Data Engineering & AI | Apache Spark
前往频道在 Telegram
This channel is created for beginner in Bigdata Engineer, Apache Spark Developers for knowledge sharing and Projects (Blog,Paid and Free Course)
显示更多7 903
订阅者
无数据24 小时
+87 天
+3730 天
正在加载数据
相似频道
标签云
进出提及
---
---
---
---
---
---
吸引订阅者
十一月 '2611月 '26
十一月 '26
+57
在0个频道中
十月 '26
+155
在1个频道中
Get PRO
九月 '26
+186
在0个频道中
Get PRO
八月 '26
+244
在0个频道中
Get PRO
七月 '26
+181
在0个频道中
Get PRO
六月 '26
+135
在0个频道中
Get PRO
五月 '26
+180
在0个频道中
Get PRO
四月 '26
+154
在0个频道中
Get PRO
三月 '26
+80
在0个频道中
Get PRO
二月 '26
+113
在0个频道中
Get PRO
一月 '26
+258
在0个频道中
Get PRO
十二月 '25
+161
在0个频道中
Get PRO
十一月 '25
+131
在0个频道中
Get PRO
十月 '25
+155
在0个频道中
Get PRO
九月 '25
+171
在0个频道中
Get PRO
八月 '25
+195
在0个频道中
Get PRO
七月 '25
+215
在0个频道中
Get PRO
六月 '25
+154
在0个频道中
Get PRO
五月 '25
+158
在0个频道中
Get PRO
四月 '25
+161
在0个频道中
Get PRO
三月 '25
+203
在0个频道中
Get PRO
二月 '25
+180
在0个频道中
Get PRO
一月 '25
+479
在0个频道中
Get PRO
十二月 '24
+581
在0个频道中
Get PRO
十一月 '24
+395
在0个频道中
Get PRO
十月 '24
+382
在0个频道中
Get PRO
九月 '24
+420
在0个频道中
Get PRO
八月 '24
+527
在0个频道中
Get PRO
七月 '24
+265
在0个频道中
Get PRO
六月 '24
+216
在0个频道中
Get PRO
五月 '24
+297
在0个频道中
Get PRO
四月 '24
+299
在0个频道中
Get PRO
三月 '24
+291
在0个频道中
Get PRO
二月 '24
+291
在0个频道中
Get PRO
一月 '24
+426
在0个频道中
Get PRO
十二月 '23
+1 106
在1个频道中
| 日期 | 订阅者增长 | 提及 | 频道 | |
| 15 十一月 | +3 | |||
| 14 十一月 | +3 | |||
| 13 十一月 | +6 | |||
| 12 十一月 | +2 | |||
| 11 十一月 | +8 | |||
| 10 十一月 | +3 | |||
| 09 十一月 | +5 | |||
| 08 十一月 | +1 | |||
| 07 十一月 | +5 | |||
| 06 十一月 | +1 | |||
| 05 十一月 | 0 | |||
| 04 十一月 | +4 | |||
| 03 十一月 | +5 | |||
| 02 十一月 | +6 | |||
| 01 十一月 | +5 |
频道帖子
🚀 New Project Guide: Predict Loan Defaults with Machine Learning! 💸📊
Are you looking for a hands-on project to add to your Data Engineering or Data Science portfolio?
In this comprehensive guide, we walk through how to use Apache Spark to build an ML model that predicts whether a borrower will default on a loan. This is exactly the kind of real-world scale problem fintech companies tackle every day.
What you will learn:
🔹 Data preprocessing and feature engineering at scale.
🔹 Training a robust classification model using Spark MLlib.
🔹 Evaluating model accuracy on real financial datasets.
