Get the latest AI, data, and technology insights from Matatika’s experts direct to your inbox.
-
Why Most SQL Server Data Tools Migrations Fail (And How to Build Better Ones
Many data teams avoid SQL Server Data Tools (SSDT) migrations due to cost, complexity, and risk, leaving them stuck with outdated systems and growing technical debt. Matatika’s Mirror Mode offers a safer, more cost-efficient alternative by enabling secure, isolated testing environments that mirror production without exposing sensitive data or inflating infrastructure costs.
Read the article -
Column vs Row: Why It’s Time to Rethink How You Pay for ETL
Most data teams remain locked into outdated ETL platforms not out of satisfaction, but due to the perceived risk and disruption of switching, yet the real risk lies in doing nothing, especially under inefficient row-based pricing models that punish growth and hinder budgeting. This blog advocates for a shift to performance-based ETL pricing, highlighting how modern approaches reward efficiency, reduce costs by 30-90%, and can be safely trialed via parallel validation methods like Matatika’s Mirror Mode.
Read the article -
Building Data Trust Through Effective ETL Staging Environments
Many teams avoid ETL staging due to cost and complexity, but this leads to production risks and data trust issues. Matatika offers secure, cost-efficient staging with parallel testing, obfuscated data, and performance-based pricing to catch issues early and deploy confidently.
Read the article -
ETL Commodity – Why Are You Still Paying a Premium?
ETL is no longer a specialised function, it’s a commodity, yet many organisations are still paying inflated prices due to outdated, volume-based pricing models. This blog explores why ETL costs remain high, and how Matatika’s Mirror Mode offers a risk-free path to modern, performance-based pricing.
Read the article -
Data Engineers Don’t Burn Out from Work They Burn Out from Pointless Work
This blog discusses key insights from a Data Matas podcast episode featuring Nik Walker, Head of Data Engineering at Co-op. It explores how data teams can reduce burnout, cut cloud costs, and build trust in their data without overhauling their entire stack. Key themes include eliminating low-value work, right-sizing syncs, prioritising discovery, and fostering psychological safety through structured leadership. The focus is on making smarter choices, not faster ones, to create scalable, resilient data delivery systems that serve both business needs and team wellbeing.
Read the article
Data Leaders Digest
Stay up to date with the latest news and insights for data leaders.