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In the realm of data engineering, the way we store and manage data is critical for our success. Many robust methodologies exist, but one standout approach is the Data Vault. This framework not only supports agile development but also enhances data warehouse design by focusing on long-term sustainability and adaptability. Our latest article explores the valuable concepts we can adopt from Data Vault practices. From improving collaboration to streamlining data integration, these principles serve as a solid foundation for anyone looking to bolster their data strategies. We all have unique experiences and insights within this field. I’d love to hear your stories about how you have implemented innovative practices in your data management endeavors. Let’s share our learnings and foster a collaborative environment. #DataVault #DataEngineering #CloudTechnologies #Collaboration #DataManagement https://lnkd.in/gsrXB6P5

"𝗪𝗲'𝗿𝗲 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗠𝗲𝘀𝗵 𝘁𝗼 𝘀𝗼𝗹𝘃𝗲 𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀." I hear this constantly. And it's usually followed by exactly the same anti-patterns that made their data lake a graveyard. Here's what I'm seeing in enterprise after enterprise: 𝗗𝗼𝗺𝗮𝗶𝗻 𝗖𝗼𝗻𝗳𝘂𝘀𝗶𝗼𝗻 Teams spend months defining "domains" based on org charts instead of actual business value flows. Then they wonder why the data doesn't align with how work actually gets done. 𝗧𝗵𝗲 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗧𝗿𝗮𝗽 Data Mesh becomes the new ETL. Teams create "data products" that are really just APIs moving operational data between systems. This isn't analytics architecture – it's expensive middleware. 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗣𝗿𝗼𝗹𝗶𝗳𝗲𝗿𝗮𝘁𝗶𝗼𝗻 Every team builds their own data stack because "decentralization." Six months later, you have 12 different security models, incompatible data formats, and nobody who can support any of it. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗯𝘆 𝗛𝗼𝗽𝗲 "We'll establish governance as we go." Translation: "We'll deal with compliance and security after we get hacked or audited." Sound familiar? 𝗜𝘁'𝘀 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗹𝗮𝗸𝗲 𝗽𝗹𝗮𝘆𝗯𝗼𝗼𝗸 𝘄𝗶𝘁𝗵 𝗻𝗲𝘄 𝗯𝘂𝘇𝘇𝘄𝗼𝗿𝗱𝘀. The uncomfortable truth: Your data problems aren't technical. They're organizational. • You don't know who owns what data • You can't define what a "product" actually is • Your teams lack the platform skills to be autonomous • You have no governance framework for federated decisions Data Mesh can work. But only if you fix the foundations first: ✓ 𝗗𝗼𝗺𝗮𝗶𝗻 𝗼𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 aligned with business reality, not IT structure ✓ 𝗔𝗰𝘁𝘂𝗮𝗹 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀 that deliver value to real customers ✓ 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 that enable teams without requiring PhD-level expertise ✓ 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗴𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 established before teams need them 𝗧𝗵𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗲𝘃𝗲𝗿𝘆 𝗖𝗧𝗢 𝘀𝗵𝗼𝘂𝗹𝗱 𝗮𝘀𝗸: 𝗔𝗿𝗲 𝘄𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗗𝗮𝘁𝗮 𝗠𝗲𝘀𝗵 𝗼𝗿 𝗷𝘂𝘀𝘁 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗻𝗴 𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘀𝘄𝗮𝗺𝗽? Parallaxis #DataMesh #DataGovernance #PlatformEngineering

📊 Cost-Aware Data Engineering: Scaling Without Overspending 💡 TL;DR (for orgs scaling data platforms under budget pressure) Building reliable, enterprise-scale data pipelines doesn’t have to mean sky-high cloud bills. Smart design choices—cost visibility, automation, and workload-aware infrastructure—let teams deliver value without overspending. 📌 What I Focus On 🔹 Cost Visibility & Accountability • Fine-grained tagging & monitoring (per-team / per-project) • Clear chargeback models → teams own their data spend 🔹 Efficient Architecture • Tiered storage (hot vs. cold) with lifecycle automation • Workload-aware design (batch for heavy loads, streaming only where it truly matters) 🔹 Optimization at Scale • Query tuning & partitioning → avoid scanning petabytes unnecessarily • Serverless + spot instances for spiky workloads → same performance, lower cost 🔹 Automation & Guardrails • Budget alerts tied to pipeline jobs • Policy-driven orchestration → prevent runaway jobs before they happen 📈 Impact Snapshot In recent projects, this approach helped: • Reduce monthly data infra spend by ~30% while scaling usage • Shorten job runtime by 45% with partitioning + query optimization • Enable cost visibility for 10+ global teams without slowing them down 🧠 Takeaway Scalable data engineering is not just about handling growth—it’s about handling it responsibly. By making cost a first-class metric, data teams can deliver resilient systems that scale with the business, not against the budget. #DataEngineering #DataOps #CostOptimization #CloudEfficiency #ScalableSystems

