Higher licensing & support cost
More tools means more licensing, integration, support, and platform rationalization overhead.
Consolidate the work into one AI engineering layer.
Thu, June 18, 2026 · 11:00 AM EST
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Live Masterclass · AI-Powered Migration
Reduce your Databricks costs up to 75% — without adding headcount.
Six structural patterns are quietly inflating your Databricks bill right now. Join Srinath Reddy, Founder CEO of BigHammer.ai, for a live session on the exact framework enterprise data teams use to find the waste, fix it, and keep it from coming back.
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Free · seats are limited
Who this is for
Built for the people who own the Databricks bill.
If cloud cost, legacy modernization, or AI delivery pressure lands on your desk, this session is built around your problem.
Data leaders
Data Engineering Managers
Heads of Data and VPs of Data Platform running the team that owns the infrastructure.
Cloud cost pressure
Leaders juggling 4–5 tools
Watching cloud spend spiral across a fragmented, point-solution stack.
Legacy modernization
Stuck on legacy platforms
Teams still tied to Informatica, Cloudera, or StreamSets and looking for a way off.
Platform cost pressure
Feeling the Databricks squeeze
Teams absorbing Databricks or Snowflake cost explosions quarter after quarter.
AI transformation
Leading, not buried by, AI
Leaders who want to drive enterprise AI transformation instead of getting stuck cleaning up after it.
The reality
Data engineering has become the AI bottleneck.
Enterprises want faster, trusted AI delivery. A fragmented stack multiplies cost, risk, and time to value before a single model ever gets trained.
Specialists become the bottleneck
Delivery queues grow as scarce specialists gate every migration and pipeline change.
Automate the work across pipelines, migration, and operations.
Migration backlogs stall AI
Backlogs slow modernization, block AI adoption, and deepen vendor lock-in.
AI-driven migration cuts TCO and accelerates modernization.
More handoffs, more incidents
More manual monitoring and more handoffs mean more risk with every release.
Govern and optimize quality, latency, cost, and reliability together.
AI programs miss business windows
Data products wait in queue while analytics and AI initiatives miss their window.
Accelerate delivery from request to governed data product.
The opportunity isn't another tool — it's replacing the engineering workflow itself with AI. BigHammer modernizes data engineering without expanding headcount or ripping out every platform at once.
Spend still rising
You've invested in AI. So where are the results?
Across the industry, AI spend is climbing faster than the value it delivers.
4 months
Time it took one major rideshare company to burn through its entire annual AI token budget, with leadership admitting ROI is still unclear.
Scaled back
One large software vendor pulled back the majority of its AI coding tool licenses after tool costs outpaced the value delivered.
$207B
Projected 2026 agentic AI software spend — up 139% year over year, per industry analyst estimates.
Still climbing
R&D, guardrails, and governance overhead keep pushing spend up even as per-token pricing falls. AI still needs data, context, platforms, and people.
"Organizations are burning through AI tokens with next to no value." — industry consensus, 2026
What you'll walk away with
45 minutes. One framework. Real numbers.
Stay for both halves — the Q&A alone has walked away with a $50 gift card for three attendees.
0 – 30 minutes
- Harnessing data leadership in a cost-constrained AI era
- How to slash TCO 40–75% and engineering labor effort by 75%
- Accelerating data platform deployment without adding risk
- A real healthcare company case study, walked through end to end
30 – 45 minutes
- Live Q&A with Srinath Reddy — bring your hardest question
- Ask the expert, get a direct answer on your specific setup
- Top 3 questions win a $50 Amazon gift card
Demo bonus
First 5 people who book a follow-up demo receive a $50 Amazon gift card.
Q&A bonus
Top 3 questions during live Q&A each win a $50 Amazon gift card.
Your host
Led by someone who's actually cut the bill.
Srinath Reddy
Founder CEO, BigHammer.ai
- Led data platforms and engineering teams for large enterprises generating $B+ in revenue.
- Managed multi-million dollar cloud and data platform infrastructure at exabyte scale.
- Cut data engineering and platform spend — including Databricks and Snowflake — by 70% annually.
- Founded BigHammer.ai to build what data engineers and data organizations actually need.
This isn't a vendor pitch dressed up as education. It's the same framework Srinath has used inside enterprise data platforms — the reality, the hidden causes, the methodology, and the automated solution, in that order.
Why costs explode
Six structural patterns. Not one bad job.
Runaway Databricks spend almost never comes from a single culprit — it's a handful of quiet, repeatable patterns compounding month over month.
Idle & off-hours waste
Interactive clusters run 24/7 at 1–2% peak CPU, with no auto-shutdown for nights, weekends, or non-prod.
Premium compute misalignment
Photon applied by default adds a 2× DBU premium on simple ETL and I/O tasks with zero ROI.
Failed run & retry leakage
Workspaces burn 57–63% of spend on jobs that fail after consuming full compute, with no fail-fast timeouts.
Setup & spin-up dominance
High-frequency micro-batch jobs spend 65–75% of runtime spinning up clusters, not processing data.
Small file & storage overhead
Uncompacted Delta files create I/O choke points — thousands of tiny files inflate compute uptime.
Governance & guardrail gaps
No tagging, no budget alerts, no compute policies — costs drift quietly until the bill arrives.
The framework
Three tiers, one discipline.
See the spend, control the spend, guard the spend — this is the exact sequence covered in the masterclass.
Tier 1 — See it
Ops & Finance observability
- Query billing and jobs-cost system tables for granular spend by SKU, job, and serverless usage.
- Enforce tagging by business unit, project, and environment — untagged clusters fail to launch.
Tier 2 — Control it
Active optimization
- Shift interactive workloads to job clusters; reserve Photon for Gold-layer aggregations only.
- Enable Delta auto-optimize and scheduled compaction to eliminate small-file overhead.
Tier 3 — Guard it
Ongoing governance
- Hard budget alerts at 50/75/90% thresholds in the Databricks Account Console.
- Compute policies mandate auto-termination, instance limits, and tag enforcement.
Our approach: assess current spend → 30-day quick-win sprint → lock in governance → monitor continuously.
Customer spotlight
A healthcare firm cut Databricks TCO by ~50%.
Same workloads, same SLAs, right-sized compute — no re-architecture required.
The situation
- Interactive clusters ran 24/7 at roughly 2% peak CPU utilization, with no scheduled shutdown.
- Photon was enabled broadly across I/O-bound ingestion jobs, paying a 2× DBU premium with no speed benefit.
- No auto-termination, tagging, or budget alerts existed across non-production workspaces.
The fix
- Right-sized clusters with global auto-termination policies on all interactive compute.
- Migrated scheduled batch and ETL jobs to job clusters; disabled Photon outside Gold-layer aggregations.
- Implemented org-wide tagging, budget alerts, and compute policies for continuous governance.
~50%
Reduction in Databricks TCO — same workloads, same SLAs, right-sized compute.
Thursday, June 18 · 11:00 AM EST
4:00 PM UK · 5:00 PM Central Europe · 8:00 AM US Pacific · 8:30 PM India
Your Databricks bill has a ceiling. Come find out where it is.
45 minutes with Srinath Reddy. Bring your hardest cost question — the top 3 during Q&A each win a $50 Amazon gift card.