Hi Everyone ! I am looking to #Connect with people who are interested in ----- 👨‍💻 Data Engineering 📊 Data Analysis ☁️ Microsoft Azure | AWS 🧱Databricks ⌨️ Coding 👨‍🔬 AI/ML/GenAI Let's grow together ☕️

Day 15 — Photon: Understand how Databricks’ native C++ vectorized engine accelerates scans, joins, aggregations, and writes for faster query performance. ☕️ #databricks #dataengineering #DataScience #DataAnalytics #MachineLearning #coding #python #ArtificialIntelligence

Day 14 — Unity Catalog Architecture: Master regional metastores, three-level namespaces, and cross-workspace governance to build secure, scalable Databricks data platforms. ☕️ #dataengineering #DataAnalytics #cloudcomputing #datascience #machinelearning #databricks #datalake

Day 13 — OPTIMIZE, ZORDER & VACUUM: Compact files, improve data skipping, and safely remove obsolete files for faster, more efficient Delta Lake tables.☕️ #dataanalytics #dataengineering #cloudcomputing #datascience #machinelearning #databricks #datalake #coding #python

Day 12 — Liquid Clustering & Deletion Vectors: Boost Delta Lake performance with smarter data layout, efficient row-level changes, and reduced maintenance overhead.☕️ #dataengineering #dataanalytics #datascience #Machinelearning #CloudComputing #bigdataanalytics #python

Day 11 — Delta Lake Transaction Log Internals: Understand JSON commits, checkpoints, and concurrency control to build reliable, high-performance lakehouse tables.☕️ #dataengineering #dataanalytics #datascience #CloudComputing #databricks #datalake #deltalake #coding

Risk analytics is very important in any datasets ☕️ #dataengineering #dataanalytics #datascience #Machinelearning

Microsoft Fabric concepts every Data Engineer should know ☕️: → OneLake → Lakehouse → Warehouse → Direct Lake → Dataflows → Pipelines → Notebooks → Semantic models → Real-Time Intelligence Understand how the pieces connect. #dataengineering #DataAnalytics

Agentic AI Architecture ☕️

Day 10 — Choosing Your Lakehouse Stack: Match cloud, team skills, streaming needs, and governance—not hype—to build the right data platform. ☕️ #dataengineering #dataanalytics #datascience #machinelearning #databricks #datalake #deltalake #datawarehouse

Day 9 — Catalogs 101: Build a governed, interoperable lakehouse with consistent namespaces, discovery, and cross-platform access. ☕️ #dataengineering #datascience #dataanalytics #CloudComputing #aws #googlecloud #microsoftazure #databricks #datalake

Spark patterns every Data Engineer should know: → Partitioning → Shuffle reduction → Broadcast joins → Skew handling → Caching → Predicate pushdown → Partition pruning → AQE Think execution plan, not just code ☕️ #bigdata #dataengineering #dataanalytics #DataScientist

Data Engineering patterns worth mastering: → Incremental loads → CDC → Idempotency → Partitioning → Deduplication → Schema evolution → Backfills → Data validation → Retry handling → Observability Master patterns, not pipelines. ☕️ #dataengineering #dataanalytics

Python skills every Data Engineer should know [🐍] ☕️ → Lists & dictionaries → map() / filter() → Comprehensions → Functions → Exception handling → JSON → CSV → Pandas → File handling → Iterators Write less code. Understand more. #pythonprogramming #dataengineering

Cloud Data Engineering patterns to master: → Medallion → Incremental loads → Event-driven pipelines → Autoscaling → Cost optimization → Secrets → Monitoring → Disaster recovery → IaC → CI/CD Master patterns. Build for production. ☕️ #dataengineering #dataanalytics

Day 8 — The Small File Problem: Fewer, right-sized files mean lower metadata overhead, faster queries, and healthier lakehouse performance. #dataengineering #dataanalytics #cloudcomputing #databricks #datalake #aws #googlecloud #microsoftazure #datascience #coding

Tea Break during work ☕️🙂 #tech #dataengineering #dataanalytics

RAG patterns every AI Engineer should understand: → Chunking → Embeddings → Vector search → Metadata filtering → Hybrid search → Reranking → Context construction → Evaluation → Guardrails → Observability Better retrieval → better answers. ☕️ #genai #dataanalytics

Microsoft Fabric patterns Data Engineers should know: → OneLake → Lakehouse → Medallion architecture → Direct Lake → Data pipelines → Spark → Shortcuts → Incremental processing → Governance → Monitoring Learn the platform. Understand the architecture ☕️ #dataanalytics

Sunday tech reminder: Don't chase every new tool. Technology will keep changing. Your ability to learn, adapt and solve problems is what stays valuable. #dataengineering #tech #technology #cloudcomputing #dataanalytics

Master the fundamentals. Learn the tools. Build at scale. 🚀☕️ #DataAnalytics #dataengineering #sql #bigdata #cloudcomputing #databricks #datalake #machinelearning #genai #coding