MVF Makes ETL 7x Cheaper While Migrating 1B+ Rows Across 60+ Sources

Background:

MVF is a global customer generation platform that depends on fast, accurate data pipelines to drive reporting and marketing performance across multiple regions and channels.

Their ETL spend had nearly tripled under a new pricing model, creating unsustainable cost pressure. Costs were escalating faster than business value, engineering teams were stretched, and they needed a simpler, more sustainable approach.

As Andonis Pavlidis, Head of Data at MVF, explained: “Prices were always increasing, SLAs were dropping. It didn’t feel like a partnership anymore, but rather like a commodity.”

---

Challenge: Spiralling Costs and Deteriorating Service

MVF’s data team was under increasing strain from a fragile and inefficient ETL setup.

• Engineering drag – half a dozen custom pipelines in Airflow consumed valuable analyst time.
• Support bottlenecks – four-day ticket cycles left the team firefighting instead of focusing on growth. As Andonis Pavlidis described it: “When something went down with Fivetran, it was four days of back and forth tickets. If something goes down with Matatika, it’s a Slack message and it’s fixed by the next day.”
• Sensitive data risk – HIPAA/GDPR disposition pipelines relied on fragile infrastructure outside MVF’s control.
• Fragmented pipelines – multiple parallel connectors inflated costs and slowed data delivery.

---

Solution: Mirror Mode Migration + Custom Connector Enablement

MVF partnered with Matatika to deliver a risk-free migration strategy that cut costs and simplified infrastructure.

• Mirror Mode migration – replicated all existing pipelines in parallel, ensuring zero disruption during validation and cutover.
• Custom connector replacement – rebuilt and stabilised bespoke Airflow jobs, including Iterable, Five9, Survicate, Everflow, Injixo, Invoca, Airtable and more. New connectors such as Baidu were also developed.
• Sensitive data improvements – re-engineered disposition pipelines from fragile webhooks to robust API calls, giving MVF safer and more controllable data flows.
• Simplified operations – decommissioned Airflow and introduced a dedicated development workspace for safe testing and deployment.

As Andonis Pavlidis noted: “We loved working with the Matatika team as they felt an extension of our team. They shared the same frustrations and reacted as we would react ourselves.”

---

Results: 7x Cheaper, Zero Risk, Superior Performance

The migration delivered more than savings. MVF gained faster pipelines, stronger reliability, and greater team productivity. Is this better?

• 7 x cheaper than renewal (86% cost reduction) – achieved with Matatika’s performance-based pricing, while migrating 1B+ rows across 60+ sources.
• Risk-free migration – completed with zero downtime or disruption.
• Up to 4x faster pipelines – Bing Ads connector reduced from 1.8 hours to 30 minutes, enabling intraday refresh and reduced Snowflake cost.
• Engineering capacity reclaimed – two days of monthly maintenance eliminated, freeing analysts for higher-value projects.
• Same-day issue resolution – from “four-day ticket cycles” to direct Slack access with Matatika engineers.
• Complete infrastructure modernisation – legacy Airflow retired, fragmented pipelines consolidated, and data architecture strengthened for scalability and compliance.

As Andonis Pavlidis concluded: “We went to Matatika for a migration. We ended up with an improvement of our stack.”

---

Appendix: Full Connector List

Fivetran connectors migratedGoogle Adwords, GA4 Export, Iterable, MySQL (x12), Facebook Ads, Appwiki, Bing Ads, Webhooks, Outbrain, Taboola, Twitter Ads, TikTok Ads, LinkedIn Ads, Google Sheets, S3 and Dbt Cloud Reporting.

Custom Airflow connectors migrated to Matatika supported connectorsAirtable, Five9, Survicate, Everflow, Injixo, Invoca, Custom S3.

New connector developedBaidu and CallMiner

---

Appendix: Full Connector List

MVF’s migration spanned a wide range of connectors across marketing, customer interaction, compliance, and operational data sources, together accounting for more than 1B rows per year.

Category	Example Connectors	Estimated Rows / Year
Marketing & Paid Media	Google Adwords, GA4 Export, Facebook Ads, Bing Ads, TikTok Ads, LinkedIn Ads, Outbrain, Taboola	600M
Customer Interaction	Iterable, Five9, Survicate, Everflow, Invoca (real-time + historical)	250M
Sensitive / Compliance Data	Webhooks (disposition bronze), Airtable, Injixo	120M
Operational / Other Sources	MySQL (x12), Google Sheets, S3 Export, Dbt Cloud Reporting, ExpertReview S3	30M
New Development	BaiduCall MIner	Included above

#Case Study #analytics #data #Data Engineering #Data Infrastructure #ETL Tools