👉 Read the step-by-step guide and start coding here:
https://projectsbasedlearning.com/apache-spark-machine-learning/predicting-possible-loan-default-using-machine-learning/
#MachineLearning #DataScience #BigData #ApacheSpark #AI #DataAnalytics #Python #Programming #100DaysOfCode
| 2 | If you’re diving into Big Data tools like Hadoop, Hive, Flume, or Kafka — this collection is gold 💎
📥 Install Apache Hadoop 3.3.1 on Ubuntu
🐝 Install Apache Hive on Ubuntu
📊 Customer Complaints Analysis (Hadoop Project)
📹 YouTube Data Analysis using Hadoop
🧾 Web Log Analytics for Product Company
All projects include end-to-end implementation steps — ideal for building a Big Data portfolio or practicing for interviews!
#BigData #Hadoop #Hive #ApacheKafka #DataEngineering #Linux #OpenSource #DataAnalytics | 163 |
| 3 |  Hey Data Engineers! 👋
Writing SQL during a live technical screen can be stressful, whether you are aspiring to be a Data Analyst or transitioning into Data Engineering. To help you ace your next round, I've just released the Ultimate SQL Cheat Sheet for Interviews—and it's completely FREE! 🎁
Inside this 10-section quick-reference guide, you will master:
🔹 The Foundations: Precision filtering, aggregations, and complex GROUP BY logic.
🔹 Relational Data: A clear breakdown of INNER, LEFT, and FULL Joins.
🔹 Advanced Logic: Replacing messy subqueries with CTEs and Window Functions (ROW_NUMBER, RANK).
🔹 Performance Tuning: Indexing, writing SARGable queries, and avoiding the SELECT * trap.
🔹 Top 20 Interview Questions: Real-world test questions complete with fully optimized solutions.
Take the first step toward landing your next big offer! 🚀
👇 Download your free copy today:
https://www.smartdatacamp.com/products/Ultimate-SQL-Cheat-Sheet-for-Interviews-69ce0f9f36607a59209299ea
Keep Learning!!!... | 190 |
| 4 | Hey everyone! 👋 I see a lot of people prepping for Data Engineering interviews lately, so I wanted to share a quick tip.
One of the biggest red flags in a Hadoop interview is calling the Secondary NameNode a "backup" for high availability. It is actually just a checkpoint node! 🛑
I just made a quick 30-second visual breakdown explaining exactly what it does behind the scenes so you don't fall into this common trap. Hope this helps anyone currently studying!
You can check out the quick breakdown here:
https://www.facebook.com/share/r/19PysXpT4D/ | 253 |
| 5 | 🚀 FREE Fresher Data Engineer Resume Template
Looking to start your career in Data Engineering? 📊
Get our Fresher Data Engineer | Big Data & Spark Specialist resume template to showcase your technical skills and hands-on projects.
✅ Clean, one-page, ATS-friendly format
✅ Apache Spark & Spark ML
✅ Hive, Delta Lake & Linux
✅ AI-Assisted Engineering with ChatGPT
✅ Project-focused resume layout
🎯 Perfect for freshers preparing for Data Engineer opportunities!
📥 Download your FREE Resume Template:
🔗 https://www.smartdatacamp.com/products/Fresher-Data-Engineer--Big-Data--Spark-Specialist-69ce2743c5363e72d8090085
📢 Share with friends who are starting their Data Engineering journey!
#DataEngineering #BigData #ApacheSpark #ResumeTemplate #Freshers #SmartDataCamp | 243 |
| 6 | Hey everyone! 👋 If you have a Data Engineering interview coming up, you will almost certainly be asked to explain Hadoop’s MapReduce.
Instead of memorizing definitions, I put together a quick 30-second video that breaks down the architecture using a super simple, real-world analogy (no heavy jargon).
Check out the quick breakdown here:
https://www.facebook.com/share/r/19oLnquT1b/
What is the toughest Big Data concept you have ever had to explain in an interview? Let me know below! 👇 | 290 |
| 7 | Apache Spark Machine Learning Projects
🚀 Want to learn Machine Learning using Apache Spark through real-world projects?
