The components:Storage: S3 as the data lake foundationCataloging: Glue Data Catalog for metadataProcessing: Glue ETL for transformationsAnalytics: Redshift for BI, Athena for ad-hoc queriesEvents: EventBridge + Lambda for real-time alertingLet's dive into each piece and the decisions that shaped them.How we built a scalable clickstream analytics pipeline that processes 500GB/day while cutting costs by 70%You're a data engineer at a growing e-commerce company. Every hour, your web application generates millions of clickstream events—page views, add-to-carts, purchases. These land as JSON files in S3, and your business teams are demanding:Sound familiar? Let me show you how we solved this with a modern AWS data pipeline.Our solution follows a medallion architecture with four distinct layers:The components:Let's dive into each piece and the decisions that shaped them.Raw data arrives at:This year/month/day/hour structure isn't arbitrary—it's our first line of cost defense.Why it matters: Athena charges $5 per TB scanned. Without partitioning, querying one day of data means scanning the entire dataset. With partitioning, we scan only what we need:95% cost reduction with one WHERE clause. That's the power of partitioning.Instead of running crawlers or MSCK REPAIR TABLE to discover partitions, we use partition projection:Now Athena automatically knows where to find data. No maintenance, zero lag. New files are immediately queryable.We transform raw JSON into a star schema with fact_clicks and dim_users. Here's why:The star schema advantage:Compared to alternatives:Our workload is 95% reads, 5% writes. Star schema optimizes for the common case.Our Glue job does three critical things:Data Quality ChecksCreate Dimension Table (SCD Type 1)Build Fact Table with MetricsOutput: Parquet files with Snappy compression. Why Parquet?One query that convinced us:Here's our controversial decision: We use both Redshift AND Athena.Hot Data (0-30 days) → Redshift:Warm Data (30-90 days) → Redshift Spectrum:Cold Data (90+ days) → S3 Glacier:Query pattern analysis:We optimized for the 80% case and made the 5% case acceptable.Before optimization (all in Redshift):After optimization (hybrid):Savings: $7,698/month (82%)Business requirement: "Alert us immediately when someone makes a $1,000+ purchase."Our solution: EventBridge + LambdaKinesis would give us true streaming, but:Lambda gives us:Simple, effective, and costs pennies.Our initial architecture was expensive:What we changed:Redshift Reserved Instances (75% savings)S3 Intelligent-Tiering (35% savings)Glue Job Bookmarks (30% time savings)Redshift Auto-Pause (Dev/Test)Total savings: $7,000/month = $84,000/yearStart Simple, Optimize LaterWe launched with:This got us to production in 3 weeks. We added:Partition EverythingEvery table is partitioned by date. This single decision:Measure Before OptimizingWe logged every query's execution time and scanned bytes. This data revealed:Implement Data Quality EarlierWe had to backfill 2 weeks of data due to a schema mismatch. A simple validation would have caught it:Use Terraform from the StartWe manually created resources initially. Big mistake. When we needed to replicate to staging, it took 2 days to document everything.Now everything is code:Spinning up new environments: terraform apply -var-file=staging.tfvarsMonitor Data FreshnessWe once didn't notice data wasn't arriving for 6 hours. Now we have:Alarm fires if lag &gt; 90 minutes.Before optimization:After optimization:Changes made:Result: 37x faster, 90% less data scannedOur most common query:We materialized it:Impact:Layer 1: NetworkLayer 2: IdentityLayer 3: DataLayer 4: AuditHandling deletion requests:Challenge: Parquet is immutable. We have to rewrite entire partitions. For frequent deletions, consider Apache Iceberg or Delta Lake.We track 5 key metrics:Data FreshnessCurrent Time - Latest Partition Timestamp &lt; 90 minutes ✓ETL Success RateSuccessful Runs / Total Runs &gt; 99.5% ✓Query Performance (P95)95th Percentile Query Duration &lt; 5 seconds ✓Cost per TB ProcessedMonthly Cost / TB Processed &lt; $50 ✓Data Quality ScoreValid Records / Total Records &gt; 95% ✓Critical Alarms (page on-call):ETL job failedData not arriving (90+ minute lag)Redshift cluster unhealthyWarning Alarms (Slack notification):Query duration &gt; P95 baselineCost spike (&gt;20% daily budget)Data quality score &lt; 95%Benefits:Building a production data pipeline isn't about using the fanciest tools—it's about making smart trade-offs:Serverless over self-managed (unless you have specific needs) Partition everything (it's free performance) Optimize for the common case (80/20 rule applies to data too) Monitor what matters (data freshness &gt; vanity metrics) Start simple, iterate (perfect is the enemy of shipped)Before:Manual data exports for analystsWeek-old analytics$15,000/month AWS bill2 engineers full-time on data opsAfter:ROI: $143,000/year savings + countless analyst hours

