Big Data, Data Engineering & Analytics
Batch and streaming data pipelines (Kafka, Spark, Airflow), scalable data lakes and warehouses (BigQuery, Snowflake, Redshift), real-time dashboards and self-service analytics. We turn large volumes of data into measurable decisions.
What we deliver
- Design and deployment of batch and streaming pipelines using Apache Kafka and Apache Spark
- Data workflow orchestration with Apache Airflow and Git-versioned DAGs
- Data lake implementation on object storage (S3, GCS) with cataloging via Apache Hive or AWS Glue
- Cloud data warehouse setup and optimization (BigQuery, Snowflake, Redshift) with partitioning and clustering strategies
- Dimensional modeling and semantic layer (dbt, LookML) for self-service analytics
- Real-time dashboards with sub-minute latency on Looker, Metabase, or Apache Superset
- Data quality and data lineage enforcement with Great Expectations and OpenLineage
- Data SLA definition: freshness, completeness, and schema drift monitoring
When you need it
E-commerce with sales data scattered across multiple channels
Data sits in separate systems — marketplace, ERP, CRM — with no connection between them. Reports are built manually in Excel and always run days behind. You need a single source of truth for pricing, inventory, and campaign performance.
B2B SaaS company wanting product analytics on user behavior
Your product generates usage events, but no one is using them. The product team has no reliable data on which features drive retention and which get dropped after day one. You need a stable event pipeline and an analytics layer your team can trust.
Manufacturing company with IoT data from production lines
Sensors generate data every second, but it gets stored in local silos with no real-time visibility. You need anomaly detection, predictive maintenance signals, and plant efficiency KPIs accessible to management without manual extraction.
Scaling company losing control of cloud infrastructure costs
Your data warehouse grew without governance. Expensive queries run unchecked and your BigQuery or Snowflake bills are unpredictable month to month. You need query optimization, partitioning, and proper access policies in place.
Frequently asked questions
How long does it take to get a first pipeline running in production?
A straightforward batch pipeline — one source, one destination, linear transformations — can go to production in 2-3 weeks. Streaming architectures with multiple Kafka sources and complex join logic realistically take 6-10 weeks. The biggest variable is the accessibility and quality of your existing data sources.
BigQuery, Snowflake, or Redshift — how do you choose?
If you're already on Google Cloud, BigQuery is the natural fit for operational simplicity. Snowflake works better when you have multi-cloud teams or need cross-organization data sharing. Redshift makes sense if you're deeply integrated in the AWS ecosystem. We always run a workload and cost estimate before recommending a platform.
Our data is sensitive — where is it processed and who can access it?
Everything is processed in the cloud region you select (e.g., eu-west for GDPR alignment). We apply row- and column-level access controls where needed, with audit logging enabled throughout. No data leaves your environment without your explicit authorization.
We already have a data warehouse — do we have to start over?
Almost never. We start with an audit: data quality, current schema, most-used queries, and running costs. Starting from scratch is only justified when the existing architecture has structural issues that make refactoring more expensive than migration. We'll tell you clearly after the audit which path makes more sense.
How do you measure ROI on a data engineering project?
We define 2-3 measurable KPIs together before the project starts — for example, reducing report generation time, increasing dashboard adoption by management, or eliminating manual data entry errors in billing. We evaluate the project against those numbers, not internal technical metrics.
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