ImagineX’s Post

🤷 Why complicate what can be simple? Databricks Materialized Views = your entire data pipeline in one CREATE statement. The image 👇 shows exactly what you get: incremental updates, event-driven processing, and Enzyme's magic, all in a cost-effective way. Check out the full blog with code examples here: https://lnkd.in/ds6CDpef (pss LATAM folks working with data warehouses: stay tuned for an upcoming Databricks collaboration 👀)

🚀 Big data isn’t just about volume. It’s about turning complexity into clarity. At DataEngi, we build scalable solutions that help businesses manage, process, and analyze massive data volumes with precision. Our platform is #Databricks. It gives us the power of Apache Spark, seamless Data Lake integration, and collaborative data workflows. All in one place. With #Databricks, we help clients: ✅ Simplify big data pipelines ✅ Enable fast analytics ✅ Empower teams with reliable, governed data ✅ Unlock insights from structured & unstructured sources With the right tools and the right team, #BigData can work for you. 🔗 See how we build Big Data solutions: https://lnkd.in/dxKA3kSv #DataEngineering #ApacheSpark #Lakehouse #RealTimeData

Databricks is making waves in the data engineering world with its latest announcement! 🚀 Table update triggers are now generally available in Lakeflow Jobs, bringing a new level of automation and efficiency to your data workflows. With this update, you can now automatically trigger downstream jobs when changes are made to your tables. This means no more manual interventions or missed updates—your data pipelines will run smoothly and seamlessly, ensuring that your business insights are always up-to-date. Whether you're working with large-scale data lakes or complex data pipelines, this feature will streamline your processes and help you stay ahead of the game. We're excited about the potential this brings to data teams everywhere. What do you think about this new feature? How do you envision it enhancing your data workflows? 🤔💡 #Databricks #DataEngineering #LakeflowJobs #Automation #DataPipelines #TechNews

I was having a conversation with a client yesterday about Databricks and Data Fabric, and they asked, “Which one should we use?” It’s a great question — and one that made me want to share a few thoughts here. ⬇️ 🔹 Databricks is ideal when you need to do things with data: building pipelines, running analytics, training machine learning models. It’s designed for data teams who need power and flexibility. 🔹 Data Fabric is more about connecting and managing data across systems. It helps organisations unify access, apply governance, and make data available wherever it lives — without duplication. One is focused on processing and insight. The other is focused on integration and accessibility. Both have their place — it really depends on what you're trying to solve. If you're exploring this space or want to chat more, feel free to reach out. 😊

🚀 Top 5 Best Practices for Designing Scalable Data Pipelines Building a data pipeline is easy — scaling it is the real art 🎨. Here are 5 golden rules every data engineer should live by: 1️⃣ Modular Design: Break your pipeline into clear stages — ingest, transform, load. Easier to debug, test, and scale. 2️⃣ Schema Enforcement: Define and validate schemas early to prevent nasty surprises. 3️⃣ Smart Partitioning: Use the right partition keys and formats (like Parquet/Delta) to boost performance and cut costs. 4️⃣ Observability: Add logs, metrics, and alerts. You can’t fix what you can’t see! 5️⃣ Cost & Elasticity: Scale up when needed, scale down when idle. Efficiency = longevity 💰 A scalable pipeline isn’t just fast — it’s reliable, maintainable, and future-proof. 🌐 #DataEngineering #ETL #BigData #DataPipelines #Analytics #CloudData

Most companies say they have a “data lake.” But few truly use a lakehouse where storage, compute, and analytics come together. 💡 The lakehouse architecture combines: The scalability of data lakes (raw data at any scale) The structure & reliability of data warehouses The flexibility for both BI and ML use cases In short, it’s the bridge between raw data and usable insight. And tools like Databricks Delta Lake make this seamless with features like: ✅ ACID transactions ✅ Schema enforcement ✅ Time travel ✅ Cost-efficient storage Lakehouses aren’t just a buzzword they’re how modern data teams build reliable, future-proof data platforms. #DataEngineering #DataLakehouse #Databricks #CloudData #DataArchitecture

💡 Handling Rescued Data while Ingesting Files in Databricks Ever wondered what happens when your incoming data doesn’t match the expected schema during ingestion? 🤔 Databricks smartly places such records into a special column called rescued_data — ensuring no data is lost even if the structure doesn’t fit perfectly. As shown below example — malformed rows are automatically captured under _rescued_data and same is being used in the output to avoid the data loss. This approach ensures data quality + reliability, especially when working with large ingestion pipelines where malformed records are inevitable. ✨ Pro Tip: Always keep _rescued_data in your ingestion design — it’s your safety net for schema evolution and unexpected file anomalies. #Databricks #DataEngineering #DeltaLake #AutoLoader #DataQuality #BigData #AzureDatabricks #DataIngestion

Data engineering is the backbone of every modern data-driven organisation. But even the best teams fall into traps that slow down projects, inflate costs, and frustrate business users. Here are the tools that help you overcome these data engineering challenges on Databricks 🚀!

There aren't many resources that discuss the infra side of data engineering Here's a collection of articles I've written that can accelerate your learning: Azure Fundamentals: https://lnkd.in/eUYJFjvi Identity and Access Management (IAM): https://lnkd.in/ei_Bh_WX How I actually implement IAM: https://lnkd.in/eCVtzMDj Networking Patterns: https://lnkd.in/eDzfn_qr My Framework for Building Real MVPs: https://lnkd.in/etUhkRRz My Code Scanning Solution for Data Platform Engineers: https://lnkd.in/eFDt7xTz Check out my Substack for content on all aspects of data engineering: https://lnkd.in/eb2nWjKq I write about everything from skills development to API fundamentals and test-driven development to help you become a valuable data engineer! What topics do you wish there was more content for? p.s. I'm very happy for the surge of recent followers! Don't be a stranger.

Companies don’t just need pipelines. They need outcomes. At Distillery, we help teams modernize data infrastructure, unify siloed sources, and enable real-time decision-making. From Databricks to Snowflake and beyond, our engineers are fluent across the modern stack, tailoring solutions to each client’s context- not just chasing tools. Discover what impact-driven data engineering looks like: https://lnkd.in/g5JYr6fv #DataEngineering #DataStrategy #ModernDataStack