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.

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Query latency

Sub-second SQL under high concurrency

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Ingest to queryable

Streaming data

10s

Data freshness

Pinterest production

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.

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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.

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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.

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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.

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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.

storage

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.

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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 KafkaApache FlinkApache SparkAWS KinesisConfluent

Storage & lakehouse

Apache IcebergDelta LakeApache HudiAmazon S3GCSADLS

BI & visualization

TableauLookerSupersetGrafanaJDBC / ODBC

In production

What teams see when they make the switch.

“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.”

Fanatics

PhoenixAI customer

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join latency

Your data infrastructure,without the batch lag.