Why Modern Data Pipelines Are Broken (And What We're Building About It)

Three years ago, I was a data engineer at a mid-sized SaaS company. We needed to sync data from Salesforce, Stripe, and our Postgres database into Snowflake for analytics. Simple, right?

We evaluated the major platforms. One boasted 700+ connectors. Another promised “zero maintenance.” A third offered a generous free tier.

Six months and $47,000 later, we were still debugging broken pipelines at 2 AM.

The “fully managed” connector broke every time Salesforce updated their API. The “predictable” pricing quintupled when our data volume spiked during Black Friday. The “enterprise support” meant waiting 3 days for a response while our dashboards showed stale data.

I left that job. But I couldn’t stop thinking: Why is this so hard?

The Three Lies We’ve Been Sold

After researching every major data pipeline and orchestration platform on the market, I’ve identified three fundamental lies the industry tells us:

Lie #1: “More Connectors = Better Platform”

The pitch: “We have 600+ connectors! We support everything!”

The reality: Only 15% are actually maintained by the vendor. The rest are community-contributed code that breaks when APIs change, has no SLA, and comes with a disclaimer: “should be used with caution in production.”

I’ve seen platforms advertise Shopify integration, only to discover it was last updated 18 months ago and fails on the new API version. The connector exists—it just doesn’t work.

What we actually need: 20–30 rock-solid connectors for the sources 90% of companies use, maintained with obsessive care. PostgreSQL, MySQL, Salesforce, HubSpot, Google Analytics, Stripe, AWS S3, Snowflake, BigQuery.

Quality over vanity metrics.

Lie #2: “Usage-Based Pricing Is Fair”

The pitch: “Pay only for what you use! It’s flexible!”

The reality: You have no idea what you’ll pay until the bill arrives.

• MAR (Monthly Active Rows) pricing: Your Salesforce data grew 40% last quarter? Surprise—your bill just increased 70%.
• Credit-based models: Matillion charges credits for pipeline execution and warehouse compute—which you don’t control. One customer reported their actual cost was 2.3x the license fee.
• DPU consumption (AWS Glue): $0.44/DPU-hour sounds cheap until you realize a single ETL job consumed 150 DPUs and cost $847 for one run.

What we actually need: Flat, predictable pricing.

“$X per month, unlimited pipelines, defined connectors.” Done. No surprises, no spreadsheets to forecast costs.

Lie #3: “Open Source Means Free”

The pitch: “It’s open source! No vendor lock-in!”

The reality: The software is free. The infrastructure, operations, debugging, and maintenance costs $50K–$150K/year in engineering time.

Apache Airflow is “free,” but you need:

• PostgreSQL database (managed or self-hosted)
• Redis for queueing
• 3–5 worker nodes
• Load balancer
• Monitoring stack (Prometheus, Grafana)
• DevOps engineer to maintain it all

A mid-sized company spends 2–3 engineer-months per year just keeping Airflow running. That’s $40K–$60K in hidden costs.

What we actually need: Managed solutions that don’t require a PhD in distributed systems—or transparent hosting costs if self-hosted is an option.

The Real Problem: Misaligned Incentives

Here’s the uncomfortable truth: most data platforms make more money when things are complicated.

• Connector bloat lets them advertise big numbers in comparison charts, even if 85% are unreliable
• Usage-based pricing means revenue grows as your data grows, regardless of value delivered
• Complex architectures create switching costs—once you’ve invested in learning their DAG syntax or proprietary transformations, migrating is painful

The incentives are backwards. Vendors profit from complexity, but customers need simplicity.

What a Better Data Platform Looks Like

After talking to 50+ data engineers, analysts, and engineering leaders, here’s what people actually want:

1. Predictability Over Features

• ❌ Stop: Chasing 700 connectors you’ll never use
• ✅ Start: Guaranteeing the 20 connectors that matter always work

People don’t want options. They want certainty. They want to know that when they wake up Monday morning, their Stripe → Snowflake pipeline synced successfully. Every time.

2. Transparent Pricing

• ❌ Stop: “Contact us for pricing” and usage-based surprises
• ✅ Start: "$499/month for 20 pipelines, $999 for unlimited"

Data teams need to budget. CFOs need predictability. Usage-based pricing optimizes for vendor revenue, not customer success.

The best pricing model is the one you can explain to your CEO in one sentence.

3. Built-In Observability

• ❌ Stop: Requiring third-party monitoring tools
• ✅ Start: Native alerting, logging, and lineage tracking

When a pipeline fails, I shouldn’t need to check:

• AWS CloudWatch (infrastructure)
• Datadog (application metrics)
• Slack (manual alerts)
• The vendor’s dashboard (pipeline status)
• My warehouse (data validation)

One dashboard. One source of truth. Pipeline status, data quality, sync frequency, error logs—everything in one place.

4. Help Users Scale Beyond You

• ❌ Stop: Trying to be everything to everyone
• ✅ Start: Partnering with specialists and helping customers integrate

Not every platform needs to do ML model training, reverse ETL, data catalog, and governance. Do pipelines exceptionally well, then help customers connect to specialists for other needs.

