The Streaming Database Landscape in 2026: A Complete Guide

Two years ago, "streaming database" was still a niche term that most data engineers associated with experimental technology. In 2026, the category has matured into a core layer of the modern data stack. Organizations running fraud detection, real-time personalization, IoT telemetry, and AI-agent pipelines now treat streaming databases as production infrastructure, not science projects.

But the landscape has grown crowded. RisingWave, Materialize, ksqlDB, Timeplus, DeltaStream, and Arroyo each claim to solve real-time data processing with SQL. The differences between them are real and consequential: architecture, SQL dialect, state management, deployment model, and pricing vary widely. Choosing the wrong tool can lock you into an ecosystem, inflate cloud costs, or force painful migrations later.

This guide surveys the entire streaming database landscape in 2026. You will learn how each platform is architected, where it excels, and which trade-offs matter for your workload. By the end, you will have a clear framework for selecting the right streaming database for your team.

What Is a Streaming Database?

A streaming database is a database system that continuously ingests, processes, and serves data in real time. Unlike traditional databases that process queries against static snapshots, streaming databases maintain incrementally updated materialized views that reflect the latest state of your data within milliseconds of changes occurring upstream.

The core idea is simple: instead of running periodic batch jobs to refresh dashboards, aggregations, or derived tables, a streaming database processes each event as it arrives and updates all dependent computations incrementally. This eliminates the latency gap between "data arrives" and "data is queryable."

Streaming databases typically provide three capabilities in a single system:

• Ingestion: Native connectors for Kafka, Redpanda, PostgreSQL CDC, MySQL CDC, and other event sources
• Processing: Continuous SQL queries, including joins, aggregations, windowed computations, and temporal filters
• Serving: Low-latency point queries against materialized views that are always up to date

This combination distinguishes streaming databases from stream processors like Apache Flink, which handle ingestion and processing but require a separate database for serving results.

Three Architecture Categories

Not every tool in this landscape is built the same way. The six platforms covered in this guide fall into three distinct architectural categories. Understanding these categories is the fastest way to narrow your shortlist.

Streaming Databases

Examples: RisingWave, Materialize

A streaming database is a fully integrated system that handles ingestion, computation, state storage, and query serving in one platform. You send data in, define transformations as SQL, and query the results directly from the same system. There is no need for an external serving layer.

Key characteristics:

• PostgreSQL-compatible wire protocol (both RisingWave and Materialize)
• Built-in state management with durability guarantees
• Support for ad-hoc queries against materialized views
• Designed for both operational and analytical streaming workloads

This category provides the simplest operational model because there are fewer moving parts. You do not need to stitch together a stream processor, a key-value store, and an API layer.

Kafka-Native Stream Processors

Example: ksqlDB

ksqlDB is tightly coupled to the Apache Kafka ecosystem. It uses Kafka topics as both input and output, stores intermediate state in RocksDB, and leverages Kafka Streams under the hood. Every piece of data must flow through Kafka before ksqlDB can process it.

Key characteristics:

• Requires Kafka as a prerequisite
• Custom SQL dialect (not ANSI SQL compliant)
• State backed by RocksDB with Kafka changelog topics for recovery
• Best suited for Kafka-centric architectures where all data already lives in topics

The coupling to Kafka is both the strength and the limitation. If your organization runs Confluent Platform and every event already flows through Kafka, ksqlDB provides a simple SQL layer on top. If you need to ingest from databases directly or serve results without Kafka consumers, the architecture becomes a constraint.

Streaming SQL Layers

Examples: Timeplus, DeltaStream, Arroyo

These platforms provide a SQL interface over stream processing engines but differ from full streaming databases in that they often delegate storage, serving, or both to external systems.

Key characteristics:

• SQL as the primary interface for defining streaming computations
• May rely on external engines (Flink, ClickHouse) under the hood
• Focus on processing rather than integrated serving
• Often designed as managed cloud services

Timeplus combines a custom streaming engine with ClickHouse for historical analytics, offering a hybrid approach. DeltaStream is built on Apache Flink and provides a managed SQL interface over Flink's processing capabilities. Arroyo, now part of Cloudflare following its acquisition in 2025, is a Rust-based stream processor that powers Cloudflare Pipelines for serverless streaming ingestion and transformation.

Platform-by-Platform Breakdown

RisingWave

RisingWave is an open-source (Apache 2.0) streaming database written in Rust. It is PostgreSQL wire-compatible, meaning any tool, driver, or ORM that works with PostgreSQL works with RisingWave without modification.

