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AI & Data

Data Engineering & Architecture

Build your data foundation for AI and analytics — engineered for scale and governed for trust.

CBTS data engineering and data modernization services build pipelines that move and transform data reliably, supported by a modern data platform.
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Break through data bottlenecks

Data streams into the enterprise from every direction, arriving in incompatible formats, trapped in proprietary tools, and siloed across teams. That’s the opposite of what analytics and AI workloads demand: high-quality data that’s unified, governed, and delivered quickly.

Because of data bottlenecks, leadership teams discover problems weeks late. Analysts devote more time to reconciling than producing reports. And every major change makes the gap wider. Fixing this problem requires data pipelines that move and transform information reliably plus a modern data platform purpose built for analytics and AI workloads.

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The CBTS approach

Engineering your data pipelines and platform together

Pipelines designed in isolation from the underlying platform get brittle. Platforms modernized without engineering discipline end up empty. That’s why CBTS designs them together: the cloud-native lakehouse, warehouse, or hybrid architecture that stores your data and the pipelines that move and transform it.

CBTS data engineers and architects have done this work across Microsoft Fabric, Azure, Snowflake, Databricks, and other major cloud analytics platforms. We reduce risk and accelerate time to value with proven patterns like medallion architecture, ELT pipelines, DataOps practices, and governed bronze/silver/gold zones. And because we’re vendor neutral, we always recommend the platform that best meets your business needs. 

Data Engineering & Architecture capabilities

 CBTS shapes every data engineering and architecture engagement where the work is needed most — the pipelines moving data, the platform underneath, or both in sequence.

Data Engineering & Architecture

Where to start

Advisory engagements

A CBTS advisory is a time-bound, fixed-fee engagement designed to give you a clear answer to a specific strategic question — fast.  

AI & Data Maturity Assessment

Best for organizations that want a clear, third-party read on where they stand on AI and data readiness and where to focus first.

You walk away with: 


  • Current-state assessment across both AI and data dimensions
  • Gap analysis against industry benchmarks and your own stated AI ambitions
  • Prioritized list of foundational gaps to close before scaling AI investment
  • Short-form executive readout deck for leadership alignment
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What success looks like

Data engineering and modernization work should change how your business operates with its data. Three outcomes show up most frequently for the clients we support.

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Operational excellence

With trusted data flowing reliably from source to consumer, leadership stops discovering problems weeks late. Analysts stop reconciling. And the data estate becomes something the business depends on.

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Improved productivity

New data sources onboard in days, not quarters. Analytics workloads that used to run overnight finish in minutes. And the data team’s capacity shifts from plumbing to value.

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Reduced risk

Modern architecture with governed zones, lineage, and quality controls dramatically reduces regulatory, audit, and AI-failure risk.

 “We see clients spend a year on AI use cases that never scale because the pipelines underneath weren’t reliable, or the platform can’t handle production workloads. The work we do is upstream, and it isn’t glamorous. But it’s what moves pilots into production and produces real business outcomes.”

Justin-Grieshop_a

 Justin Grieshop   

Senior Director, AI & Analytics, CBTS

Don’t take our word for it

“I love the creative, tailored solutions that are delivered in a consistent and reliable way while always doing what it takes to make things right.”

Chief Technology and Information Security OfficerFinancial Services / Banking

“My team at CBTS have been trusted partners for a long time. They provide excellent technical support and pre-sales work. Their breadth of knowledge and ability to bring in the right resources have helped us steer our technology into the future.”

Managing Director, CISO, Head of TechnologyPrivate Equity / Financial Services

“CBTS treats us like a partner and not just a customer. The technical expertise is next to none and the relationship management is some of the best I have experienced.”

Director, Telecom and Architecture ServicesHealthcare

The data foundation connects everything else

Data engineering and modernization sit in the middle of the AI and analytics value chain. Strategy sets the direction. Infrastructure provides the platform.

Related insights 

Frequently asked questions 

What is data engineering, and how is it different from data modernization? Data engineering is the discipline of designing, building, and operating the pipelines that move and transform data — ingestion, ETL or ELT processing, integration, quality controls, and ongoing operations. Data modernization is the work of replatforming the data estate itself, moving from legacy on-premises warehouses, siloed file shares, or aging proprietary tools onto a cloud-native architecture like a lakehouse or modern cloud data warehouse. The two are usually needed together. Pipelines without a modern platform stay brittle, while a modern platform without engineering discipline ends up empty or unreliable.
What’s the difference between a data lake, data warehouse, and lakehouse? A data lake stores raw, unstructured data in its original format; it’s flexible, but harder to query directly. A data warehouse stores structured data processed and modeled for analytics; it’s easier to query but rigid and expensive to expand. A lakehouse combines both. You get lake-style flexibility for raw and semi-structured data with warehouse-style structure and performance for the curated layers on top. Most modern enterprise data architectures are now lakehouse-based, often built on Microsoft Fabric, Databricks, or Snowflake, with bronze/silver/gold zones layered inside.
What’s the difference between ETL and ELT? Both move data from a source into a storage location. ETL (extract, transform, load) transforms the data before loading it into the destination, typically because the destination is a structured warehouse that needs clean data on arrival. ELT (extract, load, transform) loads raw data first and transforms it inside the destination, which works well with cloud-native lakehouses and modern data warehouses that handle transformation at scale. Most new pipelines are designed ELT, while many legacy environments still run ETL. CBTS designs to fit the platform and the use case, not to favor one pattern.
How does data affect the success or failure of AI projects? Industry research puts the AI project failure rate above 70 percent, and most of those failures trace back to data that’s incomplete, inconsistent, or inaccessible. It lacks the quality, lineage, or governance the model needs to be trusted. Data engineering and modernization solve this by building the pipelines and platform that deliver AI-ready data. Such data is integrated across sources, validated for quality, governed by zone, and accessible at the throughput AI workloads demand.
Which platforms does CBTS work with? CBTS is vendor neutral and platform certified across the major cloud and data ecosystems, including Microsoft Fabric and Azure, Databricks, Snowflake, AWS (Redshift, S3, Glue), and Google Cloud (BigQuery, Dataflow). Most of our recent enterprise engagements have centered on Microsoft Fabric, Snowflake, and Databricks, but the recommendation is driven by your existing technology stack, use cases, and long-term roadmap.

Start with a conversation

Your organization’s AI ambitions depend on data engineered and modernized to support the work.