Data engineers: The key to data maturity

𝗖𝗮𝗻 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝗮 𝗱𝗮𝘁𝗮 𝗺𝗲𝘀𝗵 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗗𝗮𝘁𝗮+ 𝗔𝗜 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆? Some assume that once you decentralize ownership and give domains responsibility, a data mesh will simply work. The reality: without data observability, it’s nearly impossible to scale. Here’s why: ✅ 𝗧𝗿𝘂𝘀𝘁: If data products aren’t reliable, domains will quickly lose confidence in each other’s outputs. ✅ 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Observability provides the visibility needed for teams to take true ownership. ✅ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: A mesh multiplies complexity; observability keeps it manageable. ✅ 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: Without automated monitoring, domains spend more time firefighting than innovating. With Data + AI observability in place, you can even assign each data product a Data Reliability Score, built from KPIs like freshness, completeness, accuracy, and pipeline health. This makes trust measurable, comparable, and actionable across the mesh. A data mesh is not just about architecture or org design. It’s about ensuring every data product can be trusted and that requires observability at its core. 💬 What’s your take: is data observability optional or essential for a successful data mesh? #DataObservability #AIObservability #DataMesh #DataReliability #DataEngineering #DataOps

How #DataEngineering Transforms Raw Data into #Business Intelligence The Data Engineering Process Data engineering is #indispensable in #enhancing unrefined data to an usable format. Data engineering is defined by building sophisticated pipelines of data, automating data collection, and integrating disparate systems into one. This allows #businesses to receive clean, organized, and #accurate datasets in real time. https://lnkd.in/e7g_DwZg #AIforBusiness #ArtificialIntelligence #AIstrategy #BusinessGrowth #StartupAI

Data Engineering Tip: Ensuring Data Quality:High-quality data is the foundation of reliable analytics and machine learning models. Poor data quality can lead to flawed insights and costly errors. Tip:Implement automated data quality checks at every stage of your data pipeline, from ingestion to transformation. Use tools like Great Expectations or Deequ to define expectations, validate data, and flag anomalies. This proactive approach helps catch issues early and maintains trust in your data assets. What are your favorite tools or strategies for maintaining data quality? #DataEngineering #DataQuality #DataOps #ETL #BigData #Analytics

A Data Lake is more than just storage—it’s the foundation of a modern data ecosystem. 🌊 It brings together structured, semi-structured, and unstructured data in one place, supporting everything from big data processing and log analytics to data warehousing, relational & non-relational databases, and advanced machine learning use cases. 🚀 By enabling organizations to store massive volumes of raw data and process it flexibly, data lakes open the door to faster insights, smarter decision-making, and scalable innovation. 💡 #DataEngineering #BigData #DataLake #MachineLearning #CloudComputing #Analytics

Data is an asset for companies, Data engineers create wealth from those assets, But wealth creation doesn't happen instantly, you got to go through the proper channels - from unclean and complex datasets to -> clean datasets -> proper modelling and joins -> proper governance -> then you get Golden Layer of the Data -> that creates wealth. #dataengineering #dataanalytics

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

Too many people jump right into using the "best" tools without first stopping to think, "Do we even need the best tools?". Just because something is the best in the industry doesn't mean it's the best for you. For example, Snowflake is considered the best analytical and ML data warehouse, but it isn't the best for a smaller team just starting to use data. The costs would be astronomical for the use case. Fivetran is the most reliable data ingestion tool available, but it wouldn't make sense for a team with extensive engineering support to invest in it. When choosing a tool, consider things like: - data maturity - team size - time/skills available for maintenance - budget - governance - how critical data is to business operations Until you take the time to consider all of these different factors in deciding on a data tool, you shouldn't be choosing any new tools. ➡️ Want more advice on data and analytics engineering? Subscribe to my weekly Learn Analytics Engineering newsletter.

Data engineering is the backbone of modern business. Without it, data-driven decisions would be impossible. In my 14 years of experience, I've seen how robust data pipelines transform raw data into actionable insights. Here are three key practices: 1. Build scalable data pipelines. 2. Ensure data quality and integrity. 3. Automate processes for efficiency. These steps have consistently driven success in my projects. How are you leveraging data engineering in your organization? #DataEngineering #BusinessInsights

🚀 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

Ever struggled to get reliable data when you need it most? You're not alone! 🚧 In many companies, raw data comes from countless sources—databases, APIs, devices—and often arrives messy, incomplete, or inconsistent. This creates huge roadblocks for teams who rely on timely, clean data for analysis or ML models. In my experience, I once faced frequent data pipeline failures that delayed reports and caused frustration across departments. The root cause? Lack of automation and proper data checks that made the process brittle and error-prone. The solution was to build a robust data pipeline that automated ingestion, rigorous cleansing, and transformation before loading data into a centralized warehouse. Key lessons: automate wherever possible, monitor data quality continuously, and collaborate closely with stakeholders to understand their data needs. This transformed not just our data flow, but how the business made decisions. 🎯 How do you ensure your data pipelines remain reliable and scalable as data sources grow? #DataEngineering #DataPipeline #DataQuality #TechLeadership #DataDriven #Analytics #BigData #Automation ----------------------------------------