Medallion Architecture by Databricks for Data Trust

𝗗𝗔𝗬 𝟭𝟲: 𝗧𝗵𝗲 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸 𝗳𝗼𝗿 𝟮𝟬𝟮𝟲 If you want to stay ahead in the data world this year, mastering the right tools is non-negotiable. The landscape has shifted from traditional reporting to 𝘈𝘐-𝘳𝘦𝘢𝘥𝘺, 𝘮𝘰𝘥𝘦𝘳𝘯 𝘥𝘢𝘵𝘢 𝘴𝘵𝘢𝘤𝘬𝘴. Here is the stack you should be focusing on right now: 🧱 𝗧𝗵𝗲 𝗨𝗻𝘀𝗵𝗮𝗸𝗮𝗯𝗹𝗲 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 • Excel: Still the king of quick analysis. Focus on mastering dynamic arrays and the 𝗜𝗡𝗗𝗘𝗫 & 𝗠𝗔𝗧𝗖𝗛 combo for robust lookups. • SQL: The universal language. If you want to pull and manipulate your own data, 𝘢𝘥𝘷𝘢𝘯𝘤𝘦𝘥 𝘚𝘘𝘓 (Window Functions, CTEs) is a non-negotiable must-have. ⚙️ 𝗧𝗵𝗲 𝗠𝗼𝗱𝗲𝗿𝗻 𝗗𝗮𝘁𝗮 𝗦𝘁𝗮𝗰𝗸 (𝗦𝘁𝗼𝗿𝗮𝗴𝗲 & 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻) • dbt (Data Build Tool): The industry standard for transforming data directly in the warehouse. • Snowflake & BigQuery: Cloud-native, scalable, serverless giants that are revolutionizing how we query massive datasets. 🧠 𝗧𝗵𝗲 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗘𝗻𝗴𝗶𝗻𝗲 • Python: The go-to for automation and complex modeling. Libraries like 𝘗𝘢𝘯𝘥𝘢𝘴, 𝘕𝘶𝘮𝘗𝘺, and 𝘗𝘺𝘔𝘊 are essential. • Databricks: The "Lakehouse" architecture is dominating 2026, bridging the gap between data engineering and data science. 🎨 𝗧𝗵𝗲 𝗦𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗲𝗿𝘀 (𝗕𝗜 & 𝗩𝗶𝘇) • Power BI & Tableau: Still the gold standards. The key in 2026 is leveraging their AI/Copilot integrations to maximize your workflow efficiency. 👇 Question for my network: Which of these tools are you prioritizing this year? Are you actively exploring the modern data stack? Let me know in the comments! #Day16 #DataAnalytics #ModernDataStack #SQL #Python #PowerBI #Tableau #Snowflake #BigQuery #dbt #Databricks #CareerGrowth

🚀 Exploring Functions in PySpark – Union, String & Date Operations Today’s session was focused on practicing some very useful PySpark DataFrame functions, especially union operations, string functions, and date functions. These are commonly used in real-world data transformation and ETL pipelines. Understanding these built-in functions makes data cleaning and transformation much more efficient and readable. 🔗 Union Operations 🔹 union() Used to combine two DataFrames with the same schema (same number and order of columns). 🔹 unionByName() Used to combine two DataFrames based on column names rather than column positions. This is especially useful when column order differs but names are the same. 🔤 String Functions These functions are extremely useful for data cleaning and standardization. 🔹 initcap() Converts the first letter of each word to uppercase. Example: "data engineering" → "Data Engineering" 🔹 upper() Converts all characters in a column to uppercase. 🔹 lower() Converts all characters in a column to lowercase. These functions help maintain consistency in textual data. 📅 Date Functions Date handling is very important in analytics and reporting. 🔹 current_date() Returns the current system date. 🔹 date_add() Adds a specified number of days to a date. 🔹 date_sub() Subtracts a specified number of days from a date. 🔹 datediff() Calculates the difference between two dates. 🔹 date_format() Formats a date column into a specific pattern (for example, year-month-day). 📊 Why These Functions Matter These functions are widely used in: • Data cleaning and transformation • Building time-based reports • Standardizing user input data • Creating derived columns for analytics • Combining datasets from multiple sources Today’s practice reinforced how powerful and expressive PySpark becomes when we understand and apply its built-in functions effectively. Grateful to Anurag Srivastava Sir and the DataX Bootcamp for the structured and practical learning journey. Consistency over intensity 🚀 #PySpark #ApacheSpark #DataEngineering #BigData #ETL #DataTransformation #SparkSQL #LearningJourney #DataXBootcamp

