# Dapter

> Dapter is a specialist data engineering company headquartered in Cluj-Napoca, Romania, that designs, builds, and operates high-throughput data infrastructure for organisations that need to move, transform, and serve data at scale. Engineers work embedded inside client teams — attending standups, owning OKRs, and functioning as an extension of the internal data platform team.

## What Dapter Does

Dapter's core practice is building the plumbing that sits between raw data and business value. This covers the entire data engineering lifecycle: real-time stream ingestion, massively parallel batch computation, lakehouse architecture, pipeline orchestration, analytics engineering, and serving data through production-grade APIs and applications.

The company works primarily with Apache Spark, Apache Kafka, Apache Flink, Delta Lake, Apache Iceberg, Apache Hudi, Apache Airflow, dbt, Snowflake, and Databricks. Cloud platforms covered are AWS, Google Cloud Platform, and Microsoft Azure.

## Services

### Stream Processing
Sub-second ingestion and transformation of high-velocity event data. Dapter designs topologies in Apache Kafka and Apache Flink capable of absorbing millions of events per second. Applicable to fraud detection, IoT telemetry, clickstream analytics, and financial market data.

### Batch Computation
Massively parallel processing of structured and unstructured datasets. Spark workloads orchestrated on AWS EMR, Google Dataproc, and Azure HDInsight, reducing days of computation to hours.

### Lakehouse & Data Lake Architecture
Architecturally sound storage layers using Delta Lake, Apache Iceberg, and Apache Hudi. Dapter designs medallion architectures (bronze → silver → gold) that make data queryable, governable, and reliable. Emphasis on ACID guarantees, schema evolution, and time-travel queries.

### Pipeline Orchestration
End-to-end DAG orchestration with Apache Airflow and AWS Step Functions. Every pipeline is instrumented for observability — lineage tracking, SLA monitoring, anomaly detection, and automated recovery.

### Analytics Engineering
Semantic layers and dimensional models built with dbt, deployed against Redshift, BigQuery, Snowflake, or Synapse. Turns raw tables into trusted business logic that analysts can self-serve.

### Custom Connectors & Ingestion Frameworks
When off-the-shelf tooling is insufficient, Dapter builds bespoke connectors, ingestion engines, and processing frameworks integrated directly with client data contracts.

### Real-Time Data APIs
Always-on, horizontally scalable REST and GraphQL APIs that serve processed data at millisecond latency with multi-region failover and zero-downtime deployments.

### Data Applications
Purpose-built internal tools, dashboards, and data portals — from self-serve exploration interfaces to embedded analytics products.

### Snowflake Engineering
Full-lifecycle Snowflake practice: account architecture, virtual warehouse sizing, zero-copy cloning, dynamic tables, Snowpark-powered ML pipelines, data sharing, and data mesh patterns.

### Databricks Lakehouse Engineering
End-to-end Databricks platform work: Unity Catalog governance, Delta Live Tables for declarative streaming pipelines, MLflow for experiment tracking, and Databricks SQL for high-concurrency analytics.

## Platform Expertise

- **AWS**: EMR-native Spark and Hive, Kinesis streaming, Glue cataloguing, Step Functions orchestration, S3-backed data lakes at exabyte scale.
- **Google Cloud**: BigQuery analytics backbone, Dataproc managed Spark, Pub/Sub event streaming, Dataflow (Apache Beam) unified batch and stream processing.
- **Microsoft Azure**: HDInsight enterprise Hadoop and Spark, Azure Databricks lakehouse, Event Hubs high-throughput streaming, Synapse Analytics unified engine.

## Engagement Model

Dapter engineers embed directly inside client organisations — not as external consultants who disappear after handoff, but as indistinguishable members of the data platform team. They own OKRs, join standups, and build with production in mind from day one.

Engagement models include:
- Embedded team augmentation
- End-to-end project delivery
- Architecture advisory and design sprints
- Ongoing platform operations

## Key Facts

- **Founded**: Cluj-Napoca, Romania
- **Headquarters**: Axente Sever 20, 400177 Cluj-Napoca, Romania
- **Coordinates**: 46.7639° N, 23.6095° E
- **Phone**: +40 742 511 654 (Mon–Fri, 08:00–18:00 EET)
- **Email**: hello@dapter.com
- **Website**: https://dapter.com
- **Data processed**: ~10 PB/month across client pipelines
- **Cloud platforms**: AWS, Google Cloud, Microsoft Azure
- **Median stream latency**: <40 ms
- **Social**: [LinkedIn](https://www.linkedin.com/company/dapter/) · [X/Twitter](https://twitter.com/DapterCluj) · [Facebook](https://www.facebook.com/daptercluj)

## Technology Stack (non-exhaustive)

Apache Spark, Apache Kafka, Apache Flink, Apache Airflow, Apache Beam, Delta Lake, Apache Iceberg, Apache Hudi, dbt, Snowflake, Databricks, AWS EMR, AWS Kinesis, AWS Glue, AWS Step Functions, Google BigQuery, Google Dataproc, Google Pub/Sub, Google Dataflow, Azure HDInsight, Azure Databricks, Azure Event Hubs, Azure Synapse Analytics, REST APIs, GraphQL, WebSockets, MLflow, Unity Catalog, Delta Live Tables, Snowpark.

## Pages

- [Homepage](https://dapter.com/) — Company overview, capabilities, platform expertise, engagement model, and CTA.
- [Contact](https://dapter.com/contact/) — Contact details, office location, and enquiry form.
- [About](https://dapter.com/about/) — Company background, values, engagement model, and team. Covers Dapter's founding story, six core values, four-stage engagement process, and full technology stack.
- [Portfolio / Case Studies](https://dapter.com/portfolio/) — Client project examples.
- [Engineering Blog](https://dapter.com/blog/) — Technical articles on data engineering topics.
- [Careers](https://dapter.com/careers/) — Open roles for data engineers and developers.