Software Engineering Cloud Computing

Saving Lakhs Every Month - How I Implemented an AWS Cost Optimization Automation as a DevOps Engineer! When I first joined my current project as an AWS DevOps Engineer, one thing immediately caught my attention: “Our AWS bill was silently bleeding every single day.” Thousands of EC2 instances, unused EBS volumes, idle RDS instances, and most importantly — NO real-time cost monitoring! Nobody had time to manually monitor resources. Nobody had visibility on what was running unnecessarily. Result? Month after month, the bill kept inflating like a balloon. ⸻ I decided to take this as a personal challenge. Instead of another boring “cost optimization checklist,” I built a fully automated cost-saving architecture powered by real-time DevOps + AWS services. Here’s exactly what I implemented: ⸻ The Game-Changing Solution: 1. AWS Config + EventBridge: • I set up Config rules to detect non-compliant resources — like untagged EC2, open ports, idle machines. 2. Lambda Auto-Actions: • Whenever Config detected issues, EventBridge triggered a Lambda function. • This function either auto-tagged, auto-stopped idle instances, or sent immediate alerts. 3. Scheduled Cost Anomaly Detection: • Every night, a Lambda function pulled daily AWS Cost Explorer data. • If any service or account exceeded 10% threshold compared to the weekly average, it triggered Slack + Email alerts. 4. Visibility First, Action Next: • All alerts first came to Slack channels where DevOps and owners could approve actions (like terminating unused resources). 5. Terraform IaC: • Entire solution — Config, EventBridge, Lambda, IAM, SNS — all written in Terraform to ensure version control and easy replication. ⸻ The Impact: • 20% monthly AWS cost reduction within the first 2 months. • Real-time visibility for DevOps and CloudOps teams. • Zero human dependency for basic compliance enforcement. • First-time ever — proactive action before bills got out of hand! ⸻ Key Learning: “Real success in DevOps isn’t just about automation — it’s about understanding business pain points and solving them smartly.” I learned that cost optimization is NOT a “one-time” audit. It needs real-time event-driven systems — combining AWS Config, EventBridge, Lambda, Cost Explorer, and Slack. ⸻ If you’re preparing for DevOps + AWS roles today: Don’t just learn services individually. Learn how to build real-world solutions. Show how you saved time, money, and risk — that’s what companies pay for! ⸻ If you want me to share the full Terraform + Lambda GitHub repo for this cost optimization automation project, Comment below: “COST SAVER” and I will send you the link! Let’s learn. Let’s grow. Let’s solve REAL problems! #DevOps #AWS #CostOptimization #RealTimeAutomation #CloudComputing #LearningByDoing

Don't assume your work "speaks for itself" Unseen work may as well not be done. A lot of my clients learned this the hard way. They were sure they were "layoff-proof." But it's not just layoffs that rely on visibility. Companies change, managers leave. Promotions & raises go to whose work is seen. Here are 9 ways to demonstrate your impact: (Grab the PDF of this here: https://lnkd.in/eqVSeaMG) 1/ Document process improvements ⇢ Show before and after scenarios with time or money saved from your improvements. 2/ Build strategic relationships ⇢ Connect with decision-makers and share helpful insights across different teams. 3/ Track everything ⇢ Keep a "wins" document updated weekly with metrics, feedback, and outcomes. 4/ Quantify your impact ⇢ Turn your actions into numbers because "Raised cash collected 32%" hits different. 5/ Link actions to company goals ⇢ Align your work with big objectives and speak the language of leadership. 6/ Solve problems proactively ⇢ Identify issues before they get worse and present solutions, not just problems. 7/ Maintain a project portfolio ⇢ Showcase completed projects with testimonials and results that prove your value. 8/ Gather stakeholder feedback ⇢ Request specific testimonials and save all positive responses you receive. 9/ Master the art of the update ⇢ Send regular, short progress reports that focus on value and next steps. (Grab the PDF of this here: https://lnkd.in/eqVSeaMG) Showing your impact isn't difficult. Doing it consistently is the biggest needle mover in your career. Have you ever felt invisible at work? How did you overcome it? ♻️ Repost to help others increase their impact 🔔 Follow Ashley Couto for daily career growth

