Seyfullah Ural’s Post

🚀 Just when businesses think they’ve mastered data, the rules change again. In 2025, data engineering is no longer just about moving data from point A to B. It’s about 𝐀𝐈-𝐝𝐫𝐢𝐯𝐞𝐧 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧, 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠, 𝐃𝐚𝐭𝐚 𝐌𝐞𝐬𝐡 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞𝐬, 𝐬𝐭𝐫𝐨𝐧𝐠𝐞𝐫 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞, 𝐚𝐧𝐝 𝐜𝐥𝐨𝐮𝐝-𝐧𝐚𝐭𝐢𝐯𝐞 𝐬𝐭𝐚𝐜𝐤𝐬 - the trends reshaping how businesses operate and scale. The challenge? Many organizations are still weighed down by outdated systems: - Data silos that block collaboration. - Slow, batch-based processes that can’t keep up with market demands. - Rising costs and stalled AI projects caused by weak infrastructure. 💡 Understanding these trends is no longer optional, it’s the key to staying competitive, reducing costs, and turning data into real-time business value. 👉 𝐑𝐞𝐚𝐝 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐛𝐥𝐨𝐠 𝐡𝐞𝐫𝐞: https://lnkd.in/dph4z2r2 💬 Facing data silos, outdated pipelines, or costly failed AI initiatives? Contact us - our 𝐝𝐚𝐭𝐚 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 help you modernize infrastructure, eliminate bottlenecks, and build a scalable, future-ready data foundation. #DataEngineeringTrends #AIDataEngineering #DataEngineeringTrends2025 #LatestTrends #FutureOfData #DataEngineeringServices #LatestBlog #SculptSoft

Friday – Wisdom to apply + Sneak peek next week 💡 You’ve got options—choose based on maturity and goals. If your organization still struggles with data silos and slow central teams, a Data Mesh (even partial) can supercharge agility. If you're focused on big data analytics with fewer domain needs, a Data Lake may offer simpler scale. Many of today’s architects mix both—using lakes for raw consolidation and meshes for domain empowerment. Cutting-edge approach? Autonomous data products—trusted, governed, domain-owned—and the future of scalable data ecosystems. 👉 What shall we explore next week? Potential topics: "Scalable MLOps Patterns" or "Responsible AI System Design"? Pro Tips: * Always align your architecture with org structure and culture. * Use pilots to validate before full transformation. * Build governance into your design, not as an afterthought. 📖 Read more: 🔗 https://lnkd.in/gqPBS2sG 🔗 https://lnkd.in/gfpPFGQj 🔗 https://lnkd.in/g6Q6V2Jc 🔗 https://lnkd.in/gSyUQCSf #DataMesh #DataLake #DataArchitecture #NextWeekPreview

📈 Every company wants to reach data maturity. But let’s be real: Data maturity isn’t just about collecting more data. It’s about turning that data into trusted, actionable insights. And who makes that possible? The data engineer. They: ✔ Build pipelines that scale ✔ Design secure, governed architectures ✔ Enable analytics & AI to create value The higher your maturity, the more critical your data engineers become. ✨ In the end, data maturity is the journey — and data engineers are the guides. 👉 What’s the biggest leap your team has taken on the data maturity journey? #DataEngineering #DataMaturity #BigData #MachineLearning #Analytics

A recurring theme in my client conversations: “𝘏𝘰𝘸 𝘥𝘰 𝘸𝘦 𝘵𝘳𝘢𝘤𝘦 𝘢 𝘒𝘗𝘐 𝘪𝘯 𝘉𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘋𝘢𝘴𝘩𝘣𝘰𝘢𝘳𝘥 𝘣𝘢𝘤𝘬 𝘵𝘰 𝘵𝘩𝘦 𝘳𝘢𝘸 𝘵𝘢𝘣𝘭𝘦𝘴 𝘪𝘯 𝘋𝘢𝘵𝘢𝘣𝘳𝘪𝘤𝘬𝘴, 𝘚𝘯𝘰𝘸𝘧𝘭𝘢𝘬𝘦, 𝘰𝘳 𝘦𝘷𝘦𝘯 𝘭𝘦𝘨𝘢𝘤𝘺 𝘸𝘢𝘳𝘦𝘩𝘰𝘶𝘴𝘦𝘴?” And it usually starts with business leaders saying things like: “𝘔𝘺 𝘳𝘦𝘱𝘰𝘳𝘵𝘴 𝘵𝘢𝘬𝘦 𝘵𝘰𝘰 𝘭𝘰𝘯𝘨 𝘵𝘰 𝘳𝘦𝘧𝘳𝘦𝘴𝘩!” “𝘚𝘢𝘭𝘦𝘴 𝘴𝘩𝘰𝘸𝘴 𝘢 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘯𝘶𝘮𝘣𝘦𝘳 𝘪𝘯 𝘵𝘸𝘰 𝘳𝘦𝘱𝘰𝘳𝘵𝘴—𝘸𝘩𝘢𝘵’𝘴 𝘳𝘪𝘨𝘩𝘵?” “𝘞𝘩𝘺 𝘥𝘰 𝘐 𝘯𝘦𝘦𝘥 5 𝘥𝘪𝘧𝘧𝘦𝘳𝘦𝘯𝘵 𝘳𝘦𝘱𝘰𝘳𝘵𝘴 𝘵𝘰 𝘢𝘯𝘴𝘸𝘦𝘳 𝘰𝘯𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯?” These frustrations point to a bigger challenge: Architecture that could not keep up with dynamic requirements, Lineage that takes months of efforts to trace manually, and hence the data that cannot be trusted. At Polestar Analytics, we've built solutions for the problems that keep data leaders occupied. That's why AIM Media House's report on 1Platform resonates (link in the first comment). A standout capability in client conversations is automated end-to-end lineage—tracing KPIs back to raw data to directly solve these challenges. If your org is wrestling with data fragmentation, regulatory risk, or slow time-to-value on Data and AI, this is worth a closer look. Curious to hear from you: What’s the most common “data frustration” you hear from your teams and/or business teams in your org?

