The Best 8 Materialize Alternatives for Streaming and Real-Time Analytics

These are the best Materialize alternatives:

1. Tinybird
2. Apache Flink
3. ksqlDB
4. Timeplus
5. ClickHouse Cloud
6. RisingWave
7. Apache Druid
8. Rockset

Materialize has carved out a unique position in the data infrastructure landscape with its focus on incremental view maintenance. Its ability to maintain always-up-to-date materialized views using PostgreSQL-compatible SQL makes it compelling for teams building real-time data pipelines and operational dashboards.

But Materialize isn't always the right fit. Maybe you need a storage-first approach rather than view-first architecture. Perhaps you're looking for faster query performance on historical data alongside streaming updates. Or you might need a platform that provides APIs and developer tooling beyond just maintaining views.

The real-time analytics and streaming space offers multiple approaches to similar problems. Some platforms focus on storing and querying data with extreme speed. Others specialize in stream processing. Some maintain live views like Materialize, while others take fundamentally different architectural approaches.

In this guide, we'll explore the best alternatives to Materialize, covering real-time analytics platforms, stream processing systems, and other streaming databases.

The 8 Best Materialize Alternatives

1. Tinybird

Best for: Real-time analytics with flexible querying and instant APIs

Tinybird represents a fundamentally different approach to real-time analytics than Materialize. Instead of maintaining predefined views, Tinybird stores data in ClickHouse and lets you query it any way you want with sub-100ms latency.

Key Features:

• Sub-100ms query latency on billions of rows
• Managed ClickHouse infrastructure
• Instant SQL-to-API transformation
• Streaming ingestion with automatic scaling
• Local development with CLI workflows
• Native connectors for Kafka, S3, DynamoDB, Postgres, and more
• Schema iteration with zero-downtime migrations as shown in how Tinybird automatically handles ClickHouse schema migrations.
• Tinybird Code: AI agent for query optimization
• No need to predefine views or aggregations

Architecture: Tinybird uses ClickHouse's columnar storage with vectorized query execution. Data is ingested continuously and immediately queryable. You write SQL queries that become production APIs, no view maintenance required.

How It Differs from Materialize: Materialize maintains incremental views. Tinybird stores data and queries it directly. Materialize is view-centric; Tinybird is storage-centric. With Materialize, you predefine aggregations that update continuously. With Tinybird, you write any query you want at query time.

This difference is profound. Materialize excels when you know exactly what views you need and want them always up-to-date. Tinybird excels when you need flexible querying, want to change queries without recomputation, or need to serve multiple different analytical endpoints from the same data.

Tinybird's query performance is also typically faster than querying Materialize views, especially for queries that don't perfectly match your predefined views.

When to Choose Tinybird Over Materialize:

• You need flexible, ad-hoc queries beyond predefined views
• You want to change queries without recomputing entire datasets
• You need sub-100ms query performance consistently
• You want instant APIs from SQL queries
• You value local development and modern DevOps workflows
• You need to serve multiple different analytical endpoints
• You want simpler cost predictability

Ideal Use Cases:

• Customer-facing analytics dashboards
• Real-time operational monitoring
• Usage-based billing systems
• Web and product analytics
• API-backed analytics
• Event-driven applications
• Any scenario requiring flexible real-time queries

2. Apache Flink

Best for: Complex stream processing and transformations

Apache Flink is a distributed stream processing framework that provides low-level control over how data is processed as it flows through your system.

Key Features:

• Exactly-once processing semantics
• Event time processing with watermarks
• Stateful computations and windowing
• SQL and DataStream APIs
• Integration with numerous sources and sinks
• Batch and streaming unified

Architecture: Flink uses a distributed dataflow execution model with stateful operators. It processes streams of events with precise control over timing, state, and fault tolerance.

How It Differs from Materialize: Flink is a processing framework, not a database. You write explicit processing logic for how data flows and transforms. Materialize is declarative, you define SQL views and it figures out the processing. Flink gives more control but requires more code. Typically used with a storage layer.

Ideal Use Cases:

• Complex event processing
• Real-time ETL pipelines
• Stream enrichment and joins
• Continuous analytics with custom logic
• Scenarios requiring exactly-once semantics

3. ksqlDB

Best for: Kafka-native stream processing with SQL

ksqlDB is the SQL interface for Apache Kafka, letting you process streams and create materialized views using SQL rather than writing Kafka Streams code.

Key Features:

• SQL interface for Kafka Streams
• Create streams and tables from Kafka topics
• Continuous queries and materialized views
• Push and pull queries
• Integration with Kafka ecosystem
• Stream-table joins

Architecture: ksqlDB runs on top of Kafka Streams, compiling SQL into stream processing applications. It maintains state in RocksDB and can create materialized views from Kafka topics.