Here’s a collection of 100% free, hands-on projects to build your portfolio 👇
📊 Predict Will It Rain Tomorrow in Australia
💰 Loan Default Prediction Using ML
🎬 Movie Recommendation Engine
🍄 Mushroom Classification (Edible or Poisonous?)
🧬 Protein Localization in Yeast
Each project comes with datasets, steps, and code — great for Data Engineers, ML beginners, and interview prep!
#ApacheSpark #MachineLearning #BigData #DataScience #AI #Python #100DaysOfCode | 360 |
| 8 | https://www.facebook.com/reel/1469942725117947 | 332 |
| 9 | The notorious Apache Spark shuffle bottleneck just got a massive architectural overhaul in version 4.3.0.
Historically, shuffles in Spark were strictly blocking operations. Under the standard SortShuffleManager, the execution flow was rigidly linear: the producer stage had to completely finish running and materialize all its shuffle data before the downstream consumer stage could even begin reading. If one straggler task took an extra five minutes, the entire downstream stage sat idle.
Spark 4.3 fundamentally changes this paradigm by introducing PipelinedShuffleDependency.
Here is exactly how the architecture shifts under the hood:
• Concurrent Execution: The execution flow moves from Producer → Materialize → Consumer to a continuous Producer ⇄ Streaming Shuffle ⇄ Consumer loop. Consumers can now start reading data incrementally as it is produced, while the upstream producers are still actively running.
• Coordinated Scheduling Groups: The Spark scheduler now treats stages connected by these dependencies as a single pipelined group. The scheduler only admits the group when enough task slots are available for all of its tasks, ensuring they run together and fail together, which prevents cluster deadlocks.
• The StreamingShuffleManager: Instead of routing data to a blocking manager, pipelined dependencies are handled by the new StreamingShuffleManager, which explicitly manages this incremental, low-latency data transfer.
This architectural upgrade is the core engine powering Spark 4.3's new sub-second Stateful Real-Time Mode. By eliminating the dead time between stages, Spark is aggressively closing the performance gap with dedicated streaming engines like Flink.
Are you analyzing your Spark UIs to see where pipelined shuffles can cut down your stage wait times, or are you still relying on batch-heavy architectures? Let me know below! 👇
#ApacheSpark #DataEngineering #BigData #DataArchitecture #SoftwareEngineering #StreamingData #TechTrends #SmartDataCamp | 458 |
| 10 | The biggest Big Data release of late 2026 is officially here: Apache Spark 4.3.0 just dropped, and it is directly challenging other engines in the real-time streaming space.
If you have been running a separate streaming engine just to handle low-latency stateful workloads, this release might change your architecture.
Here are the most critical updates data engineers need to know about Spark 4.3:
• Stateful Real-Time Mode: Spark 4.3 extends its sub-second Real-Time Mode to support stateful processing, including streaming aggregations and transformWithState. You can now maintain keyed state with millisecond latency using the Structured Streaming API you already know.
• ASOF JOIN & SQL Power-Ups: Time-series analysis just got much easier. Native ASOF JOIN allows seamless alignment of disparate event streams, alongside the new JSON_TABLE function for structuring complex JSON arrays directly in SQL.
• Pipelined Shuffle Dependencies: A massive execution upgrade. Consumers can now start reading shuffle data while producers are still running. These pipelined stages are scheduled together and fail together, dramatically speeding up query execution.
• SCD Type 2 Auto CDC: Building Slowly Changing Dimensions used to require pages of brittle merge logic. Spark 4.3 automates history-preserving continuous data capture (CDC), natively managing the validity windows of your records.
Apache Spark is aggressively closing the gap between batch, micro-batch, and true continuous streaming.
Are you planning to test out the new Real-Time Mode for your streaming pipelines, or are you sticking with engines like Flink for your lowest-latency workloads? Let me know your thoughts below! 👇
#ApacheSpark #DataEngineering #BigData #StreamingData #TechTrends #SoftwareEngineering #Databricks #DataArchitecture | 441 |
| 11 | 🚀 FREE Metabase Crash Course: Building Interactive Dashboards
It's time to bring all your isolated insights together! In the newest Metabase lecture, we tackle the most crucial phase of Business Intelligence: transitioning from answering single questions to telling a cohesive business story.
