We build data lakes, lakehouses and streaming pipelines on Amazon Web Services, so analytics, dashboards and machine learning all run on the same clean, governed data. And you pay only for the compute each job uses.
AWS Advanced Tier Services PartnerAWS-certified data engineers
We see the same three problems in almost every data estate.
Operational databases, SaaS tools and files each hold part of the picture, so reports get stitched together by hand every month.
Transformations run inside an always-on warehouse, so compute spend rises with every new query and dashboard.
Batch jobs land yesterday's data, and nobody can say who has access to what, which is a problem for GDPR and audits.
An open-format lakehouse on Amazon S3 with Apache Iceberg tables, a shared AWS Glue Data Catalog and fine-grained access through AWS Lake Formation.
Change data capture and event streams landed in minutes rather than overnight, on managed, pay-per-use streaming services.
Orchestrated Spark and SQL pipelines, with each engine matched to its job: Amazon Redshift for serving, Amazon EMR for heavy transforms, Amazon Athena for ad-hoc queries.
Embedded dashboards on a modeled semantic layer, plus ML and GenAI running on the same governed data.
Durable, low-cost storage in open formats such as Parquet and Apache Iceberg, so any engine can read the data and there's no lock-in.
One catalog for every engine and fine-grained, auditable permissions down to the column. This is how we meet GDPR and access-control requirements.
Serverless streaming ingestion that scales with volume and lands data in Amazon S3 within minutes, with no brokers to run.
Managed Apache Kafka for teams already built around Kafka topics and connectors.
Spark for heavy transformations on transient clusters, orchestrated with managed Apache Airflow, so compute stops when the job finishes.
Change data capture from operational databases into the lake without adding load to production.
A fast, concurrent warehouse for curated serving layers and high-traffic dashboards.
Serverless SQL directly on the lake, billed per query, for ad-hoc analysis and lightweight reporting.
Serverless BI with embedded dashboards and per-user pricing, so analytics can go inside your own product.
ML training and deployment on the same governed tables the dashboards use.
Every platform follows the same layered pattern on AWS: ingest, store and govern, process, then serve. You get a design that's proven and easy to hand over, sized to your data volume and budget.
An Amazon Redshift billing data mart fed by Amazon EMR pipelines orchestrated with Amazon MWAA replaced manual monthly invoicing across 300+ lenders.
Moved ~900 transformation queries out of Amazon Redshift into a Spark-on-EMR lakehouse on Apache Iceberg, with zero downtime.
AWS DMS, AWS Glue and AWS Lake Formation unified Postgres, MongoDB and SaaS data into a GDPR-compliant lake with embedded Amazon QuickSight dashboards.
Amazon Data Firehose, AWS Glue and Amazon Athena ingest 3.3 billion records a month, queryable within five minutes.
We'll review your current pipelines and warehouse and show you a target architecture on AWS, with the cost to run it.
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