New ISG Software Research Analyst Perspective: Nextdata Provides a Platform for Distributed Data I previously described data mesh as a cultural and organizational approach to distributed data ownership, access and governance, rather than a product that could be acquired or even a technical architecture that could be built. While that remains true, many data management software providers have adapted their products in recent years to address the four key principles of data mesh: domain-oriented ownership, data as a product, self-serve data infrastructure and federated governance. New data management providers have also emerged with products built around these principles, including Nextdata, which is led by the originator of the data mesh concept.

💡 Why “Perfect” Dev Data Turns into Chaos in Production 💡 Every data engineer and analyst has faced this: In development, source systems send beautiful, clean sample data. Pipelines run smoothly. Dashboards look great, downstream systems align perfectly. Confidence is high. Then we push to production — and suddenly, hell breaks loose. Nulls and empty strings appear out of nowhere. Reference value appear that were missing in dev data. Schemas evolve without notice. Duplicate or late-arriving records sneak in. Business rules behave differently in the real world. ⚠️ Why does this happen? Because dev data is often a “golden” subset: sanitized, clean, and missing the edge cases of real-world production. A lot of source system team, provide manually created files rather than system generated files. Production data is messy, unpredictable, and subject to business realities that test environments rarely capture. ✅ How do we prevent this? 1. Test with production-like data — partner with source teams to simulate both regular and edge-case business scenarios. The more variation you cover, the better prepared your pipelines will be. 2. Set data contracts — so source systems guarantee schemas and critical rules. 3. Embed data quality checks early — null checks, thresholds, schema validation. 4. Build resilient pipelines — bad records should quarantine, not crash jobs. Schema evolution shouldn't fail jobs. 5. Separate ingestion from consumption — introduce a latency buffer, deliver product in increments. Phase 1: Ingest data into a landing layer, monitor, and analyze for anomalies. Phase 2: Only after checks pass, make it available for business consumption, by building business layer. This creates space to detect production variations without disrupting dashboards or operations. 👉 The lesson: Don’t rely on the “perfect dev picture.” Plan for production chaos. By introducing latency buffers and phased data delivery, we can protect business stakeholders while continuously improving our processes.

Modern enterprises run on orchestrating data systems so that information can be filtered, transformed, and consumed at every level of the organization. Today I came across an interesting framework that classifies data systems into tiers. Each tier has its own purpose, and no single tool can serve them all. That’s why using the right tool for the right job is so crucial.

🚀 Day 2 / 5 of the Data Glossary Series Continuing this no-fluff series to break down core data concepts into shorthand notes - perfect for anyone building, managing, or analyzing data systems. 📘 Today’s glossary includes: Data Ops → pipeline automation · orchestration · monitoring · testing Data Mesh → domain-oriented · data as a product · self-serve platform · federated governance Data Fabric → unified architecture · virtualization · integration · orchestration Data Virtualization → abstraction · federation · integration · access Data Integration → ingestion · transformation · loading · synchronization Data Transformation → cleaning · enrichment · aggregation · normalization Data Ingestion → batch · streaming · CDC · replication Data Pipeline → ingestion · transformation · loading · orchestration Data Warehouse → modeling · storage · querying · reporting Data Lake → storage · processing · analysis · governance Data Lakehouse → lake + warehouse · unified architecture · governance · security Did I miss anything? Drop it in the comments! #DataEngineer #Governance #DataChecklist

Managing data at scale has always required a balance between performance, cost, and maintainability. At exmox GmbH, where our ETL pipelines process tens of millions of rows daily, traditional manual optimizations (OPTIMIZE, VACUUM, ANALYZE) quickly became an operational bottleneck. In early 2025, we began experimenting with Databricks’ Liquid Clustering (LC) and Predictive Optimization (PO) to reduce this burden. These features promise to: ✅ Continuously recluster and compact files ✅ Refresh statistics automatically ✅ Reduce the need for manual table maintenance In our latest blog post by our Head of BI Andreas Paech, he shares two case studies showing where LC & PO deliver big wins and where manual optimizations still matter. He also proposes a phased adoption model for introducing automation into modern DataOps practices. Read the full post here ➡️ https://lnkd.in/dvs9ZMd4

Quest Software launches unified AI-ready data management platform - IT Brief New Zealand: Quest Software's revamped erwin platform uses generative AI to unify data quality, modelling, metadata management, and data governance within a ...