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URL: https://www.phoenixdata.ai/solutions/real-time-analytics/

⇱ Real-Time Analytics | PhoenixAI

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Products

PhoenixAI Cloud Fully managed. Cloud or self-hosted. Pricing Flexible plans for every scale.

Use Cases

AI & Agents Real-time data for agentic AI workloads. Real-Time Analytics Sub-second queries on live data streams. Customer-Facing Analytics Embedded analytics built for your product. Lakehouse Analytics Query Iceberg and Delta Lake at sub-second speed.

Industries

Finance & Banking Logistics & Transportation Manufacturing Healthcare Retail & E-Commerce Energy & Utilities Telecommunications Public Safety & Government

Comparisons

PhoenixAI vs. Databricks Real-time OLAP vs. batch-first lakehouse. PhoenixAI vs. ClickHouse Managed, governed, and built for AI workloads. PhoenixAI vs. Snowflake Real-time freshness vs. warehouse latency.

Learn

Blog AI agents, real-time analytics, and data engineering. Docs Full technical documentation. Glossary Key terms in real-time analytics and AI data.

Assets & Events

Whitepapers Deep technical reads on performance and architecture. Events Conferences, meetups, and online sessions. Customer Case Studies Real-life customers stories of how they transformed their data stack.

Latest whitepaper

Real-Time Analytics for Customer-Facing Applications Whitepaper

Analytics is no longer just a tool for internal decision-makers.

Download free

About

About PhoenixAI Who we are and why we built this. Partners AWS, Confluent, Databricks, and more. Newsroom Press coverage and announcements.

Join us

Careers Open roles at PhoenixAI. Contact Talk to our team directly.

Customer story

Customer Facing Analytics Meets Agents

Eightfold’s Move from Redshift.

Read the story

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.

Book a demo Start free trial

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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.

cancelWith 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

check_circleWith 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.

stream

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.

join_inner

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.”

F

Fanatics

PhoenixAI customer

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join latency

See it on your data.

We’ll run your actual queries live — no slides, no canned demo.

Book a demo arrow_forward Start free trial