✦ Data Architecture Consulting · 13+ Years Enterprise Experience

Your Data Pipeline Is Costing You
Millions in Lost Productivity.

We help CTOs, Data Directors, and SaaS engineering teams eliminate overnight batch failures, slash ETL processing time by up to 70%, and migrate legacy systems to the cloud — with near-zero production downtime.

No spam. No sales pitch. Just a clear diagnosis of your data bottlenecks.
75%
Avg. ETL time reduction
13+
Years enterprise data engineering
$0
Production downtime on migrations
40%
Avg. cloud cost reduction

Built for Leaders Who Have Outgrown Their Data Infrastructure

If your engineering team is spending more time firefighting than building, your data pipeline is a liability — not an asset.

CTOs & Engineering VPs

Dealing with cascading pipeline failures and engineering teams buried in incident tickets instead of shipping product.

📊

Data Directors & Architects

Running aging SSIS or custom ETL that needs modernizing — but lacks the bandwidth to architect and execute safely.

🚀

SaaS Companies Scaling Fast

Growing rapidly but your data warehouse can't keep up. Queries timing out, BI dashboards stale, analysts losing trust.

🏢

IT Agencies & Integrators

Need a specialist data engineering partner to deliver cloud-native ETL work for clients without expanding headcount.

Before vs. After SnapDataTech

Before
  • 2–3 hour overnight ETL batch jobs
  • Frequent pipeline crashes & 3am failure alerts
  • Significant engineering time spent on firefighting
  • Cloud costs spiraling with no performance gain
  • Analysts working off 24-hour stale data
  • Legacy SSIS packages nobody dares to touch
  • Limited visibility into pipeline health or lineage
After
  • ETL pipelines completing in <45 minutes
  • ~99.9% reliability — near-zero unplanned alerts
  • Engineering team shipping features, not fighting fires
  • Up to 30–50% reduction in monthly cloud compute costs
  • Real-time or near-real-time data for decisions
  • Modern Azure / Databricks architecture — fully documented
  • Clear observability, lineage tracking, and proactive alerting

The 6 Data Crises We Eliminate

Every engagement starts with a root-cause audit. Here is what we find most often.

01

Broken ETL Pipelines

Pipelines that fail silently or crash under volume, creating data gaps and hours of manual intervention weekly.

02

Legacy SSIS Nobody Touches

Brittle on-premise packages with no documentation, no error handling, and no engineer confident enough to refactor.

03

Slow SQL & Deadlocks

Reports taking 20+ minutes, deadlocks blocking production writes, and a database not tuned in years.

04

Exploding Cloud Costs

Azure or AWS bills growing every month with no performance improvement — compute wasted on inefficient jobs.

05

Stale Data & Slow Dashboards

Executives making decisions on yesterday's data. Power BI reports slow, stale, and losing organizational trust.

06

No Clear Cloud Migration Path

Everyone agrees the infrastructure needs modernizing — but nobody has the bandwidth to architect it safely.

We Don't Just Build Pipelines. We Own the Architecture.

Most vendors deliver code and leave. We design systems your team can maintain, scale, and trust — long after the engagement ends.

  • 🔍

    Specialists in Fixing Failing Pipelines

    We are experts at diagnosing what others built wrong — not just building from scratch. We have taken broken SSIS packages from 2010 and rebuilt them as zero-failure cloud pipelines.

  • 💡

    Architecture-Level Thinking, Not Just Execution

    We design for scalability, cost efficiency, and long-term maintainability. Every decision is made with your 3-year roadmap in mind — not just the immediate ticket.

  • 💰

    Cost Optimization Is Non-Negotiable

    Every engagement includes a cloud cost review. We typically reduce Azure and Databricks spend by up to 30–50% through Delta optimization, partition tuning, and right-sized compute.

  • 🛡️

    13+ Years Hands-On Enterprise Experience

    Real hands-on experience from SQL Server 2008 through modern Fabric/Lakehouse architectures — across healthcare, retail, and enterprise SaaS.

