✨ Generating Match Reports from Neon Data

1Create Your Neon Project and Table

Determine which data table in Neon you will connect to and generate an Interzoid Match Report with. Any table containing company names, individual names, or street addresses is a good candidate.

If you do not have sample data, you can load the following CSV file into a Neon table. You can get this sample data at the following address:

https://dl.interzoid.com/csv/companies.csv

If you do not have a Neon account set up yet, it is easy to create one at Neon.com. Creating a project provisions a PostgreSQL database immediately, with no instance sizing or provisioning step.

Creating the Target Table

Unlike some managed Postgres platforms, Neon does not offer a browser-based CSV importer that infers a schema for you. The table must exist before the data is loaded, and its columns must line up with the columns in the CSV file. Open the SQL Editor in the Neon Console and create the table:

> CREATE TABLE companies (
  id        SERIAL PRIMARY KEY,
  company   TEXT,
  category  TEXT
);

The sample file has two columns, company and category, and contains 91 records. The id column above is added for convenience and is populated automatically by its sequence, so it does not appear in the CSV. If you are loading your own file, adjust the CREATE TABLE statement so the columns appear in the same order as the header row, and use TEXT for anything you intend to match on. Matching operates on the values as they were entered, so there is no benefit to narrower types here.

2Load the CSV with psql

Neon does not support a browser-based file upload for table data, and the server-side COPY ... FROM '/path/to/file.csv' form does not work either, because that reads from the database server's own filesystem, which is not accessible on a managed serverless platform. The supported approach is the \copy meta-command in psql, which streams the file from your local machine over the connection you already have open.

Download the Sample File

> curl -O https://dl.interzoid.com/csv/companies.csv

Connect and Load

Start a psql session using the connection string from your Neon dashboard, then run \copy. The column list is given explicitly so that the id column continues to populate from its sequence:

> psql "postgresql://neondb_owner:YOUR-PASSWORD@ep-your-endpoint.us-east-2.aws.neon.tech/neondb?sslmode=require"

neondb=> \copy companies(company, category) FROM 'C:/data/companies.csv' WITH (FORMAT csv, HEADER true, ENCODING 'UTF8');
COPY 91

neondb=> SELECT COUNT(*) FROM companies;

The \copy command reports the number of rows loaded, shown here as COPY 91 for the sample file. The COUNT query confirms it against the table. The count should match the number of data rows in the file, with the header row consumed rather than loaded.

3Copy Your Neon Connection String

Interzoid connects to Neon the same way any other PostgreSQL client does, using a standard connection string. Neon generates this for you, so there is nothing to compose by hand.

• Click Connect: The Connect button on your Neon Project Dashboard opens the connection modal, which shows the connection string for the selected branch, database, and role.
• Choose the direct connection: The modal includes a pooled connection option, which adds a -pooler suffix to the hostname. Use the direct string, without the suffix, for both the psql load and the matching wizard.
• Copy the string: The password is included in the copied string. Treat it as a credential.

A Neon connection string looks like this:

postgresql://neondb_owner:YOUR-PASSWORD@ep-cool-darkness-a1b2c3d4.us-east-2.aws.neon.tech/neondb?sslmode=require

Reading the Connection String

• Username: Neon creates a default role named neondb_owner. Your project may use a different role name if you created one.
• Host: The endpoint ID identifies the compute for a specific branch, in the form ep-name-id.region.aws.neon.tech. Each branch has its own endpoint, so make sure you are copying the string for the branch that holds your data.
• Port: Neon uses the default PostgreSQL port, 5432. It is usually omitted from the string.
• Database: Neon names the default database neondb.
• SSL: Neon requires TLS on all connections, so sslmode=require belongs in the string.

4Launch the Wizard and Enter Your API Key

Open the Postgres Data Matching Wizard in your browser. Before beginning, enter your Interzoid API key in the top-right area of the header bar. Your key is saved in your browser for future sessions.

