AI-first consulting. Practical implementations, secure architecture, measurable outcomes.

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Data Solutions & Analytics

Turn complex data into trusted business intelligence.

CiberSQL designs modern data platforms that connect fragmented sources, improve quality, enable self-service analytics, and create a dependable foundation for AI.

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Explore Capabilities
Modern data architecture
Governance by design
Analytics people can use

Unified pipelines

Connect ERP, CRM, operational, and cloud data

Actionable analytics

Dashboards, reporting, and predictive insights

AI-ready

data foundation

A stronger data foundation

Make data accurate, accessible, secure, and useful.

Most organizations do not have a data shortage. They have disconnected systems, inconsistent definitions, manual reporting, and too little trust in the numbers. We bring strategy, architecture, integration, quality, governance, and analytics together in one practical program.

Align architecture with business decisions—not technology for technology’s sake.

Build reusable pipelines and governed data products across the enterprise.

Prepare the data layer for analytics, machine learning, and agentic AI.

Discuss your data priorities

From raw information to trusted decisions

Ingest, transform, govern, analyze, and continuously improve.

Comprehensive data services

One partner across the full data lifecycle.

We connect strategy to implementation so your platform, pipelines, governance, and analytics evolve as one system.

ST

Data Strategy

Define the business case, target state, and roadmap for a data program that produces measurable value.
  • Maturity assessment
  • Use-case prioritization
  • Architecture roadmap
  • ROI and operating model

AR

Data Architecture

Design scalable data platforms for reporting, advanced analytics, and AI workloads.
  • Enterprise data models
  • Lakehouse and warehouse design
  • Integration patterns
  • Security and governance

PL

Data Engineering

Build reliable batch and real-time pipelines that connect systems and keep data current.
  • ETL / ELT development
  • API and event integration
  • Streaming pipelines
  • Monitoring and recovery

BI

Business Intelligence

Give leaders and operational teams clear, timely insight through usable analytics experiences.
  • Executive dashboards
  • Operational reporting
  • Self-service analytics
  • Semantic models

BI

Business Intelligence

Give leaders and operational teams clear, timely insight through usable analytics experiences.
  • Executive dashboards
  • Operational reporting
  • Self-service analytics
  • Semantic models

DQ

Data Quality

Create confidence with profiling, validation, ownership, lineage, and automated controls.
  • Quality rules
  • Master data practices
  • Catalog and lineage
  • Stewardship workflows

AI

Analytics & AI Readiness

Create governed feature sets and accessible knowledge that support predictive and generative AI.
  • Model-ready datasets
  • Vector and search patterns
  • Evaluation data
  • Responsible access controls

Architecture patterns

Use the right pattern for the right business need.

There is no single “best” data architecture. We help you select and combine patterns based on data types, latency, governance, team ownership, cost, and the decisions the platform must support.
Data warehouse
Data lake
Lakehouse
Real-time analytics
Real-time analytics
Data mesh
Data fabric

Technology ecosystem

Modern platforms, selected pragmatically.

Our teams work across Microsoft Azure and leading data technologies, with architecture decisions driven by your environment, skills, economics, and business goals.

Azure

Synapse, Fabric, Data Factory, Data Lake, Power BI

Open

Databricks, Spark, Kafka, dbt, Airflow

Cloud

Snowflake, BigQuery, Redshift, managed databases

Governed

Catalog, lineage, quality, access, and observability

Implementation approach

Build confidence in stages.

We make the current state visible, establish priorities, deliver a high-value foundation, and expand based on measurable adoption.

01

Assess

Map systems, data flows, users, pain points, quality risks, and business priorities.

02

Design

Define target architecture, governance, technology choices, and a phased roadmap.

03

Build

Implement ingestion, storage, transformations, quality controls, and analytics products.

04

Enable

Train users, establish ownership, document operations, and launch self-service capabilities.

05

Scale

Improve performance, expand domains, and prepare trusted data for AI use cases.

Technology Stack

Leveraging best-in-class data platforms and tools

Snowflake

Cloud data warehouse

Databricks

Lakehouse platform

Tableau

Visual analytics

Power BI

Microsoft BI platform

Apache Spark

Big data processing

Apache Kafka

Stream processing

Apache Airflow

Workflow orchestration

dbt

Data transformation

Looker

BI & analytics

Azure Synapse

Analytics service

AWS Redshift

Cloud data warehouse

BigQuery

Google cloud analytics

Turn your data estate into a foundation for better decisions and practical AI.

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