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Delivering data platforms, data pipelines, and generative AI applications.
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Case Study:
Learn how Quantatec integrated generative AI into their Movias logistics platform.
Quantatec wanted a large language model (LLM) to answer customer’s questions about their own fleet data and Patterson Consulting helped us to integrate the LLM properly and provide consistent quality answers to the user.
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Recent case studies
Enabling Hurricane Impact Prediction Through Robust Data Platform Engineering
A case study of how Patterson Consulting helped the University of Michigan's College of Nursing research team build out a Google Cloud data platform to enable their hurricane impact prediction modeling.
Read case studyData Platform Migration with Large Midwest Insurance Company
Patterson Consulting worked with the platform engineering team of a large midwest insurance company to help migrate data pipelines from AWS to GCP. New data pipelines were built out on Google BigQuery and orchestration was implemented with Airflow.
Read case studyManaged MLOps Infrastructure for Large Financial Services Company
Patterson Consulting worked with a large east coast financial services company to help them integrate Kubeflow with Kerberos, Active Directory, and NVIDIA DGX GPUs for an on-premise Jupyter Notebook-based data science environment with fixed cost profile.
Read case studyOur Services
End-to-End Services for Developing Next Generation Data Platforms
Build better data platforms
Patterson Consulting can help you review existing data platform architectures or design new ones from the ground up. We use a mix of traditional business analysis coupled with deep platform engineering expertise to best match platform architecture to what data platform configuration will best drive line of business key indicators.
If you are already in the cloud and need to migrate your data platform to another cloud, Patterson Consulting simplifies and expedites data platform migrations in the Cloud. Whether your cloud provider is Azure, AWS, or GCP, Patterson Consulting will efficiently move your data from one platform to another with minimal disruptions.
Learn moreDeploy new data pipelines with a team of experts
Leveraging the latest technologies and best practices, we design robust and scalable pipelines tailored to meet the unique needs of each client. Our expert team meticulously architects the entire process, from data ingestion to transformation and storage, ensuring seamless integration with cloud-native services for maximum efficiency and performance. Trust us to transform your data challenges into opportunities for innovation and growth in the cloud.
From data products, to analytics, to semantic layers, to building dashboards, the Patterson Consulting team will design and deploy efficient data pipelines that improve how your business operates.
Our data pipeline design process focuses on data integration integrity and lowering the latency it takes from "data intake to data answer" in the line of business.
Learn moreAccelerate your line of business with generative AI
We focus on 3 core areas of application in generative AI:
- Conversational User Interfaces
- Process Automation
- Decision Support
Focusing on these 3 areas allow our customers to frame generative AI use cases and drive towards return on investment for the line of business.
Our rigorous development process ensures that each application is rigorously tested and fine-tuned for optimal performance, reliability, and scalability.
Learn moreThe Hitchhiker's Guide to Knowledge Work Systems
The rise of large language models has accelerated AI investment and hype, but as our business blog series "The Hitchhiker’s Guide to Knowledge Work" explains, real productivity gains depend on addressing foundational data platform challenges and rethinking technology strategy to support the evolving nature of knowledge work.
Check out our business blogOur latest technical blog resources
Articles on data engineering and generative AI from the Patterson Consulting team
Building a Property Insurance Claims Data Lakehouse with Airflow and Databricks
In this article we build a data lakehouse for property insurance claims management. This allows us to build out a complete claims analysis platform complete with standardized data modeling.
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Building a Retail Product Delta Table with Databricks Unity Catalog
In this article we get you going quickly building Delta Tables with CTAS statements and Unity Catalog on Databricks.
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Understanding the Difference Between Unity Catalog and the Hive Metastore
In this article we give a breakdown of the key differences between the legacy hive metastore and the modern Databricks Unity Catalog.
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Building a Standard Claims Data Model With the Cube Semantic Layer and Databricks
Creating standard claims data models for claims knowledge work in property insurance companies.
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Driving Retail Information Architecture with the Databricks Medallion Architecture
Information architecture within the Information Layer is what transforms a company’s raw data into a single, trusted system of record that fuels knowledge work across every business function. By organizing and governing how data is modeled, related, and exposed through APIs, the Information Layer ensures that every team—from operations to finance to marketing—works from the same version of the truth.
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Building a Retail Semantic Layer with Unity Catalog Metric Views
This article explains how Databricks Unity Catalog Metric Views create a governed semantic layer that transforms complex retail data into consistent, business-ready insights—empowering teams to accelerate knowledge work and make revenue-driving decisions with confidence.
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