On your infrastructure. Under your control.
Same APIs, familiar tools and cloud-like experience — without giving up control of your data or environment.
Deployment, configuration, monitoring, backups, upgrades and day-to-day operations — handled by our engineering team.
Distributed compute
for large-scale data processing.
Run ETL, batch analytics and machine-learning pipelines across large datasets without moving the data out of your environment. Spark works naturally with object storage and fits well into lakehouse architectures alongside Iceberg and Trino.
Real-time analytics for large datasets.
Analyze billions of rows with low-latency SQL queries and high ingestion rates. ClickHouse is built for dashboards, observability, event analytics and other workloads where traditional relational databases become too slow or expensive.
Event streaming
that keeps your systems connected.
Build durable real-time data pipelines between applications, databases and analytics systems. Kafka handles high-throughput event streams reliably and gives services a common backbone for asynchronous communication and data ingestion.
Search, indexing and analytics at scale.
Index large volumes of logs, documents, and application data for fast search and real-time analysis. Elasticsearch powers full-text search, log analytics, observability, and security workloads on your own infrastructure, with hot-warm-cold tiering and snapshots stored on Petabox Object Storage.
The database your applications
already know.
Run transactional workloads on a proven relational database with replication, backups, point-in-time recovery and high-availability configurations. From a single primary to clustered deployments, PostgreSQL scales with the needs of the application.
Stateful processing for data in motion.
Process streams continuously as events arrive instead of waiting for batch jobs. Flink is designed for real-time transformations, enrichment, aggregation and complex event processing, with state, checkpoints and recovery built into the model.
A modern table layer for your data lake.
Turn object storage into a structured analytical data layer with schema evolution, snapshots, partitioning and time travel. Iceberg lets Spark, Trino and other engines work against the same datasets without locking them to one compute platform.
Orchestration for complex data workflows.
Schedule and coordinate pipelines across databases, storage, analytics and compute engines using explicit DAGs and dependencies. Airflow gives data teams one place to control recurring jobs, retries, sequencing and operational workflows.
Metrics built for modern infrastructure.
Collect and query time-series metrics from applications, servers and data services using the industry-standard Prometheus model. Exporters, service discovery and PromQL make it a natural foundation for infrastructure and application monitoring.
One view across your entire data stack.
Turn Prometheus metrics and other operational data into dashboards, alerts and visualizations your team can actually use. Grafana brings infrastructure, databases and applications together in one familiar observability interface.
Query everything
with SQL.
Run distributed SQL across object storage, Iceberg tables and multiple external data sources without copying everything into a new database first. Trino gives analysts one query layer across a heterogeneous data platform.
Reliable messaging between applications.
Handle queues, routing, pub/sub and asynchronous jobs with a mature message broker designed for application integration. RabbitMQ is a strong fit where services need dependable delivery, flexible routing and predictable operational behavior.