Typical Vendors vs. SnapDataTech

Criteria Typical Vendors SnapDataTech
FocusDelivery onlyDelivery + Cost + Scale
DocumentationMinimalFull arch docs + runbooks
Production RiskBig-bang cutoverParallel deploy + rollback
Cloud Cost ReviewNot includedIncluded in every engagement
Post-Launch SupportBilled separately30 days included

Four Core Capabilities That Drive ROI

Every service is tied to a measurable business outcome — reduced costs, faster pipelines, or higher data reliability.

☁️

Cloud ETL Migration & Modernization

Migrate legacy on-premise SSIS to fully managed, cloud-native ADF or Databricks pipelines — with zero production downtime and a validated rollback strategy built in from day one.

Azure Data Factory SSIS Migration Self-Hosted IR

Big Data Processing & Lakehouse

Design and implement Databricks Lakehouse architectures using PySpark, Delta Lake, and Unity Catalog to process terabytes reliably at 35–50% lower compute cost.

Databricks PySpark Delta Lake Unity Catalog
🗄️

SQL Performance & Database Tuning

Turn 20-minute queries into sub-second responses. We diagnose deadlocks, rebuild indexes, rewrite slow stored procedures, and design reporting-ready schemas that scale.

SQL Server Query Tuning Index Strategy Deadlock Resolution
📐

Medallion Architecture & BI Enablement

Architect a unified Bronze/Silver/Gold Medallion model on Microsoft Fabric or Synapse, enabling Power BI Direct Lake mode for sub-second executive dashboards.

Microsoft Fabric Snowflake Power BI OneLake

Real Problems. Measurable Results.

Every number below is from an actual client engagement — no rounding, no estimates.

✓ Up to 70% ETL Time Reduction · ~$2.5K/month Saved

From 3-Hour Nightly Crashes to a 45-Minute Cloud Pipeline

Healthcare IT · SQL Server (On-Premise) → Azure Data Factory + Azure SQL DB

🔴 The Problem

A 10-year-old on-premise SQL Server warehouse crashed regularly during ETL. The 3-hour batch window overran into business hours, causing deadlocks on production tables and blocking analyst access until mid-morning. The SSIS packages had minimal error handling, limited logging, and no one on the team fully understood the legacy logic.

🔵 Our Solution
  • Audited and fully documented 50+ legacy SSIS pipelines
  • Rebuilt as parameterized ADF pipelines with robust logging
  • Deployed Self-Hosted Integration Runtime for secure on-prem extraction
  • Migrated warehouse to Azure SQL DB (PaaS — zero maintenance)
  • Rewrote deadlocking stored procedures and rebuilt critical indexes
~70%
ETL processing time reduced
3h→45m
Batch window compressed
~$2.5K
Reduced cloud costs by ~$2.5K/mo
Azure Data Factory Self-Hosted IR Azure SQL DB SSIS SQL Performance Tuning Azure Key Vault
Architecture · Legacy SSIS → Azure Data Factory Migration
🏢
Legacy SQL Server
On-Premise · SSIS Packages · Windows Server
SHIR
🔐
Self-Hosted IR
Encrypted Gateway · ExpressRoute / VPN
ADF
⚙️
Azure Data Factory
Parameterized Pipelines · Mapping Dataflows
Load
☁️
Azure SQL DB
PaaS · Managed · 99.99% SLA
BI
📊
Power BI
Live Reports · Executive Dashboards
Security
Azure Key Vault · Private Endpoints · Managed Identity
Monitoring
Azure Monitor · Pipeline Alerts · Proactive Notifications
Outcome
3hr → 45min ETL · 100% reliability · $2,500/mo savings
✓ Sub-Second Dashboards · 3h → 1h Data Latency

Eliminating Data Silos with a Unified Fabric Medallion Architecture

Enterprise SaaS · Fragmented Data Sources → Microsoft Fabric + OneLake + Power BI Direct Lake

🔴 The Problem

Six disparate data sources fed three separate Power BI workspaces. Dashboard refresh took 4–6 hours in Import mode. Executives received contradicting numbers from different reports, and the data team manually reconciled CSV exports every morning.

