Data Engineering

Turn Raw Data into Business Intelligence

We build data pipelines, warehouses, and analytics platforms that transform scattered data into reliable, actionable insights.

Oronts provides data engineering services from Munich, Germany. We design and build production-grade data platforms including ETL and ELT pipelines, cloud data warehouses, real-time streaming architectures, and analytics dashboards. Our orchestration stack includes Apache Airflow, Dagster, Prefect, and dbt for transformation workflows. For processing, we use Apache Spark for large-scale batch jobs, Apache Flink for stream processing, and Pandas and Polars for lightweight transformations. Real-time streaming architectures are built with Apache Kafka, Amazon Kinesis, Google Pub/Sub, and Redis Streams. We deploy data warehouses on Snowflake, Google BigQuery, Amazon Redshift, and Delta Lake, choosing the right platform based on query patterns, data volume, and budget. Our architecture expertise covers Medallion (Bronze-Silver-Gold) lakehouse patterns, Lambda and Kappa architectures for mixed latency needs, and Data Mesh for decentralized data ownership in large organizations. Data quality is ensured through automated validation, schema testing, freshness monitoring, anomaly detection, and data contracts between producers and consumers. We implement GDPR-compliant data processing with PII detection, data masking, retention policies, and audit trails. Analytics and BI delivery uses Metabase, Looker, or custom-built visualization tools tailored to business users.

Raw Data
Extract
Transform
Load
Insights
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Data Services

End-to-end data engineering, from ingestion to visualization.

1

ETL Pipelines

Scalable extract-transform-load workflows with Apache Airflow, Spark, and dbt for reliable data movement.

2

Data Warehouses

Cloud-native warehouse design on Snowflake, BigQuery, and Redshift with medallion architecture.

3

Real-time Streaming

Event-driven data streaming with Kafka, Flink, and Kinesis for sub-second analytics.

4

Data Lakes

Centralized raw-data repositories on S3/GCS with Iceberg and Delta Lake for cost-efficient storage.

5

Analytics & Reports

Self-service BI dashboards and automated reporting pipelines that turn data into decisions.

6

Data Quality

Automated quality checks, lineage tracking, and governance frameworks for trusted data.

What Technologies Do We Use?

Modern data tools chosen for reliability, scalability, and community support.

Apache Spark
Airflow
dbt
Kafka
Snowflake
BigQuery
Databricks
Flink
Redshift
Pub/Sub
Apache Spark
Airflow
dbt
Kafka
Snowflake
BigQuery
Databricks
Flink
Redshift
Pub/Sub
Delta Lake
Iceberg
Fivetran
Kinesis
Presto
Trino
Great Expectations
Monte Carlo
Looker
Metabase
Delta Lake
Iceberg
Fivetran
Kinesis
Presto
Trino
Great Expectations
Monte Carlo
Looker
Metabase

What Architecture Patterns Do We Use?

We choose the right data architecture based on your latency needs and data volume.

Lambda

Batch + Real-time

Source
Batch
Serve
Source
Stream
Serve

Both paths merge into the serving layer

Kappa

Stream-only

Source
Stream
Serve

Single streaming path. Simpler, lower latency

Medallion

Bronze / Silver / Gold

Bronze
Raw
Silver
Cleaned
Gold
Curated

Progressive data refinement layers

5+ PB

Data Processed

*cumulative across client projects

99.99%

Pipeline Uptime

*production monitoring data

<100ms

Query Latency

*median query performance across deployments

1,000+

Tables Managed

*cumulative across active client platforms

Real-time vs Batch

Choosing the right processing approach for each workload.

Real-time

Sub-second processing for time-critical workloads.

  • Fraud detection alerts
  • Live dashboards & monitoring
  • IoT sensor processing
KafkaFlinkKinesis

Batch

High-throughput processing for large-volume workloads.

  • Daily reports & aggregations
  • ML model training pipelines
  • Historical data analysis
SparkAirflowdbt

Our Open Source Plugins & Bundles

We develop and maintain open-source Vendure plugins and Pimcore bundles. Production-tested in real client projects.

Vendure Data Hub Plugin

Enterprise ETL & data integration plugin for Vendure. Visual pipeline builder, 9 extractors, 61 transform operators, 24 entity loaders, feed generators for Google Merchant & Amazon, and real-time monitoring.

VendureETLTypeScriptE-Commerce
View on GitHub

Pimcore Asset Pilot Bundle

Intelligent rule-based asset organization for Pimcore 12. Priority-based rule engine with Twig path templates, expression language conditions, async processing via Symfony Messenger, localized folder structures, audit logging, and unused asset detection.

PimcoreDAMPHPSymfony
View on GitHub

More plugins coming soon. We actively contribute to the commerce open-source ecosystem.

