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Better late than never - Ride the AI ,Data Science , Machine Learning revolution wave
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SKILLS ***********************************************
Python , R , Julia , SAS , F# ,
C++ ,SQL , Scala , Java , MAT LAB
Big Data , Hadoop ,Hive , Pig ,Spark
Clojure, Lisp
***************************************************ENDLESS
Idea for talk?
Technical talk with implementation details about Language UseCases or related development exercise from speakers comfortable handling hands on with Q&A.
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Upcoming events
17
Series2 -Inside the Database Engine: MySQL, PostgreSQL & MongoDB – Architecture
·OnlineOnlineFrom Theory to Source Code: Internals That Drive Your Database”
🧠 Explore, Decode & Rebuild Core Database Engines Like the Pros📅 Date: Marc 7th, 2026
⏰ Time: 8:30 PM IST
Join Zoom Meetinghttps://us02web.zoom.us/j/81833278868?pwd=YpZ9QFiak9dbKXhro2YVdh0F3baRxz.1
Meeting ID: 818 3327 8868
Passcode: 875686📍 Live + Recorded + Labs Access
🎥 Hosted by: Coderrange YouTube + GitHub + Telegram Group***
### 🧬 What Makes Series 2 Different?
✅ We show the internal source code from the official GitHub repos of MySQL, PostgreSQL, and MongoDB
✅ Live debugging and instrumentation walkthroughs
✅ You run labs locally, inspect logs, retry events, and simulate failure
✅ We help you trace real crash recovery, log replay, and buffer pool internals🔧 Dive into Real Source Code • Crash Recovery • Retry Logic • Live Demos
| 🕕 Time | 💡 Session |
| ------- | ---------- |
| 8:30 PM | Kickoff + Setup Labs |
| Get access to GitHub repos, tools, data dumps, and retry labs. | |
| | |
| 8:45 PM | MySQL Recovery Internals |
| Analyze InnoDB crash recovery code (`log0recv.cc`), simulate redo logs, binlog replay, and fail recovery. | |
| | |
| 9:15 PM | PostgreSQL WAL + Vacuum Deep Dive |
| Trace WAL flush, replay, and vacuum cycles from the `xlog.c` source. Live demo of planner + stats tools. | |
| | |
| 10:00 PM | MongoDB Replication & Failover |
| Walk through Oplog, elections, failover logs, and aggregation engine from MongoDB core files. | |
| | |
| 10:00 PM | Retry Mechanisms in Action |
| Compare how MySQL (`force_recovery`), Postgres (WAL timelines), and MongoDB (retryable writes) recover. | |
| | |***
640 attendeesWhat Most Python Developers Never Learn — and How It Makes You a Pro
·OnlineOnlineHere’s a list of engaging and advanced meetup topics tailored for experienced Python developers (0–8 years). These topics go beyond the basics and delve into deeper, often overlooked areas that challenge and inspire.
