7 Powerful DBeaver Tips and Tricks to Improve Your SQL Workflow

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7 Powerful DBeaver Tips and Tricks to Improve Your SQL Workflow

DBeaver is the most powerful open-source SQL IDE, but there are several features people don’t know about. In this post, I will share with you several features to speed up your workflow, with zero fluff. I’ve learned these as I’m currently digging deeper into the tools I use daily, starting with Dbeaver. In a future post, I’ll compare the workflow between DBeaver versus building your SQL development environment on VSCode (or Cursor). If you’re interested in that, be sure to follow my publications! Today, though, the focus is on learning the cool features of DBeaver. Let’s get started. The Command Palette This is one of the most powerful yet hidden features in DBeaver. Maybe people overlook it because it’s not called a “Command Palette”. You can open it with CMD + 3 (Mac) or CTRL + 3 (Windows). From here, you can access basically any action in the IDE. I mostly use it for: Switching between Sql scripts. Navigating to specific settings. Quickly accessing actions like Export Results, Refresh Schema, Open Templates, Rename File, etc. (Officially, this feature in DBeaver is called “Find Actions.”) Custom SQL formatter Did you know you can easily set up a different formatter in DBeaver? I’m personally not a fan of the default formatting, and since I mainly use PostgreSQL, I prefer pg_formatter. Let me show you how it works to set up pg_formatter, but keep in mind that the process will be similar for any SQL formatter that you can call via the terminal. # Install PG Formatter brew install pgformatter # Find where the program is located. # In my case: opt/homebrew/bin/pg_format which pg_format Next, go to Preferences → Editors → SQL Editor → Formatting, select an “External Formatter,” and then paste the path to your desired formatter. 💡 Or you can simply open the command palette and search for “Formatting”. Expand columns on SELECT Often, you might need to select most columns from a table, excluding just a few. DBeaver makes this easier by expanding your SELECT * into explicit column names. You can do this with the CTRL + Space hotkey, both on Mac or on Windows. This might not work if you have it bound to another system shortcut, in which case you can look up for “Content Assist” in the command palette. Quickly find column statistics DBeaver has a bunch of features to speed up your analysis. One of them that I use frequently is the “Calc tab”, located to the right of your query results. It lets you quickly get the information about columns in your query results. Here is what you can do with it: Find the amount of unique and non-null values of categorical columns. Get min, max, mean, median, etc., of numerical columns. Very handy for quickly understanding your dataset! Ad-hoc groups Similarly to the Calc tab, the “Groupings tab” lets you quickly create group-by queries without writing SQL manually. What you can do with it: Quickly count occurrences of values. Add multiple aggregations. Although this one is pretty nice for simple aggregations, I find it a bit underwhelming as there is no way to count unique values, as I do in the GIF above, without having to manually write the metric function. SQL templates SQL Templates are extremely powerful, although I admit I don’t use them as often as I should. Templates save you from repeatedly writing common expressions. You can see the built-in templates by opening the command palette and searching for “Templates”. You will see shortcuts for: SELECT * FROM {table} SELECT * FROM {table} WHERE {col} = {value} And others, like selecting and ordering, counting by groups, etc. All you need to do is to write the shorthand for the query and press tab: You can also create your own templates, which is not hard if you just copy the existing ones and adapt them. Advanced copy tips You might already know DBeaver has extensive data export options. However, the standard export wizard can feel a bit overwhelming, as it shows you tons of configurations, even when you just want to quickly export a CSV. A quicker way is selecting data in the Results tab, right-clicking, and choosing “Advanced Copy”. By doing this, you can copy your data in many formats like CSV, JSON, Markdown, TXT, and even SQL Insert Statements. I find this one super helpful, especially when I need to quickly send data to a teammate. A bonus tip is that you can copy this data to TSV, which gets properly recognized by Excel and Google Sheets into their proper cells! For this one though, you have to be double advanced, according to DBeaver 😅 Conclusions I love DBeaver as a SQL IDE. It’s incredibly powerful and the interface is very clean. It’s honestly mindblowing that this tool is free and open-source. If you haven’t tried it yet, I recommend it a lot! I haven’t seen that many people talking about some of its nicest features and tricks, and most tips I’ve shared came from just using the software. There is probably a lot that I missed, especially when it comes to Plugins, which I haven’t used much. I know I went through all of the tips very fast, so if you’re in doubt, feel free to reach out. Also, if you have more workflow tips, I would love to hear about them! I hope you learned something new! If you’re curious about other tips that didn’t make it into this post, or enjoy learning about general data topics, subscribe to my free newsletter on Substack. I publish whenever I have something genuinely interesting to share. Want to connect directly or have questions? Reach out anytime at mtrentz.com. All images and animations, unless otherwise noted, are by the author

I’ve learned these as I’m currently digging deeper into the tools I use daily, starting with Dbeaver. In a future post, I’ll compare the workflow between DBeaver versus building your SQL development environment on VSCode (or Cursor). If you’re interested in that, be sure to follow my publications!

Today, though, the focus is on learning the cool features of DBeaver. Let’s get started.

The Command Palette

This is one of the most powerful yet hidden features in DBeaver. Maybe people overlook it because it’s not called a “Command Palette”. You can open it with CMD + 3 (Mac) or CTRL + 3 (Windows).

From here, you can access basically any action in the IDE. I mostly use it for:

Switching between Sql scripts.
Navigating to specific settings.
Quickly accessing actions like Export Results, Refresh Schema, Open Templates, Rename File, etc.

