Data Warehousing Services
You need one governed source of truth for reporting from the systems you already run, without an enterprise-scale migration.
By the Numbers
of benefit plan claims processed by AI agents for a Taft-Hartley client.
lower operating cost and +30% CSAT for a regional insurer using AI-powered service automation.
reduction in deposition and interview transcription time for a law enforcement agency using custom SPARK Transcriber.
Sound Familiar?
You Don’t Trust Your Own Dashboards
The same metric shows a different number depending on which system it came from, so nobody fully trusts what’s on the screen.
You Spend Every Reporting Cycle Reconciling by Hand
Your team pulls data from Oracle, SAP, JD Edwards, and department spreadsheets, then spends hours lining up the numbers before anyone can actually use them.
You’ve Been Quoted More Than You Need
Vendors keep proposing enterprise-scale lakehouse builds or full rip-and-replace migrations when what you actually need is a warehouse sized for the data volume you have right now.
Why Sparkhound for Your Data Warehousing Project
We Connect the Systems You Already Run
Oracle, SAP, and JD Edwards connect through modern cloud data infrastructure without ripping out what’s already working.
We’ll Tell You When You Don’t Need a Lakehouse
If a right-sized warehouse solves your problem, that’s what we recommend.
We Stick Around on After Go-Live
Your warehouse stays monitored and supported in production around the clock through our onshore NOC. The Data & Analytics practice carries the work through to Power BI, so consolidation turns into reporting people actually use.
How We Help
Cloud Data Warehouse Design and Architecture
A warehouse designed with dimensional modeling and star schema principles that match how your business actually reports.
ETL/ELT Pipeline Development
Extraction and transformation pipelines that move your data into 1 consistent structure.
Legacy System Integration
Direct connections to Oracle, SAP, JD Edwards, and other legacy systems, so you get 1 consolidated view without a full system replacement.
Data Governance and Lineage
Governance and lineage built into the architecture from day 1, so reporting stays consistent as new data sources get added later.
Power BI and Reporting Integration
A direct path from your warehouse into Power BI, so consolidated data turns into dashboards your team actually uses.
Ongoing Monitoring and Support
Onshore monitoring and support after your warehouse is in production, so issues get caught before they show up in a report.
Data Warehousing Services for Your Data Tech Stack
How We Work
Step 1: Assess Your Current Data Landscape
A clear view of the systems you run today and where your data actually lives before any architecture is recommended.
Step 2: Right-Size the Architecture
A warehouse or lakehouse decision based on your data volume and use case, not the biggest platform a vendor can sell.
Step 3: Build Pipelines and Bake in Governance
Extraction and transformation pipelines that connect Oracle, SAP, JD Edwards, and other systems in place, with governance and access controls built into the model from the start.
Step 4: Hand Off to Reporting and Support in Production
A direct connection to Power BI, so your team gets daily use out of the warehouse, followed by onshore monitoring and support after go-live.
What Our Clients Say
We needed a flexible partner who could respond and adapt to our needs. Sparkhound has exceeded our expectations every step of the way.
Eric Abrams, IT Manager, Noble Energy
Case Studies
A National Alcohol Distributor
Process and task mining identified exactly where manual order entry was consuming staff time, then eliminated it with a purpose-built workflow, cutting manual order-entry time by 97 percent.
A Customer Quoting Portal
A Power Apps rules engine paired with Azure-hosted infrastructure gave a client a self-service quoting portal projected to save up to $100,000.
Offshore Drilling Operations
A custom .NET application aggregating operational data across offshore rigs shipped four weeks ahead of schedule.
Ready for 1 Source of Truth?
Book a Call With a Data Warehousing ExpertFrequently Asked Questions About Data Warehousing Services
What Is Data Warehousing?
Data warehousing brings data from your business systems into 1 structured, governed repository for reporting and analysis. It gives your team a consistent place to work from and reduces the manual reconciliation that happens when each system reports the same metric differently.
What Are Data Warehousing Services?
Data warehousing services cover the design, build, and ongoing support of a data warehouse. That includes cloud data warehouse architecture, ETL and ELT pipelines, legacy system integration, data governance, and the Power BI connection after the warehouse is live.
What Is Data Warehousing and Business Intelligence?
Data warehousing and business intelligence work together. The warehouse stores and standardizes the data. Business intelligence tools, such as Power BI, report from that warehouse so every dashboard uses the same definitions.
What Is the Difference Between a Data Warehouse, a Database, and a Data Lake?
A database handles fast, real-time transactions. A data warehouse stores current and historical data from multiple systems in a structure designed for reporting. A data lake stores raw structured and unstructured data at scale, often for machine learning.
How Long Does It Take to Implement a Data Warehouse for a Mid-Market Company?
A simple 1-source warehouse can take 2–3 months. A mid-market build with several systems may take 4–8 months. A complex multi-source rollout can take 9 months or more. The timeline depends on the number of source systems, data quality, reporting needs, and integration complexity.
What Does a Data Warehouse Project Typically Cost?
Data warehouse costs depend on the number of source systems, data volume, integration complexity, reporting needs, technology choices, and ongoing support. A scoping assessment identifies the right architecture and provides a project estimate before implementation begins.
Can a Data Warehouse Connect to Legacy Systems Like Oracle, SAP, or JD Edwards Without Replacing Them?
Yes. A data warehouse can connect to Oracle, SAP, JD Edwards, and other legacy systems without replacing them. Extraction and transformation pipelines move data into a central warehouse for reporting while the source systems stay in place.
Do We Need a Full Lakehouse Architecture, or Is a Smaller Warehouse Enough for Our Size?
Most mid-market companies don’t need a full lakehouse. A right-sized data warehouse is often enough when the primary need is governed reporting, dashboarding, and integration across a defined set of business systems.