How to use higher-order functions in Spark SQL for data cleaning

Our new Data Science Agent transforms Databricks Assistant into an autonomous partner for data science and analytics. Turn hours of work into minutes while staying in control Integrated into Notebooks and the SQL Editor, this agent explores data, trains models, fixes errors, and more — all with governed access: https://lnkd.in/gFBh7VSh

Excited to see Databricks unleash a new era for data practitioners! The new Data Science Agent transforms Databricks Assistant from a helpful copilot into a truly autonomous partner—planning, executing, and refining complete analytics workflows right inside Notebooks and the SQL Editor. With features like end-to-end lifecycle support (from EDA to feature engineering to model evaluation), Planner Mode for transparent multi-step execution, and deep Unity Catalog integration, this truly feels like the intelligent, collaborative leap we’ve been waiting for. No longer just about code suggestions—agents can now accelerate insight while ensuring trust, security, and governance. As our field moves from “automation” to “autonomous collaboration,” solutions like the Data Science Agent show that the future of data science is agentic, fast, and enterprise-ready. Can’t wait to see teams set free to spend more time driving strategy and storytelling, while trusted AI handles the heavy lifting. Cheers to the innovators making intelligent workflows our new normal! 🚀 #AI #DataScience #Innovation #Databricks #AgenticAI

👏 Exciting news from our Partner - Databricks! The new Data Science Agent elevates the Databricks Assistant into an autonomous partner for data science and analytics by helping teams explore data, train models, resolve errors, and more. ⚡ Turn hours of work into minutes while staying fully in control with governed access! At TriSeed, we help organizations unlock the full potential of Databricks. As your consulting partner, we can guide you in adopting these innovations to maximize business impact. 👉 Ready to accelerate your data journey with Databricks? Let’s connect! https://lnkd.in/gpfvJWJx #TriSeed #Databricks #DataScience #DataEngineering #AI #Analytics #DataTransformation #DatabricksPartner

Impressive innovation from Databricks, the new Data Science Agent streamlines analytics by accelerating tasks while maintaining governance. At TriSeed, we’re committed to helping organizations leverage advancements like this to drive smarter, data-driven decisions. #TriSeed #Databricks #DataScience #Analytics #DatabricksPartner

#BigDataOdyssey – Fortnight 7 Reflections This fortnight was dedicated to strengthening my understanding of how Spark optimizes computation under the hood through memory management and data organization: 1.Deep-dived into memory management in Spark, exploring how execution and storage memory are balanced during distributed processing. 2.Compared sort vs. hash aggregations and learned how each affects performance based on dataset characteristics. 3.Examined different Spark execution plans and how they evolve through stages of query optimization. 4.Explored the Catalyst Optimizer to understand how Spark generates efficient physical plans from logical ones. 5.Studied row- and column-based data formats and their performance tradeoffs in analytical workloads. 6.Analyzed specialized formats like Parquet, Avro, and ORC, focusing on schema evolution and data compression techniques for efficient storage and retrieval. Concluded the fortnight by revisiting all the major topics covered so far in the Big Data journey as preparation for the next hands-on project. Thanks to Sumit Mittal sir for the support via TrendyTech - Big Data By Sumit Mittal #ApacheSpark #DataEngineering #BigData #LearningJourney #SparkOptimization

Tired of writing boilerplate code just to wrangle customer data? Chuck Data runs natively in your terminal and uses natural language to build and manage customer data models in Databricks. What used to take days of coding now takes minutes. Built for data engineers, by data engineers. Check it out!

𝐃𝐚𝐭𝐚𝐛𝐫𝐢𝐜𝐤𝐬 𝐡𝐚𝐬 𝐦𝐨𝐫𝐞 𝐭𝐨 𝐨𝐟𝐟𝐞𝐫 𝐭𝐡𝐚𝐧 𝐒𝐩𝐚𝐫𝐤 𝐣𝐨𝐛𝐬 𝐚𝐧𝐝 𝐒𝐐𝐋 𝐝𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬.  𝐁𝐞𝐡𝐢𝐧𝐝 𝐭𝐡𝐞 𝐬𝐜𝐞𝐧𝐞𝐬, 𝐭𝐡𝐞𝐫𝐞 𝐚𝐫𝐞 𝟐𝟎+ 𝐮𝐧𝐝𝐞𝐫𝐫𝐚𝐭𝐞𝐝 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬 👉 Debug smarter with system tables & query history 👉 Save cost with instance pools & spot instances 👉 Build cleaner pipelines with DLT & constraints 👉 Collaborate better with repos & secrets management Start exploring them, and you’ll see why Databricks is more than just a data platform. Follow Abhishek Agrawal for updates on interview questions. 🔗 𝑱𝒐𝒊𝒏 𝑶𝒖𝒓 𝑾𝒉𝒂𝒕𝒔𝒂𝒑𝒑 𝑫𝒂𝒕𝒂 𝑬𝒏𝒈𝒊𝒏𝒆𝒆𝒓𝒊𝒏𝒈 𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈 𝑷𝒍𝒂𝒕𝒇𝒐𝒓𝒎: https://lnkd.in/dUuscrch 🚀

KDnuggets™ News 21:n46, Dec 8: How to Get Certified as a Data Scientist; 5 Practical Data Science Projects That Will Help You Solve Real Business Problems for 2022 https://lnkd.in/eVuvgWQR

Ever wondered what happens when you submit a Spark job? 👩💻 ➡️ From SparkSession creation to result return/storage, Spark follows a well-defined flow — covering logical plan generation, optimization, physical plan creation, DAG scheduling, and task execution. Understanding this flow helps in optimizing performance and building scalable data pipelines effectively. #BigData #ApacheSpark #DataEngineering #ETL #DataProcessing #CloudComputing #Databricks #Analytics

Delete Rows—Not Entire Files! Title: Databricks Deletion Vectors & Liquid Clustering: The Secret Sauce for Faster Delta Tables Link: https://lnkd.in/gwDwfasN Caption: Deleting 1 row shouldn’t rewrite a 2GB Parquet file. 😩 With Deletion Vectors, Delta Lake marks rows as deleted—no full rewrites. With Liquid Clustering, your large tables auto-optimize based on query patterns—no manual Z-Ordering! Result? 60%+ faster queries, lower DBU costs, and happier engineers. #DeltaLake #Databricks #BigData #DataEngineering #PerformanceOptimization #LiquidClustering #DeletionVectors #Spark