You’ll watch step-by-step as we construct a complete, 10-visual Video Game Sales Analytics Dashboard—covering design principles, visual hierarchy, and dynamic cross-filtering.
📺 Watch the full tutorial here: https://youtu.be/EBz28VrNOVg
🎓 Want the Complete Masterclass?
Stop paying for expensive legacy BI tools like Tableau or Power BI. Go from an absolute beginner to a production-ready BI practitioner.
Deploy Metabase & PostgreSQL locally with Docker 🐳
Build a governed Semantic Layer 📊
Leverage Metabot AI for native SQL generation 🤖
💸 Get the full course at a discount directly from SmartDataCamp:https://www.smartdatacamp.com/courses/Metabase-an-Open-Source-Business-Intelligence-Platform-660b94fc4ec3390ec18bfe7a
(Also available on Udemy: https://www.udemy.com/course/metabase-an-open-source-business-intelligence-platform/?referralCode=B132AB08BEE5DA4F9C8F)
🌐 Visit https://www.smartdatacamp.com for more open-source Data Engineering resources! | 452 |
| 12 | 🚀 Make November the month you finally master Data Engineering.
🚀 MASSIVE CAREER UPGRADE: 28 Data Engineering Courses for just ₹2,000! 🚀
Stop wasting money on individual, overpriced tech courses. The ultimate Full Stack Data Engineering Bootcamp is now live on SmartDataCamp with an unbeatable offer.
Get a complete career roadmap containing 28 masterclasses covering Big Data, Cloud, and AI analytics.
💡 What You Get Inside This Bundle:
Core Big Data: Apache Spark, Scala, and Hadoop Essentials.
Data Warehousing: Apache Hive, Cassandra, Delta Lake, and Druid.
BI & Dashboards: Apache Superset, Metabase, Redash, and Zeppelin.
AI & Next-Gen: ChatGPT automation frameworks for Data Engineers.
Real-World Portfolios: 5+ Machine Learning and Weblog Analytics projects.
Interview Prep: 300+ actual interview Q&As for Spark, Hive, and Hadoop.
🔥 Why You Can't Miss This:
Unbelievable Value: That is less than ₹72 per course!
Zero to Hero: No prior Big Data experience required to start.
Instant Access: Study anytime via your phone, laptop, or tablet.
🎯 Perfect for: Tech students, software engineers, ETL developers, and job seekers aiming for high-paying data roles.
👇 Secure your 28-in-1 bundle now for ₹2,000:
🔗 https://www.smartdatacamp.com/courses/All-Courses-Package-62945a740cf2b5e046a88f54 | 471 |
| 13 | 🚀 Build a Loan Approval Prediction Model with Machine Learning!
Want to work on a practical Machine Learning project while learning Apache Spark and Scala?
In this project, you’ll explore how machine learning can be applied to loan approval prediction using real-world data and data-processing techniques.
🔹 Machine Learning
🔹 Apache Spark
🔹 Scala
🔹 Data Science & AI
🔹 Hands-on project experience
👉 Explore the complete project:
https://projectsbasedlearning.com/apache-spark-machine-learning/machine-learning-project-loan-approval-prediction/
#MachineLearning #ApacheSpark #Python #DataScience #AI #BigData #Coding #100DaysOfCode #NLP #OpenSource | 526 |
| 14 | 🚀 Want more bite-sized Data Engineering tutorials and daily tech updates?
While we love sharing deep dives and resources here on Telegram, we are expanding our daily content over on Facebook!
