Why you should choose a collaborative SQL editor

How a collaborative SQL editor is different

Collaborative SQL editors help your team explore data faster and save precious time. They bridge the gap between solo, offline SQL tools and dashboard-centric visualization tools. Think of it like this:

• Legacy SQL tools = a kayak 🛶 They're made for one person. Everyone paddles independently. You don't go any faster by tying your kayaks together.

• Business intelligence (BI) tools = an oil tanker 🚢 They steadily carry tons of cargo to the exact same place, but move slowly and need lots of maintenance.

• Collaborative SQL editor = a speedboat 🚤 It’s built for nimble exploration and can quickly go anywhere.

Deep dive into different SQL tools

	Command-line interface	Database management tool	Legacy SQL editor	Data warehouse web UI	BI tool
Collaboration	🔴 Not for collaboration Built for individual use	🔴 Not for collaboration< Users have to share SQL files outside of tool	🔴 Not for collaboration Users have to share SQL files outside of tool	🟡 Limited query sharing & organization	🟡 Built for rigid dashboards Data instrumentation creates dependency & bottlenecks	🟢 Built for collaboration No silos or bottlenecks
Exploration	🔴 Extremely limited	🟡 Limited	🟡 Limited	🟡 Limited	🔴 Exploration only within the bounds of instrumented data	🟢 Built for rapid, iterative, & unbounded exploration
Visualization	🔴 None	🔴 None	🔴 None	🔴 None (some connect to external visualization tools)	🟢 Expansive, complex visualization	🟢 Charts & dashboards at your fingertips
Ease of use	🔴 Steepest learning curve	🟡 Clunky, dated UIs	🟡 Clunky, dated UIs (but 30% more clunky features!)	🟡 Clunky, dated UIs	🟡 Great for business users Not designed for heavy SQL writing	🟢 Built for all levels of SQL writing
Built for the cloud	🟢 There are (surprisingly) packages for most cloud warehouses	🔴 Most lack cloud warehouse support	🔴 Most lack cloud warehouse support	🟢 Yes, by default	🟢 Supports all major data warehouses & databases	🟢 Supports all major data warehouses & databases via shared connections
Pricing	🟢 Free	🟢 Free or low, one-time fee	🟡 Annual licenses required for each database type	🟢 Free (as you’re already paying for data warehouse)	🔴 Expensive, with opaque pricing & locked-in contracts	🟢 Clear, upfront pricing

Collaborative SQL editor use cases & questions:

Use cases

Exploring uncharted territory

Any update to your data model (e.g., a feature launch) takes you into uncharted territory. For most BI tools, new territory is a bottleneck. You have to wait for the few experts on that BI tool to instrument the data. A few days (weeks?) later and you can finally explore. In a collaborative SQL editor, you can explore your new uncharted data immediately.

Visualizing data

In a collaborative SQL editor, you'll find all the core visualizations. The focus is speed. You shouldn't have to take a thousand steps to produce a chart. For niche or advanced visualizations (e.g. sankey diagrams, chloropleth maps, etc.), a BI tool may be needed.

Querying both a production database and a data warehouse

You shouldn't need multiple tools just because you have multiple data sources. Legacy SQL tools were built long before the advent of cloud data warehouses. The web user interfaces (UI) in cloud data warehouses only run queries against themselves. Collaborative SQL editors work with all major data sources.

Questions

What if I already have a BI tool?

BI tools and collaborative SQL editors are not mutually exclusive. Collaborative SQL editors focus on accelerating data exploration through uncharted territory, while BI tools typically focus on the output of that work, which is a formalized dashboard or report for business stakeholders.

Supplementing your BI tool with PopSQL ensures your team has the best collaborative SQL editor to improve productivity, speed, and data trust for your stakeholders. Also, there are several scenarios where it’s disadvantageous to start your dashboard development directly in a BI tool (e.g., you need a quick dashboard for a new feature launch that doesn’t yet merit all the tedious data plumbing required or you want a faster dashboard iteration process with a stakeholder before productionizing it in your BI tool).

What if I need to manage a database?

Database management tasks (e.g., creating a table, importing a CSV, etc.) can be handled with simple DDL statements. If a graphical UI is needed, we'd recommend supplementing PopSQL with one of the many free database management tools for that specific use case. But use a collaborative SQL editor for fast data exploration.

Why shouldn’t I just use a free tool?

There are many hidden costs to using free legacy tools. From clunky UIs to team silos, free tools ultimately decrease productivity, create data trust issues, cause delays, and lead to redundant cloud run costs. Here are some helpful questions to ask your team if it's using free tools: How many unnecessary Zoom calls are we jumping on per week to review code? How often are we rewriting redundant queries from scratch? How often are our SQL experts getting pinged by new hires or junior analysts on repeat data model questions? If we stumble across a teammate’s query in Slack or an internal doc, do we know with full certainty that it’s the latest version? How much time does all this waste?

See the difference for yourself

Try PopSQL for free to see how a collaborative SQL editor can help your data team work faster, smarter, and happier 😊