Modernize Your Data Platform. Cut the Cost.
Enterprise data modernization services that move Teradata, Netezza, Exadata, or Hadoop to a governed lakehouse on Databricks, Snowflake, or Microsoft Fabric, with a defended cost case before migration starts.
- Up to40%lower cloud data spend
- Up to70%faster queries
- About6 monthsaverage modernization
- Zerodowntime cutovers
Your Data Warehouse Is Holding Back BI and AI.
Legacy warehouses were built for nightly reports, not live dashboards and machine learning.
- 01Appliance Costs Keep ClimbingTeradata, Netezza, and Exadata renewals arrive with bigger bills and end-of-life dates.
- 02Queries Too Slow for the BusinessReports queue overnight while teams wait for answers.
- 03Not Ready for AISiloed, ungoverned data can't feed machine learning or generative AI safely.
Definition
What Is Data Modernization?
Data modernization is the move from legacy data warehouses and on-premises platforms to a modern cloud architecture that is faster, cheaper to run, governed, and ready for analytics and AI. SMI TechSolutions delivers enterprise data modernization services with a cost model in week one and parity-validated cutover.
What we do
What We Modernize
Teradata
FromTeradata EDW applianceToDatabricks, Snowflake, or Fabric lakehouseLegacy data warehouse modernization with workload-by-workload cutover.
Netezza
FromNetezza appliance near end of lifeToCloud data warehouseOff the appliance before renewal.
Exadata
FromOracle Exadata data warehouseToCloud lakehouse or warehouseLower licensing and compute costs.
Hadoop
FromOn-premises Hadoop clustersToManaged lakehouseFewer clusters to run; governance built in.
Governance
FromSpreadsheets and tribal knowledgeToUnity Catalog, Purview, or CollibraData contracts, lineage, and access control enforced in CI/CD.
Target architectures compared
Cloud Data Warehouse, Data Lake, or Lakehouse?
Choose a cloud data warehouse for structured BI, a data lake for cheap raw storage, and a lakehouse when you need both plus machine learning and AI on one governed platform. SMI TechSolutions presents two target architectures with cost models before you commit.
| Architecture | Best for | Platforms | Trade-off |
|---|---|---|---|
| Cloud data warehouse | Structured reporting and BI | Snowflake, BigQuery, Fabric Warehouse | Less suited to unstructured data and ML |
| Data lake | Low-cost storage of raw data | ADLS, S3, GCS | Weak governance and slow BI on its own |
| LakehouseBest fit for BI and AI | BI, data science, and AI on one copy of data | Databricks, Microsoft Fabric, Snowflake | Needs strong data contracts and design discipline |
Built on a medallion (bronze, silver, gold) layout with data contracts at every layer.
How we deliver
Our Six-Stage Data Modernization Method
- 1
Discover
Inventory the estate: workloads, pipelines, users, costs, and dependencies.
- 2
Options
Two target architectures, each with a cost model, presented to IT and finance.
- 3
Plan
Migration waves sequenced by value and risk; the CFO signs off before cutover planning.
- 4
Build & Migrate
Medallion layout with data contracts; workloads moved wave by wave.
- 5
Validate
Row-count, query-result, and performance parity signed off per workload.
- 6
Retire
Legacy decommissioned in phases, with a rollback path live until day 90.
- FinOps built in
- Cost model in week one
- FinOps controls live by week four
- Spend tracked per workload
- No migration without a defended cost case
FAQ
Frequently Asked Questions
What is a data lakehouse?
A data lakehouse is a data architecture that combines the low-cost storage of a data lake with the performance, structure, and governance of a data warehouse. SMI TechSolutions builds lakehouses on Databricks, Microsoft Fabric, and Snowflake so BI, data science, and AI share one governed copy of the data.
When should we modernize our data warehouse?
Modernize when appliance renewals or end-of-life dates approach, when queries can't keep up with the business, or when AI and real-time analytics need data the warehouse can't serve. SMI TechSolutions starts with a 60-minute architecture review to test the business case.
How long does data modernization take?
SMI TechSolutions averages about six months for a focused data modernization program. Larger estates move in quarterly migration waves, each delivering value on its own, so the business never waits for one big-bang cutover.
Databricks or Snowflake: which should we choose?
Databricks suits organizations with heavy data science, ML, and streaming needs. Snowflake suits SQL-centric BI and data sharing. Microsoft Fabric fits organizations standardized on Microsoft 365 and Power BI. SMI TechSolutions offers Databricks consulting and Snowflake consulting and models the cost of each for your workloads.
How do you control cloud data platform costs?
SMI TechSolutions builds a cost model in week one and puts FinOps controls live by week four: right-sized compute, auto-suspend, workload isolation, and per-workload spend tracking. No migration starts without a cost case your finance team has reviewed.
What does data warehouse consulting include?
Data warehouse consulting at SMI TechSolutions covers estate discovery, target architecture options with cost models, migration planning, and performance tuning. The same team then builds and migrates, so recommendations become running platforms rather than reports.
What does data governance consulting cover?
Data governance consulting defines data ownership, definitions, quality rules, and access policies, then enforces them in the platform. SMI TechSolutions uses Unity Catalog, Microsoft Purview, or Collibra, with row-level security, column masking, and data contracts enforced in CI/CD.
What does data architecture consulting cover?
Data architecture consulting designs how data is stored, modeled, integrated, and governed across the enterprise. SMI TechSolutions presents two target-state architectures, each with performance and cost benchmarks, so IT and finance choose together.
How do you avoid downtime during data modernization?
SMI TechSolutions moves workloads in waves with parallel running, then signs off row-count, query-result, and performance parity before each cutover. The legacy platform stays available as a rollback path until day 90.
What is the difference between data modernization and data migration?
Data migration moves data from one platform to another, often as it is. Data modernization redesigns the platform itself, with new architecture, governance, and cost controls. SMI TechSolutions handles both; see Data Migration for lift-and-shift projects.
How do you benchmark performance and cost before and after?
SMI TechSolutions baselines query performance, data freshness, and monthly spend on the legacy platform, then reruns the same benchmarks on the new platform before cutover. Results are shared with IT and finance as part of the sign-off.
Ready to Modernize Your Data Platform?
Book a 60-minute architecture review. See two target options and a first cost model before you commit to anything.
- Nearly two decades of delivery
