Ditch fragmented, high-maintenance data pipelines — transition to a unified, SaaS data platform that scales without complexity.
Fragmented data platforms — Data is scattered across separate databases, lakes, and third-party SaaS apps — making it impossible to get a unified operational view.
High maintenance overhead — Teams waste hours managing virtual machines, clusters, and complex database infrastructure instead of analyzing datasets.
Slow & stale analytics reports — Pipelines refresh too slowly; executives make decisions based on yesterday's (or last week's) reports instead of real-time realities.
Poor self-service data access — Business departments must request data files from IT, waiting in line for weeks because there's no governed self-service hub.
Spiralling storage & compute costs — Paying separate vendor fees for storage, ETL, and warehousing causes data budgets to spin out of control.
Lakehouse vs Warehouse confusion — Data architects struggle to define when to use Spark-driven Lakehouses, SQL Warehouses, or Kusto databases in Fabric.
Throttled Fabric capacities — Badly written Spark notebooks or Power BI queries exhaust capacity unit (CU) limits, slowing down reports.
Migration risks from Synapse — Lifting custom SQL pipelines or Azure Databricks workloads onto Fabric without breaking production reports is complex.
Fragile Direct Lake configurations — Direct Lake mode in Power BI drops back to import mode unexpectedly, causing slow performance and failures.
OneLake governance gaps — Allowing developers workspace access leads to messy data structures, duplicate datasets, and loose security.
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Unified analytics platform implementations delivered to global clients.
In the highly competitive world of Esports, especially in fast-paced games like Valorant, massive volumes of data are generated from each match, round, and individual player performance. This data includes everything from round outcomes and player stats to map usage and agent selection. However, analyzing this data holistically posed several key challenges:
Urban mobility is at the core of smart city planning. For companies, the need to understand trip behavior, station utilization, and rider demographics is crucial. However, the absence of an automated, scalable analytics solution hindered their ability to make timely, data-driven decisions. INKEY IT Solutions delivered a full-stack data engineering solution on Microsoft Fabric, transforming unstructured bike-sharing data into rich, actionable insights via a layered Medallion Architecture and Power BI dashboards. The implementation simplifies planning, optimizes operations, and empowers stakeholders across operations, planning, and marketing.
In the intricate landscape of financial technology, the security of applications is paramount, given the critical and sensitive nature of the data they manage. To address these challenges effectively, a strategic amalgamation of Microsoft Fabric (MSFabric) and Machine Learning (ML) stands as a pioneering solution, providing a robust framework for fortifying the security posture of financial applications.
We are certified Microsoft Fabric partners, helping organizations architect scalable, governed, and optimized analytics estates.
Our Microsoft Fabric practice helps enterprises transition from complex data structures to a unified SaaS lakehouse. We specialize in OneLake architecture, Synapse data engineering pipelines, Real-Time intelligence, and Power BI Direct Lake reports.
From initial tenant configuration to deploying governed lakehouses, we build analytics engines that perform.
We guide you from initial feasibility workshops to tenant-wide Fabric migration and CU optimization.
Configuring workspace topologies, domain delegation, capacity security rules, and performance settings.
Designing star schemas, table partitions, Delta format tables, and semantic models in OneLake.
Building Spark notebooks, copy data operations, and data pipelines to ingest SaaS data.
Ingesting, cleaning, and querying streaming data from IoT devices, logs, and events.
Building semantic models that bypass refresh schedules and import sizes — querying OneLake directly.
Re-platforming pipelines, data lakes, and SQL views from Synapse onto modern Fabric capacities.