Most people in data know SQL. But ask them to design: • a star schema • an incremental ingestion pipeline • a curated analytics layer …and things start getting uncomfortable. Because knowing queries is different from knowing data engineering. So I’m launching a hands-on cohort where we actually build these systems. 🚀 Data Engineering Launchpad 📅 1 April – 30 April 2026 In 4 weeks we’ll cover: • Platform architecture fundamentals • Data modeling (OLTP → OLAP → Star Schema) • Production-grade SQL patterns • Batch + incremental ingestion pipelines No theory-heavy slides. Just live engineering + building things together. 💻 100% live sessions 🎯 Limited seats If you're serious about moving toward Data Engineering roles, this will help you build the right foundation. Registration link in comments 👇 ----------------------------- #dataengineering #datascience #machinelearning #bigdata #dataanalytics #artificialintelligence #data #bigdataanalytics #sql #dataanalysis #bigdataanalysis #programming #dataengineer #database #deeplearning #ai #coding #analytics #fintech #bigdataexperts #bigdataacademicprojects #contactus #datamanagement #datavisualization #sqlserver #datamining #businessintelligence #cloudcomputing #python #technology

Last week, I shared my project on building a lightweight Data Lake using DuckDB, DBT, and MinIO. The response was incredible — thank you for all the conversations 🙌 But here’s the part no one talks about 👇 👉 Building a Data Lake is easy. Designing it well is the hard part. While working on this project, I made a few mistakes that completely changed how I think about data systems: ⸻ 🔹 Mistake #1: Over-engineering too early Tried to build like enterprise systems. 👉 Reality: Simplicity scales better than complexity. ⸻ 🔹 Mistake #2: Ignoring file formats Moved from CSV to Parquet. 👉 Result: Faster queries + lower storage cost. ⸻ 🔹 Mistake #3: Treating DBT as just SQL Used it only for transformations. 👉 But DBT is actually modeling + testing + documentation. ⸻ 🔹 Mistake #4: Not planning for scale Built for today, not tomorrow. 👉 Even small pipelines need future-proof design. ⸻ 💡 Biggest takeaway: You don’t need expensive tools like Snowflake or Databricks to build powerful data systems. 👉 Good architecture beats expensive infrastructure. ⸻ Now I’m exploring: • Orchestration with Airflow • Real-time ingestion • Data quality & observability If you’re learning data engineering tell me - What’s one mistake that changed the way you think about data systems? Let’s learn together #DataEngineering #DataLake #DBT #DuckDB #OpenSource #AnalyticsEngineering #LearningInPublic #TechCareers

 Weeks 2 & 3 of Data Engineering Zoomcamp by DataTalksClub are complete! Module 2 — Workflow Orchestration with @Kestra Kestra is an open-source orchestration platform that makes building and scheduling data pipelines is incredibly simple — all through YAML. -Orchestrate data pipelines with Kestra flows -Use variables and expressions for dynamic workflows -Implement backfill for historical data -Schedule workflows with timezone support -Process NYC taxi data (Yellow & Green) for 2019-2021 Built ETL pipelines that extract, transform, and load taxi trip data automatically! Module 3 — Data Warehousing with BigQuery What blew my mind? BigQuery lets you build and deploy ML models using just SQL — no Python, no separate ML pipeline, just SQL. -Create external tables from GCS bucket data -Build materialized tables in BigQuery -Partition and cluster tables for performance -Understand columnar storage and query optimization -Analyze NYC taxi data at scale Working with 20M+ records and learning how partitioning and clustering reduce query costs by 90%+! Thanks to the @Kestra team and @DataTalksClub for the amazing free resources! Homework solution:https://lnkd.in/gdJ74GCi Appreciate the DataTalksClub team, Alexey Grigorev, for delivering an insightful and hands-on learning experience 🙏 📚 Course: https://lnkd.in/dzWg4hed #DataEngineering #DataPlatforms #Bruin #DEZoomcamp #ELT #ETL #LearningInPublic

