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.

DataOps Managed Services

Accelerate time-to-value.

Automate testing, CI/CD, and deployment environments to ship updates and new products in days or hours. Receive faster BI insights to maintain a competitive edge and accelerate growth.

Launch products faster ->

Grow BI and analytics with accurate data.

Stop schema drift, corrupt data, and silent pipeline failures before they affect BI dashboards or pollute AI models. Empower executives with accurate reporting for confident, strategic business decisions.

Secure data trust ->

Optimize operational costs.

Lean on our Data Engineers to optimize cloud computing resource usage and pipeline execution. Reduce operational costs by up to 60% compared to maintaining in-house platform engineering teams.

Slash costs ->

Align technical priorities with revenue goals and uncover hidden architectural risks.

Trace how your data moves across source systems (CRMs, ERPs, databases) into warehouses, lakes, or analytics to identify bottlenecks and data debt. Evaluate your existing data architecture, source systems, ETL/ELT pipelines, and overall engineering practices, and outline a continuous delivery roadmap for high ROI.

Identify pipeline risks →

Ensure zero-disruption DataOps integration with automated governance to build platform safety and scalability.

Onboard external DataOps engineers and integrate advanced operational tooling with zero disruption to your core business operations. Automated governance frameworks, role-based access control (RBAC), and CI/CD deployment pipelines ensure continuous platform safety and scalability.

Onboard DataOps seamlessly →

Modernize workflows with automated pipelines to accelerate time-to-insight.

Deploy modern orchestrators like Apache Airflow, Dagster, and dbt alongside automated CI/CD pipelines. Eliminate manual errors, test code updates reliably, and free internal data teams from daily firefighting so they can focus on predictive analytics and AI innovation.

Automate data pipelines →

Keep cloud spend predictable and ensure your data platform scales effortlessly for AI.

Audit system performance continuously to refine query execution, optimize cloud consumption, and introduce new tools and techniques (e.g., AI/ML data enablement) as business requirements evolve. Ensure your platform scales effortlessly with increasing data volume and adapting AI use cases without requiring massive rewrites every few years.

Save and adapt →

Guarantee high data trust and maximize system uptime through round-the-clock proactive monitoring.

Deploy automated observability tools to validate data freshness, schema changes, and volume anomalies 24/7/365. Achieve rapid Mean-Time-To-Resolution (MTTR) with global engineering teams that resolve midnight pipeline breaks before business hours.

Receive 24/7 support →

Frequently asked questions (FAQ) about DataOps Managed Services

DataOps Managed Services delivers 24/7 proactive monitoring, automated quality control, and continuous architecture modernization for enterprise data pipelines. Unlike traditional IT support that only reacts after dashboards crash, DataOps Managed Services apply database-grade discipline to prevent schema drift, eliminate null records, and resolve pipeline failures before they disrupt business reporting.

Enterprise data engineers and architects often spend over 50% of their bandwidth firefighting broken batch jobs and triage notifications. Partnering with experienced DataOps specialists provides immediate operational advantages:

80%+ faster time-to-market: Build automated ELT pipelines and deploy new analytics products in days rather than months.
Up to 60% operational cost reduction: Right-size virtual compute resources, eliminate runaway queries, and avoid hiring large internal platform engineering teams.
Pristine data quality: Intercept missing records and schema errors before corrupt data impacts executive dashboards or predictive machine learning models.
Zero developer burnout: Offload 24/7 alert triage and incident remediation so internal engineers focus entirely on high-value analytics.

Generative AI models and predictive analytics fail when trained on brittle pipelines or corrupted data sources. Pythian’s DataOps Managed Services executes automated schema validation, freshness checks, and continuous log analysis across Snowflake, BigQuery, and Databricks ecosystems. By resolving data quality issues before data reaches your storage engines, your internal data science teams deploy production-ready AI models with complete data trust.

DataOps services continuously monitor pipeline execution speed, warehouse resource consumption, and query performance. By auditing cloud consumption patterns across Snowflake, BigQuery, or Databricks, DataOps engineers prune idle compute nodes, optimize table partitioning, and restructure slow SQL queries. This proactive tuning keeps cloud spend predictable and prevents budget bloat as data volumes scale.

While DevOps Managed Services automate code deployment and application infrastructure, DataOps focuses specifically on the continuous flow, freshness, and quality of data moving through pipelines. DataOps manages data schema drift, volume anomalies, and pipeline state across platforms like Airflow, Snowflake, and Databricks. Pythian integrates DataOps frameworks into your existing CI/CD pipelines to guarantee data trust alongside code deployment velocity.