Real-Time Analytics
Your data infrastructure, without the batch lag.
Stream Kafka and Flink events into PhoenixAI’s own real-time storage and serve sub-second SQL on data that’s seconds old. Join or union those live tables with your historical lakehouse in the same query.
The problem
Batch pipelines were built for a different era.
With batch ETL today
Data that’s always hours behind
- Dashboards reflect data from the last batch run, not now
- Operational decisions made on stale information
- Engineering time spent maintaining fragile ETL pipelines
- Scaling requires expensive pre-aggregations and denormalization
With PhoenixAI
Live data, same SQL, same tools
- Query mutable streaming data within seconds of ingestion
- SQL joins directly on terabytes of normalized data, no denormalization required
- Native ingestion from Kafka, Flink, and Spark Streaming
- Join or union real-time tables with historical lakehouse data in one SQL query
Capabilities
Everything your real-time stack needs.
Built for the workloads that break batch-oriented databases.
Second-level data freshness
Ingest from Kafka, Flink, Spark, or Kinesis into PhoenixAI’s native real-time tables. Even mutable data — with appends, updates, and deletes — is queryable within seconds of arrival.
Sub-second queries under load
Vectorized columnar execution and intelligent caching maintain stable p99 latency even under thousands of concurrent queries on billions of rows.
On-the-fly JOINs
Multi-table joins across normalized fact and dimension tables, executed on the fly. A cost-based optimizer picks the join order; vectorized execution delivers sub-second latency — no denormalization required.
Intelligent materialized views
Materialized views refresh incrementally: only the partitions touched by new data are recomputed, not the full view. Queries auto-rewrite to hit the MV, so dashboards stay fresh without manual pipelines.
Lakehouse queries, no copy
One SQL query, real-time and historical unified. PhoenixAI’s native real-time tables sit side-by-side with your Apache Iceberg and Delta Lake tables — join or union them without copying data.
Enterprise governance
SOC 2 certified. Row-level security, column masking, audit logging, and fine-grained access controls built into the database — not bolted on.
Works with your stack
PhoenixAI connects to the streaming, storage, and BI tools you already use. Most teams are in production within two to four weeks.
Streaming ingestion
- Apache Kafka
- Apache Flink
- Apache Spark
- AWS Kinesis
- Confluent
Storage & lakehouse
- Apache Iceberg
- Delta Lake
- Apache Hudi
- Amazon S3
- GCS
- ADLS
BI & visualization
- Tableau
- Looker
- Superset
- Grafana
- JDBC / ODBC
In production
What teams see when they make the switch.
<1s join latency
PhoenixAI is at the center of our real-time data analytics. We strive for quicker and easier insights into day to day operations. We chose PhoenixAI for its ability to upsert data in real-time, support for joins across large fact tables with very low latency, and the ability to serve and join native and external tables from the same cluster.
See it on your data.
We’ll run your actual queries live — no slides, no canned demo.