Data is messy. As an analyst, your job is to clean it up, connect the dots, and pull clear insights from a swirling pool of raw events. But you don’t need to be a software engineer to build good data models. There are small-scale tools out there — simple, powerful, and easy to use — that can help you transform chaos into clarity.
TL;DR
Modern data analysts love lightweight modeling tools that sit between raw data and dashboards. They’re often easier and faster to use than big, bulky platforms. Tools like dbt have made data transformation fun, and there are other tools too that play well in this space. This list explores 6 user-friendly tools you can start using today to turn raw data into clean metrics.
Why “dbt-adjacent” Tools?
In recent years, dbt has changed how analysts think. It helped make SQL modular, tested, and documented. But not everyone needs the full weight of dbt. Or maybe they want something more visual, or better for metrics. These dbt-adjacent tools offer powerful ways to slice data smarter, often without writing too much code.
The Top 6 Small-Scale Data Modeling Tools
1. dbt (Data Build Tool)
Let’s begin with the OG. dbt is the reason this new wave of modeling tools even exists. With dbt, you write modular SQL models that build on each other. You define relationships, test assumptions, and document things as you go.
It’s kinda like version control + SQL legos + analytics sanity.
Best for: Teams who love SQL and version control, and want robust modeling pipelines.
Cool features:
- Auto-generates lineage graphs
- Built-in testing and validation
- Works with most major warehouses
Tip: Start with dbt Cloud if you don’t want to mess with setting it up locally. Skip the command line pain!
2. Transform Data
Transform is like a layer on top of your warehouse that gives you a controlled way to define metrics. Think: you define what “conversion rate” means once, and then re-use it everywhere — safely.
It’s centered around their “Metric Layer”. This helps avoid the classic problem where every dashboard defines the same metric slightly differently.
Best for: Mid-sized teams who need consistent, reusable KPIs across tools and dashboards.
Cool features:
- Central metric definitions
- Integrates with Looker, Mode, Hex and more
- Makes metrics explorable with metadata
Analyst quote: “Once we added Transform, dashboards finally made sense company-wide.”
3. Lightdash
Lightdash takes your dbt models and gives them a beautiful, interactive front-end. It feels like Looker, but you can host it yourself and control more. It uses your dbt YAML files to define explores, dimensions, and measures.
If you love dbt but want a less manual way to let users explore data, Lightdash is it.
Best for: Analytics teams that already use dbt and want lightweight BI, without paying Looker prices.
Cool features:
- Connects directly to dbt projects
- Users can self-serve metrics
- Open-source and cloud options
Tip: Lightdash isn’t just for dashboards. Use it to declutter your metrics warehouse too.
4. Metrics Layer (by Cube)
Cube is another player in the Metrics Layer game, but it offers more engineering-focused tools. You define data models and metrics in JavaScript or YAML, and it sits between your warehouse and any front-end tools.
Imagine a clean API layer for all your company metrics. You define once, and feed into anywhere: dashboards, apps, experiments.
Best for: Data-savvy teams that work with embedded analytics or want a developer-friendly metric service.
Cool features:
- GraphQL and REST APIs for metrics
- Caching layer for faster queries
- Customizable logic and transformations
Bonus: Works amazing with frontend developers who want metric power without SQL.
5. Evidence
Evidence brings the flavor of data notebooks + reporting into a sleek platform. You write Markdown files (yes, Markdown!) and interweave SQL queries to produce clean, version-controlled reports.
It’s great for recurring updates, investor reports, or any doc where you want your charts to refresh automatically.
Best for: Analysts who want structured writing, storytelling, and precise messaging with fresh data.
Cool features:
- Markdown + SQL = beautiful reports
- Git-based versioning
- Open-source and lightweight
Quirky tip: Think of it like a Google Doc made for data teams… with way more power.
6. Rill Developer
Rill Developer is a fast, local way to build reports and dashboards using just CSVs or Parquet files. It’s designed to feel more like a desktop app than a cloud platform.
You import a file, play with the dataset, define metrics, and boom — instant insights.
Best for: Solo analysts or small teams working with flat files who want quick dashboards without spinning up a warehouse.
Cool features:
- Built for speed and simplicity
- No complex setup needed
- Good for CSV and Parquet explorations
Fun fact: It’s open-source and runs smoothly on most laptops.
Which Tool Should You Pick?
It depends on your vibe:
- Want structure and modular SQL pipelines? Use dbt.
- Need standardized metric definitions across tools? Try Transform or Cube.
- Already using dbt, and want BI on top? Go with Lightdash.
- Love notebooks and structured reports? Evidence.
- Working with flat files, no warehouse? Check out Rill.
The good news? These tools are small, fast, and easy to try. You don’t have to rebuild your stack to test one out. Most are open-source or have simple cloud options.
Final Thoughts
Modern analytics is about turning messy raw events into beautiful, reusable truths. These tools help bridge that gap without needing a huge data engineering team. Whether you love code or hate it, there’s something here for every analyst.
So grab a dataset, pick a tool, and start modeling!
