AI-Enabled Data Engineering
Trust Starts with High-Fidelity Engineering
You can't run a business on 'mostly accurate' data. As the 2025 Confluent Enablement Partner of the Year, we bring the most certified data streaming team in the Americas to solve your most complex scale problems. That credential wasn't given to us. It was earned in production environments, for enterprise clients.
From Raw Data to Business Decisions, We Own Every Stage in Between
Most data problems that look like technology problems are actually architecture, governance, or trust problems. We've seen every version. Each engagement at Improving starts with an honest assessment of where your data foundation actually stands, then we build the right thing for the right stage.
Most organizations have more data than insight. Not because the data isn't there. Because the foundation beneath it isn't ready. We meet you where you are and build toward where the business needs to go.
The Platforms Your Data Teams Trust, Partners Built on the Same Foundation
Every platform in our data ecosystem earned its place through real delivery. We work with the tools your data teams already depend on, and we bring the certification depth to prove it. Here's how our ecosystem works across a full data engagement.
Canada Partner of the Year — Public Sector
Recognized for delivering measurable outcomes on Google Cloud for public sector clients across Canada. Validated by Google based on technical delivery, client impact, and certification depth.
Assessing Your Data Foundation
Before a single pipeline is built
Every data engagement starts with understanding where you actually are — not where you think you are. Our strategy-phase partners help us assess your current platform, identify gaps, and align on what the right architecture looks like for your specific scale and goals.
Where does your data live, and how trustworthy is it?
Which cloud platform is right for your data workloads?
What does a realistic 90-day data roadmap look like?
Data Maturity Assessment
Platform Selection
Cloud Strategy
90-Day Roadmap
Google Cloud Professional Data Engineer — expertise in cloud data strategy and platform architecture
Designing Your Data Platform
The work most teams skip — and regret
Architecture decisions made here determine everything downstream. Our platform-phase partners help us design lakehouse architectures, cloud-native data platforms, and governance frameworks that scale with your business — not just your current data volume.
Lakehouse, warehouse, or hybrid — what's right for your workloads?
How do you govern data across teams without slowing them down?
What does an AI-ready data model actually look like at your scale?
Lakehouse Design
Data Architecture
Governance Model
Data Catalog
Databricks Certified Data Engineer Associate — expertise in lakehouse architecture and ML-ready data platforms
Building and Engineering Your Pipelines
Where architecture becomes data in motion
This is where we build — streaming pipelines, batch processing, integrations, and the real-time data infrastructure that ties everything together. Our engineering-phase partners give us the tools to move data reliably at enterprise scale, from any source to any destination.
How do you connect data from 20+ source systems without fragility?
What does real-time data availability actually require at your scale?
How do you build pipelines that fail gracefully and recover fast?
Streaming Pipelines
ELT/ETL Architecture
Integration Layer
Data Observability
Confluent Systems Integrator Elite — expertise in real-time streaming and event-driven data pipelines
Turning Data Into Decisions, Then Making It Better
Where data becomes a competitive advantage
Getting data into a platform is only half the job. This stage is where we activate it. building the BI layer, AI-powered analytics, and governed data models that give your teams a single version of the truth. Then we keep improving it, monitoring quality, reducing cloud spend, and evolving the platform as your business changes.
How do you give every team self-service analytics without losing governance?
What does an AI-powered analytics layer look like on top of your data platform?
How do you maintain data quality as source systems change over time?
BI Platform
Semantic Layer
AI Analytics Model
Cost Optimization
Snowflake SnowPro Core Certified — validated expertise in cloud data warehousing and analytics activation
Data Foundations That Hold at Enterprise Scale
Real-Time
Automated IoT data capture, no manual entry
Predictive
Equipment issues surfaced before they cause downtime
Scalable
Expands across production lines and sites
Modern Real-Time Data Pipelines for Medical Device Manufacturing
Medtronic relied on manual entry and paper whiteboards to track production metrics across factory floors. Improving implemented Kafka and Confluent to capture live IoT and MES data in real time — enabling predictive maintenance, eliminating manual reporting delays, and giving production teams instant visibility into equipment health and line performance.
Our Data Engineers Teach What They Build, Watch Them Do It
From Data Vault with dbt to real-time streaming architecture to multi-cloud observability, our data engineers teach what they build, every week, for free.
Data in Motion: Operationalizing the Modern Data Platform
Nick Larson
VP of Technology
Data Ecosystems for Today: Your Data Needs a Supply Chain
Luke McGrath
President, Toronto Enterprise
Automate DV (formally dbtvault) & Snowflake: Best Practice for a Data Vault Model in 2025
Preston Mesarvey
Technical Director
Ready to Build a Data Foundation Your Business Can Trust?
Tell us where you are and we'll tell you exactly how we can help. No generic proposals, no sales pitch, just a direct conversation about your data situation.
Nick Larson
VP of Technology
Rich McCraw
VP of Technology
Preston Mesarvey
Technical Director