General Comparison

While HeidiSQL and Navicat Premium share many similarities, they are in fact different product types. Navicat Premium is what's known as a Universal Database Tool, which means that it supports ALL popular databases, including MySQL, MariaDB, MongoDB, SQL Server, Oracle, PostgreSQL, and SQLite. Moreover, Navicat is compatible with cloud databases as well, such as Amazon RDS, Amazon Aurora, Amazon Redshift, Microsoft Azure, Oracle Cloud, Google Cloud and MongoDB Atlas. HeidiSQL began as a MySQL/MariaDB client and evolved to suport a few additional database types. That being said, the two products are homogenous enough to warrant a side by side comparison. In this installment we'll be taking a high level inventory of pros and cons, while the next part will focus on specific features.

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Meet the Contestants

There is no shortage of either free or commercial relational database clients. Some provide basic functionality, while others offer advanced tools that help professionals achieve their many day-to-day activities in an efficient manner. While there is some correlation between cost and the number of features offered, each product needs to be evaluated on its own merits when deciding which product(s) to use yourself.

Back in June of last year, we compared DBeaver, a popular free tool, to Navicat Premium 15. Now, it's high time that we did it again. In this installment of Database Tools Showdown, we'll be taking a look at another freebie called HeidiSQL, and see how it stacks up against Navicat Premium 16.

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A CSV is a Comma-Separated Values file, which allows data to be saved in a tabular format. It's long been the preferred format for transferring data between databases. More recently, Internet-driven formats such as XML and JSON have also gained much traction. CSV files are well suited to databases because they represent table data exceptionally well and can be used with just about any spreadsheet program, such as Microsoft Excel or Google Spreadsheets. In today's blog, we'll be taking a look at a few ways to export table data to CSV in MySQL.

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In some cases, running a well crafted UPDATE statement in production can save the day. Other times, a botched UPDATE can cause more harm than the initial issue. You can always execute your Data Manipulation Language (DML) statements on a development or test database, but due to differences in the data, this approach makes determining the statement's effects on the production data a craps shoot at best.

So what are some options to accurately predict what the result of an INSERT, UPDATE, or DELETE statement will be on production data before running it? Well, that depends on the database vendor and product, at least in part. There are also some solutions that enjoy widespread support. We'll be taking a look at both options in this blog.

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