SourceMedium vs assembling a Modern Data Stack

Why teams choose SourceMedium over assembling a Modern Data Stack

The "modern data stack" for ecommerce means assembling a minimum of six tools: a data warehouse, an ELT tool, a transformation layer, a BI tool, an orchestrator, and reverse ETL. Add data observability, a data catalog, and governance tooling — and the architecture diagram hits 8–12 logos before anyone writes a single query.

The realistic cost: $9K–$68K/month in tooling and labor, or $110K–$816K/year — plus 6–12 months before the first useful insight. A Capella Solutions TCO analysis found Year 1 costs exceeded projections by 60%, reaching $2.3M by Year 3.

The industry has a name for this: "MDS fatigue." As dbt Labs founder Tristan Handy described it: "plate tectonics: slow, but inevitable. And we are headed towards Pangea."

Where the approaches differ

One product vs 6–8 tools: Instead of stitching together a warehouse, ELT pipeline, transformation layer, BI platform, and orchestration tools—SourceMedium delivers the entire verified stack as a single integrated platform. Commerce, ads, email, subscriptions, and ops are reconciled into one schema with 2,500+ automated quality checks.

Days to value vs 6–12 months: SourceMedium delivers pre-built data models and 20 analytics modules from day one. The MDS approach takes months 1–2 for hiring, 3–4 for tool selection, 5–8 for building ecommerce data models, and 9–12 for dashboards and bug fixes.

Predictable cost vs runaway budgets: 53% of DIY analytics projects cost 189% of their original estimate. 31% get cancelled entirely. SourceMedium's pricing is predictable — no multi-vendor billing, no budget overruns, no surprise costs in Year 2.

Dedicated support, not a ticket queue: Every SourceMedium customer gets a named Customer Solutions Analyst — US-based, with hands-on DTC experience across marketing, ops, finance, and executive teams. Solution hours are included in your plan, scoped during the sales process so the highest-value work fits within what's already budgeted. No offshore support teams, no $250/hr professional services surprises, no "implementation packages" as a separate line item. If you need additional hours, pricing is transparent — and you always have the option to resource work with your own team instead.

No assembly required — but fully extensible: Your data lives in BigQuery with included compute. Tableau, dbt, Python, Hex — anything that speaks BigQuery connects natively, no wiring needed. Unlimited storage means you can centralize beyond ecommerce — finance, ops, or any custom source — without managing the infrastructure yourself.

Vendor finger-pointing vs one throat to choke: When source APIs change — and Shopify, Meta, and Google Ads APIs change multiple times per year — each MDS vendor blames the other. One support experience called it "not-my-problem finger-pointing that leaves you stuck in the middle." SourceMedium owns the full pipeline from source to dashboard.

Bus factor risk: When the single data engineer who built your MDS leaves, the replacement cost alone runs $65K–$260K, plus 6–12 months of lost productivity and institutional knowledge.

Forkable Looker Studio templates: Every Looker Studio template is open-source and forkable — copy it, customize it, build something entirely new. No Looker license required.

No more "add another tool": An assembled data stack has no built-in AI analyst — you'd need yet another vendor. SourceMedium's AI Analyst answers ecommerce questions in Slack and shows the SQL, so your team gets verified answers without adding another logo to the architecture diagram.

The vendor fragility problem

The MDS approach is only as durable as its weakest vendor. Recent examples: Stitch Data was acquired by Talend, then Qlik, and is now in a soft sunset. Rivery was acquired by Boomi and is being absorbed into their platform. The Fivetran + dbt Labs merger in 2025 — approaching $600M combined ARR — has pricing implications that concern existing customers. When one vendor in your six-tool chain gets acquired, sunset, or pivots, the ripple effects hit everything downstream.

The alternative — building in-house — costs $300K–$500K/year, has a 53% chance of going over budget, and creates bus factor risk when the engineer who built it leaves.

When assembling an MDS might make sense

A custom-assembled data stack works for organizations with dedicated data engineering teams (3+ engineers), workloads that extend far beyond ecommerce, and the budget to sustain $200K–$500K+/year in tooling and personnel. If ecommerce analytics is one small piece of a larger enterprise data strategy, a custom stack provides maximum flexibility at maximum cost.