Code: <a href="https://gitlab.com/arif-rahman4/mini-data-pipeline">https://gitlab.com/arif-rahman4/mini-data-pipeline</a> Questions? Reach out on <a href="https://www.linkedin.com/in/arif-rahman-da/">https://www.linkedin.com/in/arif-rahman-da/</a> Tags: #DataEngineering #AWS #Analytics #BigData #CloudComputing

Building a Production-Ready Data Pipeline on AWS: A Practical Guide

Most enterprises are sitting on goldmines they can't access. Data silos everywhere. Partners waiting months for integrations. Revenue opportunities locked behind technical debt.What if you could package your data like a SaaS product — governed, subscription-based, and monetized?Welcome to **Data-as-a-Product (DaaP)** — the platform model that's transforming how companies unlock value from their data assets.The Business CaseThe Problem:Organizations struggle to share data securely and consistently. Every partner integration becomes a custom project. Data teams are overwhelmed. Business value remains unrealized.The Opportunity:According to Gartner, organizations that successfully productize their data see revenue growth rates 2-3x higher than competitors. The data monetization market is projected to reach $15.5B by 2030.The Solution:A platform that treats data like a product — with packaging, contracts, governance, and delivery channels. Think Stripe for data instead of payments.Real-World ExampleImagine a healthcare network with patient outcomes data across 50 hospitals. Before DaaP:- Research partners wait 6+ months for custom data extracts- Legal reviews each request individually- No standardized delivery mechanism- Zero recurring revenue from data sharingAfter implementing DaaP:- Pre-approved, de-identified datasets available in 24 hours- Automated HIPAA compliance and audit trails- API access for research institutions at $15K/month- New recurring revenue stream generating $2M+ annuallyThis isn't hypothetical. Leading healthcare, financial services, and retail companies are already doing this.The Architecture (Simplified)Foundation Layer:S3 + Iceberg (governed data lake), MSK/Kafka (real-time streaming), Lake Formation (access control)Processing Layer:Flink/Spark for transformations, schema registry, automated PII masking, multi-format outputsProduct Layer:AWS Data Exchange subscriptions, REST/GraphQL APIs, interactive dashboards, entitlement managementBuilt on cloud-native services. Scales from terabytes to petabytes. Fully governed and auditable.Three Paths to ValueWe've designed subscription tiers that match how organizations actually consume data:Basic: Data Explorer — $1,000/monthFor:Analysts, BI teams, smaller partners exploring possibilitiesWhat you get:- Curated datasets refreshed daily- Secure SQL query access via Lake Formation- Row and column-level filtering- Standard supportUse case:A retail partner wants to analyze customer purchase trends to optimize their product mix.Pro: Insights Accelerator — $5,000/monthFor: Mid-sized partners, SaaS platforms, data-driven decision makersWhat you get- Near-real-time updates (15-60 minute refresh)- Interactive dashboards via OpenSearch/Kibana- REST and GraphQL API access- SLA-backed freshness and uptime guarantees- Priority supportUse case:A fintech startup embeds your transaction data into their app to provide real-time fraud alerts to customers.Enterprise: Real-Time Innovator — Starting at $25,000/monthFor: Large enterprises, regulated industries, mission-critical applicationsWhat you get:- Real-time event streams via Kafka/MSK or AWS PrivateLink- Full Iceberg table access with multi-tenant governance- Dedicated AWS Data Exchange products- Custom integration support- HIPAA, GDPR, and CCPA compliance included- White-glove onboarding and dedicated success managerUse case:A pharmaceutical company streams clinical trial data in real-time to power AI-driven drug discovery models.Why Governance = Competitive AdvantageTrust is the currency of data sharing. Without it, no partnership happens.Our platform bakes in security:- **Automated PII/PHI masking** at the processing layer- **Granular entitlements** with Lake Formation (row/column/table level)- **Complete audit trails** via CloudTrail and OpenSearch- **Regulatory compliance** out of the box (HIPAA, GDPR, CCPA)- **Zero-trust architecture** with PrivateLink for sensitive workloadsThis isn't bolted on. It's the foundation.The Business Impact**Faster Partner Onboarding:**Reduce integration time from months to days. No more one-off data pipelines.**New Revenue Streams:**Subscription-based model creates predictable, recurring revenue. One financial services client added $8M in annual recurring revenue within 18 months.**Ecosystem Growth:**Partners build on your data products, creating network effects. Their success becomes your success.**Market Differentiation:**In B2B, data access is increasingly a competitive requirement. Be the platform, not just a vendor.The Bottom LineThe future isn't about hoarding data. It's about creating **trusted, productized, consumable data assets** that drive ecosystem growth.Companies that master this model will:- Build moats through data network effects- Create new revenue streams without new infrastructure- Accelerate partner innovation- Win in their marketsLet's ConnectIf you're exploring data monetization or building data partnerships, I'd love to hear from you.**Question:** Which path resonates with your organization?- **Basic** — We're exploring what's possible- **Pro** — We need insights to drive decisions faster- **Enterprise** — We're ready to build mission-critical applications#DataStrategy #DataMonetization #DataAsAProduct #CloudArchitecture #AWS #DataGovernance #B2BData #EnterpriseArchitecture

