Introduction
Apache Flink is a distributed stream processing framework for stateful computations over unbounded and bounded data streams. Flink specializes in stream processing, but it relies on other datastores for ingesting input data and serving real-time data, which may not be optimized for that purpose.
Materialize is a SQL-based real-time data integration and transformation platform that combines cloud storage, incremental view maintenance, and a built-in, Postgres-compatible serving layer. This design enables streaming data processing within a single framework, reducing complexity and cost.
Materialize vs Flink: Key decision factors
- Development velocity: Enterprise customers deploying Materialize in production report approximately 50% faster deployment cycles than their previous Flink installs.
- Cost efficiency: These same customers say that deploying Materialize is 45-50% of the cost they experienced deploying a stream processor like Flink
- Data consistency: Materialize provides strict serializability vs. Flink’s eventual consistency
- Team accessibility: Standard Postgres SQL-only (Materialize) vs. JVM programming and the FlinkSQL dialect (Flink)
- Consistency: Guaranteed global consistency through strict serializeability (Materialize) vs. eventual consistency through “exactly-once state semantics” (Flink)
- Composability for higher order data products: Materialize’s strict consistency model makes Materialize views safe to compose, cache, and expose to applications or agents vs Flink’s fundamental timing inconsistencies break system composability.
Not quite an apples to apples comparison
When comparing Flink and Materialize, we are comparing a stream processor and a real-time data integration platform:
- Stream processors handle the core data transformation, but they also rely on Kafka for intermediate storage, and some type of serving layer (i.e. a separate database, or a Redis-like tool). In practice, stream processors work in a context of multiple supporting services:
- Materialize is one tier up in the software abstraction stack, combining the compute, orchestration, and load balancing into one platform so users see a unified real-time data integration and transformation platform that presents as a SQL database.
The functional difference between Flink and Materialize is fundamental:
- Flink is a stream processing engine with some SQL capabilities
Flink delivers raw stream processing. It functions as the compute layer, and requires external services to materialize data views: a message bus or CDC connector (e.g., Kafka, Debezium) as the input layer, and an output system (e.g., Kafka, Elasticsearch, a database) to persist results.
- Materialize is a database-like experience with streaming capabilities
Materialize’s database abstraction means Materialize doesn’t simply deliver raw stream processing. At the heart of Materialize, Timely Dataflow is mature and stable open source software originally developed at Microsoft Research. Materialize builds database functionality and distributed architecture around the core stream processing library, in a single unified platform.
For the user, Materialize presents as a Postgres wire-compatible data streaming platform with incrementally — and continually — updated materialized views. This means you can store raw data, transform, and serve all within a single system.
To read more download our comprehensive Materialize vs Flink platform analysis. It covers:
- Architecture and system integration: Overview of Flink’s extended platform requirements for stream processing vs. Materialize’s integrated ingestion, computation, storage, and serving platform
- Real-time data integration: How organizations can achieve real-time data integration that minimizes architectural sprawl, reduces operational cost, and allows teams to focus on business logic instead of pipeline plumbing
- Consistency and composability: Examine how Flink’s “exactly-once state semantics” achieve eventual consistency vs how Materialize achieves, and guarantees, strict consistency — and the consequences each has for system composability
- Developer experience and data accessibility: Differences in the user experience for Materialize’s streamlined stream-processor-wrapped-in-a-database approach vs Flink’s power-through-bespoke-complexity approach, and the impacts of each on data accessibility
- Enterprise implications and TCO (total cost of ownership): Comparative analysis of Flink vs Materialize for data stream processing, including resources and skills required to deploy, cost structure, data consistency ramifications, and business value