Architecture: RisingWave uses a decoupled compute-storage architecture. Compute nodes handle stream processing and query serving, while state is persisted to object storage (S3, GCS, or MinIO). This design enables elastic scaling and keeps storage costs low because you pay object storage prices rather than provisioned SSD prices.

Processing model: All streaming logic is defined as SQL. You create materialized views that are incrementally maintained as new data arrives. RisingWave supports complex multi-way joins, window functions, temporal filters, and sub-queries in streaming context.

Ingestion: Native CDC connectors for PostgreSQL and MySQL eliminate the need for Debezium or other CDC middleware. Kafka and Redpanda connectors are also supported, along with S3, Kinesis, and Pulsar sources.

Serving: Because RisingWave speaks the PostgreSQL protocol, you can query materialized views directly from your application using any PostgreSQL client. Point lookups on materialized views return results in single-digit milliseconds.

Deployment: Self-hosted (free, Apache 2.0) or RisingWave Cloud (managed service starting at approximately $0.14/hour).

Materialize

Materialize is a streaming database built on Timely Dataflow and Differential Dataflow, both originating from Microsoft Research. It is PostgreSQL wire-compatible and focuses heavily on incremental view maintenance with strong consistency guarantees.

Architecture: Materialize uses a Snowflake-style architecture with separated storage and compute. It offers multiple cluster sizes for different workload requirements. The system supports recursive queries, which is uncommon among streaming databases and useful for graph-style computations.

Licensing: Materialize's source code is available under the Business Source License (BSL 1.1), which converts to Apache 2.0 after four years. A free self-managed community edition is available with resource limits (24 GiB memory, 48 GiB disk). Cloud pricing starts at $0.98/hour, making it significantly more expensive than open-source alternatives for production workloads.

Strengths: Strong consistency across materialized views, recursive query support, and a mature approach to incremental computation based on years of academic research.

Trade-offs: Higher cost than open-source alternatives. The BSL license means you cannot freely modify and redistribute the source code for competing services. Fewer native source connectors compared to RisingWave.

ksqlDB

ksqlDB is Confluent's SQL streaming engine built on top of Kafka Streams. It provides a SQL interface for creating streaming applications that read from and write to Kafka topics.

Architecture: ksqlDB is not a standalone database. It requires a running Kafka cluster as a prerequisite. All input data must be in Kafka topics, and all output is written back to Kafka topics. State is stored in RocksDB with changelog topics in Kafka for fault tolerance.

SQL dialect: ksqlDB uses a custom SQL dialect that is not ANSI SQL compliant. The syntax includes streaming-specific constructs like EMIT CHANGES and EMIT FINAL but lacks support for complex subqueries and some standard SQL features. This means skills and queries are not portable to other databases.

Limitations: No PostgreSQL compatibility, no transaction support, no native CDC connectors (data must already be in Kafka), and limited join capabilities compared to full streaming databases. ksqlDB does not support serving ad-hoc queries against materialized state the way RisingWave or Materialize do.

Best fit: Teams that are deeply invested in the Confluent/Kafka ecosystem and need a lightweight SQL layer for simple transformations, filtering, and aggregations on Kafka topics.

Timeplus

Timeplus is a streaming analytics platform that combines a custom-built streaming engine with ClickHouse for historical query processing. Its open-source core, Timeplus Proton, is available on GitHub.

Architecture: Timeplus takes a hybrid approach. Its streaming engine handles real-time computations including tumble/hop/session windows, watermarks, and incremental materialized view maintenance. For historical queries, it leverages ClickHouse's columnar storage engine. This dual-engine design serves both real-time dashboards and historical analytics from a single platform.

SQL dialect: Timeplus uses a SQL dialect that extends standard SQL with streaming-specific syntax. It is not PostgreSQL-compatible.

Deployment: Timeplus offers a cloud service and the open-source Proton engine for self-hosting. Proton is a single C++ binary designed for fast ETL pipelines.

Best fit: Teams that need both real-time streaming analytics and historical analytics in one platform and are comfortable with a non-PostgreSQL SQL dialect.

DeltaStream

DeltaStream is a managed stream processing platform built on Apache Flink. It provides a unified SQL interface for querying and transforming streaming data without requiring users to manage Flink clusters.

Architecture: Under the hood, DeltaStream compiles SQL queries into Flink jobs. This gives it access to Flink's mature processing capabilities, including exactly-once semantics and sophisticated event-time processing. However, it also inherits Flink's operational complexity behind the managed service abstraction.