• API Key Field: Type or paste your API key into the input field in the header. Click the lock or eye icon to toggle visibility.
• Check Credits: Click the Credits button to verify your current balance before starting a job. The sample company file is small, so a demonstration run costs very little.
• Language Selection: Click the language dropdown in the navigation bar to switch between any of the 17 supported languages. The entire interface updates immediately. You can also set the language by URL parameter: ?lang=fr for French, ?lang=ja for Japanese, and so on.

Once your API key is entered, click Get Started on the introduction screen to begin.

5Select a Matching Function

The wizard presents six matching functions. Choose the one that fits your data and use case. Each function card shows a description and the column parameters it requires.

Single-Column Functions

Combination Functions

These functions use two columns together for higher matching precision:

For the sample company data, select Company Name Matching. Click the card for your chosen function, then click Next.

6Connect to Neon

This is where the Neon connection string from Step 3 comes in. The wizard connects to PostgreSQL in real time and presents the available objects at each level through cascading dropdown menus.

Connection Flow

• Switch to connection string mode: Use the toggle at the top of the form to select the connection string option rather than individual fields.
• Paste your string: Drop in the direct Neon connection string, without the -pooler suffix. The field is masked by default. Use the eye icon to reveal it and the clipboard icon to copy it back out.
• Click Connect: The wizard validates the connection. Because the database name is already carried in the connection string, the database dropdown is skipped and you go straight to schema selection.
• Select the schema: Choose public, which is where the table you created in Step 1 lives. Internal schemas such as pg_catalog and information_schema are filtered out of the list.

Using Connection Fields Instead

If you prefer to fill in the fields individually, take the values from the same Neon connection string: host is ep-your-endpoint.region.aws.neon.tech, port is 5432, username is neondb_owner or your custom role, database is neondb, and SSL Mode is require.

Saving Your Connection Details

Below the credentials is a Remember these connection details on this browser toggle. It is off by default, and nothing connection-related is saved until you turn it on. When enabled, the wizard stores your connection mode, host, port, username, password, SSL mode, and connection string in your browser. When disabled, any previously saved values are erased immediately. Because the stored values include your password, leave this toggle off on shared computers.

CREATE ROLE interzoid_reader LOGIN PASSWORD 'strong-password-here';
GRANT CONNECT ON DATABASE neondb TO interzoid_reader;
GRANT USAGE ON SCHEMA public TO interzoid_reader;
GRANT SELECT ON ALL TABLES IN SCHEMA public TO interzoid_reader;
ALTER DEFAULT PRIVILEGES IN SCHEMA public
  GRANT SELECT ON TABLES TO interzoid_reader;

7Select Your Table, Columns, and Options

Choose the table to match against and configure which columns to use for matching and which columns to include in the output.

Table Selection

The wizard presents a dropdown of all tables and views available in the schema you selected. Choose the table holding the records you want to match, such as the companies table you loaded in Step 2. Once selected, the wizard loads the column names from that table in their natural column order.

Match Columns

For each matching parameter required by your chosen function, select the corresponding column from the dropdown. Having chosen Company Name Matching, select the column containing company names.

• Single-column functions: Select one column for the matching parameter.
• Combination functions: Select two different columns, one for each parameter. The two columns must be different.

Output Columns

Use the checkboxes to select which columns you want to include in the match report output. At least one column must be selected. The match columns are automatically included even if you do not check them separately, which ensures the data you matched on always appears in the results.

Output Options

• Show Similarity Keys: When enabled (the default), each output record includes the generated similarity key as the last column. Records sharing a key are matches. Disable this if you want clean output with only the selected data columns.
• Matches Only: When enabled (the default), only records that have at least one other matching record are shown. Disable this to see every record in the table after processing, sorted by similarity key. If you plan to load the results back into Neon, turn this option off. See Step 10 for why.

Click Next when your selections and options are configured.