🔵 Our Solution
  • Designed Medallion Architecture on Microsoft Fabric OneLake
  • Unified all 6 sources into a single Bronze ingestion layer
  • Built PySpark cleansing pipelines for the Silver layer
  • Created Gold reporting schema connected via Power BI Direct Lake
  • Eliminated all manual CSV reconciliation with automated data contracts
<1s
Dashboard query response
3h→1h
Data latency reduction
6→1
Data silos consolidated
Microsoft Fabric OneLake PySpark Delta Lake Power BI Direct Lake Azure Data Factory
Architecture · Microsoft Fabric Medallion Data Platform
🥉 Bronze
Raw Ingestion Layer
Ingest from 6 disparate sources (APIs, databases, files) into OneLake as-is. No transformations applied. Full historical replay capability.
ADF Pipelines Auto Loader Delta Parquet OneLake
🥈 Silver
Cleansed & Conformed
PySpark jobs apply data quality rules, deduplication, schema conformance, and SCD Type 2 history. Single source of truth established.
PySpark Delta Merge SCD Type 1&2 Data Quality Rules
🥇 Gold
Curated Business Layer
Pre-aggregated, business-friendly tables optimized for Power BI Direct Lake. Sub-second query performance. Zero Import mode latency.
Power BI Direct Lake Star Schema Z-Order Indexing Semantic Models
✓ Up to 35% Cloud Cost Reduction · 1TB+ Daily Pipeline Optimized

Optimizing 1TB+ Daily Production Pipelines on Databricks at 35% Lower Compute Cost

Enterprise SaaS · Raw APIs → Databricks Lakehouse + Delta Lake + Unity Catalog

🔴 The Problem

The team needed to process and optimize 1TB+ of complex nested JSON daily from multiple SaaS APIs across production environments. Existing Spark jobs ran on over-provisioned clusters, took 6+ hours to complete, and the data team had limited governance, lineage tracking, or access controls in place.

🔵 Our Solution
  • Implemented Databricks Lakehouse with Auto Loader ingestion
  • Rebuilt Spark transforms using Photon engine + PySpark optimization
  • Implemented SCD Type 1 & 2 with Delta Lake merge operations
  • Deployed Unity Catalog for improved data lineage and access governance
  • Right-sized clusters with autoscaling and spot instance policies
~35%
Azure compute cost reduction
1TB+
Daily production pipeline optimized
6h→2h
Daily pipeline runtime cut
Databricks PySpark Delta Lake Unity Catalog Auto Loader Photon Engine Azure ADLS Gen2
Architecture · Databricks Enterprise Data Lakehouse
📡
Data Sources
SaaS APIs · Databases · Event Streams
Ingest
🔄
Auto Loader
Incremental · Schema Evolution · ADLS Gen2
Process
Databricks + Photon
PySpark · SCD 1&2 · Delta Merge
Store
🗂️
Delta Lake
Z-Order · Compaction · ACID Transactions
Govern
🔒
Unity Catalog
Lineage · Access Control · Governance
Optimization
Auto-scaling clusters · Spot instances · Z-Order indexing · File compaction
Outcome
Optimized 1TB+ daily data pipelines in production · 35% cost reduction · 6hr → 2hr runtime

The SnapData Zero-Downtime Migration Framework™

A battle-tested 4-phase system for modernizing enterprise data infrastructure without disrupting your business.

Phase 01

Architecture Audit

Deep technical review of your pipelines, database schemas, query plans, and cloud spend. You receive a prioritized bottleneck report with estimated ROI for each fix — within 3 business days.

Phase 02

Architecture Design

We design the target-state architecture with full documentation: data flow diagrams, infrastructure specs, compute sizing, and cost forecast. Everything approved before a single line of code is written.