The most unpopular but true career lesson you'll receive today: (From a top tech Solutions Architect who spent 20+ years learning it the hard way) Your fancy projects are killing your career growth. I proudly shared with my manager that I've developed a new project leveraging the latest AWS Gen AI capabilities, combined with Serverless and Kubernetes. He paused, then simply asked: "What was the impact?" That's when it hit me - using cutting-edge tools feels exciting, but true success comes from measurable impact: accelerating time-to-market, reducing churn, boosting productivity, or increasing revenue. And those impactful projects are way more effective in your resume for career or job switch. I had focused on the technology itself rather than how it addressed business objectives. Innovation is powerful, but it's meaningless if it doesn't serve your customers or align with strategic goals. Here are some actionable tips: - Before starting a new project, clearly define its impact—ask yourself: "How will this improve the business? What's the measurable outcome?" - Shift your project narrative from tech-focused to outcome-focused. Instead of: "Implemented Gen AI with Kubernetes," Say: "Reduced customer onboarding time by 40% with Gen AI." - Regularly communicate your project's impact to leadership. Managers prioritize outcomes, not tools. - Review your past projects and have a short pitch: Identify which ones created measurable impact and highlight these prominently on your resume. Have a short pitch ready, highlighting the impact that you can communicate to recruiters and hiring managers Question to readers: What's one career lesson you have discovered late in your career? --- Get byte sized system design, behavioral, and other interview and career switch tips in weekly newsletter : https://lnkd.in/eG7XdHmN

Amazon's Gen AI Q&A chat assistance (86% accuracy) # Problem Answering queries from customers takes from hours to days. Root Cause: Accounts Payable and Accounts Receivable analysts (especially new hires) don’t have immediate access to the necessary information # Solution Finance Automation developed a LLM based Q&A chat assistant to rapidly retrieve answers to customer queries # Architecture 1. User submits a query 2. Query Routing 1. Get Contexts from OpenSearch Service which is used as the vector store for embedding documents 2. Titan Multimodal Embeddings G1 (on Bedrock) is used as embedding model (pre-trained on large and unique datasets and corpora from Amazon) 3. Diversity ranker is responsible for rearranging the results obtained from vector index to avoid skewness or bias 4. Lost in the middle ranker is responsible for efficiently distributing most relevant results towards the top and bottom of the prompt 5. Foundation model (on Amazon Bedrock) is used for its balanced ability to quickly deliver highly accurate answers 6. Validation engine removes PII from the response (using Bedrock Guardrails) and checks whether the answer aligns with the retrieved context 1. If not, it returns a hardcoded “I don’t know” response to prevent hallucinations 7. Streaming response is returned back to users (using Streamlit) Over to you, how to avoid "too brief" response from LLM? # Reference and Image Credit - aws .amazon .com/blogs/machine-learning/how-amazon-finance-automation-built-a-generative-ai-qa-chat-assistant-using-amazon-bedrock/ #SystemDesign #architecture #scalability --- Understanding the First Principles is the key to Effective Learning! 🔔 Follow Kamran to Improve System Design Skills through case studies

🤖 Multi-Step Agents and Compounding Mistakes - TL;DR, mitigating compounding mistakes in complex agents and multi-step agents via Amazon Bedrock Capabilities. As AI agents tackle increasingly complex tasks, we face a critical challenge: compound mistakes. Imagine an AI system performing a 10-step task with 95% accuracy per step - the cumulative error could reduce overall task success to a mere 60%, turning potentially reliable systems into unpredictable black boxes. With each step, the risk of errors multiplies, potentially tanking overall accuracy. Here are some evolving strategies to keep our AI agents on track using Amazon Bedrock: ⚡ Improving Individual Step Accuracy: Leverage advanced models like Claude 3.5 Sonnet, Amazon Nova Pro, etc. which achieve SOTA accuracy on multi-step reasoning tasks and implement smart data augmentation techniques along with better prompting. Guardrails and Automated Reasoning Checks in Bedrock can validate factual responses for accuracy using mathematical proofs - https://lnkd.in/gdEyUrGE ⚡ Optimize Multi-Step Processes: Utilize frameworks like ReAct for interleaving reasoning and acting along with custom reasoning frameworks. Bedrock Agents now support custom orchestrator* for granular control over task planning, completion, and verification - https://lnkd.in/gQasM7kX ⚡ Monitoring and Metrics : Implementing robust monitoring and establishing clear quality metrics are essential. CloudWatch has an automatic dashboard for Amazon Bedrock added to provide insights into key metrics for Amazon Bedrock models - https://lnkd.in/gee_zdiv ⚡ Hybrid Data Approaches that combine structured and unstructured data can generate more accurate outputs. Bedrock Knowledge Base now has out-of-box support for structured data - https://lnkd.in/gfthHvsi ⚡ Self-reflection and Correction: Amazon Bedrock Agents Code Interpretation support the ability to dynamically generate and execute code in a secure environment enabling complex analytical queries. https://lnkd.in/gQzxdK3P #amazon #bedrock #agenticAI

