Data Platforms and Data Architecture consulting for businesses
At Galde, we act as a data consultancy specializing in the design, deployment and optimization of complex infrastructures. We help your organization configure a customized cloud data platform, unifying your information flows under a high-performance data architecture, ready for advanced analytics and the use of enterprise artificial intelligence models.
What problems do we solve on your data platform?
The value of a modern data platform lies not in the technology you buy, but in how it's configured. Our data platform engineering team intervenes to solve the four major pain points of data-driven organizations:
- We curb cloud waste: We optimize computing processes to drastically reduce monthly bills on AWS, Azure or GCP.
- We eliminate vendor lock-in: We design open architectures. If you decide to switch providers or discontinue our services in the future, your internal team will have complete control over the code and data.
- We break down information silos: We unify databases from different departments under a single, accessible data architecture (Lakehouse architecture).
- We guarantee production-ready data: We prepare the underlying infrastructure to successfully answer the big technical question: how to deploy AI models to production without breaking existing systems.
Data Platform Engineering with the methodology MVP60™
Your infrastructure in production in 60 days
Unlike traditional technology consulting models, at Galde we eliminate the endless development phases that delay obtaining immediate value.
From infrastructure complexity to production in 60 days
Using our method, we build an MVP (Minimum Viable Product) of a module for your enterprise data platform in just 8 weeks. We focus on solving a specific technical problem for your business, deploy it to production, and provide a complete technology transfer. Your team learns to control it from day one, ensuring your technical independence.
Specialists in the leading Data Platforms on the market
We configure, integrate and optimize industry-leading environments, adapting their capabilities to your organization's specific infrastructure. This technical expertise allows us to guide your team through the design and internal linking phases toward future dedicated implementations:
- Palantir Foundry Consulting and Implementation: Specialization in structuring the operational ontology and deploying native AI and analytics solutions with Palantir AIP.
- Databricks Consulting and Optimization: Design of efficient architectures using Delta Lake and metadata management with Unity Catalog.
- Snowflake Consulting and Migration: Configuration of virtual data warehouses with high concurrency capacity and optimized compute costs.
- Cloud Data Platforms (AWS, Azure and GCP): Engineering and orchestration of native data services in secure and scalable multi-cloud environments.
Benefits of an integrated Enterprise Data Platform
The real impact on your business: What value does it add?
- Elimination of information silos: Full connectivity between databases, transactional systems and data lakes.
- Native governance: Ability to automatically monitor data lineage and quality through a robust data governance platform.
- Production readiness: Optimized infrastructure to mitigate technical bottlenecks, addressing the challenge of securely deploying AI models to production.
- Optimized performance: Dramatic reduction in the execution times of business-critical queries.
Success stories and technical validation
The experience we've gained in demanding environments allows us to design solutions that address high-volume problems. We collaborate closely with technical teams on Databricks platforms with millions of records (such as InfoJobs / Adevinta) or Palantir Foundry platforms with comprehensive data products based on them (such as Sonnedix), optimizing technical documentation and defining architectures that dramatically accelerate the time developers need to locate and process useful information. This eliminates the common reasons why data projects fail due to a lack of structure or accessibility.
FAQs
What differentiates Galde from a traditional Data Platform provider?
A traditional vendor typically resells licenses or implements generic methodologies that create dependencies. At Galde, we act as engineers under a co-creation model, taking on the role of Forward Deployed Engineers (FDEs); we design the architecture on your stack, giving you complete control over the developed code.
How does Galde optimize costs in a Data Platform?
We analyze and audit the consumption patterns of your cloud infrastructure to redesign inefficient pipelines, apply appropriate compression techniques, and automate the shutdown or rescaling of computing clusters when they are not in use, defining and implementing autoscaling strategies.
Is it necessary to migrate our entire infrastructure to adopt a Modern Data Platform?
No. We design progressive modernization strategies. Our engineering allows us to natively connect legacy systems and gradually migrate computing to modern architectures without disrupting daily business operations.
Do your Data Platform implementations include Data Governance functionalities?
Yes. We consider governance not an add-on, but a fundamental part of the infrastructure. Every platform we design includes traceability mechanisms, automated data dictionaries and access policies by design, which, in the case of the major market providers (Palantir, Databricks, Snowflake), are offered in their own way and are configurable and/or integrable with third parties.
Discover the rest of our services for businesses
Discover our articles on Data Platforms and Infrastructure
We keep the technical community up-to-date with our insights on the evolution of data. Explore our specialized publications:
FinOps in Databricks and Snowflake: Keys to Curbing Compute Overspend
Where the money goes in Databricks and Snowflake, and how to curb it with three concrete levers: automatic shutdown, consumption limits and cost tagging by team.
How to Stop Software Changes from Breaking Your Data Pipelines
Why an application change, such as renaming a column, breaks your pipelines and dashboards, and how to prevent it with data contracts, compatible changes and…
Data Pipeline Governance Architecture in Multi-Cloud Environments
Data Pipeline Governance Architecture: Automating Metadata in Multi-Cloud Environments How to stop governance from depending on someone filling in a field by hand…