Phase 03

Parallel Deployment

New pipelines run in parallel with your existing system. We validate data integrity row-by-row before any production cutover — guaranteeing zero data loss and zero downtime on switchover day.

Phase 04

Optimization & Handover

Post-launch, we tune performance under real production load, reduce compute costs, and provide full runbooks, monitoring dashboards, and 30 days of free post-launch support.

All changes are validated in staging before any production rollout. Your data integrity is non-negotiable.

Full-Stack Data Architecture Capability

From raw API ingestion to executive dashboard — we cover the complete modern data engineering stack.

📥

Data Ingestion

Connect to any source — REST APIs, databases, file systems, and message queues — with reliable, monitored ingestion patterns.

REST APIs Azure Event Hubs Azure Data Factory Auto Loader SSIS AWS Glue
⚙️

Processing & Transformation

Apply business logic, data quality rules, and complex transformations at scale — from megabytes to terabytes.

Databricks PySpark Azure Synapse dbt T-SQL Python Photon Engine
🗄️

Storage & Warehousing

Design the right storage tier for every use case — raw Data Lakes to curated analytical warehouses optimized for speed and cost.

Delta Lake Snowflake Azure SQL DB OneLake SQL Server PostgreSQL
🎛️

Orchestration & Monitoring

Every pipeline is orchestrated, monitored, and alerting on failure — so you know about problems before your analysts do.

Azure Data Factory Apache Airflow Azure Monitor Unity Catalog Key Vault
📊

Analytics & Visualization

Connect your curated Gold layer to BI tools — optimized for speed, with semantic models your analysts can trust completely.

Power BI Direct Lake Tableau SSRS Azure Analysis Services
☁️

Cloud Platforms

Platform-agnostic expertise across all major cloud providers, with deepest specialization in the Microsoft Azure ecosystem.

Microsoft Azure Microsoft Fabric AWS Google Cloud Databricks Snowflake

Built for Enterprises That Cannot Afford Downtime

Every engagement includes a full risk mitigation strategy. Your production environment is never touched until everything is validated in staging.

🔄

Parallel Deployment — Zero Cutover Risk

New pipelines run alongside your existing system. We only cut over after row-count, checksum, and business-logic validations pass completely.

↩️

Full Rollback Strategy — Always Included

Every deployment includes a documented rollback plan. If anything is wrong post-cutover, we restore the previous system within 15 minutes.

🔒

Data Integrity Validation at Every Layer

We validate data integrity at source, staging, and target before any business data is considered migrated and production-ready.

📋

Staging Environment — Always First

All changes are validated in a staging environment mirroring production. Nothing touches live data until it has been tested and signed off.

Trusted by IT Leaders and Data Teams

★★★★★

"The ETL migration was seamless. Critical reports were rewritten and our reporting is now 40% faster. The technical expertise and communication were outstanding — they clearly understood the business impact, not just the code."

RC
Ryan Choate
IT Director, ACOE
★★★★★

"Outstanding experience. Professional, detail-oriented, and highly skilled — they exceeded every expectation. A top-notch Data Engineer who made a complex migration feel completely manageable. Collaboration was seamless throughout. 🙌"

D
Derrick
Manager, Data Team
★★★★★

"Excellent work delivered once again — clear communication, consistently high quality, and impressively fast turnaround every single time. This is a team you can genuinely rely on for mission-critical data work."

JP
Jason Pedely
Associate Director, Data Team
⏱ Limited audit slots available each week

Get Your Free 30-Min Data Architecture Audit

Identify your biggest data bottlenecks, get a cost reduction estimate, and leave with a clear action plan — in 30 minutes, at no cost.

Get clear insights into performance bottlenecks and cost leaks — before they get worse.

🔒 No spam. No sales pressure. Just actionable insights. Results vary based on system complexity, but most clients see measurable improvements within weeks.