How to Turn Your Data Into a Revenue Engine

In today's fast-paced digital landscape, the difference between proactive response and reactive damage control can make or break your business. After architecting and implementing numerous real-time alert systems across various industries, I'm excited to share how a well-designed 3-tier architecture can power multiple critical use cases that every enterprise needs.

From predictive maintenance to fraud detection, it all runs on a proven 3-tier architecture. Scalable infrastructure handles the plumbing — the real magic happens inside the “black box” of stream processing.After building real-time alert systems across industries, I've seen how the right architecture transforms reactive operations into proactive excellence. Here's what's possible:The ArchitectureInfrastructure: Kafka + AWS (S3, DynamoDB) + PostgreSQL + ElasticsearchDynamoDb - Hot Path + Rules EnginePostgres - ContextElasticsearch - AnalyticsS3 - Cold PathProcessing:Apache Flink for real-time stream processingAlert Processing (Rules Engine + Context)Application:FastAPI + Intelligent Alert ConsumerFive Game-Changing Use CasesMaintenance AlertsMonitor equipment sensors → Prevent $50K/hour downtime Result: 73% reduction in unplanned downtime.Equipment Malfunction DetectionProcess thousands of telemetry points → Instant failure response Result: 99.7% uptime, 18 major incidents preventedPredictive MaintenanceML-powered predictions → Optimal maintenance timing Result: 23% longer equipment life.Network Threat AlertsReal-time traffic analysis → Sub-minute threat response Result: 94% threats mitigated in 60 seconds.Fraud DetectionSub-100ms transaction analysis → Real-time fraud blocking Result: 99.2% fraud caught, 67% fewer false positives.Smart Alert ConsumerMulti-Channel Communications: Slack alerts with rich context Status page updates Email/SMS notificationsPagerDuty integration Custom webhooksSmart Processing:Contextual enrichment with historical dataDe-duplication to prevent alert fatigueAutomatic escalation rulesComplete audit trailsWhy This WorksScale: Millions of events per secondSpeed: Sub-second processingFlexibility: Rules-as-data, no deploymentsIntegration: Works with existing systemsGetting StartedStart with one use case, scale gradually. The modular design adapts to your specific challenges.

hashtag#RealTimeAlerts hashtag#StreamProcessing hashtag#ApacheFlink hashtag#AWS hashtag#DigitalTransformation hashtag#PredictiveMaintenance hashtag#FraudDetection hashtag#Architecture

Building Enterprise-Grade Real-Time Alert Systems: From Equipment Failures to Fraud Detection

Cloud bills are exploding. Many enterprises spend $500–$2000/month just to keep data lakehouse environments idling.

That’s wasteful! Clients want live proofs in minutes, not static slides. Consultants need to show value without burning budget. Startups need to present to Proof of Concepts to Investors and Clients.

That’s where the Proof-in-Minutes Lakehouse changes the model:These costs are GCP based and all deployment is Code as Infa using GitHub Actions. We deploy 2 separate cost center layers. #1 Foundation Layer #2. Compute Layer. Here are some cost analysis.