How It Differs from Materialize: ksqlDB is tightly coupled to Kafka and operates on Kafka topics. Materialize supports multiple sources with PostgreSQL CDC and offers more sophisticated incremental computation. ksqlDB is simpler for Kafka-centric architectures but less flexible for other data sources.

Ideal Use Cases:

• Kafka-native architectures
• Stream processing with SQL
• Real-time data pipelines on Kafka
• Organizations already using Confluent

4. Timeplus

Best for: Real-time stream analytics with SQL

Timeplus (built on Proton) is a streaming analytics platform that lets you query data as it flows through the system, similar to Materialize but with different architectural choices.

Key Features:

• Streaming-first SQL analytics
• Query data in motion and at rest
• Time window aggregations
• Streaming joins
• Native Kafka integration
• Real-time materialized views

Architecture: Timeplus processes data as streams with a streaming SQL engine. It can query both streaming data (in motion) and historical data (at rest) with unified SQL.

How It Differs from Materialize: Timeplus focuses on querying streams directly, not just maintaining views. It's more flexible for querying data in motion before storage. Materialize focuses more on maintaining incremental views. Both support streaming SQL but with different optimization goals.

Ideal Use Cases:

• Real-time analytics on streaming data
• Stream processing with SQL
• IoT and sensor data analysis
• Real-time monitoring and alerting

5. ClickHouse Cloud

Best for: High-performance analytical queries without view maintenance

ClickHouse Cloud provides the raw power of ClickHouse, the fastest analytical database, in a managed service. It's a storage-first approach optimized for analytical queries.

Key Features:

• Columnar storage with extreme compression
• Sub-second query performance on billions of rows
• Real-time data ingestion
• Full SQL support with analytical functions
• Automatic scaling and high availability
• Materialized views (computed at insert time, not incrementally)

Architecture: ClickHouse uses columnar storage with vectorized execution. Materialized views in ClickHouse are computed at insert time, not incrementally like Materialize. This makes inserts slightly slower but queries much faster.

How It Differs from Materialize: ClickHouse is a database optimized for query performance. Materialize is optimized for incremental view maintenance. ClickHouse materialized views are computed at insert time; Materialize updates them incrementally. ClickHouse is faster for queries but doesn't offer the same incremental computation benefits.

Ideal Use Cases:

• High-performance analytical queries
• Real-time dashboards
• Log and event analytics
• Time-series data analysis
• When query speed matters more than incremental updates

6. RisingWave

Best for: PostgreSQL-compatible streaming database

RisingWave is an open-source streaming database that, like Materialize, maintains incremental materialized views. It's PostgreSQL-compatible and offers a similar approach with different implementation details.

Key Features:

• PostgreSQL wire protocol compatibility
• Incremental materialized view maintenance
• Built on top of cloud object storage
• Separation of compute and storage
• Open-source with commercial support
• Integration with Kafka and CDC sources

Architecture: RisingWave uses a distributed streaming architecture with separation of compute and storage. It maintains materialized views incrementally using a streaming execution engine.

How It Differs from Materialize: RisingWave is very similar to Materialize in approach, both maintain incremental views with PostgreSQL compatibility. RisingWave is fully open-source and separates storage/compute differently. Performance and feature sets are comparable.

Ideal Use Cases:

• Similar to Materialize use cases
• Organizations preferring fully open-source
• Real-time monitoring dashboards
• Streaming ETL with views
• Incremental aggregations

7. Apache Druid

Best for: High-concurrency real-time analytics

Apache Druid is a real-time OLAP database designed for high-concurrency analytical queries. Unlike Materialize's view maintenance, Druid stores data and queries it directly with fast performance.

Key Features:

• Column-oriented storage with bitmap indexes
• Real-time and batch ingestion
• High concurrency support
• Time-based partitioning
• Approximate algorithms for speed
• SQL and native query support

Architecture: Druid uses a distributed architecture with specialized node types (broker, historical, real-time). Data is stored in segments with heavy indexing for fast queries.

How It Differs from Materialize: Druid is a storage and query database, not a view maintenance system. You ingest data and query it directly. Druid is optimized for many concurrent users running different queries. Materialize is optimized for maintaining specific views that update incrementally.

Ideal Use Cases:

• Interactive dashboards with many users
• User-facing analytics
• Network telemetry and monitoring
• High-concurrency scenarios
• Ad-tech and digital analytics

8. Rockset

Best for: Real-time analytics on semi-structured data

Rockset is a real-time analytics database built on RocksDB, designed for fast queries on semi-structured data with automatic indexing.

