# Navicat Premium 18: A PostgreSQL Developer's Review (Part 1)

> A hands-on PostgreSQL review of Navicat Premium 18 using Neon: query plans, data masking, SQL editing and schema dumps, compared with my DataGrip workflow.

- URL: https://avikmukherjee.com/blog/navicat-premium-18-review
- Author: Avik Mukherjee
- Published: 2026-10-31
- Updated: 2026-10-31
- Tags: postgresql, databases, navicat, review

I spend a lot of time looking at PostgreSQL queries and their plans. While building [QueryGuard](/blog/queryguard), I had to think about what a planner estimate can tell you before a query runs and what it cannot. So when Navicat reached out and asked me to try Navicat Premium 18 from a PostgreSQL perspective, I wanted to use it on a real workflow rather than just go through a list of features.

I normally use DataGrip. That is the comparison I kept coming back to: how does Navicat feel when I connect to a database, write a query, look at its plan, and work with test data?

**Disclosure:** Navicat gave me access to Premium 18 for this review and offered a complimentary one-year Premium subscription in return for publishing a review. They did not ask for a positive review. The observations here are my own.

## My verdict

Navicat Premium 18 is a capable database client, and its data masking workflow was the highlight of this test. I liked seeing the original and replacement values side by side before exporting them. I also preferred how its visual, JSON, and text plan views stayed close to the query editor.

For everyday SQL work, I still prefer DataGrip: its interface is a little more to my taste, its autocomplete felt better to me, and its `pg_dump` dialog gave me more control over the export I compared. Navicat's interface itself is good and easy enough to use. I did experience one unexpected app closure while setting up data masking, then completed the export after reopening it.

If you want one client for several database systems or a guided way to prepare test data, Navicat is worth evaluating. I tested only its PostgreSQL workflows, though, so I cannot judge its multi-database experience firsthand.

## What is Navicat?

Navicat is a family of desktop database tools. Instead of working exclusively in a terminal, you can connect to a database through a graphical interface, browse schemas and tables, write SQL, inspect results, and carry out administrative tasks. It is a client: the database itself still runs on your own machine or a server such as Neon.

The product I tested, **Navicat Premium 18**, brings multiple database platforms into one application. The supported types include PostgreSQL, MySQL, SQL Server, Oracle, SQLite, MariaDB, MongoDB, Redis, and Snowflake. That breadth is useful if your work takes you across different database systems; my hands-on testing here was limited to PostgreSQL, so I cannot judge how well the other connections work.

There are a few different choices in the Navicat family:

- **Navicat Premium** is the multi-database product I tested. Navicat offers paid licensing options; check the [Premium product page](https://www.navicat.com/en/products/navicat-premium) for current details.
- **Database-specific products**, such as [Navicat for PostgreSQL](https://www.navicat.com/en/products/navicat-for-postgresql), focus on a particular database platform. These are worth looking at if you only need one system.
- **Navicat Premium Lite** is a free, more limited multi-database edition for core database operations. It is separate from the paid Premium experience I reviewed here.

Within a tool like this, the workflows fall into several categories: connecting and browsing objects, writing and running queries, understanding execution plans, moving or backing up data, and preparing data for development. Navicat also offers other capabilities, including modelling and AI features, but I did not evaluate those in this review.

## There is a free version: Navicat Premium Lite

One thing that is easy to miss is that [Navicat Premium Lite](https://www.navicat.com/en/products/navicat-premium-lite) is free. Navicat lists support for PostgreSQL, MySQL, SQL Server, Oracle, SQLite, MariaDB, Snowflake, MongoDB, and Redis. It says Lite can be used commercially as well as non-commercially, with **up to five complimentary user accounts per organization**.

Lite is intended for core database operations and does not have the same feature set as the paid product. I tested **Premium 18**, not Premium Lite, so do not assume that every feature or workflow in this review is available in the free edition. If you are deciding between them, check Navicat's current feature matrix and licence terms for your platform.