***
## 🧠 1. Metaprogramming & Reflection
- Metaclasses, custom class creation, and `init_subclass` hooks
- Advanced decorators and dynamic attributes
- Runtime introspection with `inspect` and `ast`
Good for enabling DSLs, frameworks, and highly dynamic systems
***
## ⚙️ 2. Concurrency, Parallelism & Async Patterns
- Deep dive into threading vs multiprocessing vs `asyncio`
- GIL internals, thread safety, and `queue`, `concurrent.futures`
- Async architecture: tasks, event loops, server patterns
***
## 🧪 3. Performance Profiling & Memory Optimization
- Using `cProfile`, `memory_profiler`, and `objgraph` for bottlenecks
- `slots`, `weakref`, and manual GC tuning
- Leveraging Cython, PyPy, Numba and building native extensions
***
## 🌐 4. Packaging, Distribution & CI/CD
- Building robust Python packages using `setuptools`, `wheel`, and `poetry`
- Publishing to PyPI, automating tests, and versioning
- Using linting (mypy, flake8), formatter (black), and CI pipelines
***
## 🔐 5. Advanced Networking & Asynchronous I/O
- Building WebSocket servers, Pub/Sub systems
- Async frameworks like FastAPI, `httpx`, and `uvloop`
- Designing robust, scalable networked applications
### 🔗 Meetup Lineup
- Metaprogramming & DSLs in Python
- Async & Concurrency: Threads vs Async
- Profiling & Memory Efficiency in Live Apps
- Building & Publishing Python Packages
- Advanced Indexing & Data Structures
## 🎯 Why These Topics?
- Advanced but practical, solving real-world issues
- Underexplored but essential for senior engineers
- Build a compelling learning journey for motivated developers
Join Zoom Meeting
https://us02web.zoom.us/j/87612944665?pwd=7ZlyoczF724etqWZ3OpZebY6dU90P9.1
Meeting ID: 876 1294 4665
Passcode: 538113167 attendees
From UI to Insights: Building End-to-End Data Pipelines in the Cloud| Segment |
Location not specified yetFrom UI to Insights: Building End-to-End Data Pipelines in the Cloud
🕒 3‑Hour Agenda
| Segment | Duration | What You’ll Learn |
| ------- | -------- | ----------------- |
| | 20 min | Walk through the full pipeline, why it's critical in modern apps |
| | 25 min | REST/GraphQL → API Gateway → Message buses |
| | 25 min | Service mesh, event-driven APIs, microservices |
| ☕ Break | 10 min | — |
| | 30 min | ETL/ELT pipelines, streaming vs batch, data lakes |
| | 30 min | Serverless analytics, BOM-ing real-time insights |
| | 45 min | Attendees design and diagram a full-stack pipeline |
| | 15 min | Recap best practices, tools, next steps ||
### 🔍 1. Introduction & Overview (20 min)
- Show a complete flow diagram—from user click in frontend to analytic dashboard.
- Explain use cases: real-time insights, personalization, A/B testing, ML features.
- Map tools/components: frontend tech, APIs, backend services, data hubs.
***
### 🔗 2. Frontend → Integration (25 min)
- Interactive UI calls → API Gateway (AWS API Gateway / GCP Apigee).
- Common patterns: REST, GraphQL, WebSockets.
- Middleware: Authentication, rate limiting, request validation.
- Choosing cloud iPaaS for hybrid/on-prem needs data.folio3.com+11cloud.google.com+11rudderstack.com+11en.wikipedia.org+3geeksforgeeks.org+3integrate.io+3cloud.google.comen.wikipedia.orgen.wikipedia.org+1cloud.google.com+1.
***
### ⚙️ 3. Integration → Backend (25 min)
- Service-to-service communication: REST, gRPC, message queues.
- Event-driven architecture: API call can trigger multiple services via events .
- Use of Kafka, RabbitMQ, or NiFi to decouple and scale .
- Data integration architecture patterns: hub-and-spoke, pipelines, federation en.wikipedia.org+11hevodata.com+11airbyte.com+11.
***
### ☕ Break (10 min)
***
### 🧪 4. Backend → Data Analytics (30 min)
- ETL/ELT concepts: raw → clean → curated zones airbyte.com+2aws.amazon.com+2rudderstack.com+2.
- Tools: Airflow, NiFi, DBT for orchestration integrate.io+1en.wikipedia.org+1.
- Streaming vs batch analytics: Lambda architecture, Kinesis, Kafka, Spark, Flink en.wikipedia.org+1geeksforgeeks.org+1.
***
### ☁️ 5. Data Analytics → Cloud (30 min)
- Choosing cloud stores: BigQuery, Redshift, Snowflake .
- Serverless pipelines: Dataflow (Apache Beam) for seamless ingestion & analytics en.wikipedia.org+1geeksforgeeks.org+1.
- Governance, security, monitoring: encryption, IAM, audit trails aws.amazon.com.
***
### 🧠 6. Architecture Deep Dive Lab (45 min)
- Break into teams: design a pipeline for a scenario (e-commerce analytics, IoT sensor data, etc.)
- Sketch full-stack: from UI event → API → backend event → data lake → dashboard.
- Integrate key patterns: streaming, batch, governance, fault-tolerance.
- Teams present diagrams + rationale.
***
### ✅ 7. Wrap-up + Q&A (15 min)
- Quick recap of best practices and patterns.
- Tool recommendations: API Gateway, Kafka/NiFi, Airflow, Dataflow, BigQuery.
- Share resource links and invite ongoing pipeline architecture community.
- Open Q&A.
***
## ✨ Why This Meetup Will Stand Out
- Offers a panoramic view of modern application-to-analytics systems.
- Blends architecture theory with hands-on design.
- Covers cloud-first, hybrid, and serverless approaches.
- Tools and patterns are highly relevant to real production systems today.
127 attendees
Past events
98