Key Features:

• Automatic indexing of all fields
• Native JSON and semi-structured data support
• Converged indexing (row, column, search)
• Real-time data ingestion
• SQL query interface
• Mutable data with updates

Architecture: Rockset uses Converged Indexing™ that combines columnar, row, and inverted indexes. It's built on RocksDB and optimized for semi-structured data queries.

How It Differs from Materialize: Rockset stores and queries data directly with automatic indexing. Materialize maintains incremental views. Rockset is better for semi-structured data and queries that weren't predefined. Materialize is better when you need specific views that update efficiently.

Ideal Use Cases:

• Real-time search and analytics
• Semi-structured data (JSON, nested objects)
• Log analytics
• Applications requiring mutable data
• Flexible schema requirements

Understanding Materialize and Incremental View Maintenance

Before exploring alternatives, it's important to understand what makes Materialize unique and what problems it solves.

The Materialize Approach Materialize is a streaming database that maintains materialized views incrementally. Instead of recomputing entire views when underlying data changes, it updates only the affected portions. This makes it possible to maintain complex analytical views in real-time without expensive full recomputation.

Key Characteristics:

• PostgreSQL wire protocol compatibility
• Incremental view maintenance using dataflow graphs
• ANSI-standard SQL for view definitions
• Real-time updates as source data changes
• Support for complex joins, aggregations, and window functions
• Integration with Kafka, PostgreSQL CDC, and other sources

Materialize's Architecture Under the hood, Materialize uses a dataflow-based architecture derived from Timely Dataflow and Differential Dataflow research. When you define a materialized view, Materialize compiles it into a dataflow graph that processes changes incrementally.

The Use Case Materialize excels when you need to maintain complex analytical views that update continuously as data streams in. Think of it as "always-up-to-date dashboards" or "real-time reporting" where the views themselves are the primary output.

Why Look for Materialize Alternatives?

Organizations explore Materialize alternatives for several reasons:

• Storage and Query Patterns Materialize is view-centric. You define views that update continuously, and you query those views. But many use cases need to query raw data directly with different aggregations and filters. Storage-first platforms like Tinybird let you query data any way you want without predefining views.

• Performance for Historical Queries Materialize optimizes for keeping views up-to-date. Querying large amounts of historical data or running ad-hoc queries that weren't predefined as views can be slower than specialized OLAP databases like ClickHouse.

• Developer Experience and APIs Materialize provides a PostgreSQL interface for querying views. Building production applications requires creating APIs, handling authentication, and managing deployments. Some alternatives provide complete platforms with API generation and developer tooling.

• Cost Model Complexity Materialize's pricing based on CCUs (Cluster Credit Units) and memory usage can be difficult to predict and optimize. Alternative platforms offer different pricing models that may align better with your usage patterns.

• Operational Complexity Managing Materialize deployments, optimizing view performance, and understanding dataflow resource usage requires specialized knowledge. Fully-managed platforms with automatic optimization can reduce operational burden.

• Different Architectural Philosophy Some teams prefer stream processing frameworks (like Flink) where they control the processing logic explicitly. Others want simple storage and query platforms (like Tinybird) where data is ingested and queried without intermediate view definitions.

Streaming Views vs. Storage-First: Two Philosophies

Understanding the architectural difference between Materialize's approach and storage-first platforms is critical:

View-First (Materialize, RisingWave):

• Define materialized views that update continuously
• Query the views, not the raw data directly
• Optimized for keeping predefined aggregations up-to-date
• Changes to views may require recomputation
• Best when you know your query patterns in advance

Storage-First (Tinybird, ClickHouse, Druid):

• Ingest data into a fast analytical database
• Query the data any way you want
• Optimized for flexible, ad-hoc queries
• No need to predefine views
• Best when query patterns are dynamic or unknown

See how to count 100B events: Comparing architectures for a real-world example.

Stream-Processing-First (Flink, ksqlDB):

• Process data as it flows through the system
• Transform, aggregate, and route before storage
• Explicit control over processing logic
• Typically paired with a storage layer
• Best for complex event processing and transformations

Your choice depends on whether you need predefined always-up-to-date views (Materialize) or flexible querying of stored data (Tinybird).

Comparison Table

The Incremental Computation Tradeoff

Materialize's incremental view maintenance is powerful but comes with tradeoffs:

Benefits of Incremental Computation:

• Views stay up-to-date without expensive recomputation
• Efficient for complex aggregations that change slowly
• Good when you have well-defined, stable views
• Reduces query latency for predefined aggregations

Limitations of Incremental Computation:

• Views must be predefined before data arrives
• Changing views may require expensive recomputation
• Memory usage can be high for complex views
• Ad-hoc queries outside predefined views are less efficient
• Resource usage can be unpredictable

Storage-First Advantages:

• Query data any way you want
• Change queries without recomputation
• Consistent, predictable performance
• Simpler resource management
• Better for exploratory analytics

For many use cases, storing data in a fast analytical database (like Tinybird's managed ClickHouse) and querying it directly provides better flexibility and performance than maintaining incremental views.

