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Compare Yalantis vs DQO

Side-by-side comparison of features, pricing, services, ratings and more.

Yalantis Agency logo

Yalantis

Data & Analytics Website

Yalantis is a compliance-first engineering partner specializing in complex digital product development with deep expertise in IoT. The company designs and develops embedded systems, software, hardware, and AI services for businesses launching new compliance-ready products or scaling existing ones.

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DQO Agency logo

DQO

Data & Analytics Website

DQOps is an open-source data quality platform designed for data quality and data engineering teams that makes data quality visible to business sponsors. Founded in 2020, DQO is headquartered in Warsaw, Poland.

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Feature Comparison

Attribute Yalantis DQO
Category Data & Analytics Data & Analytics
Rating - -
Location Warsaw, Poland Warsaw, Poland
Services
  • IoT Development
  • Embedded Engineering
  • Hardware Engineering
  • AI Development
  • Software Development
  • Product Development
  • Cloud & DevOps
  • Data Engineering
  • Staff Augmentation
  • UI/UX Design
  • Big Data Analytics
  • Data Quality Monitoring
  • IoT Development
  • Embedded Engineering
  • Hardware Engineering
  • AI Development
  • Software Development
  • Product Development
  • Cloud & DevOps
  • Data Engineering
  • Staff Augmentation
  • UI/UX Design
  • Big Data Analytics
  • Data Quality Monitoring
Industries
Automotive Fintech Finance Manufacturing Real Estate Healthcare Transportation & Logistics Industrial Agriculture Home Services MedTech Smart Building Defence
FMCG Public Health
Pros
  • 17 years of experience solving complex engineering challenges
  • 500+ engineers with strong domain and technical expertise
  • ISO 13485, ISO 9001, ISO 27001, IEC 62304, TISAX, and AWS certified
  • Partnerships with AWS, ARM, Raspberry Pi, and Google
  • Trusted by clients like RAKwireless, Healthfully, Toyota Tsusho, and Bosch
  • Strong project management and proactive problem-solving praised in reviews
  • Open-source platform with over 150 built-in data quality checks and support for custom checks
  • DevOps-friendly with YAML-based definitions in Git, pipeline execution, and a Python client
  • Deployable locally or as a SaaS platform
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