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High-Performance Databricks Data Matching

Identify inconsistent, matching, and duplicate data across your Databricks lakehouse tables in just a few clicks.

A guided, read-only analysis tool that works with every Databricks workspace, on AWS, Azure, and Google Cloud.

Launch the Matching Wizard Read the Documentation
Built for Databricks Unity Catalog, Delta Lake, Any Cloud

Better Data. Better Decisions. No Exports.

The Databricks Matching Wizard connects directly to your lakehouse through a SQL warehouse in read-only analysis mode, on any cloud and in any region. In a guided, step-by-step flow, you'll generate AI-enhanced match reports that surface inconsistent values, redundant records, and duplicate entries across any of your Delta tables and views, see the cost before you run, and review the results in an interactive spreadsheet view. Parallel processing delivers maximum performance, even on large tables, and the similarity keys in every report load straight back into a Delta table for consolidation with ordinary SQL.

Open databricks-match.interzoid.com

See It in Action

A quick look at the Matching Wizard experience, from connecting to Databricks through reviewing your match report.

Choose Matching Type

Choose Matching Type

Select the type of matching you want to perform: company names, individuals, addresses, and more.

Connect to Databricks

Connect to Databricks

Enter your workspace hostname, SQL warehouse HTTP path, and access token, or paste a connection string, and connect in read-only mode over HTTPS.

Select Tables and Columns

Select Tables and Columns

Pick a catalog, schema, and table and the wizard pre-selects the matching columns, previews the data, and counts the rows so you know the cost before you run.

Run and Review Results

Run and Review Results

Run the job and explore the match report in a searchable, sortable spreadsheet view with each match group highlighted, then save it as a file.

Why Teams Use the Matching Wizard

Effortless to Use

A guided wizard walks you through connecting to Databricks, choosing your data, and generating a match report. No SQL, notebooks, or scripting required.

Read-Only and Safe

The wizard operates entirely in read-only analysis mode. Your lakehouse data is never modified and no job or notebook runs in your workspace, making it safe to run against any of your tables, including production.

Connects to Any Workspace

Supply a workspace hostname, SQL warehouse HTTP path, and access token, or paste a connection string. Everything travels over HTTPS on port 443, so no firewall rules are needed, and Unity Catalog permissions govern exactly what the wizard can read.

Know the Cost Before You Run

Choose a table and the wizard counts its rows, shows the credit estimate against your balance, and previews the data with your match columns highlighted. No surprises on large tables.

AI-Enhanced Matching

Advanced AI-enhanced similarity algorithms cluster matching records and reveal duplicates and inconsistencies that exact-match queries, LIKE patterns, and soundex or levenshtein comparisons miss entirely.

Better Data, Better Decisions

Cleaner data means more reliable analytics, better customer insights, accurate joins across systems, and fewer wasted compute cycles processing redundant records.

Parallel Processing for Maximum Performance

A multi-threaded, distributed cloud architecture processes large lakehouse tables in parallel, delivering match reports fast, even at high data volumes.

Actionable Match Reports

Review results in an interactive spreadsheet view that shades each match group, with search, sorting, and paging across even the largest reports. Save the report as a file, or load the similarity keys back into a Delta table and consolidate with a single JOIN.

How It Works

From connection to match report in three simple steps.

1

Connect to Databricks

Enter your workspace hostname, SQL warehouse HTTP path, and access token, or paste a connection string, and connect. A dedicated read-only service principal is recommended.

2

Select Tables and Columns

Choose a catalog, schema, and table; the wizard pre-selects the columns to match on, previews the data, and shows the row count and credit estimate before you commit.

3

Run and Review Results

Run the matching job and explore the AI-enhanced report in an interactive spreadsheet view, with each cluster of matching, duplicate, and inconsistent records highlighted.

Data Matching Example

See the Matching Wizard in action, generating similarity keys to match company and organization name data directly from within lakehouse tables.

Interzoid match report from a Databricks table showing company name variations grouped into clusters by similarity key

Resources

Everything you need to get started, from a sample report to step-by-step documentation.

Sample Match Report

See an example match report for company name data, showing how matching clusters are presented.