(Officially, this feature in DBeaver is called “Find Actions.”)

Custom SQL formatter

Did you know you can easily set up a different formatter in DBeaver? I’m personally not a fan of the default formatting, and since I mainly use PostgreSQL, I prefer pg_formatter.

Let me show you how it works to set up pg_formatter, but keep in mind that the process will be similar for any SQL formatter that you can call via the terminal.

# Install PG Formatter
brew install pgformatter

# Find where the program is located.
# In my case: opt/homebrew/bin/pg_format
which pg_format

Next, go to Preferences → Editors → SQL Editor → Formatting, select an “External Formatter,” and then paste the path to your desired formatter.

💡 Or you can simply open the command palette and search for “Formatting”.

Expand columns on SELECT

Often, you might need to select most columns from a table, excluding just a few. DBeaver makes this easier by expanding your SELECT * into explicit column names.

You can do this with the CTRL + Space hotkey, both on Mac or on Windows. This might not work if you have it bound to another system shortcut, in which case you can look up for “Content Assist” in the command palette.

Quickly find column statistics

DBeaver has a bunch of features to speed up your analysis. One of them that I use frequently is the “Calc tab”, located to the right of your query results. It lets you quickly get the information about columns in your query results.

Here is what you can do with it:

Find the amount of unique and non-null values of categorical columns.
Get min, max, mean, median, etc., of numerical columns.

Very handy for quickly understanding your dataset!

Ad-hoc groups

Similarly to the Calc tab, the “Groupings tab” lets you quickly create group-by queries without writing SQL manually.

What you can do with it:

Quickly count occurrences of values.
Add multiple aggregations.

Although this one is pretty nice for simple aggregations, I find it a bit underwhelming as there is no way to count unique values, as I do in the GIF above, without having to manually write the metric function.

SQL templates

SQL Templates are extremely powerful, although I admit I don’t use them as often as I should. Templates save you from repeatedly writing common expressions.

You can see the built-in templates by opening the command palette and searching for “Templates”. You will see shortcuts for:

SELECT * FROM {table}
SELECT * FROM {table} WHERE {col} = {value}
And others, like selecting and ordering, counting by groups, etc.

All you need to do is to write the shorthand for the query and press tab:

You can also create your own templates, which is not hard if you just copy the existing ones and adapt them.

Advanced copy tips

You might already know DBeaver has extensive data export options. However, the standard export wizard can feel a bit overwhelming, as it shows you tons of configurations, even when you just want to quickly export a CSV.

A quicker way is selecting data in the Results tab, right-clicking, and choosing “Advanced Copy”. By doing this, you can copy your data in many formats like CSV, JSON, Markdown, TXT, and even SQL Insert Statements.

I find this one super helpful, especially when I need to quickly send data to a teammate.

A bonus tip is that you can copy this data to TSV, which gets properly recognized by Excel and Google Sheets into their proper cells! For this one though, you have to be double advanced, according to DBeaver 😅

Conclusions

I love DBeaver as a SQL IDE. It’s incredibly powerful and the interface is very clean. It’s honestly mindblowing that this tool is free and open-source. If you haven’t tried it yet, I recommend it a lot!

I haven’t seen that many people talking about some of its nicest features and tricks, and most tips I’ve shared came from just using the software. There is probably a lot that I missed, especially when it comes to Plugins, which I haven’t used much.

I know I went through all of the tips very fast, so if you’re in doubt, feel free to reach out. Also, if you have more workflow tips, I would love to hear about them!

I hope you learned something new!

If you’re curious about other tips that didn’t make it into this post, or enjoy learning about general data topics, subscribe to my free newsletter on Substack. I publish whenever I have something genuinely interesting to share.

Want to connect directly or have questions? Reach out anytime at mtrentz.com.

All images and animations, unless otherwise noted, are by the author

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Amazon is facing scrutiny and significant pushbacks over its plan to install 250 diesel backup generators for a proposed data center in Becker, Minnesota. Much of the concern had been due to the fact that the hyperscaler was seeking an exemption from the state’s standard permitting process, a move that has sparked opposition from environmental groups and state officials. Aggregate Power that Matches Nuclear Power Generation Amazon’s proposed fleet of diesel generators would have a maximum power output almost equivalent to the 647 MW that is produced by Xcel Energy’s nuclear plant in Monticello, one of the two existing nuclear generation stations in the state. Meanwhile, as reported by Datacenter Dynamics, according to a real estate filing published with the Minnesota Department of Revenue, the land parcel assigned for the Amazon data center in Becker was previously part of Minneapolis-based utility Xcel’s coal-powered Sherco Site. Amazon argues that the diesel generators in question are essential to ensuring reliable and secure access to critical data and applications for its customers, including hospitals and first responders. However, opponents worry about the environmental impact and the precedent it may set for future large-scale data center developments in the state. The Law and Its Exception Under Minnesota state law, any power plant capable of generating 50 megawatts or more that connects to the grid via transmission lines must obtain a Certificate of Need from the Public Utilities Commission (PUC). This certification ensures that the infrastructure is necessary and that no cheaper, cleaner alternatives exist. Amazon, however, contends that its generators do not fall under this requirement because they are not connected to the larger electric grid; power generated would be strictly used by the data center suffering an outage from its primary power source. That power would be generated locally, and not transmitted over

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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