📷 What we learned this week at @dataengwithaws 📷 Where to start as a beginner 📷 AWS tools for smarter pipelines 📷 Personal stories from the trenches 📷 How to build a modern lakehouse stack

"Fun Friday / Weekly Wrap" Light content, summarize week, build community feel. data eng tip before the weekend: take time to document your pipeline. yes even if it’s ugly. especially if it’s ugly. future you will thank you 📷

(Summary thread): 📷 What we learned this week at @dataengwithaws 📷 Where to start as a beginner 📷 AWS tools for smarter pipelines 📷 Personal stories from the trenches 📷 How to build a modern lakehouse stack Follow for more next week 📷 and don’t forget to touch grass

"Fun Friday / Weekly Wrap" Light content, summarize week, build community feel. data eng tip before the weekend: take time to document your pipeline. yes even if it’s ugly. especially if it’s ugly. future you will thank you 📷

Want a Notion board with step-by-step AWS projects? thinking of putting one together for free. Like this tweet if that sounds helpful 📷 might just drop it soon 📷

"Deep Dive Thread Day" Showcase expertise, provide value, attract retweets. 📷 Building a Data Lake on AWS (2025 Edition) Tired of theory? Here’s a real-world stack that works 📷 Ingest → Kafka / Kinesis Buffer → S3 (raw zone) Process → Glue / Lambda / EMR Store → S3

(Engagement tweet): Data engineers, what’s one AWS service you wish you learned earlier? I’ll start: 📷 Step Functions. Saved me from messy spaghetti code flows. reply and share yours 📷

"Real Talk Wednesday" Show vulnerability, personal story, engagement driver. I failed my first data pipeline project. like BAD. no retries broke on nulls cost $700 overnight but I learned more in those 3 days than any course ever taught me

One underrated AWS combo for beginners: 📷 CloudWatch + SNS Monitor logs or ETL job failures Automatically send alerts to email/Slack It's like having a robot yell at you when stuff breaks 📷 and honestly.. we all need that

"Tips & Tools Tuesday" Share actionable tips and resources for current & aspiring data engineers. If you’re still writing batch jobs for everything, try this instead: 📷 Lambda + EventBridge for real-time triggers 📷 Glue streaming jobs for incremental ETL 📷 Kinesis for

(Visual + Short Thread): 📷 5 AWS services every beginner data engineer should mess with: (a tiny thread) S3 – your data lake starts here Glue – managed ETL + crawlers Lambda – serverless magic Athena – SQL on files 📷 CloudWatch – logs or it didn’t happen Just play with them.

"Foundations & Vibes" Attract beginners, build emotional connection, start light. data engineering seems complex when you're just starting out. pipelines, spark, cloud, kafka.. it’s a lot but here's the truth — you don't need to learn everything at once. start small. pick a tool.

💬 Did you get both answers right? Let me know in the replies! 🧵👇 #AWS #Serverless #EventDriven (5/5) Let me know if you need any tweaks before posting! 🚀

🎯 Key Takeaway: Lambda + SQS = Event-driven powerhouse 💪 🔹 Decouples producers & consumers 🔹 Handles spikes in workload 🔹 Scales automatically This duo is the backbone of many serverless architectures! (4/5)

🔍 Why not the others? ❌ SNS – Good for pub/sub but not core to event-driven workflows. ❌ DynamoDB – Useful for data storage, but not essential for event processing. ❌ EC2 – Provides compute, but not serverless like Lambda. ❌ EBS – Block storage, not related to event-driven

💡 Correct Answers: A) Lambda & E) SQS Why? 🤔 ✅ Lambda – Runs code in response to events, no servers to manage. ✅ SQS – Message queue that helps decouple producers & consumers. Together, they enable scalable & fault-tolerant event-driven architectures. (2/5)

⚡ Question: Which TWO AWS services are essential for building an event-driven architecture? A) Lambda B) SNS C) DynamoDB D) EC2 E) SQS F) EBS Drop your two answers below! 👇 (1/5)

💬 Did you get it right? Drop a ✅ in the comments if you did! If not, let me know what confused you. Let's learn together! 🚀 #AWS #Cloud #DataEngineering (4/4)

🔍 Why not the others? ❌ DynamoDB – NoSQL key-value & document DB, but not optimized for graph queries. ❌ RDS – Relational database, not ideal for graph-based relationships. ❌ EC2 – Just provides compute capacity, not a DB service. Neptune specializes in graph relationships!

💡 Answer: B) Amazon Neptune Amazon Neptune is a fully managed graph database service designed for applications that need to analyze highly connected data. Use cases: 🔹 Social networking 🔹 Recommendation engines 🔹 Fraud detection 🔹 Knowledge graphs (2/4)