Data Warehouse Solutions for Business Intelligence

Consolidate operational data with ETL pipelines, dimensional modeling, and query optimization for unified business intelligence.

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Trusted and top rated tech team

Centralized data infrastructure for enterprise analytics

Enterprise analytics require more than collecting information in one place. Warehouses provide ETL integration, dimensional schemas, and query optimization that turn disparate operational systems into reliable reporting infrastructure. Our teams work with CTOs managing fragmented sources where inconsistent formats and siloed databases prevent accurate business intelligence and slow analytical queries.

Our capabilities include:

ETL pipeline design and implementation
Dimensional modeling with star schemas
Cloud and on-premise warehouse architecture
Data quality and governance frameworks
Query performance optimization
Legacy system integration and migration

Who we support

We work with organizations where operational data lives across multiple systems and manual reporting processes prevent timely business intelligence and strategic decision-making.

SaaS Companies With Fragmented Data

Your product generates records across application databases, payment systems, and customer tools. Manual SQL queries and spreadsheet exports slow reporting, and executives lack real-time visibility into business metrics.

Enterprises With Legacy Data Systems

You operate dozens of operational databases accumulated through acquisitions and organic growth. Each system uses different formats, and consolidating information for reporting requires custom scripts that break frequently.

Financial Services Firms

You need historical transactions for compliance reporting and risk analysis. Current databases prioritize transactional speed over analytical queries, and running reports impacts production performance.

Ways to engage

We offer a wide range of engagement models to meet our clients’ needs. From hourly consultation to fully managed solutions, our engagement models are designed to be flexible and customizable.

Staff Augmentation

Get access to on-demand product and engineering team talent that gives your company the flexibility to scale up and down as business needs ebb and flow.

Retainer Services

Retainers are perfect for companies that have a fully built product in maintenance mode. We'll give you peace of mind by keeping your software running, secure, and up to date.

Project Engagement

Project-based contracts that can range from small-scale audit and strategy sessions to more intricate replatforming or build from scratch initiatives.

We'll spec out a custom engagement model for you

Invested in creating success and defining new standards

At Curotec, we do more than deliver cutting-edge solutions — we build lasting partnerships. It’s the trust and collaboration we foster with our clients that make CEOs, CTOs, and CMOs consistently choose Curotec as their go-to partner.

Helping a Series B SaaS company refine and scale their product efficiently

Why choose Curotec for data warehousing?

Warehouse success depends on understanding your operational systems before designing schemas. Our engineers map existing structures, identify quality issues, and build ETL pipelines that maintain accuracy during transformation. You get analytics infrastructure that consolidates fragmented sources while preserving the context needed for meaningful business intelligence.

1 Extraordinary people, exceptional outcomes

Our outstanding team represents our greatest asset. With business acumen, we translate objectives into solutions. Intellectual agility drives efficient software development problem-solving. Superior communication ensures seamless teamwork integration.

2 Deep technical expertise

We don’t claim to be experts in every framework and language. Instead, we focus on the tech ecosystems in which we excel, selecting engagements that align with our competencies for optimal results. Moreover, we offer pre-developed components and scaffolding to save you time and money.

3 Balancing innovation with practicality

We stay ahead of industry trends and innovations, avoiding the hype of every new technology fad. Focusing on innovations with real commercial potential, we guide you through the ever-changing tech landscape, helping you embrace proven technologies and cutting-edge advancements.

4 Flexibility in our approach

We offer a range of flexible working arrangements to meet your specific needs. Whether you prefer our end-to-end project delivery, embedding our experts within your teams, or consulting and retainer options, we have a solution designed to suit you.

Data warehouse implementation capabilities

ETL Pipeline Architecture

Design ETL processes to consolidate operational information, manage schema changes, and ensure quality during migration.

Dimensional Modeling & Schema Design

Structure star and snowflake schemas with fact tables and dimension hierarchies that optimize query performance for analytical workloads.

Cloud Data Warehouse Migration

Migrate on-premise warehouses to Snowflake, Redshift, or BigQuery with data validation, performance testing, and minimal disruption to reporting.

Data Quality & Governance

Implement validation rules, lineage tracking, and governance frameworks that ensure accuracy and compliance across consolidated sources.

Query Performance Optimization

Tune indexing strategies, partitioning schemes, and materialized views that reduce query execution times for complex analytical reports.

Real-Time Data Integration

Configure streaming ETL pipelines and change data capture that update warehouses continuously for near real-time business intelligence dashboards.

Our data warehouse technology stack

Cloud Data Warehouse Platforms

Our engineers implement cloud-native warehouses that provide scalable storage, compute separation, and automated infrastructure.

