Realtime Intelligence.Real Future.

A one-stop AI-driven streaming lakehouse platform for data integration, development and operations across your infrastructure.

Built on Apache StreamPark. From its original creator.

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CatalogShared data context

Flink CDCReal-time data integration

AIDevelopment & diagnosis

LakehouseStorage & analytics

Production experience from the Apache StreamPark ecosystem

The business case

Put your streaming lakehouse to work.

Connect ingestion, catalogs, AI-assisted development and operations so teams can turn changing data into usable tables and applications.

Time to value

Reach productionsooner.

Reuse catalog definitions and develop with AI, validation and data previews before publishing.

Shared context from draft to production

Operating efficiency

Run more jobs withless overhead.

Manage pipelines, resources and runtime environments through a shared workspace.

One workspace for development and operations

Production confidence

Loweroperational risk.

Keep recovery, observability, access control, and audit in one lifecycle, backed by enterprise support.

Permissions, operational history and support

Product overview

From catalog to working pipeline.

Explore your catalogs, write SQL and build CDC or ETL pipelines with AI assistance, validation and data previews.

Realfuture development workspace with catalog browsing and a SQL editor

Data Integration

Real-time data integration.

From source databases to lakehouse tables, manage full snapshots and continuous change streams in one platform.

Sources

Targets

SQL & CDC pipelinesRouting & transformsSchema evolution

Connect dozens of source types for real-time sync with Flink SQL and Flink CDC pipelines.

Build, validate and deploy pipelines in Realfuture, with filtering, transformations and field mapping.

Sync entire databases to your lakehouse, with configurable schema evolution for supported schema changes.

Platform capabilities

AI for development and operations.

Develop with catalog context. Diagnose with runtime evidence.

01 · AI-assisted SQL & ETL

Start with your data. Develop with AI.

Describe the logic you need. AI uses catalog schemas to draft Flink SQL and visual ETL flows, explain errors and suggest corrections in your editor.

Catalog contextFlink SQLVisual ETLError explanation

AI developmentSQL draft example

Illustrative output · review and validate

02 · AI-assisted CDC

Build CDC pipelines with AI.

Describe what to sync and where to send it. AI drafts Flink CDC YAML for selected tables or entire databases, with routing, filters and field mappings. Review the configuration, set schema evolution policies, then validate, deploy and monitor your pipeline in Realfuture.

Whole-database syncRouting & transformsSchema evolutionPipeline validation

AI-assisted CDCCDC YAML example

Illustrative output · review and validate

03 · AI-assisted operations

Investigate issues with runtime evidence.

Ask AI about a failing or slow job. It examines metrics, checkpoints, exceptions and logs to explain likely causes and suggest next steps for your team.

Job diagnosisMetrics & logsCheckpointsRead-only analysis

AI job diagnosisIllustrative analysis

10:41:07MetricsBackpressureSink operatorread

10:41:08StateCheckpointsRecent historyread

10:41:09LogsExceptionsTaskManagerread

10:41:10AISlow sink writesLikely causereview

10:41:11AICheck sink loadSuggested next stepreview

Platform foundation

Your streaming lakehouse. All in one.

Bring catalogs, real-time data integration, AI-assisted development and operations into one platform.

Platform architecture

Catalog context and AI connect sources to Flink and Flink CDC, then to lakehouse storage and analytics.

Sources

Kafka

Pulsar

MySQL

PostgreSQL

Oracle

SQL Server

MongoDB

ClickHouse

Elasticsearch

Shared context

Catalog & resources

Schemas & SQL lineage
Connectors, UDFs & formats
AI development context

Realfuture · AI-driven streaming lakehouse

Streaming lakehouse platform

DevelopValidateDeployOperateObserve

Flink SQLFlink JARVisual ETLCDC pipelinesStreaming & batch

Production control

Reliable by design

Publishing, state & recovery
Observability, RBAC & audit
Scheduling & AI diagnosis

Flink compute

Dedicated runtimesStandalone / dedicated

Existing Hadoop clustersHadoop YARN

Cloud-native computeKubernetes

Lakehouse & analytics

PaimonFlussDorisStarRocks

Built on Apache StreamPark

Enterprise enhancements bring shared catalogs, AI-assisted development and diagnosis, Flink CDC and lakehouse integrations into one platform.

01Catalog & metadata

Define once. Reuse across workflows.

Shared schemas for development and AI.

Browse catalogs and manage tables and views. Reuse their metadata in SQL, CDC and ETL workflows.

Catalog & metadata

02Flink & Flink CDC

Process streams. Sync databases.

Flink and Flink CDC in one workspace.

Develop, validate and run SQL, JAR, ETL and CDC workloads with shared connectors, dependencies and runtime settings.

Flink & Flink CDC

03Context-aware AI

Keep AI close to the work.

From the editor to job diagnosis.

Use schemas, job definitions and runtime evidence to support development and troubleshooting, with changes reviewed by your team.

Context-aware AI

04Lakehouse & analytics

Connect lake storage and analytics.

Paimon, Fluss, Doris and StarRocks.

Work with Paimon lake tables, Fluss streaming storage, and Doris or StarRocks analytics through integrated catalogs and connectors.

Lakehouse & analytics

Runtime deployment

One platform. Multiple clusters.

Deploy Realfuture once to manage Flink jobs across multiple YARN and Kubernetes clusters, spanning clouds and data centers.

Across clouds and data centers

Public cloud, private cloud, or your own data center

Private cloud / data centerCustomer-owned

Alibaba CloudPublic cloud

AWSPublic cloud

Google CloudPublic cloud

Microsoft AzurePublic cloud

Realfuture runtime managementChoose a cluster for each job.

Run jobs on AWS Kubernetes, Google Cloud Kubernetes and on-premises YARN from the same Realfuture platform.

One Realfuture deploymentMultiple YARN clustersMultiple Kubernetes clustersMulti-cluster, multi-cloud operations

Connected runtime clusters

Add clusters to the platform as your infrastructure grows

Dedicated runtimes

Standalone / dedicated

Manage jobs on existing standalone Flink clusters alongside your YARN and Kubernetes deployments.

Target 01
Target 02
Target N

Multiple YARN clusters

Hadoop YARN

Choose from multiple Hadoop environments and YARN queues when deploying each Flink job.

YARN 01
YARN 02
YARN N

Multiple Kubernetes clusters

Kubernetes

Select a Kubernetes deployment target for each job, across cloud providers and data centers.

AWS K8s
Google K8s
Private K8s

One platformAcross clouds and data centersYARN × N · Kubernetes × N

Open ecosystem

Build on an open data ecosystem.

Use Flink for processing, Paimon for lake tables, Fluss for streaming storage, and Doris or StarRocks for analytics. Realfuture connects their workflows.

01Data sources & messaging

MySQL · PostgreSQL · Oracle · SQL Server · MongoDB · Apache Kafka · Apache Pulsar

02Processing, CDC & AI

Apache Flink · Apache Flink CDC · Flink SQL · Visual ETL · AI assistance · AI diagnosis

03Storage & analytics

Apache Paimon · Apache Fluss · Apache Iceberg · Apache Doris · StarRocks · ClickHouse · Elasticsearch

04Runtime & infrastructure

Standalone · Hadoop YARN · Kubernetes · On premises · Private cloud · Public cloud · Hybrid cloud · BYOC

Build the business case

Start with a workload that matters.

Bring a representative workload and your constraints. Leave with a target architecture, expected impact, and practical proof-of-concept plan.

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