8Review and Run the Match

The final screen shows a summary of all your selections: matching function, server, SSL mode, database, schema, table, column assignments, and output options. Review these carefully before proceeding.

Click the green Run Match button to start processing. The wizard will:

• Validate your API key and check that your account has sufficient credits for the job.
• Connect to Neon and read the selected columns from your table.
• Process each record through the selected matching algorithm using concurrent workers for performance.
• Generate the match report with records sorted and grouped into clusters of matching entries.

A progress indicator is shown while the job runs. Processing time depends on the number of records. The sample company file completes in seconds, while very large tables (up to 500,000 records) may take a minute or more.

9Interpret the Match Report

The match report appears in the results panel at the bottom of the screen. Records are organized into clusters, groups of records that the AI has determined to be matches. Each cluster is separated by a blank line for readability.

Example Output

For a company name match on the sample table, with the company and category columns selected for output and similarity keys enabled:

IBM Corporation,Technology,d477E1d7sG6dja3hDNsk9P
I.B.M. Corp,Information Technology,d477E1d7sG6dja3hDNsk9P

Microsoft Inc.,Software,k8Rp2mNx4wQjL9vB3cYh7T
Microsoft Corporation,Software,k8Rp2mNx4wQjL9vB3cYh7T
MSFT Corp,Technology,k8Rp2mNx4wQjL9vB3cYh7T

In this example, the first cluster contains two records identified as variations of IBM, and the second cluster contains three records identified as variations of Microsoft. The last column in each row is the similarity key. All records sharing the same key are considered matches, even though not one of the company name values is spelled identically to another. Notice that the category values also vary within a cluster, which is exactly the kind of inconsistency a match report surfaces.

What the Clusters Tell You

• Duplicate records: Two or more rows in the same cluster that should be a single record. These are candidates for merging.
• Inconsistent data: Clusters where the entity is clearly the same but the formatting varies. These reveal where standardization is needed upstream.
• Entity resolution: A cluster establishes that separate rows refer to one real-world organization, person, or location, which is the foundation for building a single view of a customer.
• Join keys: The similarity key gives you something to join on across tables where no shared identifier exists. Two tables processed with the same matching function produce the same key for the same entity.

10Save and Use Your Results

Click the Save Results button above the results panel to download the match report as a CSV file. On supported browsers, a save dialog appears allowing you to choose the file name and location. On other browsers, the file downloads automatically.

The saved file is clean, delimited text that can be imported directly into spreadsheets, databases, or other data processing tools for further analysis.

Bringing the Results Back into Neon

If your goal is to load the results back into Neon rather than simply review them, go back to the options in Step 7 and uncheck the Matches Only option, leaving Show Similarity Keys enabled.

With Matches Only turned off, every input row is included in the output with its similarity key appended, not just the rows that landed in a cluster. That gives you a complete, one-to-one copy of your source data with a new key column, which is exactly what you want to load back into a Neon table. Records that matched nothing still carry a key, so nothing is silently dropped and row counts reconcile against the original table.

Loading it back uses the same psql approach as Step 2. Create a table for the results, remove the blank cluster separator lines from the file, then run \copy:

CREATE TABLE match_results (
  company         TEXT,
  category        TEXT,
  similarity_key  TEXT
);

\copy match_results FROM 'C:/data/match_report.csv' WITH (FORMAT csv, ENCODING 'UTF8')

Once the keyed data is in a table, matching becomes an ordinary SQL operation, and you can perform fuzzy joins within your Neon data tables by joining on the similarity key instead of on exact text:

-- Count the records in each match cluster
SELECT similarity_key, COUNT(*) AS record_count
FROM match_results
GROUP BY similarity_key
HAVING COUNT(*) > 1
ORDER BY record_count DESC;

-- Fuzzy join: match customers to prospects on entity
-- similarity rather than on exact text
SELECT c.company, p.company, c.similarity_key
FROM customer_matches c
JOIN prospect_matches p ON c.similarity_key = p.similarity_key;