English Video - https://youtu.be/WGRXLrd684Y Telugu Video - https://youtu.be/nHbr8-nuHq0 Understand the real difference between Apache Hadoop & Spark vs enterprise platforms like Databricks, EMR, Dataproc, and Cloudera. In this video I explained: • What is Apache • Open Source vs Enterprise • Hadoop & Spark ecosystem • Why companies use managed platforms • Real-world industry usage Perfect for Data Engineering beginners and professionals. 𝐌𝐲 𝐁𝐨𝐨𝐤𝐬 & 𝐆𝐮𝐢𝐝𝐞 https://topmate.io/dataengineering/ 𝐀𝐥𝐥 𝐅𝐫𝐞𝐞 𝐌𝐚𝐬𝐭𝐞𝐫 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 ----------------------------------------- 𝟗 𝐇𝐨𝐮𝐫𝐬 𝗦𝗤𝗟 𝗠𝗮𝘀𝘁𝗲𝗿 𝗖𝗹𝗮𝘀𝘀 in Tamil - https://youtu.be/JtaOmwnR6AM 𝟒 𝐇𝐨𝐮𝐫𝐬 𝐨𝐟 𝐋𝐢𝐧𝐮𝐱 𝐌𝐚𝐬𝐭𝐞𝐫 𝐂𝐥𝐚𝐬𝐬 in Tamil - https://bit.ly/45pOI0y 𝟏𝟐 𝐇𝐨𝐮𝐫𝐬 𝐏𝐲𝐭𝐡𝐨𝐧 𝐌𝐚𝐬𝐭𝐞𝐫 𝐂𝐥𝐚𝐬𝐬 𝐢𝐧 𝐓𝐚𝐦𝐢𝐥- https://youtu.be/HAxm8n9QY50 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐌𝐚𝐬𝐭𝐞𝐫 𝐂𝐨𝐮𝐫𝐬𝐞 𝐓𝐚𝐦𝐢𝐥 - https://bit.ly/3yF5uVD 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐌𝐚𝐬𝐭𝐞𝐫 𝐂𝐨𝐮𝐫𝐬𝐞 𝐄𝐧𝐠𝐥𝐢𝐬𝐡 - https://youtu.be/Tyg1FVNq40g 𝐏𝐲𝐭𝐡𝐨𝐧 𝐒𝐞𝐫𝐢𝐞𝐬 𝐢𝐧 𝐓𝐚𝐦𝐢𝐥 - https://bit.ly/3GnEYZU 𝐁𝐢𝐠 𝐎 𝐏𝐥𝐚𝐲𝐥𝐢𝐬𝐭 - https://bit.ly/4ascpI0 𝐆𝐞𝐧 𝐀𝐈 𝐏𝐥𝐚𝐲 𝐋𝐢𝐬𝐭 𝐓𝐚𝐦𝐢𝐥 - https://bit.ly/4le7NZ3 𝐆𝐞𝐧 𝐀𝐈 𝐏𝐥𝐚𝐲 𝐋𝐢𝐬𝐭 𝐄𝐧𝐠𝐥𝐢𝐬𝐡 - https://bit.ly/3EmIqn9 𝐒𝐲𝐬𝐭𝐞𝐦 𝐃𝐞𝐬𝐢𝐠𝐧 𝐓𝐚𝐦𝐢𝐥 - http://bit.ly/45OYCbE 𝐏𝐲𝐭𝐡𝐨𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐕𝐢𝐝𝐞𝐨𝐬 𝐄𝐧𝐠𝐥𝐢𝐬𝐡 - https://bit.ly/4iJStRQ 𝐏𝐲𝐭𝐡𝐨𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐕𝐢𝐝𝐞𝐨𝐬 𝐓𝐚𝐦𝐢𝐥 - https://bit.ly/4bIPRBr 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐕𝐢𝐝𝐞𝐨 𝐄𝐧𝐠𝐥𝐢𝐬𝐡 - https://bit.ly/3DxUkKb 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐕𝐢𝐝𝐞𝐨 𝐓𝐚𝐦𝐢𝐥 - https://bit.ly/3BIcnga 𝐀𝐖𝐒 𝐂𝐥𝐨𝐮𝐝 𝐓𝐚𝐦𝐢𝐥 - https://bit.ly/4jZEZ5Q 𝐆𝐨𝐨𝐠𝐥𝐞 𝐂𝐥𝐨𝐮𝐝 𝐓𝐚𝐦𝐢𝐥 - https://bit.ly/2Katf2B 𝐀𝐳𝐮𝐫𝐞 𝐂𝐥𝐨𝐮𝐝 𝐓𝐚𝐦𝐢𝐥 - https://bit.ly/4hBK5U8 𝐒𝐐𝐋 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐄𝐧𝐠𝐥𝐢𝐬𝐡 - https://bit.ly/4e0sXFS 𝐒𝐐𝐋 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐓𝐚𝐦𝐢𝐥 - https://bit.ly/47x9slC 𝐒𝐨𝐜𝐢𝐚𝐥𝐬 🎥𝐘𝐨𝐮𝐓𝐮𝐛𝐞 - https://www.youtube.com/@dataengineeringvideos 📸𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦 - https://instagram.com/dataengineeringtamil 💼𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 - https://www.linkedin.com/in/sbgowtham/ 🌐𝐖𝐞𝐛𝐬𝐢𝐭𝐞 - https://dataengineeringtamil.com/ 💻𝐆𝐢𝐭𝐇𝐮𝐛 - http://github.com/Gowthamdataengineer 📧𝐄𝐦𝐚𝐢𝐥 - atozknowledge.com@gmail.com Technology in Tamil & English #DataEngineering #BigData #CloudComputing #DataAnalytics #ETLTutorials #SQLTutorials #DataEngineeringTamil #BigDataTamil #TamilTech #TamilTutorials #TamilITProfessionals #TamilDataEngineeringChannel #HadoopTutorial #ApacheSpark #FlinkTutorial #NoSQLDatabases #MongoDBTutorial #AWSDataEngineering #GCPDataEngineering #AzureDataEngineering #EngineeringStudents #TamilEngineeringStudents #ITCareerTamil #TechJobsTamil #LearnInTamil #TamilTechChannel Tamil Data Engineering Tutorials, Best Tamil Data Engineering Channel, Data Engineering in Tamil, Big Data Tamil Tutorials, Hadoop Tamil Tutorials, Spark Tamil Videos, Flink Tamil Tutorials, Data Engineering Concepts Tamil, AWS Data Engineering Tamil, GCP Data Engineering Tamil, Azure Data Engineering Tamil, NoSQL Tamil Tutorials, BigQuery Tamil Tutorials, Data Pipeline Tutorials Tamil, Cloud Computing Tamil, Hadoop Tamil Tutorial, Hadoop Ecosystem Tamil, MapReduce Tamil Explanation, Apache Spark Tamil, Spark Streaming Tamil, Spark SQL Tamil, MongoDB Tamil Tutorial, Cassandra Tamil Videos, NoSQL Tamil Explanation, AWS Tamil Big Data Tutorials, Google Cloud Tamil Data Engineering, Azure Data Engineering Tamil, SQL Tamil Tutorials, ETL Process Tamil Videos, Data Warehouse Tamil, Tamil Tech Channel, Data Engineers Tamil Nadu, Tamil IT Professionals, Engineering Students Tamil Tutorials