Foundation Layer – $3.19/monthThe “always-on skeleton” that keeps everything production-ready at coffee-cost:- BigQuery Storage: $1.00 (50 GB demo data, empty by default = no cost until loaded)- GCS Bronze/Silver/Gold Lakes: $1.86 combined (lifecycle transitions keep storage cheap)- Secret Manager: $0.18 (3 secrets with replication)- Monitoring &amp; Logging: $0.15 (budget alerts + observability)- Pub/Sub Topics: $0.00 when idleTotal = $3.19/month idle cost

Compute Layer – $0.75–$4.00/hourWake the giant only when you demo. Four levels, cost-controlled:- Minimal Demo ($0.75/hr): Cloud Run API, external tables- Standard Demo ($1.25/hr): BI Engine acceleration, streaming pipeline- Enterprise Demo ($2.50/hr): Multi-service orchestration, analytics API- ML Showcase ($4.00/hr): Vertex AI + BigQuery ML + high-memory compute

Auto-cleanup ensures you never pay for forgotten demos.The EconomicsTraditional Lakehouse: $500–$2000/month always runningProof-in-Minutes Lakehouse:Idle: $3.19/monthActive: $0.75–$4.00/hourThat’s 99.1–99.8% savings while staying enterprise-grade.

Proof-in-Minutes Lakehouse

What Problem Does This Solve?Ever inherited a mysterious codebase? Joined a new team with unfamiliar tech stack? Need to audit security across multiple projects?

Traditional approach: Hours of manual exploration, guesswork, and documentation diving.Agentic AI Pro approach: 30 seconds of intelligent analysis with actionable insights.

Core Innovation: Agentic DiscoveryUnlike static analysis tools, CodeMaster Pro uses agentic intelligence to understand context, relationships, and business impact.Agentic AnalysisUnderstands architecture patternsContextual security assessmentProject-specific action plansFull-stack polyglot expertiseReady-to-execute roadmaps

Key StrengthsProblem-Solution Focus: Opens with a relatable pain point (mysterious codebases) and shows dramatic improvement (hours → 30 seconds).Concrete Metrics: Real performance numbers (95% accuracy, 3x faster, $50K+ saved) that demonstrate business value.Visual Comparisons: Side-by-side tables showing traditional tools vs. your agentic approach.<a href="https://gitlab.com/burnside-project/blog-examples">Full Code Here</a>

Agentic Code Discovery & Analysis

Last week I read an article that said agentic coding agents are turning engineering teams into symphony conductors. And honestly — it’s true. A conductor doesn’t play every instrument. They guide the tempo, the harmony, the big picture. That’s exactly where engineering is headed. But here’s the thing: to be a conductor, you still need to know your tunes. In our world, that means systems design, architecture, data flows, and constraints. Without that foundation, you’re just waving your hands. I’m such a huge fan of subagents and MCP in Claude Code. With subagents, you can create cohorts that feel just like real-world teams: - A subagent dedicated to frontend UI - A subagent for backend APIs - A subagent focused on data pipelines Each one plays their part. And you, as the engineer, become the orchestrator — aligning all the moving pieces into something cohesive. But it doesn’t stop there. The MCP (Model Context Protocol) server takes orchestration further by letting subagents connect seamlessly to external tools, APIs, and data sources. It’s like adding new instruments to your orchestra. Suddenly, your frontend, backend, and data pipeline subagents can pull in real-time context, query databases, or trigger CI/CD pipelines — all without leaving the flow. MCP makes the whole system extensible, pluggable, and closer to how real engineering teams collaborate. And here’s the kicker, try claude --permission-mode plan. This plan mode is like giving your conductor’s baton a practice session. It produces a read-only plan of action: analyzing code, surfacing steps, and outlining how the agents would execute — without touching your codebase. I’ve been amazed at how well it thinks through orchestration before acting. Too many people still think agentic coding is just “write me some code.” The truth? It’s about orchestrating specialized agents, with planning, practice, and precision. The future isn’t about typing faster. It’s about directing an intelligent team of subagents — powered by MCP, guided by a clear plan — that can scale your vision.

From Coders to Conductors!

Imagine this: you’ve built an agentic AI assistant to help with customer support. It can query your website forms tablein real time and draft replies on your behalf.

Sounds powerful, right? But here’s the nightmare scenario:The agent runs an unoptimized query → CPU spikes → your production DB slows to a crawl.A misconfigured prompt generates a DELETE FROM forms; → catastrophic data loss.A tight loop of “helpful” queries locks tables → outage in the middle of the workday. Direct access to prod is a gamble.

