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