5. Data Sovereignty Options

• ❌ Stop: Cloud-only, black-box execution
• ✅ Start: Self-hosted options for regulated industries

Healthcare, finance, and government customers can’t send sensitive data to third-party cloud services. Give them the option to run pipelines in their own infrastructure.

Managed cloud for convenience. Self-hosted for compliance. Let customers choose.

The Market Gap We’re Filling

Notice the pattern? Every solution optimizes for one thing while sacrificing another critical need.

There’s a clear gap: Predictable, reliable data integration for teams who can’t afford enterprise chaos or open-source operational burden.

What We’re Building (It’s Cooking 🔥)

We’re currently in pre-launch, actively working with early design partners to shape a product that solves real pain—not imaginary feature checklists.

If you lead a data team tired of broken syncs, unpredictable bills, and patchwork observability—we’d love to partner with you.

We’re looking for data teams (50–500 employees) who want to manage their data movement better—without the chaos.

Our Principles

• Quality > Quantity: 20 connectors we maintain obsessively > 600 connectors that sometimes work
• Predictability > Flexibility: Flat pricing you can budget > usage-based surprises
• Partnership > Pride: Integrate with specialists > build mediocre features in-house
• Scale What We Serve: Every feature guaranteed to handle enterprise volumes from day one

Our Stack

• PostgreSQL (Supabase) for rock-solid data storage
• FastAPI for high-performance API layer
• DuckDB for in-process analytics
• Gemini AI for intelligent error detection and auto-recovery
• Cloudflare for global edge distribution
• Claude AI for natural-language pipeline configuration

Our Architecture

• Visual pipeline designer with live preview (catch errors before production)
• <5 minute sync frequency (faster than Fivetran’s 15-min standard)
• Built-in observability (logs, metrics, lineage—no third-party tools required)
• Git-native workflows (version control, branch deployments, CI/CD ready)
• Self-hosted or cloud (your choice, your data sovereignty)

Our Pricing (Pre-Launch Tiers)

• Starter: $159/month – 5 pipelines, core connectors
• Professional: $499/month – 20 pipelines, all connectors, priority support
• Enterprise: $999/month – Unlimited pipelines, self-hosted option, SLA

✅ No MAR
✅ No credits
✅ No surprises

You’ll know your bill before you start—and it won’t change as your data grows.

Why Now?

Three forces are converging:

1. Market Frustration: Teams are fed up with pricing unpredictability and broken connectors. We’ve seen 40–70% price increases (Fivetran), 4x pricing jumps (Prefect), and hidden costs (Matillion).
2. Technology Maturity: Modern tools (DuckDB, Claude AI, Gemini) make it possible to build intelligent, self-healing pipelines that were impossible 3 years ago.
3. Demand for Simplicity: The pendulum is swinging back from “infinite configurability” to “just works.” Teams want boring, reliable infrastructure, not cutting-edge complexity.

The Road Ahead

We’re in private beta with 12 design partners—data teams at companies ranging from 50 to 500 employees. Here’s what they’re telling us:

“Finally, a platform that doesn’t punish us for growth. Our data doubled, bill stayed the same.”
— Data Lead, B2B SaaS ($20M ARR)

“We migrated from Fivetran and cut costs 73% while improving sync frequency.”
— Engineering Manager, E-commerce ($50M ARR)

“The Salesforce connector just works. Every day. That’s all I wanted.”
— Analytics Engineer, FinTech startup

What We Need From You

If you’ve felt the pain I described—broken connectors, exploding bills, 2 AM debugging sessions—we’d love to partner with you during our pre-launch phase.

We’re opening limited beta slots in February 2026.

👉 Interested? Comment “PIPELINE” below or DM me.

We’re specifically looking for:

• Mid-market companies (50–500 employees)
• Teams syncing 5–30 data sources
• Organizations tired of usage-based pricing surprises
• Data engineers who value reliability over feature lists

As a design partner, you’ll get:

• Early access to the platform
• Direct influence on roadmap priorities
• Dedicated support during onboarding
• Locked-in founding-tier pricing

The Bottom Line

The data pipeline market is broken because vendors optimize for complexity (more features = more revenue) instead of simplicity (reliable pipelines = happy customers).

We’re building the platform we wish existed when I was debugging Salesforce connectors at 2 AM.

• 20 connectors that always work > 600 that sometimes don’t
• $499/month predictable > “contact us for a quote based on your MAR”
• Built-in observability > duct-taping 5 monitoring tools together
• Help you scale > vendor lock-in

If this resonates, let’s talk.

The data infrastructure your team deserves is cooking. 🔥

P.S. — If you’re a data platform vendor reading this: I’m not saying you’re evil. I’m saying the incentives are misaligned. Let’s fix this together.

Want to follow the journey? I’ll be sharing updates on architecture decisions, beta learnings, and hard truths about building in this space. Follow me for real talk about data infrastructure.