PostgreSQL Compatibility: Benefit or Limitation?

Materialize's PostgreSQL compatibility is often cited as a benefit, but consider the implications:

Benefits of PostgreSQL Compatibility:

• Familiar SQL dialect
• Compatible with PostgreSQL tools
• Easy for teams with PostgreSQL experience
• Standard ANSI SQL support

Limitations:

• PostgreSQL wasn't designed for analytical workloads
• Missing analytical database optimizations
• Performance characteristics differ from purpose-built OLAP databases
• Tool ecosystem optimized for OLTP, not OLAP

Platforms like Tinybird use ClickHouse SQL, which is purpose-built for analytics with functions and optimizations that PostgreSQL lacks. The learning curve is minimal, and the performance benefits are substantial.

Cost Model Considerations

Understanding the cost model differences is important:

Materialize Costs:

• Based on CCUs (Cluster Credit Units)
• Memory usage heavily impacts costs
• Complex views consume more resources
• Can be difficult to predict and optimize
• Development clusters also incur costs

Tinybird Costs:

• Usage-based pricing (storage, compute, data transfer)
• Predictable based on data volume and queries
• Free tier for development
• Scales with actual usage
• Clear cost relationship to business metrics

Self-Managed Options (Flink, Druid, RisingWave):

• Infrastructure costs only
• But requires engineering time for operations
• Hidden costs in complexity and maintenance
• Need dedicated platform teams

For most organizations, fully-managed platforms with predictable pricing models (like Tinybird) offer better total cost of ownership than either CCU-based models or self-managed options.

Developer Experience and Time to Production

How quickly can you build and deploy real-time analytics?

Materialize Development:

1. Define source connections (Kafka, PostgreSQL CDC)
2. Create materialized views with SQL
3. Optimize view definitions for resource usage
4. Connect applications to PostgreSQL interface
5. Build API layer (separate from Materialize)
6. Monitor CCU usage and optimize

Tinybird Development:

1. Define data sources locally
2. Write SQL queries (any aggregation you want)
3. Queries automatically become APIs
4. Deploy with single command
5. Automatic scaling and monitoring
6. Iterate on queries without recomputation

The developer experience difference is significant. Tinybird's local-first development and instant API generation eliminate weeks of infrastructure work.

When Materialize Makes Sense

Despite alternatives, Materialize is the right choice for specific scenarios:

• Well-Defined Views with Complex Joins When you know exactly what aggregations you need and they involve complex joins that benefit from incremental computation.

• PostgreSQL Ecosystem Integration When PostgreSQL compatibility is essential for tool integration or team expertise.

• Incremental Updates Are Critical When you have large aggregations that would be expensive to recompute fully but can be updated incrementally.

• Dataflow-Style Thinking When your team thinks naturally in terms of dataflows and continuous computations.

The Future of Real-Time Analytics

The real-time analytics landscape continues evolving:

• Convergence of Approaches Storage-first platforms are adding stream processing capabilities. Stream processors are adding better storage and query layers. The boundaries are blurring.

• Simplified Operations Managed platforms are hiding more complexity, making real-time analytics accessible to broader teams without specialized expertise.

• AI-Assisted Development Tools like Tinybird Code make query optimization and development easier without deep database knowledge.

• Cost Optimization Focus As data volumes grow, platforms offering predictable, optimized pricing gain adoption.

• API-First Architectures The ability to turn analytics into production APIs is becoming critical for modern applications.

Conclusion

Materialize pioneered incremental materialized view maintenance and offers a unique approach to real-time analytics. For specific use cases involving complex views that benefit from incremental updates, it's a powerful solution.

However, many organizations evaluating Materialize ultimately need flexible real-time analytics more than they need incremental view maintenance. For these scenarios, storage-first platforms like Tinybird provide better results with simpler operations and faster performance.

Tinybird's approach, storing data in ClickHouse and enabling any query you want with sub-100ms latency, offers more flexibility than predefined views. You can change queries without expensive recomputation, serve multiple analytical endpoints from the same data, and get instant production-ready APIs from your SQL.

If you need complex stream processing with explicit control, Flink or ksqlDB provide powerful frameworks. If you're deeply invested in Kafka, ksqlDB offers native integration. If you specifically need incremental view maintenance, RisingWave provides a similar approach to Materialize with different tradeoffs.

The right choice depends on your use case. But for most real-time analytics needs, user-facing dashboards, operational monitoring, API-backed analytics, storage-first platforms like Tinybird deliver better performance, simpler operations, and faster time to production than view-maintenance systems like Materialize.