SQL dialect: DeltaStream offers a SQL interface that bridges standard SQL and streaming SQL concepts. It supports both batch and streaming queries through a unified syntax.

Deployment: Cloud-only managed service. There is no self-hosted option.

Best fit: Organizations that want Flink's processing power without the operational burden of managing Flink clusters, and that prefer a SQL-first interface over Java/Python Flink applications.

Arroyo

Arroyo is a distributed stream processing engine written in Rust, built on Apache DataFusion. In 2025, Arroyo was acquired by Cloudflare and now powers Cloudflare Pipelines for serverless streaming ingestion and transformation.

Architecture: Arroyo is designed for high throughput and low latency, leveraging Rust's performance characteristics. It supports SQL-based stream processing with features like windowed aggregations, joins, and sessionization. Post-acquisition, Arroyo integrates with Cloudflare's ecosystem including Workers, R2, and Queues.

Open source: The Arroyo engine remains open-source (Apache 2.0) and self-hostable. However, the primary development focus has shifted toward Cloudflare's platform needs.

Best fit: Teams building on Cloudflare's platform who want native streaming pipelines, or organizations that want a lightweight, Rust-based stream processor they can self-host.

Comprehensive Comparison Table

How to Choose: A Decision Framework

Selecting a streaming database is not just a feature comparison. It depends on your existing infrastructure, team skills, and workload characteristics. Here is a practical decision framework.

Start with your data sources

If all your data already lives in Kafka topics and you have no plans to change that, ksqlDB or DeltaStream can work. If you need to ingest directly from PostgreSQL or MySQL via CDC without Kafka as an intermediary, RisingWave is the strongest option with native CDC support that eliminates middleware like Debezium.

Consider your SQL skills and tooling

If your team knows PostgreSQL and uses tools like psql, DBeaver, Grafana, or Metabase, RisingWave and Materialize offer the smoothest experience because they speak the PostgreSQL wire protocol. Queries, drivers, and ORMs work without modification. ksqlDB, Timeplus, and DeltaStream each have custom SQL dialects that require learning new syntax.

Evaluate your deployment preferences

If you need self-hosted, open-source software with no license restrictions, RisingWave (Apache 2.0) and Arroyo (Apache 2.0) are the clear choices. Materialize's BSL license allows self-hosting but restricts commercial redistribution. ksqlDB requires Kafka infrastructure. DeltaStream is cloud-only with no self-hosted option.

Factor in total cost of ownership

The cheapest option on paper is not always the cheapest in production. RisingWave's object storage-based state management (S3) keeps storage costs dramatically lower than systems that use provisioned SSDs. For comparison, storing 1 TB of state in S3 costs roughly $23/month, while the same state on provisioned EBS volumes can cost $100-300/month depending on IOPS requirements.

Think about the serving layer

If you need to query streaming results directly from your application (dashboards, APIs, microservices), RisingWave and Materialize are the only options that provide an integrated serving layer via the PostgreSQL protocol. With ksqlDB, Timeplus, DeltaStream, and Arroyo, you typically need to sink results to a separate database or cache for application queries.

Real-World Architecture Patterns

Pattern 1: CDC to Materialized Views (Streaming Database)

PostgreSQL/MySQL → CDC → RisingWave → Materialized Views → Application (via PG protocol)

This pattern uses RisingWave's native CDC connectors to replicate changes from operational databases, transform them in real time with SQL, and serve the results directly to applications. No Kafka, no external serving database, no batch jobs.

Pattern 2: Kafka-Centric Enrichment (Kafka-Native Processor)

Applications → Kafka → ksqlDB → Kafka → Downstream consumers

In this pattern, ksqlDB acts as a SQL transformation layer within the Kafka ecosystem. Data enters via Kafka topics, is filtered/enriched/aggregated by ksqlDB, and results are written back to Kafka topics for other consumers.

Pattern 3: Managed Flink with SQL (Streaming SQL Layer)

Kafka → DeltaStream (managed Flink) → Data warehouse / Object storage

DeltaStream abstracts Flink's complexity behind a SQL interface. Data flows from Kafka through SQL-defined transformations and lands in a data warehouse or object storage for downstream analytics.

What Is the Difference Between a Streaming Database and a Stream Processor?