𝘼𝙄 𝙧𝙚𝙖𝙙𝙞𝙣𝙚𝙨𝙨 = 𝙙𝙖𝙩𝙖 𝙧𝙚𝙖𝙙𝙞𝙣𝙚𝙨𝙨 (a CEO’s 5-point quick check) Models are only as reliable as the metadata and controls behind them. Before adding another tool, check the foundations: 1) 𝗟𝗶𝗻𝗲𝗮𝗴𝗲 Key fields are traceable end-to-end: source → transforms → owners. 2) 𝗢𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 Every critical dataset has a named owner and a clear escalation path. 3) 𝗣𝗜𝗜 𝗰𝗼𝗻𝘁𝗿𝗼𝗹𝘀 Sensitive data is classified, masked where needed, and access is enforced. 4) 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 What’s kept, why it’s kept, and when it’s deleted are defined—and applied. 5) 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗦𝗟𝗔𝘀 Freshness, completeness, and accuracy have thresholds that are measured and visible. 𝘖𝘯𝘦 𝘱𝘳𝘢𝘤𝘵𝘪𝘤𝘢𝘭 𝘵𝘪𝘱 Label authoritative sources in the platform itself (schemas, tags, views). Slides drift; governed labels travel with the data. 𝘐𝘯 𝘱𝘳𝘢𝘤𝘵𝘪𝘤𝘦 Consolidating scattered “source-of-truth” notes into a governed knowledge store tied to lineage reduces review loops and cuts hallucinations in LLM workflows. Bookmark for your next roadmap review. #AIReadiness #DataGovernance #DataStrategy #Metadata #Leadership #MLOps #GenAI

Data Guiding Principle: Purpose-Optimized Persistence Here's another set of "guiding principles" that should be part of every company's data strategy: Rule 1: Persist is optimized for purpose. Rule 2: There should be 1 (and only 1) environment for each purpose. A lot of people talk about “single source of truth.” While that’s noble and important at the data element level (each data element is born in 1 place), it’s not practical (or wise) to treat that source as the only place that data can live. You wouldn’t run machine learning directly against the same database that powers your customer-facing app serving 30M users. Technically possible? I suppose. Practically disastrous? You betchya. Instead, copy the data into an environment built and optimized for ML, and let the production database do what it’s meant to: keep the app running at scale. But here’s the trap: once you have that ML environment, do you need another one? No. In fact the answer is, HELL NO. The minute you spin up multiple environments for the same purpose, you dilute the value of your data, complicate governance, and waste real money on licenses and infrastructure. Companies often justify these overlaps with hair-splitting logic: “This ML environment is for Sales, that one is for Operations.” What that usually reveals is either weak governance, weak leadership, or someone buying into the sales pitch that “our sales-specialized tool will boost sales performance by 5%.” Spoiler alert: it’s almost never the tool, it’s the human using it. If they want it to be better, they’ll make it better and you’ll never know what could have happened with your "standard" tool. Strong leadership and strong governance keep your environment lean and effective. Otherwise, your architecture ends up looking like a NASCAR hood, plastered with every logo under the Sun, none of which are really providing the value they promised to the car you’re driving. #DataStrategy #DataGovernance #DataArchitecture #DataManagement #DataLeadership

