the monitors watching it and the reverse ETL sync that pushes it to Salesforce. It also lists the dashboards that read from it. The engineer approves the change knowing exactly what it touches.

Matia MCP recommends. You confirm every action before anything happens.

Trace a bad answer back to its source

Now the other direction. A Cortex Agent answers a revenue question with a number that looks off. The table it queried was built by a stored procedure.

Matia follows the path back from the output table, through the procedure, to the source table and the Matia sync that loaded it. That's where it finds the freshness monitor that failed at 8 a.m. The agent gets root cause and downstream impact in one answer, instead of piecing it together from separate tools. In our testing, workflows that took an agent ten or more tool calls against single-vendor MCPs took one or two against Matia.

When a question needs live numbers, Matia calls Snowflake MCP and combines the results with its own context.

The operational layer around Snowflake

Matia is the operational data infrastructure around Snowflake. It brings ETL, Reverse ETL, Observability, and Catalog together in one Unified DataOps Platform. Data comes in reliably and gets checked before it lands. Then it goes back out to the tools your business runs on. Snowflake stays your source of truth.

Ramp runs its Postgres-to-Snowflake replication through Matia. Historical resyncs that used to take 3.5 days now take 4 hours.

Matia is available on Snowflake Marketplace. Find us there, or book a demo to see your Snowflake transformations in Matia lineage.

Product Updates

September 2026 Product Updates: Matia MCP Goes GA

Your favorite AI tools can now ask Matia a question and get a real answer back, right from where you already work.

Matia MCP is now available, Power BI's lineage integration is live, and we've added new connectors to bring even more of your stack into one place.

Our goal is to help you unify your data stack, cut tool bloat, and make it simple to see what's broken, where it started, and how to fix it. This month's releases do exactly that.

Matia MCP Is Live

Plenty of companies are now using an MCP to bolt their tools together after the fact. Matia didn't have to. Our platform was unified from day one: ETL, reverse ETL, observability, and catalog in one place. This month, we launched Matia MCP, which is now available to any customer. Because Matia MCP sits on top of the unified platform, it’s not calling four separate data tools, which means less room for error and more accurate information in one central place.

What the Matia MCP does:

  • Traces downstream impact: Follow a failing connector through the transformation and the sync to the dashboard it breaks, all in one conversation
  • Find root causes: Ask about your broken dashboard, and it traces back to the source so you know where the issue started
  • Flags what's at risk: Ask what a change will affect before you make it, so you see the impact before you decide
  • Waits for your approval: Matia surfaces the recommendation. Running a sync or toggling a monitor still needs your confirmation, every time.

Matia MCP has rolled out to our customers now, connecting through OAuth with permissions enforced by RBAC, so your agent only sees what your own system access already allows.

Read the full launch post for how it works and what teams are already doing with it, or go straight to matia.io/mcp to book a demo to see it live.

Power BI Lineage Is Live

Power BI joined Matia's Catalog last month, alongside Tableau, Omni, and Hex. Once you connect a Power BI workspace, Matia picks up:

  • Descriptions. Asset descriptions carry over from Power BI, so you don’t redocument what a report already explains
  • Lineage. Native Power BI lineage traces relationships between datasets, reports, and dashboards down to the column level, running upstream through Snowflake and Databricks so you can follow a column back to where it started
  • Usage. Usage metrics land in the Catalog's usage tab, so you can see what's actually being read, not just what exists

Read the full Power BI lineage post for what that looks like in practice.

New Sources Pull Data From

  • Workday HCM: Worker records, positions, payroll, and org structure sync straight from Workday, so HR reporting doesn't mean a manual pull every time
  • Superfiliate: Affiliate, influencer, and referral program data lands in your warehouse next to the rest of your marketing data
  • Navan: Travel and expense data syncs directly from Navan into your warehouse

New Destinations to Send Data

  • Linear: Push customer records from your warehouse straight into Linear, so product and support teams work from the same data without a manual sync

Want to cut your sync times by 80%, like we did for Ramp? Book a demo here.

View all
Crunching Data
Product Updates
Case Studies
Blog

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Adding undo to a data lineage canvas forced us through Memento, Command, and event sourcing — and into checkpointed forward-command replay.

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Matia's Catalog now connects to Omni, giving data teams column-level lineage from source through warehouse all the way to the dashboard.

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