GenAI Architecture – Week 7 Project 7: Enterprise RAG for messy, real-world documents When I first started experimenting with RAG, everything looked easy — clean PDFs, perfect datasets, and sample code that “just worked.” But when I tried applying it in the enterprise world, reality hit hard. I remember sitting with a stack of documents: scanned contracts with faded text, multi-column PDFs where tables broke alignment, and 100+ page policy docs that felt like they were written in a different century. My “neat” RAG pipeline just collapsed. That’s when it clicked: enterprise RAG isn’t about retrieval — it’s about resilience. So in Week 7 of my 10-week GenAI Architecture series, I’m sharing how I built an approach to handle messy, real-world enterprise data. How the architecture works" - A developer ( poking around in Kiro or Cursor IDE) fires off a query. - Retriever Agent uses hybrid embeddings in vector DBs (Chroma, Redis, FAISS) to widen the net. - Enterprise Data Layer swallows PDFs, DOCs, scanned files, intranet pages — basically, the jungle of enterprise knowledge. - GroundX + EyeLevel step in to make sense of chaos: layouts, tables, multi-column pages, even OCR corrections. - Amazon Bedrock AgentCore, with Claude/Nova, ties it all together — interpreting, reasoning, and giving back a response that feels business-ready. Why this matters Because real data is messy. And unless your RAG system is built to handle it, the answers you get will be brittle. This design gives - Confidence that no doc was “too ugly” for the pipeline - A way to explain why an answer came back the way it did - The flexibility to run this at both small team scale and enterprise scale Where I see this shine - Legal/compliance teams are swimming in decades of contracts - Internal policy Q&A across multiple, outdated intranet sites - Research copilots digging through regulatory docs 🛠 Tech Stack Kiro IDE | Cursor IDE | GroundX | EyeLevel | AWS Bedrock AgentCore | Claude | Nova | ChromaDB | Redis | FAISS | SQLite | S3 📚 For more tips and deeper dives, check out my books: - Generative AI for Software Developers: https://lnkd.in/gy8vU-Nr - AWS for Solutions Architect (3rd Edition): https://lnkd.in/gsR_qMEN Looking back, this was the week when I stopped thinking of RAG as a “cool demo” and started seeing it as enterprise infrastructure. Next up → Week 8: Federated data with MindsDB + MCP Unified Server. ⚡ #GenAI #AWSBedrock #AgentCore #Claude #Nova #EnterpriseAI #RAG #GroundX #EyeLevel #VectorDB #10WeeksOfGenAI #KiroIDE #CursorIDE