Snowflake — Cloud platform with separated storage and compute, automatic scaling, and multi-cloud support for flexible analytics workloads
Amazon Redshift — AWS-native columnar database with massive parallel processing, S3 integration, and Spectrum for querying lakes directly
Google BigQuery — Serverless analytics engine with automatic infrastructure management, real-time analysis, and ML integration for predictive modeling
Azure Synapse Analytics — Microsoft’s unified platform combining integration, enterprise warehousing, and big data analytics in single environment
Databricks — Lakehouse architecture merging lakes and warehouses with Delta Lake for ACID transactions and unified batch-streaming processing
Teradata Vantage — Enterprise-grade platform with advanced analytics, multi-cloud deployment, and workload management for complex analytical requirements

ETL & Data Integration Tools

Extract, transform, and load platforms automate data movement from operational systems while maintaining quality and transformation logic.

Apache Airflow — Workflow orchestration platform for scheduling ETL pipelines with dependency management, retry logic, and visual DAG monitoring
Fivetran — Automated connector service with pre-built integrations for SaaS applications, databases, and event streams requiring minimal configuration
Talend — Open-source integration suite with visual design tools, quality modules, and transformation libraries for complex ETL workflows
AWS Glue — Serverless ETL service with automatic schema discovery, job scheduling, and integration with S3, Redshift, and other AWS services
Informatica PowerCenter — Enterprise integration platform with metadata management, profiling capabilities, and transformation tools for large-scale operations
dbt (data build tool) — SQL-based transformation framework enabling version control, testing, and documentation for analytics engineering workflows

Database & Query Engines

Curotec configures relational databases and columnar storage engines optimized for analytical queries and large-scale processing.

PostgreSQL — Open-source relational database with advanced indexing, partitioning, and window functions for analytical query processing
Apache Hive — SQL query engine for Hadoop with support for large-scale batch processing and integration with distributed file systems
Presto & Trino — Distributed SQL engines for querying multiple data sources simultaneously with low-latency interactive analytics capabilities
ClickHouse — Columnar database optimized for real-time analytical queries with compression algorithms and parallel processing for high throughput
Oracle Exadata — Enterprise database platform combining hardware and software optimization for mission-critical OLAP workloads and high availability
Apache Druid — Real-time analytics database designed for sub-second queries on event streams with time-series optimization and rollup capabilities

Business Intelligence & Reporting

BI platforms connect to data warehouses for interactive dashboards, scheduled reports, and self-service analytics across business teams.

Tableau — Visual analytics platform with drag-and-drop interface, interactive dashboards, and live connections to warehouse data sources
Power BI — Microsoft’s business intelligence suite with data modeling, report publishing, and integration across Office 365 ecosystem
Looker — Modeling-based BI tool with LookML for defining business logic, embedded analytics, and centralized metric definitions
Qlik Sense — Associative analytics engine with self-service visualization, AI-powered insights, and mobile app support for distributed teams
Metabase — Open-source analytics platform with SQL editor, visual query builder, and scheduled reporting for teams without dedicated analysts
Apache Superset — Modern data exploration tool with rich visualizations, dashboard creation, and SQL Lab for ad-hoc analysis

Data Modeling & Orchestration

Our developers use modeling and orchestration tools to define schemas, manage dependencies, and automate pipeline execution.

dbt (data build tool) — Transformation framework with version control, automated testing, and documentation generation for maintaining analytics code quality
Apache Airflow — Workflow scheduler with directed acyclic graphs for managing pipeline dependencies, retries, and execution monitoring across complex workflows
Prefect — Modern orchestration engine with dynamic workflows, parameterized runs, and failure handling for resilient pipeline management
Dagster — Asset-oriented orchestrator with type checking, dependency graphs, and observability features for production pipeline operations
Erwin Data Modeler — Enterprise modeling tool for designing dimensional schemas, generating DDL scripts, and maintaining architecture documentation
SQLDBMs — Cloud-based database design platform with collaborative modeling, version control, and automatic schema generation for warehouse structures

Monitoring & Data Quality

Monitoring systems and validation frameworks track pipeline performance, detect anomalies, and ensure warehouse accuracy.

DataDog — Infrastructure monitoring platform with pipeline observability, performance metrics, and alerting for ETL jobs and warehouse operations
Monte Carlo — Observability tool with anomaly detection, lineage tracking, and incident management for identifying warehouse quality issues
Great Expectations — Python framework for defining quality tests, profiling datasets, and validating pipeline outputs with automated documentation
Apache Griffin — Open-source quality platform measuring accuracy, completeness, and consistency across distributed warehouse environments
Soda Core — SQL-based testing framework with profiling, freshness checks, and schema validation for continuous quality monitoring
dbt Tests — Built-in testing capabilities for verifying transformations, enforcing uniqueness constraints, and validating business logic in pipelines

FAQs about our data warehouse solutions

How long does implementing a data warehouse take?

Implementation timelines depend on source complexity and volume, typically 3-6 months for mid-sized organizations. We start with high-priority sources and expand incrementally, delivering value while building comprehensive integration.

Should we build on-premise or use cloud platforms?

Cloud platforms like Snowflake and Redshift eliminate infrastructure management and scale automatically. We recommend cloud for most organizations unless data sovereignty requirements or existing infrastructure investments dictate on-premise deployment.

Ready to have a conversation?

We’re here to discuss how we can partner, sharing our knowledge and experience for your product development needs. Get started driving your business forward.