Frequently Asked Questions

ETL transforms data before loading it into the destination, which was the standard approach when storage was expensive and compute was limited. ELT loads raw data into a modern cloud warehouse first, then runs transformations inside the warehouse using tools like dbt, Spark SQL, or native SQL. We typically recommend ELT for cloud-based platforms because it preserves the original data for auditability, lets analysts iterate on transformations without re-ingesting, and takes advantage of the massive compute power in platforms like Snowflake and BigQuery. However, ETL still makes sense when data must be cleansed or redacted before it enters the warehouse, for example when handling PII under GDPR constraints. In practice, many of our projects use a hybrid approach where sensitive fields are masked during extraction while the bulk of transformation happens post-load. We evaluate your data volume, compliance requirements, and team skills before recommending the right pattern.
A basic data pipeline with warehouse setup starts around €25K, covering ingestion from two to three sources, transformation logic, and a single BI dashboard. Full data platforms with real-time streaming, multiple data sources, complex transformation layers, and self-service BI dashboards typically run €50K–€200K depending on scope and complexity. Infrastructure costs for services like Snowflake, BigQuery, or managed Kafka clusters are separate and scale with data volume and query frequency. We help you optimize these costs from the start through techniques like partition pruning, materialized views, and intelligent data tiering between hot and cold storage. Every engagement begins with a scoping workshop where we map your data sources, define transformation requirements, and provide a transparent cost breakdown covering both development and projected infrastructure spend. We also design for incremental build-out, so you can start with a focused MVP and expand the platform as data needs grow without rearchitecting.
Yes, and this is how most of our engagements begin. We audit your current data infrastructure including databases, APIs, file systems, legacy ETL jobs, and existing warehouse schemas to understand what is working and what needs improvement. From there, we design an integration plan that connects to your existing systems without disrupting current operations. For example, we might layer Apache Airflow on top of your existing PostgreSQL database, add dbt transformations to your current Snowflake instance, or build a Kafka streaming layer alongside a legacy batch pipeline. We have integrated with systems ranging from on-premise Oracle and SQL Server databases to cloud services like Salesforce, Shopify, HubSpot, and custom REST and GraphQL APIs. Our approach is incremental modernization rather than rip-and-replace. We keep your existing data flows running while gradually migrating workloads to the new platform, ensuring zero downtime and continuous data availability throughout the transition.
We implement automated data quality checks at every stage of the pipeline, not just at the end. During ingestion, we validate schemas, check for nulls in required fields, and verify row counts against source systems. During transformation, we run assertion tests using dbt tests and Great Expectations to catch logic errors, duplicate records, and referential integrity violations. Post-load, we monitor data freshness so dashboards never display stale information, and we run anomaly detection to flag unexpected spikes or drops in key metrics. We also establish data contracts between producer and consumer teams, defining exactly what schema, format, and freshness guarantees each data source must meet. When a quality check fails, the pipeline halts automatically and alerts the responsible team through Slack or PagerDuty. This layered approach means data issues are caught at the earliest possible stage, preventing bad data from ever reaching your analysts or downstream systems.
Yes, data privacy and compliance are built into our pipeline architecture from day one rather than bolted on afterward. We implement GDPR-compliant data processing with automated PII detection that scans incoming data for personal identifiers like names, emails, phone numbers, and IP addresses. Sensitive fields are handled through data masking, pseudonymization, or encryption depending on your compliance requirements and downstream use cases. We build retention policies directly into the pipeline logic so that data is automatically purged or anonymized after the defined retention period expires. Every data access and transformation is logged in audit trails that document who accessed what data, when, and for what purpose, which is critical for GDPR Article 30 compliance. We also implement role-based access controls at the warehouse level, ensuring analysts only see the data they are authorized to access. Beyond GDPR, we have experience with HIPAA, SOC 2, and industry-specific data governance frameworks.
Most companies need batch processing for the majority of their workloads and real-time streaming for specific, time-sensitive scenarios. Batch processing with tools like Airflow, Spark, and dbt handles daily or hourly aggregations, reporting, ML model training, and historical analysis cost-effectively. Real-time streaming with Kafka, Flink, or Kinesis is essential for use cases like fraud detection alerts, live dashboards, IoT sensor processing, and real-time personalization where latency matters. We typically see an 80/20 split: 80% of workloads run perfectly well on batch schedules while 20% genuinely require sub-second processing. Over-engineering everything as real-time is a common and expensive mistake. Streaming infrastructure costs significantly more to build and operate than batch pipelines. We design hybrid architectures, often using Lambda or Kappa patterns, that route each workload to the appropriate processing path based on actual latency requirements. This approach balances performance with cost and operational simplicity.

Ready to Unlock Your Data?

Let's build a data platform that turns your raw data into competitive advantage.