Season 2: Episode #25 | Building a Reusable SQL Library: Best Practices & Pitfalls

🔍 Tired of finding five versions of the same SQL file – all with different numbers? In this episode, Vadym and Helen break down the mess (and the fix) behind reusable SQL libraries. 

Whether you're cleaning up metrics, onboarding faster, or scaling your analytics stack, this episode is your practical guide to building a library that works.

What you’ll learn:
🪤 Common pitfalls teams face when SQL logic is scattered
🧱 What makes a good SQL library – and why it boosts speed, not slows it
🔁 Real-world story: from 400 queries to 25 clean data marts
🛠 Tools like dbt, Dataform, GitHub, Looker, and OWOX Data Marts
📁 5 best practices for building a clean, trusted, and versioned library

➡️ Explore OWOX BI and simplify your SQL workflows

Podcast listing

Vadym:
Hey friends, welcome back to The Data Crunch Podcast! I’m your host Vadym, and today we’re tackling a question most data teams mutter under their breath: How do we stop rewriting the same query a dozen different ways – and start building a SQL library everyone can trust? Today, we dig into the chaos... and the cure.

And joining me is Helen, our Head of Customer Success here at OWOX. Helen, welcome back! Are there any fun “SQL déjà-vu” moments to set the stage?

Helen:
Thanks, Vadym! Always happy to be here.

Well, you know, last week I opened a shared folder and – no joke – found six files called revenue_final_v3.sql. Same folder, totally different numbers. I mean… classic Tuesday, right?

Vadym:
Ha-ha. That is the chaos, and I’m really glad we’re here to break this down today and, more importantly, help you fix it. But before we dive into the guts of SQL libraries, a quick reminder to our listeners:

If you like practical stories with a side of analytics therapy, hit subscribe on YouTube or your favorite podcast app – we drop new episodes every Thursday, and we always bring the good stuff. Also, feel free to share topics or questions that interest you in the comments. We might even choose your suggestion for a future podcast episode.

So, to set the scene, I keep hearing people say, “Yeah … SQL libraries sound cool, but isn’t that a bunch of overhead?” Helen, let’s explore what a SQL library really is, and why on Earth anyone should bother instead of just slapping queries into dashboards.

Helen:
Great question – and, honestly, a super-useful one. Well… think of a SQL library as giving your analytics logic a brain and a memory. Without this library, you’re stuck in Groundhog Day reporting. Every new request causes another copy-paste query, numbers drift, and dashboards disagree like siblings on a road trip.

Vadym:
Yeah, and when the CFO asks, “Which number is the real revenue?” the room suddenly goes very quiet.

Alright. Let’s talk pain points. What problems do teams face when they don’t have this in place?

Helen:
Well… 

The first one: logic drift – one tiny tweak in a copied query, and ROAS jumps 20 percent for no reason.

The other one: maintenance nightmares – fixing a metric means crawling through ten dashboards you forgot existed. 

And the big consequence? Trust evaporates. Once people find one mismatch, every report looks suspect.

Vadym:
That’s brutal. And I imagine it also burns out the analysts who have to answer the same metric questions 17 times.

Helen:
Yup. They become bottlenecks instead of enablers. It’s frustrating for everyone.

Vadym:
Let’s bust a quick myth while we’re here. I think some people assume a SQL library slows you down or makes you less agile. Your thoughts?

Helen:
That’s a big misconception. A good SQL library is a speed enabler, not a blocker. When done right, it acts like LEGO blocks. You’re not reinventing the wheel every time – you’re assembling trusted pieces faster.

The other myth I heard about: “Only data engineers care.” Sorry, analysts, marketers, product managers – everyone benefits when definitions stay consistent.

Vadym:
I love that you also busted another myth right away.

Now, quick story time – Could you share a real-world story where a library (or lack of one) made all the difference?

Helen:
Sure, I think I have a story in mind.

Alright. One retailer had 400 ad-hoc queries floating around. We collapsed them into 25 clean data marts. Result? 40 percent drop in ad-hoc asks, onboarding a new analyst shrank from three weeks to three days, because the knowledge finally lived in code, not someone’s head.

Vadym:
Wow, 3 weeks to 3 days, that’s impressive.

Now, before we dive into best-practice tips, let’s talk about tools for a sec – what should people actually look at when they want to build and manage a solid SQL library?

Helen:
Sure thing. 

• dbt – Great for modular SQL and documentation.
  Build models, reuse code, and even write tests.
  The downside? It takes some setup, and your analysts might need a crash course in Jinja first.
• GitHub + Actions – For version control and collaboration.
  Great for keeping track of who broke what and when.
  But it won’t hold your hand – no SQL templating, no dependencies, just good old code reviews.
• Metabase models or Looker PDTs – If you’re BI-first.
  Perfect option if your team lives inside dashboards – quick to build and easy to explain.
  But don’t expect much in terms of versioning or complex logic. It's fast food for data modeling.

Vadym:
Ok, got it. And what about Dataform, is that on the list?

• Dataform – Also worth checking out for SQL workflows.
  Feels like dbt’s friendlier cousin, with a nice UI and smooth BigQuery vibes.
  Still, it’s not as flexible, and you may outgrow it if things get too custom.
• And our shiny new OWOX Data Marts on GitHub – you can already find a number of Connectors to help you get your data from popular advertising platforms. 

And soon, a Data Marts library not just for analysts – but for marketing, product, and anyone who lives in data.

Vadym:
That’s a great list of tools right there. Quick, useful, to the point as we love it.

I am wondering… are there any common pitfalls people should watch out for?

Helen:
Yes, sure. There are a couple I can share based on my own experience.

1. Over-engineering on day one. Fancy DAGs are cool, but honestly, start with naming conventions.
2. No ownership. A library without code owners is a public gym – mess everywhere.
3. Ignoring usage metrics. If a mart isn’t queried in 90 days, archive it instead of letting it rot.

Vadym:
Great, perhaps some of you have recognized these pitfalls and can improve next time. Alright, Helen, let’s land the plane with some practical tips. 

What are your best practices for building a SQL library that doesn’t become a mess?

Helen:
Sure, here are five to keep handy:

• Start small: Tackle your top 5 metrics first.
• Name things clearly: No cryptic abbreviations. Your future self will thank you.
• Document everything: Even if it feels obvious now, it won’t in 6 months.
• Use folders and tags: Organize by subject area or team.
• Review regularly: Set a calendar reminder to prune or update logic.

Vadym:
Thank you, Helen, this is great. And if you’re listening and thinking, “Wow, we need that,” then here’s the deal:

If you want hands-on experience with a platform that keeps your data versioned, tested, and explorable, head over to owox.com and start using OWOX BI for free. Get your models, transformations, and automated reporting under control – no credit card needed.

If you’re eager to jump straight into ready-made SQL, grab OWOX Data Marts on GitHub. Copy, tweak, deploy – instant clarity for marketing, product, and any data analytics. Link is in the description below.

Helen:
So here’s the key takeaway: A reusable SQL library turns chaos into clarity. You get speed and consistency – no more choosing one and praying.

Start small, stay organized, and remember: you don’t need perfection – you need visibility. A solid SQL library gets you there.

Vadym:
Thanks for tuning in, guys! Subscribe, leave us a comment, and share your best duplicate-query thriller story. Catch you next week on The Data Crunch Podcast. Bye for now!