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

Eliminate pipeline downtime.

Automate pipeline monitoring and alert triage to catch schema drift and missing data before BI dashboards crash. Free your internal engineers from daily break-fix tasks to accelerate production AI.

Maintain pipeline health ->

Optimize your cloud spend.

Identify runaway queries, unoptimized storage, and inefficient pipeline execution. Reduce operational costs by up to 60% compared to maintaining in-house platform engineering teams.

Slash costs ->

Accelerate time to market.

Construct scalable ETL/ELT pipelines, implement Infrastructure as Code, and automate environment provisioning across your data estate. Launch strategic analytics and enterprise AI solutions 80%+ faster.

Improve pipelines ->

Uncover hidden data failure risks and establish clear operational SLAs.

Catalog all active data sources, pipeline dependencies, data quality rules, and system pain points. Assess tooling and team workflows against best practices. Uncover hidden failure risks and establish target SLA baselines before quality issues impact executive reporting.

Identify pipeline risks →

Gain full operational visibility without paying expensive software licensing fees.

Deploy an open-standard Zabbix and Grafana observability stack, or integrate directly with your existing Datadog or Monte Carlo environment. Retain full control over all performance metrics, alerting rules, and customized dashboards with zero vendor lock-in.

Cut licensing fees →

Eliminate escalation friction and establish direct communication channels for seamless handover.

Configure role-based access control (RBAC), establish secure connectivity, and connect direct operational communication channels. Implement rule-based and automated DQ checks, alerts, and dashboards. This guarantees seamless handover to Pythian's command center with zero disruption to active business workflows.

Minimize downtime →

Minimize Mean Time to Resolution (MTTR) with automated, repeatable incident runbooks.

Document clear standard operating procedures (SOPs) for rapid alert triage and multi-tier escalation pathways. Establish standardized remediation workflows to resolve pipeline failures instantly and protect downstream data quality.

Build response runbooks →

Reclaim internal engineering bandwidth and stop daily alert fatigue with 24/7 monitoring.

Execute continuous monitoring, rapid alert triage, log analysis, and root cause analysis. Receive transparent Monthly Operating Reports and performance dashboards to maintain pristine data quality and pipeline availability. Lean on DataOps Experts for continuous support while freeing internal teams to focus 100% on strategic AI initiatives.

Secure data freshness →

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.

Pythian builds DataOps observability using open-standard frameworks like Zabbix, Prometheus, and Grafana, or integrates directly into your existing tools like Datadog, Monte Carlo, or Splunk. You own all dashboard code, monitoring rules, and telemetry data. If your engagement with Pythian ever changes, you retain 100% of the intelligence and infrastructure without paying proprietary software licensing fees or black-box platform lock-in penalties.

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.

Inefficient SQL queries, repetitive pipeline failures, and unindexed warehouse tables drive unpredictable cloud spend spikes. Through routine performance checks and compute tuning, Pythian identifies runaway queries and eliminates warehouse compute waste. This continuous optimization reduces platform operational expenses by up to 60% compared to recruiting and maintaining full-time in-house infrastructure teams.

Pythian executes a structured four-week onboarding process designed to establish guardrails with zero disruption to active business operations. Week 1 catalogs pipeline dependencies and data quality rules; Week 2 deploys open-standard observability dashboards; Week 3 configures role-based access control (RBAC) and communication channels; and Week 4 finalizes standard operating procedures (SOPs) and incident runbooks for 24/7 handover.

DataOps Managed Services

Stop firefighting broken data pipelines and deliver continuous data trust to accelerate AI and analytics.

3

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Global

Customer network, across all industries

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Net promoter score year-over-year (YoY)