🎬 Exploring Streaming Data with Microsoft Azure & Databricks! 🚀 Over the past few days, I tried hands-on building an end-to-end data engineering project combining Netflix and IMDB datasets to gain insights into global streaming content. 🔗 My tech stack: ✅ Azure Databricks (Spark) ✅ Azure Data Lake Storage Gen2 (ADLS) ✅ Azure Synapse Analytics (Serverless SQL) ✅ Power BI Here’s what I did: 1️⃣ Data Ingestion Loaded Netflix dataset from Kaggle into Databricks. Connected to IMDB datasets stored in ADLS using SAS tokens. 2️⃣ Data Transformation Cleaned and joined Netflix + IMDB data in Spark. Unified show titles, genres, release years, and other attributes. 3️⃣ Data Storage Saved the final transformed dataset as a Delta table in ADLS Gen2. 4️⃣ Analytics Layer Created an external table in Synapse Serverless SQL, pointing to my Delta table. Queried and validated data via SQL on-demand. 5️⃣ Visualization Connected Power BI to Synapse Serverless. 🎯 Key learnings: Working with Spark on Azure Databricks for large data transformations. Integrating multiple Azure services seamlessly. Using Delta Lake for efficient storage and querying. Building analytics pipelines that scale from small datasets to big data scenarios. 🔍 Why this matters: Streaming data keeps growing exponentially. Learning how to build scalable pipelines—even for smaller datasets—is essential for modern data engineers. Github repo with more details: https://lnkd.in/em7UH3Zi Architecture idea from Darshil Parmar Happy to discuss if you're building something on this! #DataEngineering #Azure #Databricks #Netflix #DeltaLake

🧵 Real Stories of Generative AI in Action   (Feature 10 of a multi-part series, you can access the full series at #AWSGenAIinAction)   🧾 S&P Global Market Intelligence, part of S&P Global, understands the importance of accurate, deep, and insightful information. It integrates news, comprehensive market and sector-specific data, and analytics into a variety of tools to help track performance, generate alpha, identify investment ideas, understand competitive and industry dynamics, perform valuation and assess credit risk. To support credit analysts in their work for answering broad questions about the economic outlook of a given country or industry, S&P Global Market Intelligence partnered with the AWS Generative AI Innovation Center (#GenAIIC).   🗣️ Tell me more! The credit analysts at S&P Global Market Intelligence are asked to answer questions and provide secondary analysis of industries and countries. Responses and analysis require synthesis of broad context from long documents (not just short snippets). The analysts need to prioritize retrieving and synthesizing the most relevant information among multiple relevant documents with similar titles (e.g. XX Industry Outlook). Furthermore, none of the "off the shelf" solutions were able to address the specific challenges of commentary articles, so S&P Global Market Intelligence needed a custom solution.   🧑💻 AWS scientists built a custom Retrieval-Augmented Generation (#RAG) solution powered by Amazon #Bedrock and Anthropic #Claude Haiku to achieve desired outcome. First, PDF documents were converted to markdown format using Amazon #Textract with Layout mode. Documents were then chunked according to markdown section delimiters, to construct chunks with broad and complete context. Chunks were vectorized with #Titanv2 embeddings, and Amazon #OpenSearch queries weighted documents by semantic relevance and date to favor the latest commentaries.   🎯The custom pipeline achieved 💯 % top 10 accuracy in document retrieval, effectively generative comprehensive answers to broad research questions using the latest insights. The solution took days and weeks of manual document search, review, and synthesis down to minutes and seconds, saving valuable time for analysts.   What's exciting about this solution is the real, immediate impact for end users: 🔹 Near-instant access to relevant financial insights 🔹 Enhanced accuracy in document analysis 🔹 Dramatic reduction in research time 🔹 Improved decision-making capability.   See more of AWS and S&P Global's partnership using Amazon generative AI services here: https://lnkd.in/g3zWND69   #AWS #GenerativeAI #FinancialServices #Innovation #ArtificialIntelligence #CloudComputing #RAG #DataAnalytics

