How do you handle security and governance during a Fabric migration, especially for regulated industries?

Security and governance are built into every phase of our Fabric engagements—not bolted on at the end. We start with a comprehensive assessment of your existing security posture, including Entra ID configuration, network isolation, and data classification. During migration, we implement Microsoft Purview governance from the outset—sensitivity labels, data classification, lineage tracking, and policy enforcement. Access controls are designed using RBAC, row-level security (RLS), and object-level security (OLS) mapped to your organizational roles. Workspace hierarchy and naming conventions are designed to enforce data domain boundaries. For regulated industries, we configure private endpoints, network isolation, and audit logging to meet compliance requirements before any production data moves.

What kind of ROI can we expect from consolidating onto Microsoft Fabric?

The ROI comes from multiple sources. Infrastructure cost savings are often the most immediate win. Beyond cost reduction, organizations typically see significant improvement in Power BI report render times through DirectLake optimization, dramatic reductions in operational complexity (from managing five or more separate Azure services to a single governed platform), and the ability to enable self-service analytics and production AI capabilities that were impossible on the fragmented estate. Our phased approach means you start seeing returns on high-value workloads early—not just at the end of a multi-year project.

We have hundreds of SSIS packages and Synapse stored procedures. How complex is the migration to Fabric?

This is the hardest part of any Fabric migration—and the part most firms underestimate. SSIS packages can't be lifted and shifted into Fabric. They require fundamental re-architecture from procedural ETL to ELT patterns using Fabric Pipelines, Dataflow Gen2, or Spark Notebooks. Each package needs to be assessed for complexity, dependencies, and the optimal Fabric-native replacement. Synapse dedicated SQL pool workloads require DDL refactoring (distribution keys, indexes, and workload management settings don't exist in Fabric Warehouse), data extraction via CETAS to staging, and T-SQL compatibility remediation. Azure Data Factory pipeline migrations need a feature-parity gap assessment since Fabric Data Factory differs from ADF in key ways—no SSIS Integration Runtime, different connection handling, and no datasets concept. Pythian has done this work across complex, multi-workload environments and understands where the hidden compatibility gaps live.

Is Microsoft Fabric mature enough for enterprise-scale production workloads?

Fabric has matured significantly since its general availability in November 2023. As of mid-2025, over 28,000 paying organizations use it, including 80 percent of the Fortune 500. Microsoft is treating Fabric as its flagship data and analytics platform with monthly feature updates, and is actively steering customers from legacy services—retiring Power BI Premium P-SKUs, feature-freezing Synapse dedicated SQL pools, and migrating ADF users toward Fabric Data Factory. That said, features still move between preview and GA, and capacity-based pricing requires careful management to avoid unexpected costs. This is exactly where Pythian adds value. We know where the sharp edges are—which features are production-ready, which need workarounds, and how to architect around current limitations. We've helped dozens of organizations take Fabric from prototype to production, and we design every implementation with the operational discipline to handle enterprise-scale workloads reliably.

AI for Retail: Agentic AI, Automation and Machine Learning

Optimize profit margins with dynamic pricing

Leverage machine learning models to automatically optimize pricing in real time based on competitor trends, demand surges, and actual stock levels.

Enable AI powered shopping experiences

Bridge the digital and physical divide with generative AI and computer vision. Eliminate buying hesitation with AI powered shopping experiences.

Empower hyper-personalized commerce

Analyze browsing history, real-time behavior, and local context to instantly deliver bespoke product recommendations that convert.

Automate verifying product integrity at scale

Deploy multimodal agents to analyze supplier images, descriptions and specifications, instantly resolve hidden contradictions to ensure your product catalog is accurate.

Achieve SKU and inventory accuracy

Continuously crawl your ERP, POS, and WMS, merge duplicate SKUs and eliminate phantom inventory, gain a high-fidelity, real-time view of your stock to confidently fulfill every omni-channel and BOPIS promise.

Forecast for real-time market response

Analyze historical sales, regional demographics, and real-time weather patterns to align supply with localized demand. Minimize costly waste and optimize stock.

Eliminate tedious back-of-office work

Automate operational workflows, deploying AI reasoning agents that handle manual task such as scheduling, purchase order and invoice reconciliation.

Wayfair scales AI across their global eCommerce engine.

Wayfair worked with Pythian to deploy an advanced reasoning AI solution designed to automate product data validation across millions of items. Our solution seamlessly standardizes and validates this incoming data in real time. By integrating directly into Wayfair’s core production systems, the AI-driven engine drastically reduces manual overhead, accelerates time-to-market for new products, and ensures a seamless shopping experience for millions of customers worldwide.

We needed AI to intelligently validate, reconcile, and flag conflicts in ourmost critical data points—the dimensions—which directly impact the bottomline."

Matt Ferrari

Head of Platforms, Wayfair

Schnucks migrates to Google Cloud to accelerate with data, analytics and AI | Pytian Customer Success Story

Urban Outfiters is a retail customer of Pythians

Fresh Direct is a retail customer of Pythian- Data and AI Service Provider

Identify high impact AI use cases.

Map technical feasibility against commercial impact—move from ideation to a concrete, risk-managed AI roadmap. Build an agile architecture to hit market-moving growth targets.

Define AI roadmap ->

Assess your data and AI readiness.

Audit your current legacy systems (ERP, POS, WMS) and data hygiene. Build a scalable architecture, resolve the technical debt slowing down customer-centric innovation.

Build data strategy ->

Deploy AI solutions in production faster.

Accelerate deployment by integrating enterprise-grade AI into existing workflows—minimize operational friction, with real-time insights to drive immediate margin growth.

Accelerate with AI ->

Be confident in your business decisions, with clean business data.

Create a unified dashboard that provides a single and accurate source of truth for every SKU—across all of your physical stores and digital fulfillment centers.

Automate cross-system reconciliation that syncs your ERP (Enterprise Resource Planning), POS (Point of Sale), and WMS (Warehouse Management Systems) in real time. Build an agile, scalable infrastructure—connect your business systems and tools, with governed, clean data. Accelerate without having to endure a multi-year IT overhaul.

Discuss your data strategy →

Eliminate costly returns and recapture abandoned carts.

As your data volumes increase, product catalogs grow and you expand through acquisitions, automate to assure accuracy.

Build an AI automated compliance engine that automatically flags mismatched product descriptions, incorrect dimensions, or inaccurate images. Set up multimodal agents that read text descriptions and analyze product images simultaneously to reconcile data entry errors.

Automated data validation ->

Set up dynamic pricing to protect profit margins.

Reduce the burden of managing spreadsheets, gain faster access to market research in real-time.

Real-time dynamic pricing engines that continuously Leverage machine learning to actively monitor external market forces and automatically adjust prices. Build solutions that continuously evaluate local competitor pricing, sudden demand surges, and localized stock overstocks. Set up data governance rules specific to your industry to protect margins.

Set up Machine Learning ->

Deploy real-time intent engines to fuel revenue growth.

Build agile AI models that adapt instantly to live user intent.

Analyze a customer's immediate clickstream behavior, browsing history, and regional context (like weather or local trends) to serve highly contextualized product suggestions.

Gain customer insights ->

Boost online traffic with with AI optimized product listings.

Ensure that your product catalogs are readable to the next generation of search engines.

Deploy data-cleansing and standardization tools that convert legacy SKU sheets into structured, machine-readable formats, ensure your products remain visible to search engines and the autonomous AI shopping assistants of the future.

Speak with AI experts ->

AI for Retail

Deploy AI solutions faster—scale smarter, protect profit margins and outpace competitors.