Blog
MotherDuck Guides: What to Put in Them, and What to Push Down
MotherDuck Guides are a routing layer for analytics agents, not documentation. What to put in them, what to push down, and how to keep them correct.
By Ross KatzRead
Your data is scattered. Your decisions shouldn't be.
We build the pipelines, warehouses, and integrations that turn fragmented data into a single source of truth your whole team can trust.
Data Engineering
Most growing organizations hit the same wall: data lives in 15 different SaaS tools, two legacy databases, and a folder of CSVs someone emails around on Mondays. Reports conflict. Analysts spend more time wrangling extracts than answering questions. The "data warehouse" is a single Postgres instance that locks up when finance runs month-end. By the time leadership asks for a dashboard, the underlying data is already stale.
11 weeks
Full warehouse rebuild, 2-person team
50+
Production pipelines built across 20+ clients
1B+/day
Sensor readings ingested from 60+ IoT machines at 5ms intervals
Use Cases
Engagements where this is the right work.
Unify siloed data sources
Consolidate data from dozens of SaaS tools, databases, and internal systems into a single warehouse your entire team trusts.
Modernize legacy pipelines
Replace brittle ETL jobs and manual processes with modern, testable, version-controlled data pipelines that run themselves.
Cut data platform costs
Architecture changes that reduce Snowflake, Fivetran, and Databricks spend by 40-80% without sacrificing performance.
Prepare for AI and ML
Build the clean, structured, accessible data foundation that makes machine learning and AI work in production.
Streaming or near-real-time pipelines
You need data fresh enough to drive operational decisions. We design and build the streaming or CDC pipelines that meet the latency the business actually needs.
Our Process
A structured approach that delivers results at every stage.
01
Assess Your Data Environment
Every engagement starts with a thorough assessment of your current data environment: sources, pipelines, storage, and consumption patterns. We identify quick wins that deliver immediate value while designing the long-term architecture that will support your growth. Our engineers embed directly with your team, working in your codebase, your cloud environment, and your communication channels.
Output: Source-system inventory, schema map, and ingestion gap analysis
02
Build Your Data Infrastructure
We set up orchestration with tools like Dagster or Airflow to schedule and monitor pipeline runs. We configure ingestion through Fivetran, Estuary, or custom connectors to pull data from your source systems. We build transformation layers in dbt to model clean, tested, documented datasets. And we design the warehouse architecture in Snowflake, BigQuery, Databricks, or MotherDuck to store and serve it all. We are not dogmatic about tools. We choose what fits your organization, not what is trendy.
From our podcasts: Reducing Dropout Rates in Clinical Trials with Miguel Cacho Soblechero, Challenges of NGS Data Sets with Joseph Pearson, and Serverless Analytics Data Warehousing with MotherDuck.
Output: Production pipelines, dbt models, and monitoring with alerts
03
Maintain and Extend
Our data engineering clients span biotech, healthcare, education, ecommerce, finance, and professional sports. What they share is a recognition that reliable data infrastructure is a competitive advantage, not a cost center. See how we modernized a data warehouse for NLx or built a data platform for an NBA team. If your team is spending more time fixing pipelines than building products, we can help.
Output: Runbook, on-call rotation, and a backlog of follow-on work
Insights
Perspectives from our team on data engineering.
Blog
The Context Layer: What to Build, What to Skip, and Where to Start
A staged framework for deciding which data architecture components AI agents need, when to build them, and why, based on the value you are creating.
By Ross KatzRead
Blog
MCP in the Enterprise: Cost, Security, and Workload Routing
MCP is now core infrastructure. Its real cost at enterprise scale, where the security model breaks, and how to route agent workloads deliberately.
By Ross KatzRead
Blog
Skip the Data Stack, Get the Wrong Answer Faster
AI agents querying raw source systems inherit every data quality problem the transformation layer solves — then present wrong answers with confidence.
By Ross KatzRead
Results
Real outcomes from real engagements.
Enterprise Technology
Embedded Analytics Modernization for a Payroll SaaS Platform
Sub-second for payslip, payroll register, and leave balance reports
Query latency
~6 minutes end-to-end, tunable to under 5
Data freshness
Healthcare / Home Care Services
Government Reporting Overhaul for a Home Care Platform
~100 CMS-required reports
Government reports stabilized
February 1 CMS filing
Regulatory deadline met
Automotive / Retail
ML Customer Segmentation for an Automotive Dealer Group
300+ features from vehicle sales, service history, and customer survey
Feature integration
8 clustering algorithms with Bayesian hyperparameter optimization
Algorithms evaluated
Frequently Asked
Questions
Can't I just stick with my existing data provider?
You can, but if you are here it is probably because something is not working. We start with an assessment that has standalone value. If your current provider is the right fit, we will tell you.
Do you work with our existing cloud provider?
Yes. We are cloud-agnostic and have deep expertise across AWS, GCP, and Azure. We also work with multi-cloud and hybrid environments. Our approach is to optimize within your existing infrastructure before recommending migration.
What makes CorrDyn different from other data engineering consultancies?
Over 80% of our business comes by referral. Our average client relationship is 5+ years. We are technologists who embed with your team, transfer knowledge throughout the engagement, and build systems your team can own after we leave.
How long does a typical data engineering engagement take?
Most initial engagements run 3-6 months. We can typically stabilize critical pipelines within the first month, then systematically rebuild and optimize. Ongoing support agreements are available for teams that need continued partnership.
What industries or verticals do you work in?
Biotech, healthcare, financial services, e-commerce, sports, education, and government. The industries vary but the problems are the same: scattered data, unreliable pipelines, and reports nobody trusts.
What are common types of projects you take on?
Pipeline development, warehouse architecture, data migrations, cost optimization, data quality frameworks, and ongoing team augmentation. Most clients start with one and expand to others.
Ready to stop fighting your own data?
Your pipeline should be working for you, not the other way around. Let’s see what you’re working with.
Book an Intro CallOur Other Services
Or, see what else we do.
Data Quality & Governance
Fix the trust problem in your data. CorrDyn implements testing, validation, governance frameworks, and metric definitions your organization can maintain.
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Managed Data Team
Get a full data team without the hiring timeline. CorrDyn embeds the specific skill sets you need and owns outcomes, not just hours.
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Data Cost Optimization
Optimize data platform performance to reduce Snowflake, Fivetran, and Databricks spend by 40-80%. Faster queries, right-sized compute, lower bills.
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Business Intelligence
Turn data into decisions with BI platforms that your team will use. CorrDyn builds dashboards, reports, and analytics workflows.
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