The Safer Play: Read ReplicasInstead of pointing your agent to the primary DB, set up a read replica. Most databases make this straightforward:Postgres/MySQL: CREATE_REPLICA of your prod DBThen:Give the replica a read-only user (no INSERT, UPDATE, or DELETE).Point the agent to the replica’s connection string.Add monitoring/limits so the agent can’t exhaust resources.This way the agent can safely:Query the forms table.Generate summaries or replies.Run complex analytics workloads.All while your primary DB stays clean, fast, and write-safe.Risk Scenarios: With vs Without ReplicaWithout replica:Data loss from accidental writes.Outages due to heavy queries.Stress every time you “let the agent loose.”With replica:Production stability remains intact.Data safety is guaranteed (read-only).Peace of mind while experimenting with agentic workflows.

Guardrails in PracticeThe takeaway: don’t deny your agent access — redirect it smartly.Replicas turn “what if” risks into “what then” experiments.

Would You Give an AI Agent Access to Your Database?

After watching companies burn through $50k+/month on Big Warehouse Platforms for relatively simple data workflows, we built a cost-effective alternative that doesn't sacrifice reliability.

The problem: Most data teams are stuck between expensive managed services and the operational nightmare of rolling their own infrastructure. We've seen startups with &lt;100GB of data paying enterprise prices, and mid-size companies afraid to experiment because each query costs real money.

Our approach: We've packaged battle-tested open source tools (Apache Airflow, dbt, ClickHouse/DuckDB) into a managed platform that:Costs 80-90% less than traditional cloud data warehousesScales from gigabytes to terabytes without architectural rewritesGives you actual ownership of your data and transformationsTakes &lt;30 minutes to get your first pipeline running

What makes this different:No vendor lock-in - everything runs on standard tools you can migrateTransparent, predictable pricing (no surprise bills)Built for teams who want data platform benefits without platform engineering overhead We're currently in beta with a few customers processing everything from e-commerce analytics to IoT sensor data. The cost savings have been dramatic - one customer went from $8k/month to $500/month for the same workload.

We know many of you have built internal data platforms. We're curious - what were the biggest pain points? What would make you choose a managed service over building in-house?We're also happy to share more technical details about our architecture choices and how we achieve the cost savings.<a href="https://burnsideproject.ai/contact">Contact Us</a>

We're making enterprise data pipelines 10x cheaper (and actually maintainable)

The agentic coding is not giving random instructions and hoping the agent gets it right. It’s about building a solid plan, providing clear content and testing with intent.

I told my Terraform modules to build these exact components for my GCP project.  No hand-waving. No guesswork. Just declarative infrastructure, defined with clarity.

In less than 10 min my GCP stack was ready to be deployed with Github actions. (These are real instruction!)

## Closing Note Everyone fears the agent that codes — but the real danger is unclear prompts. This isn't magic. It’s **infrastructure as thought**.

Agentic Coding with Claude Code

Small companies rely on analysts who pull data manually from 5-10 different systems (Salesforce, Stripe, Google Analytics, customer support tools, etc.) into Excel/Google Sheets for weekly/monthly reports. This process takes 2-3 days every month and the data is always outdated.

Stop burning money on manual processes and get data that actually helps you make decisions.<a href="https://burnsideproject.ai/contact">Act Now!</a>

Your Google Sheets Are Costing You $50K a Year - And You Don't Even Know It

mall SaaS companies typically have revenue data scattered across Stripe, subscription management tools, and sales CRMs. Leadership has no idea about real-time revenue, churn, or growth metrics. Monthly reports take a week to compile and are immediately outdated.

From monthly guessing to daily confidence in your business metrics. <a href="https://burnsideproject.ai/contact">Take Action!</a>

We Built a Real-Time Revenue Dashboard for a $2M SaaS Company in 48 Hours

Google's Vertex AI Studio provides a comprehensive platform for building, training, and deploying machine learning models at scale. Combined with RAG (Retrieval-Augmented Generation) engines, it becomes a powerhouse for creating intelligent applications.

RAG architecture bridges the gap between large language models and real-time data, enabling AI apps to provide accurate, up-to-date responses based on your specific knowledge base.

Pro Tip: Start with a well-curated knowledge base. The quality of your RAG system directly depends on the quality of your source documents.

EngineeringInsights