Overcoming Microsoft Fabric Challenges to Unlock Snowflake Power
An approach to overcome Microsoft Fabric limitations by leveraging Snowflake for scalable, high-performance data and AI workloads.
From legacy ETL modernization to real-time streaming to Snowflake-native Dynamic Tables, we build data pipelines that move fast, scale automatically, and don't break. Complete implementation including pipeline development, orchestration setup, and production monitoring. Matillion, dbt, Snowpark, Snowpipe Streaming, and Kafka integration with automated observability. Production-ready from day one.
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Modern data pipelines from legacy ETL migration to real-time streaming—production-ready from day one
Modernize legacy ETL pipelines to Snowflake-native solutions that run faster, cost less, and are easier to maintain. Automated conversion from Informatica, SSIS, and Talend reduces rebuild effort by 45-55%.
Stream data into Snowflake with sub-second latency for real-time dashboards and operational analytics. Support both streaming and batch workloads based on business requirements, with no architectural compromises.
Production-ready transformation pipelines using the right tool for each job—visual ETL, SQL-based analytics engineering, or native incremental processing. Pre-built templates reduce setup time by 35-45%.
Connect Snowflake to any SaaS platform with built-in error handling, rate limit management, and automated retry logic. Maintain data freshness without hitting API limits or losing failed requests.
Eliminate complex orchestration with tables that refresh automatically as source data changes. Cache expensive queries for instant performance without manual pipeline maintenance.
Proactive monitoring catches pipeline failures before business users notice. Automated alerts route issues to the right team with diagnostic context for faster resolution.
45-55%
Faster Conversion
Legacy ETL migration to Snowflake-native solutions
60%
Setup Time Savings
DevOps and orchestration starter kits
75%
Faster Queries
Unified Snowflake platform vs. legacy systems
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Cloud-to-Cloud Migration
75% faster queries | Real-time data confidence | 8-week migration timeline
A Snowflake-powered solution enabling real-time reporting and collaboration for corporate real estate teams
Faster reporting | Improved data accuracy | Enhanced cross-team collaboration
A Snowflake-based data foundation that centralizes and modernizes data to enable faster insights and improved customer experience
Faster insights, improved customer experience, and enhanced data accessibility across teams
Discuss your specific pipeline challenges with an engineer who has built production-grade Snowflake pipelines dozens of times.
It depends on your use case. Matillion excels at visual ETL and SaaS integrations. dbt is best for analytics engineering teams writing SQL-based transformations with version control. Snowflake Dynamic Tables eliminate orchestration for incremental processing. We typically recommend dbt for core transformations, Matillion for complex integrations, and Dynamic Tables for real-time incremental workloads—often using all three in combination.
Timeline depends on pipeline complexity and volume. Our Legacy Pipeline Modernization Kit automates 45-55% of conversion work—a 200-pipeline Informatica migration that would take 12-15 months manually completes in 6-8 months with our accelerators. Simple pipelines convert in days; complex pipelines with heavy business logic take 2-4 weeks each.
We specialize in real-time architectures. Snowpipe Streaming for sub-second latency, Kafka integration for event-driven workflows, CDC pipelines that keep Snowflake in sync with operational systems, and Dynamic Tables that auto-refresh as source data changes. Real-time doesn't mean rebuilding everything—we identify which workloads benefit from streaming vs. batch and architect accordingly.
Our Orchestration & DevOps Starter Kit includes automated alerting, data freshness tracking, pipeline health dashboards, and SLA monitoring. When failures occur, alerts route to the right team with enough context to diagnose quickly. We also build idempotent pipelines with retry logic—transient failures self-heal without human intervention.
dbt is designed for analytics engineers and data analysts who know SQL—you don't need software engineers. Our dbt Accelerator Framework includes 150+ pre-built macros, testing standards, and CI/CD templates that make dbt production-ready from day one. Most teams maintain their own pipelines after a 2-4 week knowledge transfer period.
We build error handling and retry logic into every API integration—respecting rate limits, handling pagination, managing authentication tokens, and logging failed requests for replay. For high-volume SaaS integrations (Salesforce, Workday), we use incremental extraction patterns that minimize API calls while keeping data fresh.