## How I tested it

I connected Navicat Premium 18 on macOS to a **Neon-hosted PostgreSQL 18 database**. I created a separate `navicat_review` schema with synthetic data: 1,000 customers and 20,000 orders. The customers had generated names and `example.com` email addresses, and each order referenced a customer.

I used that dataset to browse tables, write a join, examine a query plan before and after adding an index, mask customer fields, and export the schema. The distribution was deliberately simple: each customer had 20 orders. It was useful for understanding the tools, but it was not a performance benchmark or a substitute for a production workload. I also tried renaming an alias. I compared the editor, plan presentation and dump workflow with DataGrip, my usual client. I did not measure client performance, restore the dump, evaluate other database engines, or test Navicat's separate Backup feature. This is part one of my review. Navicat has many other features I have not tested yet, including its AI capabilities. I plan to explore those in the next review.

## Connecting to Neon and browsing the database

I connected to Neon using the PostgreSQL connection and worked in its `neondb` database. The interface felt familiar, and I could find my way around without much trouble. Overall, I think Navicat's UI is good. I like DataGrip's UI a bit more, but that comes down to my personal taste.

One small point in the connection screen: Navicat lists presets for several cloud vendors, but I did not see DigitalOcean in that list. A DigitalOcean PostgreSQL database can still be connected using the regular PostgreSQL connection; a dedicated entry would make that path more obvious.

![Navicat's connection screen with database types and cloud vendor filters](/images/navicat-review/connections.png)

The database overview was useful too. Once I opened the test schema, I could quickly see its tables and their row counts. It is a nice thing to have when getting familiar with a database, although it was not by itself a reason for me to switch tools. I did not independently determine whether the displayed counts are exact or estimated, so I would check that before relying on them for a critical task.

## Writing SQL and renaming an alias

I wrote a query joining customers to orders and counting the orders per customer. Navicat ran it successfully, but as I typed, I found DataGrip's autocomplete more helpful. This is one of those differences that is hard to capture in a single screenshot: it is the accumulated experience of writing SQL in the editor.

Navicat 18 also has a Rename Symbol action. I tested it on the `c` alias in my join query, changing it to `customer`. It updated the declaration and all four visible references, while leaving the similarly named `customers` table and `customer_id` columns alone.

![The join query after Navicat renamed its customer alias](/images/navicat-review/rename.png)

It worked as expected for this simple case. I do not use this kind of refactoring very often, so it is a nice addition rather than a deciding feature for me. I have not tested it on more complicated SQL or across files.

## Looking at a query plan

This was the part I was most interested in. I started with a simple lookup:

```sql
SELECT *
FROM navicat_review.orders
WHERE customer_id = 273;
```

Before adding an index on `customer_id`, PostgreSQL chose a sequential scan. Navicat's visual plan showed the scan and its estimated cost of 434. The planner estimated that the query would return 20 rows.

![Navicat visual explain showing a sequential scan before adding an index](/images/navicat-review/plan-before.png)

I then created an index and opened the plan again:

```sql
CREATE INDEX orders_customer_id_idx
ON navicat_review.orders (customer_id);
```

This time PostgreSQL chose a bitmap index scan followed by a bitmap heap scan. In practical terms, the index identifies matching locations and the heap scan visits the table pages to retrieve the rows. Whether PostgreSQL chooses this plan depends on its estimates and the database conditions; adding an index does not guarantee the same plan for every query. The estimated total cost was 62.38, with the same estimate of 20 rows. An `EXPLAIN (ANALYZE, BUFFERS)` run after adding the index returned 20 rows as well.

![Navicat visual explain after adding the customer ID index](/images/navicat-review/plan-after.png)

A quick clarification: **planner cost is not elapsed time**. The single-node plan also labels that node as 100% of the plan cost; that is not a CPU-utilization reading. The cost change tells me what PostgreSQL estimated for these plans; it does not establish a measured speedup. I did not collect a comparable actual execution measurement before creating the index.

