Databricks’ Post

Discover how a leading life sciences brand transformed its operations, migrating from a legacy Hadoop platform to Databricks. With MSRcosmos' expertise, they reduced OPEX by 50% using a Hybrid Delivery Model. Learn more about their journey towards data-driven excellence. https://lnkd.in/gk92duW5 #DataTransformation #SuccessStory #Databricksmigration #datamigration #DataDriven

In the web of modern data ecosystems, #Starburst emerges as a technical marvel. Its federated query engine is designed to access data across massive and disparate sources, be it Hadoop clusters, data lakes, or traditional databases, without the overhead of ETL processes. By leveraging cost-based query optimization, Starburst ensures rapid data retrieval, even from vast and diverse datasets. Join us on Nov 22nd for a technical exploration into Starburst's architecture and its transformative impact on data access. Engage in a Technical Exploration with Us: https://hubs.ly/Q0244z2w0 #Innovate #DataMesh #LakehouseArchitecture #LakeHouse #Starburst #Looker #BigData #Insights #AdvancedAnalytics #Analytics #DataAnalytics #DataStrategy #DataArchitecture #LookML

This chart from dbt Labs' State of Analytics Engineering report is actually uplifting. Today, the biggest two challenges that data teams face (poor data quality and ambiguous ownership) are mostly related to people and processes rather than technical complexity or the data platform itself. This is a big shift from the struggles of the pre-Modern-Data-Stack world. As someone who started working on data platforms during the Hadoop era, it's inspiring to see the industry move past spending endless engineering resources on maintaining complex data infrastructure that had a terrible return on investment. In a way, this is a direct acknowledgment of the Modern Data Stack’s role in solving one of the Hadoop era’s biggest problems: today’s data platform (mostly) just works. Gone are the days of wasting expensive engineering time on building and maintaining the platform. The Modern Data Stack may be dead, but for all its flaws, it solved the data platform’s technical hurdles. Now it’s time to solve the business ones. #dataengineering #moderndatastack #dbt

Hello All, Just completed the course of week 3 of the Ultimate bigdata programme under the guidance of Sumit Mittal sir.. Completed the session on the following:- 1.Distributed processing with mapreduce 2.Real time industry usecases 3.Apache spark vs databricks 4.Spark execution plan 5.Example in Apache spark. #bigdataengineer #dataengineer #data #learninginprogress #mapreduce #apachespak #datalakes

🚀Hey Data Fam, I updated my data pipeline to transform NYSE text data into partitioned Parquet format using Spark SQL leveraging Delta Tables instead of Hive! 🔧 Workflow: 📂 Uploaded NYSE data to the HDFS. 🗃️ Created a database and tables, then loaded NYSE text data into a staging table using Spark SQL. 📊 Inserted data into the partitioned Delta table from the staging table based on the trading month. 📝 Ran the Spark SQL script to load and partition the data. Special Thanks to Durga Gadiraju #DataEngineering #BigData #AWS #EMR #Hive #Parquet #NYSEData #ETL #Deltatables💡

For all Data Engineers out there, here is The State of Data Engineering 2024 Some of the highlights: ✅ More and more, data observability tools are used not just to monitor data sources, but also the infrastructure, pipelines, and systems after data is collected. ✅ Companies are now seeing data observability as essential for their AI projects. Gartner has called it a must-have for AI-ready data. ✅ Like in 2023, Monte Carlo is leading in this area, with G2 naming them the #1 Data Observability Platform. Big organizations like Cisco, American Airlines, and NASDAQ use Monte Carlo to make their AI systems more reliable. #BigData #Hadoop #HDFS #DataStorage #DataEngineering #Hive #Sqoop #Spark Karthik K.

"Excited to announce my completion of the Hive course! 🐝📊 Ready to harness the power of distributed data storage and processing for seamless big data analytics. Hive opens doors to scalable solutions and deeper insights. #Hive #BigData #Analytics"

The week-1 of Elite Data Engineering Program started with the throughout understanding from high level what is big data and how it works. We have learnt below topics - 1. Big data definition 2. Hadoop overview 3. Apache Spark overview 4. Cloud VS On prem systems 5. Importance of distributed storage and processing 6. Overview on Database, DWH and Data Lake. 7. Role of data engineers. Huge thanks to TrendyTech and Sumit Mittal great start. This has created the big picture. #bigdata #cloudengineer #azuredataengineer #learningexperience

Little research from last 10 years on ai Working very hard for turing award 2030 #turingaward2030 #Nobletransformationhub #drfaknoble #ArtificialIntelligence #deeplearning #BigData #bigdataanalytics #bigdatatechnologies #bigdatadeveloper #BigDataAnalysis #bigdatahadoop #dataengineer #dataengineering #dataengineerjobs #cloudcomputing #DataWarehouse #datawarehousing #DataLake #dataanalytics #dataanalysis #StatisticallySignificant #datasciencecourse #distribution #dataanalytics

🎉 Thrilled to Announce 🎉 🚀 Exciting Times Ahead in Big Data! 🚀Just earned my certificate in Big Data, Hadoop, and Spark Basics from IBM, and it's an eye-opener! The data world is vast, and the potential for innovation is massive. But what really caught my attention was the gap between data generation and utilization. 📊💡 Did you know 90% of the world's data was created in the last two years, yet we've barely scratched the surface in understanding it fully? The journey to harnessing this potential is filled with learning and adapting. Joining forums and discussions, I've realized that collaboration is key. To all data enthusiasts: How do you perceive the future of Big Data, and what tools are you excited about? Let's connect and share our insights! 🌐 #BigData #Hadoop #Spark #DataScience #IBM #BigDataChallenges #ContinuousLearning #ProfessionalGrowth