View Sample Report

Documentation

Read the complete documentation covering access tokens, service principals, connection options, and matching options.

Read the Docs

Also for Snowflake and SQL Databases

The same guided matching experience is available for Snowflake, PostgreSQL, MySQL, and Azure SQL.

Snowflake Matching Wizard

Works With Every Databricks Deployment

The wizard connects through a SQL warehouse, so it reads from any workspace on any cloud, whether governed by Unity Catalog or a legacy metastore.

Databricks on AWS
Azure Databricks
Databricks on Google Cloud
Unity Catalog Catalog, schema, and table
hive_metastore Legacy workspaces supported
Serverless SQL Warehouses
Classic and Pro Warehouses
Delta Lake Tables
Views and Materialized Views Match a filtered view of any size
Delta Sharing Recipients Shared data read in place
Any Workspace Region
HTTPS on Port 443 No firewall rule needed

No connector to install, no notebook to run, and nothing deployed inside your workspace. Point the wizard at a catalog, schema, and table, and it reads only the columns you select.

What You Can Match

Four data types, plus combinations that raise precision when one field alone is too broad

Organization Names

Resolve Acme Corp, ACME Corporation, and Acme Inc. to a single organization, across the legal suffixes, punctuation, abbreviations, and spelling variations that make company names so inconsistent.

Individual Names

Match James Johnston, Jim Johnston, and J. Johnston as the same person, handling nicknames, initials, middle names, and name ordering.

Street Addresses

Reconcile 400 E Broadway St with 400 East Broadway Street, resolving directional abbreviations, street type variations, unit designations, and spacing.

General Text

Generate similarity keys for other text values, so product names, descriptions, and free-form fields cluster the same way that names and addresses do.

Combination Matching

Require two fields to agree at once: organization with address, organization with individual name, or address with individual name. This separates the Dallas branch from the Phoenix branch while still recognizing that both spell the parent company four different ways.

Similarity Keys and Clusters

Every value receives a key representing the entity behind the text rather than the characters themselves. Records sharing a key are grouped into clusters, which makes each match auditable instead of something to take on faith, and the keys join straight back to your tables.

Frequently Asked Questions

Which Databricks deployments are supported?

All of them. Databricks workspaces on AWS, Azure, and Google Cloud are supported in any region, reading through a serverless, pro, or classic SQL warehouse. Unity Catalog workspaces and legacy hive_metastore workspaces are both supported, as are Delta Lake tables, views, materialized views, and data shared in through Delta Sharing.

Does Interzoid modify my Databricks data?

No. Processing issues SELECT statements against the table and columns you choose and nothing else. Nothing is written, updated, or deleted, no tables are created, and no job or notebook runs in your workspace. Results are returned to your browser, and you decide what to do with them.

Do I need to export my data first?

No. Interzoid connects directly to your SQL warehouse and reads the columns you select. There is no CSV export, no staging table, no cloud storage copy, and nothing to secure, track, or delete afterward.

Do I need to open a firewall rule for the wizard?

No. The wizard reaches your SQL warehouse over HTTPS on port 443, the same endpoint Databricks SQL clients use, so no special firewall rule or network change is required.

How is my access token handled?

The personal access token is sent to Interzoid over HTTPS only when a request is made and is never stored by Interzoid. Because a token inherits the permissions of whoever created it, and because processing is read-only, the recommended practice is a dedicated service principal granted only USE CATALOG, USE SCHEMA, and SELECT, which can be revoked at any time.

Can I see what a run will cost before I start it?

Yes. When you choose a table, the wizard counts its rows and shows the number of records and the estimated credits alongside your current balance, before you click Run.

How is usage measured?

Each record processed consumes one Interzoid API credit. Trial credits are included with a new API account, so a first table can be matched at no cost, and high volume tables are supported for production workloads.

Can I run this from my own applications instead of the browser?

Yes. The same processing is available as a REST API that can be called from scripts, applications, and data pipelines, which allows Databricks matching jobs to be automated without using the browser wizard.

Ready to See What's Hiding in Your Databricks Data?

Generate your first match report in minutes. No installation, no code, no changes to your data.

Launch the Matching Wizard Read Documentation

Questions? Contact our team at support@interzoid.com