What I liked was the layout. Navicat kept the visual, JSON, and text views together near the query, so I could switch between them without moving far from the editor. DataGrip also has Explain and a graphical plan; when I tried it, its diagram opened in a separate editor tab. For this workflow, I preferred having Navicat's views nearby.

## Data masking was a pleasant surprise

Data masking was the feature that stood out to me most. I had not come across a similar workflow in my own use of DataGrip.

In Navicat, I selected the synthetic `customers` table and chose `full_name` and `email` as the fields to mask. I left `customer_id` alone. The wizard let me choose replacement rules and showed the original and output values side by side before I exported anything. I used realistic replacement names and configured the email domain as `example.com`.

![Navicat's side-by-side preview of original and masked synthetic customer data](/images/navicat-review/masking-preview.png)

I liked being able to see the result before committing to an export. The preview also made it clear which fields were being changed and which were being carried over. It is a useful way to catch an obviously wrong rule early, although previewing a sample does not prove that every exported row is valid or safe. Navicat reported that it processed and masked all 1,000 records. I inspected the resulting SQL file: it contained 1,000 distinct customer IDs and 1,000 distinct email addresses, all using `example.com`. The names no longer followed the original `Customer N` pattern.

This was a small, synthetic, single-table test. It does not establish how a real dataset with multiple sensitive columns and relationships will behave. The unchanged customer IDs would allow a corresponding orders dataset to refer to the same IDs, but I did not export or restore both tables as a masking test.

There was one interruption: **the whole Navicat app closed unexpectedly while I was configuring masking**. I reopened it, set up the export again, and completed it. I could not determine the cause and only observed it once, but it was part of my experience and worth mentioning.

## Dumping the test schema

I initially thought Navicat did not have a dump option. I was wrong: right-clicking the schema showed **Dump SQL File**, with options for structure only or structure and data. I chose structure and data for `navicat_review`.

The resulting file contained both test tables, 1,000 customer inserts, 20,000 order inserts, the primary keys, the unique email constraint, the foreign key, and the index I had created. I inspected the file but did not test a restore.

The generated SQL also contained `DROP TABLE IF EXISTS` statements targeting the test schema. Unlike the masking export, this was a structure-and-data dump of the original synthetic records, not a masked export. That is worth knowing before running a dump against an existing database. I would inspect the file and test a restore in a separate disposable destination first.

For someone who wants to review export choices before generating a file, this difference matters. I preferred DataGrip's `pg_dump` dialog for flexibility. In the dialog I used for comparison, it exposed the executable path, schema and table selection, output format, statement style, and an option to add drop statements. Navicat's schema-level **Dump SQL File** path went straight to a save dialog. Navicat also has a separate Backup feature that I did not evaluate here, so this comparison is limited to these two export workflows. I also did not establish whether Navicat's SQL dump uses `pg_dump` internally.

## Who is it for?

Navicat looks most appealing to someone who wants a graphical database client and values guided workflows such as data masking. Its support for several engines may also appeal to teams that move between database systems, though I only connected it to PostgreSQL for this review. The free Lite edition is an accessible starting point for basic operations, but check its feature matrix before expecting the paid features I tested.

If your work is mostly writing SQL and you already like DataGrip, the choice is less straightforward. I would try Navicat's plan and masking workflows against your own tasks, then weigh their value against the editor and export options you already rely on. I have not tested enough to make a recommendation for production backup or recovery.

## Would I switch?

For my day-to-day PostgreSQL work, I still prefer DataGrip's UI and autocomplete, and I liked the control offered by its `pg_dump` dialog. Navicat's interface is good overall, but DataGrip is still a little more to my taste.

That said, the visual Explain layout and the masking wizard were useful in my test. Keeping the plan views close to the query made inspecting them convenient, and the masking preview made it easy to understand what would change before exporting. The unexpected closure is a drawback I would want to keep an eye on.

If you spend time investigating PostgreSQL plans or preparing test datasets, those two workflows are worth trying for yourself. My testing was limited to one Neon PostgreSQL database and the scenarios above; your experience may differ with a larger schema or a different workflow.