Your data, ready to be used
We build Data Lakes, ETL pipelines and analytics infrastructure your business can actually consume.
Having data scattered across systems is not the same as having useful data. We build data platforms on AWS — Data Lakes, automated ETL, data warehousing, BI layer — enabling your organization to make decisions with reliable, timely and traceable information.
What you get with Caleidos
Scalable Data Lake
S3 + Glue + Athena architecture that grows incrementally, preserving existing code. Productive fintech cases operating with multi-source data (see case studies).
ETL automation
Pipelines orchestrated with AWS Glue + Step Functions + Lambda. Integration of internal sources (ERP, CRM, transactions) and external (APIs, files).
Quality and traceability
Data lineage, automatic validations, quality alerts. You know where each metric you report comes from.
AI-ready
Structure prepared to feed ML models, RAG agents and GenAI. Your data becomes actionable asset, not dormant archive.
Amazon QuickSuite + Quick Flows
Intelligent BI with Direct Query connection to BigQuery, Snowflake or Redshift, QuickSight dashboards with SPICE for performance, and Quick Flows for automated alerts without human intervention (e.g. detect cards expiring within 7 days, fraud spikes or KPI deviations).
Regulatory Data Lake House
For regulators and public entities: ingestion of reports (PDF, Word, Excel) from concessionaires, cataloging with AWS Glue Data Catalog, processing with Step Functions, layered storage (raw S3 + analytics + Glacier) and dashboards for compliance, financing, investments, RAB and penalties.
KasNet
Productive multi-source Data Lake
Data Lake implementation on AWS S3 + Glue + Athena + Redshift. Automation of internal and external source integration, processing time optimization, data quality and traceability.
Read full case →Tech stack
What we get asked the most
Data Lake or Data Warehouse first?
Depends. Data Lake (S3 + Glue + Athena) if you have varied data and want flexibility. Data Warehouse (Redshift) if you need fast SQL queries on structured data with concurrency. Generally: both. Lake as raw layer + warehouse as serving layer.
How much does operating a Data Lake on AWS cost?
Cost depends on data volume, processing frequency and query patterns. We model it with you in the assessment so you have a predictable TCO aligned to your real volume. Let's have a conversation to put together a tailored proposal.
Do you do Business Intelligence too?
We implement data infrastructure and connect to BI tools you prefer: QuickSight, Power BI, Tableau, Metabase. Semantic modeling and executive dashboards are done with your analytics team or dedicated BI partner.
Ready to get started?
Tell us about your challenge. No pitch, no commitment. Just understanding.
Diagnostic of your data platform