Join the official Projects Based Learning Facebook Page to get: 🎬 Quick Tech Reels: 30-second concepts on Kafka, Spark, and LLM architectures 📊 Project Breakdowns: Hands-on Big Data and BI project guides 💡 Industry Updates: The latest trends in Data Engineering and Agentic AI
👉 Connect with the community and follow us here: https://www.facebook.com/projectsbasedlearning123
Hit "Like" and "Follow" so you never miss a new video or project update. See you there! | 527 |
| 15 | NameNode vs DataNode in 30 Seconds ⚡️ | Hadoop Architecture
https://youtube.com/shorts/44YleEpPGDs | 577 |
| 16 | Kafka Offset Explained in 30 Seconds 🚀 | Apache Kafka Series #Shorts
https://youtube.com/shorts/YI6R5mufKAY | 624 |
| 17 | 🚀 New Lecture Live: Apache Superset Dashboard Publication, Security & Access Governance!
Taking a dashboard from local development to production is more than just building charts—it’s about ensuring the right people see the right data securely.
In our latest lecture for the Apache Superset course, we cover everything you need to know about locking down and scaling your BI deployments:
🔑 What's covered:
• Safe dashboard publication workflows from draft to production
• Configuring Role-Based Access Control (RBAC) & user permissions
• Implementing Row-Level Security (RLS) for data isolation
• Enterprise security best practices to protect sensitive metrics
Ready to master production-grade governance in Superset? Dive into the new lesson now!
👉 Watch here: https://youtu.be/T2-zPav1rUw
🌐 Explore more projects: https://projectsbasedlearning.com
📚 Access all courses: https://smartdatacamp.com
#ApacheSuperset #DataEngineering #BusinessIntelligence #SmartDataCamp | 675 |
| 18 |  🚀 FREE Apache Spark Streaming Project
Clickstream Behavior Analysis – Real-Time User Tracking using Kafka, Spark & Zeppelin
Want to build a real-world Data Engineering project? This hands-on project will help you understand how companies track user behavior in real-time!
🔥 What you’ll learn:
• Real-time data streaming with Kafka
• Processing data using Spark Streaming (Scala)
• Storing results in MySQL
• Building dashboards in Zeppelin
• End-to-end data pipeline architecture
🎥 Watch the complete project series:
https://youtu.be/jj4Lzvm6pzs
https://youtu.be/FWCnWErarsM
https://youtu.be/SPgdJZR7rHk
💡 Perfect for beginners & aspiring Data Engineers
Start building real-time projects today! 🚀 | 660 |
| 19 | https://youtube.com/shorts/C9ugCvHhS2o?feature=share | 590 |
| 20 | (End to End) 20 Machine Learning Project in Apache Spark
Hi Guys,
I hope you are well.
Free tutorial on Machine Learning Projects (End to End) in Apache Spark and Scala with Code and Explanation
Life Expectancy Prediction using Machine Learning
Predicting Possible Loan Default Using Machine Learning
Machine Learning Project - Loan Approval Prediction
Customer Segmentation using Machine Learning in Apache Spark
Machine Learning Project - Build Movies Recommendation Engine using Apache Spark
Machine Learning Project on Sales Prediction or Sale Forecast
Machine Learning Project on Mushroom Classification whether it's edible or poisonous
Machine Learning Pipeline Application on Power Plant.
Machine Learning Project – Predict Forest Cover
Machine Learning Project Predict Will it Rain Tomorrow in Australia
Predict Ads Click - Practice Data Analysis and Logistic Regression Prediction
Machine Learning Project -Drug Classification
Prediction task is to determine whether a person makes over 50K a year
Machine Learning Project - Classifying gender based on personal preferences
Machine Learning Project - Mobile Price Classification
Machine Learning Project - Predicting the Cellular Localization Sites of Proteins in Yest
Machine Learning Project - YouTube Spam Comment Prediction
Identify the Type of animal (7 Types) based on the available attributes
Machine Learning Project - Glass Identification
Predicting the age of abalone from physical measurements
I hope you'll enjoy these tutorials. | 684 |