🏗️ 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗣𝗿𝗼𝗷𝗲𝗰𝘁: 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 & 𝗧𝗼𝗼𝗹𝘀 🔍 In today’s data-driven world, building scalable, secure, and efficient pipelines is critical. Here’s a breakdown of a real-world end-to-end data engineering project on Azure that combines modern tools, automation, and best practices. 🔷 🔧 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄: 📈 Objective: Ingest, process, store, and visualize data from multiple sources to enable actionable business insights. 🔷 🚀 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸 & 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: ✅ 𝗗𝗮𝘁𝗮 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻: Azure Data Factory (ADF): Pulls data from on-prem databases, REST APIs, and SaaS sources. Event Hub / IoT Hub: Real-time streaming ingestion for logs/sensor data. ✅ 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲 𝗦𝘁𝗼𝗿𝗮𝗴𝗲: Azure Data Lake Storage Gen2 (ADLS): Stores raw, curated, and enriched datasets in a hierarchical structure (Bronze, Silver, Gold). ✅ 𝗗𝗮𝘁𝗮 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 & 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Azure Databricks (PySpark/Scala): Cleansing, joining, enrichment, and aggregations. Delta Lake: Versioning, ACID transactions, and schema enforcement for reliability. ✅ 𝗠𝗲𝘁𝗮𝗱𝗮𝘁𝗮 & 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲: Azure Purview / Data Catalog: Centralized metadata management, lineage, and classification. ✅ 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗲 & 𝗤𝘂𝗲𝗿𝘆 𝗟𝗮𝘆𝗲𝗿: Azure Synapse Analytics: For analytical workloads, advanced SQL, and integration with Power BI. SQL Dedicated Pool / Serverless SQL Pool depending on use case. ✅ 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 & 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴: Power BI: Dashboards with role-based access and real-time refreshes for stakeholders. ✅ 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 & 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: Azure Monitor, Log Analytics, and Defender for Cloud: Monitor, alert, and protect the pipeline. RBAC, VNET, Private Endpoints, and Key Vault: Ensure compliance and secure data access. 🔷 🎯 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗢𝘂𝘁𝗰𝗼𝗺𝗲: ✅ Reduced data latency by 70% ✅ Enabled real-time analytics & self-service BI ✅ Achieved enterprise-level scalability and compliance #AzureDataEngineering #AzureSynapse #Databricks #ADF #DataLake #PowerBI #BigData #ETL #DataPipeline #PySpark #CloudComputing #DataEngineer #CareerGrowth #Azure #DElveWithVani

“I want to build a real-world data engineering project… but where do I even start?” If you've asked yourself this, you're not alone—and today, I'm going to show you exactly how. When I was starting out, most tutorials only covered one part of the puzzle—just ingestion, or only cleaning data, or simply creating a dashboard. But in the real world, you need to stitch together the full story: > Ingest raw data → Clean & Transform it → Store it efficiently → Analyze it → Automate it. So I built a hands-on project on Azure Databricks—one that mirrors what happens in real data teams. And here's how you can do it too. --- Project Blueprint: End-to-End Data Engineering on Azure Databricks Use case: Let’s say you're building a pipeline to analyze global cryptocurrency prices from a public API. Step 1: Source the Data (Ingestion) Find a free public API. (Example: https://lnkd.in/gqdy2iJA) Use Databricks Notebooks to write a Python script to call the API. Store the raw JSON response into Azure Data Lake Storage Gen2 (ADLS) or the Databricks File System (DBFS). Step 2: Raw Zone Storage Save the data as-is into a raw/bronze folder. Use autoloader if you're saving as files incrementally. df.write.format("json").save("/mnt/datalake/raw/crypto/") Step 3: Transform the Data (Clean & Enrich) Create a Silver table by selecting relevant fields like name, symbol, price, market cap, etc. Handle missing/null values, convert timestamps, standardize currency format. df_cleaned = df_raw.selectExpr("name", "symbol", "current_price", "market_cap") Step 4: Data Modeling (Delta Lake) Store your cleaned data as a Delta Table for efficient querying and versioning. df_cleaned.write.format("delta").mode("overwrite").saveAsTable("silver.crypto_prices") Step 5: Build Aggregations (Gold Layer) Aggregate trends like average price per day, top gainers, etc. Store these insights in a Gold Delta Table. df_gold = df_cleaned.groupBy("date").agg(avg("current_price").alias("avg_price")) df_gold.write.format("delta").mode("overwrite").saveAsTable("gold.crypto_summary") Step 6: Automate with Workflows Schedule your pipeline with Databricks Workflows (formerly Jobs). Set it to run hourly or daily depending on your use case. Step 7: Visualize & Share Use Databricks SQL or connect to Power BI to create dashboards. Share insights with stakeholders or simulate client reports. Bonus Tips: Use Unity Catalog to manage data governance. Add notebook versioning with GitHub to simulate collaboration. Document everything like you're presenting to your future employer. If you're serious about learning data engineering, build this end-to-end project and join my data engineer bootcamp cohort in the future And if you're stuck, drop a comment or DM—I’ll point you in the right direction.