A stream processor like Apache Flink handles computation: it reads events from sources, transforms them, and writes results to sinks. It does not store queryable state or serve ad-hoc queries. You need external systems (PostgreSQL, Redis, Elasticsearch) to make results accessible to applications.

A streaming database combines processing with storage and serving. It ingests events, computes streaming transformations, stores the results in materialized views, and lets applications query those views directly. RisingWave and Materialize are streaming databases. Flink, Arroyo, and DeltaStream (which wraps Flink) are stream processors with SQL interfaces.

The practical difference matters: a streaming database reduces your infrastructure from three systems (processor + database + cache) to one. This simplifies operations, reduces latency (no hop between systems), and lowers cost.

Do I Need Kafka to Use a Streaming Database?

No. This is one of the most common misconceptions in the streaming space. While Kafka is a popular event streaming platform, it is not a prerequisite for streaming databases like RisingWave or Materialize.

RisingWave can ingest data directly from PostgreSQL and MySQL via built-in CDC connectors, from S3, Kinesis, and Pulsar, or from Kafka if you already use it. Kafka is one of many supported sources, not a requirement.

ksqlDB is the exception: it requires Kafka because it is built on Kafka Streams and uses Kafka topics for all data input and output.

If you are starting a new streaming project and do not already have Kafka, consider whether you actually need it. For many use cases, direct CDC from your operational database into a streaming database is simpler, cheaper, and lower latency.

How Does Pricing Compare Across Streaming Databases in 2026?

Pricing varies significantly and depends on your deployment model:

Self-hosted (free): RisingWave (Apache 2.0) and Arroyo (Apache 2.0) are fully free to self-host with no feature restrictions. Materialize offers a free community edition but with resource limits (24 GiB memory, 48 GiB disk). ksqlDB is free to run but requires Kafka infrastructure, which has its own costs.

Managed cloud services: RisingWave Cloud starts at approximately $0.14/hour. Materialize Cloud starts at $0.98/hour, roughly 7x more expensive at the entry level. Confluent Cloud (which includes ksqlDB) uses consumption-based pricing that can be difficult to predict. DeltaStream and Timeplus use custom cloud pricing.

Hidden costs to watch: State storage is often the largest hidden cost. RisingWave stores state on object storage (S3), which costs roughly $0.023/GB/month. Systems that use provisioned block storage or local SSDs for state can cost 10-50x more per GB. Also factor in the cost of Kafka infrastructure if a platform requires it.

Which Streaming Database Is Best for AI and Agent Workloads?

The rise of AI agents and real-time ML pipelines has created new requirements for streaming databases: low-latency feature serving, event-driven triggers, and the ability to maintain real-time aggregations that feed into model inference.

RisingWave is well-suited for these workloads because its PostgreSQL compatibility means AI frameworks (LangChain, LlamaIndex, and others) that support PostgreSQL can connect directly. Materialized views can serve as real-time feature stores, providing fresh aggregated features for model inference without a separate feature store system.

Materialize also supports this pattern through its PostgreSQL protocol. ksqlDB, Timeplus, DeltaStream, and Arroyo require additional infrastructure to serve features to ML models.

Conclusion

The streaming database landscape in 2026 is more mature and more differentiated than ever. Here are the key takeaways:

• Streaming databases (RisingWave, Materialize) combine ingestion, processing, and serving in one system. They offer the simplest architecture and the lowest operational overhead for teams that need queryable real-time data.
• Kafka-native processors (ksqlDB) are best when your entire data platform is built on Kafka and you need simple SQL transformations within that ecosystem.
• Streaming SQL layers (Timeplus, DeltaStream, Arroyo) provide SQL interfaces over specialized engines. They excel in specific niches: Timeplus for hybrid streaming/historical analytics, DeltaStream for managed Flink, and Arroyo for Cloudflare-native pipelines.
• PostgreSQL compatibility is a major differentiator. RisingWave and Materialize let you use existing PostgreSQL tools, drivers, and skills. This reduces learning curves and integration effort.
• Open source matters for flexibility. RisingWave (Apache 2.0) provides the most permissive license among full streaming databases, with no feature restrictions on the self-hosted version.
• Total cost of ownership goes beyond sticker price. Factor in state storage costs, Kafka infrastructure requirements, and the operational complexity of multi-system architectures.

The right choice depends on your data sources, team skills, deployment requirements, and budget. For most teams starting fresh with streaming in 2026, a PostgreSQL-compatible streaming database with native CDC support and object storage-based state management provides the best balance of simplicity, cost, and capability.

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