🚀 From Skeptic to Believer: 30 Minutes to a Full Data Pipeline with Snowflake Cortex Code I'll be honest—I've been disappointed with Cortex. Even simple query help felt frustrating. But today? Everything changed. I decided to test Snowflake's Cortex Code (CoCo) with a basic prompt from the docs: "Build a Streamlit dashboard with filters for date and region. Create a dbt project to transform raw sales data." What happened next blew my mind: 📊 Database not found? I pointed it to Snowflake Sample Data—CoCo adapted instantly. ⚡ It generated the complete Streamlit Python code AND a full dbt project with staging, intermediate, and mart models—right before my eyes. 🔧 Query running slow with millions of rows? I asked for a data subset. It added limits... but broke referential integrity. 💡 When I mentioned the issue? It understood and fixed the joins automatically—filtering by year while preserving data relationships. Result: A fully functional Streamlit dashboard with real data in 30 minutes. The best part? Everything runs entirely within Snowflake's ecosystem—RBAC, security, governance all intact. No external tools. No data leaving the platform. This is the AI-assisted development experience I've been waiting for. 🙌 #Snowflake #CortexCode #DataEngineering #dbt #Streamlit #AI #DataPipeline #Analytics

Moving from Pandas to Spark has been a gamechanger 🙌 I've been working on a really exciting data transformation project where I'm leading the automation of some key treasury processes to reduce our team's manual workload. Of course, this involves a lot of data and important data architecture decisions. Don't get me started on the rabbit hole I found myself in while learning about all of the "lake"-type data structures (lake, lakehouse, data lake, delta lake, OneLake...). One of the cool things about using Spark with Delta tables is that you can filter data without having to read it all into memory first. In Pandas, data is typically loaded into memory and then filtered, which can become memory-intensive as data grows. Spark, on the other hand, uses metadata to determine which chunks of the data should be read into memory. This is a lot like filtering data before committing it to memory and is much more sustainable as data grows. You know you're a real data nerd when this sounds exciting. 😎 The code snippet shows what I mean. With Pandas, we read a CSV file called 'transactions.csv' into memory before we can filter (e.g., by date), meaning that the entire dataset gets loaded. With Spark, we read a Delta table called 'transactions' at the same time that we apply filtering, so only the portion of data that we're interested in gets loaded.

🧠 Core Design Principles — 6 cards covering Immutability, Separation of Concerns, Incremental Processing, Idempotency, Data Contracts, and Quality Gates ∙ 🎯 When To Use Medallion Architecture — Great fit vs. when to consider alternatives ∙ 🛠️ Popular Tools by Layer — 8 real-world tools (Kafka, dbt, Delta Lake, Databricks, Snowflake, Spark, Great Expectations, Apache Atlas) Core Topic #MedallionArchitecture #DataEngineering #DataArchitecture #BronzeSilverGold #DataLakehouse Tools & Tech #Databricks #Snowflake #ApacheSpark #DeltaLake #dbt #ApacheKafka #BigQuery Data Concepts #DataQuality #DataPipeline #ETL #ELT #DataTransformation #DataModeling #DataLineage #DataGovernance #DataCatalog Career & Community #DataEngineer #DataScientist #AnalyticsEngineer #DataAnalytics #BusinessIntelligence #MLOps #DataOps Learning & Growth #LearnDataEngineering #TechEducation #DataTips #DataCommunity #TechContent #KnowledgeSharing #100DaysOfData Trending & Reach #Tech #AI #MachineLearning #CloudComputing #BigData #Python #SQL #LinkedIn

Looking to build up your Pandas skills? Ibrahim Salami devotes his new tutorial to data types, index alignment, and defensive Pandas practices to prevent silent bugs in real data pipelines.

I spent 3 months building a medallion architecture on Databricks. Bronze ingestion from 5 sources. Silver with deduplication, type casting, business rules. Gold aggregations optimized for the analytics team. The day I demo'd it to the business lead, he looked at the dashboard for about 10 seconds. Then he asked: "Can I get this in Excel?" That moment taught me something most data engineers learn the hard way: your architecture is only as good as its last mile delivery. We obsess over Bronze/Silver/Gold. We optimize Spark jobs, tune partitions, build governance layers. And then the person who actually makes decisions with our data opens a spreadsheet. This isn't a failure of business users. It's a failure of engineering empathy. The Lakehouse doesn't win by replacing Excel. It wins by becoming invisible underneath it. Databricks just launched native Excel and Google Sheets connectors with auto-refresh. This changes the "last mile" problem: 1. Business users query Unity Catalog tables directly from their spreadsheet. No exports, no stale CSVs. 2. Scheduled refreshes (hourly, daily, weekly) keep the data current without anyone touching a pipeline. 3. Your Single Source of Truth stays in the Lakehouse. The spreadsheet is just a window into it. The architecture I wish I had 3 months earlier: governed data in Unity Catalog, consumed through a familiar interface, refreshed automatically. Zero training required for the end user. Are you still fighting the "Excel problem" in your org, or have you started building bridges to it? #DataEngineering #Databricks #Excel #BusinessIntelligence #DataArchitecture