Data Warehouse and Lake Consulting

Make your data a strategic asset rather than a source of stress.

Speak with a data engineer today ->

Engineer high-velocity data environments to maximize investments.

How we work with you

Stabilize revenue with more reliable systems.

When your service is disrupted, it is restored immediately. You get a deep-dive Root Cause Analysis (RCA) that pinpoints whether the culprit was a disk-full error, schema drift, or a network misconfiguration. Your data is protected by a proactive safety net of high availability and automated backup testing, ensuring your operations remain resilient.

Eliminate bill shock and right-size your systems.

Identify hidden risks and performance bottlenecks through query refactoring, indexing, and right-sizing cloud resource allocations. This step is vital for scaling the system without a massive budget increase.

Migrate business data with confidence.

Move data from aging on-premise hardware to elastic cloud environments. Unlike a simple lift and shift, schema conversion and validation is performed to ensure zero data loss and total logic parity.

Gain better control over your data management.

Manage user permissions, encrypt sensitive data, and provide recommendations to achieve further savings and reliability.

Transform your data from a cost center into a strategic growth engine.

Speak with a data engineer today ->

We support the technologies you use

Cloud native
data warehouse and lakes

Amazon Redshift

Reduce costs and eliminate data skew, building the right AWS ecosystem and architecture for analytics and AI.

BigQuery

Leverage Gemini and Vertex AI to turn massive datasets into real-time, conversational insights without the overhead of infrastructure management.

Azure Synapse

Modernize your infrastructure by transitioning to Azure Synapse for a governed, more cost-efficient foundation built for production-ready AI.

Databricks

Optimize your Photon engine performance and Serverless SQL. Leverage Unity Catalog to ensure your lakehouse is governed, secure, and ready to deploy models via Mosaic AI.

Microsoft Fabric

Accelerate your transition from legacy Synapse or SQL environments, optimizing Fabric Capacities to prevent cost overruns while ensuring your data is primed for integration with Azure OpenAI and Copilot.

Snowflake

Transform to a high-performance Data Cloud. Leverage Snowpark and Cortex AI to deploy governed, production AI models directly within your secure data perimeter.

Hybrid cloud and legacy
data platforms, warehouses, and lakes

Oracle Autonomous

Transform self-driving features into strategic business outcomes by optimizing OCPU consumption and refactoring legacy logic into AI-ready lakehouse architectures.

Teradata

Stabilize and optimize the performance of your existing Teradata environments to prepare for cloud-native platforms like BigQuery, Snowflake or Databricks.

Netezza

Reclaim capacity and eliminate data inefficiencies. Migrate to cloud-native platforms, transforming legacy logic into an AI-ready data architecture.

IBM Db2

Maximize the ROI of your IBM Db2 environments to eliminate costly hardware and ensure your systems can power modern AI and hybrid workloads. Migrate to resolve data silos.

Hadoop

Stabilize aging clusters to reclaim storage and secure data while optimizing query performance for AI integration. Migrate to enable real-time analytics and scalable infrastructure.

Vertica

Eliminate performance-draining table locks and unused storage, ensuring sub-second latency for AI and real-time analytics. Right-size your cloud-native Eon Mode architecture.

SAP BW

Reduce HANA memory costs and refactor complex legacy logic. Migrate your insights to Snowflake, Databricks, or SAP Datasphere before support deadlines.

Greenplum

Eliminate data skew and resource contention to ensure peak query speeds for AI and analytics. Migrate to replace manual cluster management with elastic architectures.

Stop managing infrastructure. Start delivering business value.

Speak with a data engineer today ->

Canadian retailer accelerates insights with a unified data platform

The customer partnered with Pythian to accomplish a centralized source of truth.

A leading Canadian clothing retailer struggled with siloed data and slow, manual reporting that prevented timely decision-making. By deploying Pythian's enterprise data platform (EDP) quickstart for Google Cloud, the company created a centralized source of truth. The new data platform enables self-serve analytics, allowing business users to access critical insights in near real-time and build a more data-driven culture.

Read the success story ->
Canadian retailer accelerates data insights partnering with Pythian.

$8M+

First year savings

10x

Faster performance

99%

Data reliability 

Frequently asked questions (FAQ) about data warehouse and lake consulting services

How do you ensure data security and governance during a platform migration?
We implement a robust security and governance framework from day one. This includes establishing clear policies for data access, handling, and classification, along with encryption, data catalogs, metadata management, and fine-grained access controls. During migration, we maintain strict data integrity validation and audit trails so your sensitive information is protected throughout the transition—not just after go-live.
How do you measure and ensure ROI on a data warehouse or data lake investment?
Every engagement starts with a strategic assessment that maps your platform investment to specific business outcomes—whether that's reducing total cost of ownership, accelerating time-to-insight, or enabling new analytics and AI capabilities. We track quantifiable milestones throughout delivery, and our post-launch optimization services ensure your platform continues to deliver value as your data volumes and business needs evolve.
What's the difference between a data warehouse and a data lake, and which one do I need?
A data warehouse stores structured, processed data optimized for business intelligence and reporting. A data lake stores all types of data—structured, semi-structured, and unstructured—in its raw format, making it ideal for data exploration, data science, and machine learning. Many organizations benefit from both, often converging on a "lakehouse" architecture. Pythian provides vendor-neutral guidance to help you choose the right approach based on your workloads, analytics goals, and budget—not on platform vendor preferences.
Can you help us modernize without migrating to a completely new platform?
Absolutely. Not every modernization requires a full platform migration. For many organizations, the right move is to optimize, tune, and harden governance on their current platform—especially for modern environments like Snowflake or Databricks that are scaling, not sunsetting. Pythian meets you where you are and helps you get more value from what you've already built, whether that means optimizing performance, reducing costs, or maturing your data engineering practices.
Back to top