AI-ready data isn’t a workshop, it’s an operating discipline. If exec dashboards or GenAI features wobble, it’s rarely the model. It’s the data plane. >> Here’s the checklist I use with CXOs to turn “governance” into runtime controls: >> Inventory the revenue-critical data products. Give each a DRI, SLA, and named downstream consumers. If nobody owns it, nobody saves it. >> Instrument lineage end-to-end (column-level). Source → lake/warehouse → transforms → BI/models. Impact analysis should take seconds, not meetings. >> Define drift thresholds + SLOs. Freshness, volume, distribution, schema. Treat violations like pager incidents. >>Embed governance at the table level. Auto-classify PII, wire retention/consent, and tag materiality in metadata. Policy should be executable, not decorative. >>Automate incident routing. Pipe observability alerts into the on-call tools you already live in (Snowflake/BigQuery hooks, Opsgenie/PagerDuty, Slack). >>Report quality KPIs like SRE. MTTD, MTTR, recurrence rate right next to uptime/latency. If you can’t measure trust, you can’t manage it. >>Run a controlled pilot. One pipeline, clear thresholds, prove MTTR/accuracy gains—then roll out. >>What “good” looks like in the wild >>95% owner coverage • 99% SLO adherence on tier-1 tables • MTTD < 15 min • Zero incidents reaching execs If you want a copy of the AI-Ready Data Checklist, drop “expert” below and I’ll set up a free 1:1 with our data engineering lead or grab the guide here and run it yourself. #AIReadyData #DataObservability #DataGovernance #DataLineage #DataDrift #MLOps #DataTrust Rakuten SixthSense

Data silos aren’t just an inconvenience - they’re silent profit killers. Every isolated database. Every locked department. Every “we’ll sync later” moment… They all cost you: speed, clarity, and ultimately - growth. The signs are everywhere: – Reports that never align – Teams working in the dark – Missed opportunities hidden in plain sight Now imagine this instead: Your data flows seamlessly - from one team to another, one decision to the next - with zero friction. Here’s what makes it possible: > Centralized Data Platforms – A single source of truth instead of fragmented chaos > ETL & ELT Pipelines – Structured + unstructured data, connected in real time > Data Governance & Accessibility – Secure yet collaborative access for every stakeholder > AI & Automation – Metadata driven categorization for instant discoverability At Brilliqs, we help businesses unlock seamless, interconnected data ecosystems that drive faster, smarter decisions. Because when data flows freely, so does innovation. Because when data flows, innovation follows. Want to break down the silos slowing your progress? Comment below or message us directly - let’s start unlocking your next wave of growth. #DataSilos #DigitalTransformation #BusinessIntelligence #DataStrategy #EnterpriseData #Brilliqs

How do you scale Data Products without losing control? It’s a question I hear from many organizations. As data ecosystems decentralize, cover many technologies the opportunities grow — but so do the risks. Governance is NOT an after thought, NOT a reactive action it should be embeded in the full process from ideation to deployment and runtime of datat products. Take the active approach because... I see common challenges keep surfacing: > Schema and data drift that silently break dependencies > Quality issues that erode trust in analytics and AI > Increasing compliance demands across multiple jurisdictions > Teams moving fast, but without a shared framework > Traditional governance approaches — manual checks, post-facto audits, endless documentation — can’t keep up. They slow delivery instead of enabling it. We’ve taken a different path: automated computational governance. Policies and data contracts are embedded directly into the Data Product lifecycle. The result: ✅ Producers and consumers know exactly what to expect ✅ Compliance is built in, not added later ✅ Teams keep autonomy, while the business gains trust and explainability This is not just technology — it’s about building a formal way of working that lets organizations innovate fast and responsibly. I’d love to exchange thoughts with peers on how you’re approaching this balance in your own data strategy. So let’s connect and share some knowledge around Witboost the data product management paltform with automated computational governance. #DataProducts #GovernanceByDesign #DataContracts #Witboost #AIReady

STOP THE DATA MESS: Improve Your Data Quality Today Tired of making critical decisions based on shaky data? It's a common professional pain point, and frankly, it's exhausting. The reality is, poor data quality isn't just an inconvenience; it can cripple your analytics, inflate operational costs, and lead to flawed strategic choices. Many teams spend more time cleaning data than actually analyzing it. This is where the real challenge lies: moving from reactive data cleanup to proactive data health. Here's a quick tip for a practical resolution: • Implement automated data profiling tools. These AI-powered solutions can rapidly identify anomalies, inconsistencies, and missing values across vast datasets. • Instead of manual, time-consuming checks, AI can flag issues at the source, allowing your team to focus on root cause analysis and proactive remediation. • Think of it as an early warning system for your data health, ensuring your insights are built on a solid foundation. What's your go-to strategy for tackling data quality issues? Share your insights below! #DataQuality #DataAnalytics #AIinData #DataManagement #BusinessIntelligence Sunil Zarikar