Last week, I noticed that Anthropic was hiring for a partner success manager. The very first line of the description was: Consulting and systems integration firms are racing to build Claude practices. From there, the posting went on to explain how important it is for Anthropic to support consultancies, systems integrators, and implementation partners as they bring Claude into the enterprise. And all I could think was: Wait. If the AI is good enough to replace engineers, why does it need a small army of consultants to help companies use it? On one hand, we keep hearing that AI is going to automate software engineering, data work, analytics, customer support, operations, and almost every other form of knowledge work. It will end work as we know it! And yet, every major AI company seems to be investing in partner ecosystems, systems integrators, implementation teams, enterprise success teams, and consultants in one way or another. I had a similar thought about Salesforce recently. If your AI is so good, why do companies still spend millions of dollars paying consultants to set up Salesforce, customize workflows, clean up CRM data, integrate systems, train teams, and keep the whole thing from becoming a very expensive mess? Shouldn’t AI be able to just… do that? But of course, anyone who has worked inside a real company knows the answer. The hard part is rarely clicking the buttons. The hard part is understanding the business, the process, the edge cases, the incentives, the data, the politics, and the mess that already exists. So why? Why do the LLM labs want to partner with consultancies? My thought? AI is powerful enough to change work, but not simple enough to magically reorganize companies by itself. If you enjoyed this video, check out some of my other top videos. If you enjoyed this video, check out some of my other top videos. Common Data Pipeline Patterns You’ll See in the Real World - Types Of Data Pipelines You'll Build https://youtu.be/htAipJ6yYFs What Is BigQuery - Breaking Down What BigQuery Is And Diving Into A Hands On Walkthrough https://youtu.be/pud-vuNE15g If you're looking for help ingesting your data in batch or real time, then you need to check out Estuary - https://bit.ly/4eQC3oQ If you'd like to read up on my updates about the data field, then you can sign up for our newsletter here. https://seattledataguy.substack.com/​​ Or check out my blog https://www.theseattledataguy.com/ And if you want to support the channel, then you can become a paid member of my newsletter https://seattledataguy.substack.com/subscribe Tags: Data engineering projects, Data engineer project ideas, data project sources, data analytics project sources, data project portfolio _____________________________________________________________ Subscribe: https://www.youtube.com/channel/UCmLGJ3VYBcfRaWbP6JLJcpA?sub_confirmation=1 _____________________________________________________________ About me: I have spent my career focused on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission and redesign insurance provider policy to help reduce the overall cost of healthcare. I have also helped develop analytics for marketing and IT operations in order to optimize limited resources such as employees and budget. I privately consult on data science and engineering problems both solo as well as with a company called Acheron Analytics. I have experience both working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data. *I do participate in affiliate programs, if a link has an "*" by it, then I may receive a small portion of the proceeds at no extra cost to you.