Apache Flink Alternatives for Teams That Prefer SQL

The best Apache Flink alternatives for SQL-first teams are RisingWave, ksqlDB, and Materialize. All three replace the JVM-heavy Flink operational model with SQL interfaces that data engineers already know. Among them, RisingWave stands out as the only option that combines PostgreSQL-compatible SQL, disaggregated cloud storage, and a full serving layer in a single system, with no Java required at any layer.

This guide explains why teams move away from Flink, what each SQL-native alternative actually offers, and how to choose the right one for your workload.

Why Teams Leave Apache Flink

Apache Flink is a serious piece of engineering. It processes trillions of events per day at companies like Alibaba and Netflix, and its exactly-once semantics are battle-tested. The problem is not what Flink does. The problem is what Flink requires.

The JVM Tax

Every Flink deployment starts with a JVM conversation. You need a JobManager and one or more TaskManagers, each tuned for heap allocation, garbage collection, and off-heap memory budgets. A typical production cluster carries multiple YAML configuration files, a Kubernetes operator or standalone cluster manager, and a team with JVM expertise to keep it stable.

For teams whose core competency is SQL and data modeling, this stack is a barrier that delivers no business value. The garbage collector pauses have nothing to do with your fraud detection pipeline.

The Code Barrier

Even with Flink SQL, most real production pipelines touch the DataStream API. Changing a join strategy or adding a new dimension requires a Java or Scala codebase with a build pipeline, a JAR submission process, and a redeploy cycle. A simple window resize becomes a multi-step engineering task.

State Management Complexity

Flink stores operator state in RocksDB on local task manager disks. As pipelines grow, checkpoint sizes balloon. A job with hundreds of gigabytes of state can take minutes to checkpoint, and a failed checkpoint can destabilize the entire job. You end up hiring people whose primary job is tuning Flink state.

Flink 2.0 introduced disaggregated state via ForSt (Flink on RocksDB over S3), which addresses some of this. But it is an opt-in feature added to an existing runtime, not a ground-up rethink. Many teams find the operational complexity unchanged.

Who Actually Stays with Flink

Flink remains the right choice when you need low-level control: custom operators in Java, complex event processing with MATCH_RECOGNIZE, or CEP patterns that SQL cannot express. If your team already has Flink expertise and your use case requires that level of control, the cost is justified.

For the majority of data engineering teams running aggregations, joins, filters, and CDC pipelines over Kafka, the cost is not justified.

The SQL-Native Alternatives

Three systems have emerged as credible SQL-first alternatives to Flink for stream processing: RisingWave, ksqlDB, and Materialize.

RisingWave

RisingWave is a PostgreSQL-compatible streaming database built from scratch in Rust. It is open source under Apache 2.0. The core design principle is that stream processing should look and feel like working with a PostgreSQL database: you define sources, write SQL, and query results. There is no DAG to configure, no cluster manager beyond the database itself, and no JVM anywhere in the stack.

Streaming pipelines in RisingWave are defined as materialized views. The database continuously maintains these views as new data arrives and serves query results at low latency.

State is stored in Hummock, RisingWave's purpose-built LSM-tree storage engine that writes to object storage (S3, GCS, Azure Blob). Compute and storage scale independently, which eliminates the RocksDB disk pressure that Flink teams fight constantly.

RisingWave also includes native CDC ingestion from PostgreSQL, MySQL, and MongoDB, a built-in connector framework for Kafka and other sources, and sink support to downstream systems including Kafka, PostgreSQL-compatible databases, and data warehouses.

ksqlDB

ksqlDB is Confluent's streaming SQL layer for Apache Kafka. It is tightly integrated with the Kafka ecosystem: sources and sinks are Kafka topics, the processing engine runs on Kafka Streams under the hood, and state is stored in Kafka compacted topics (backed by RocksDB on local disks).

ksqlDB uses a SQL-like dialect with Kafka-specific extensions. It supports CREATE STREAM, CREATE TABLE, push queries, and pull queries. The syntax is approachable for engineers who think in Kafka-first terms.

The trade-off is lock-in. ksqlDB is designed for Confluent Cloud or self-managed Confluent Platform. Standalone ksqlDB deployments exist, but operational support is limited outside the Confluent stack. State management mirrors Kafka Streams limitations: local RocksDB storage, manual partition rebalancing, and limited support for large-state operations.

Materialize

Materialize is a cloud-native streaming SQL database that uses ANSI SQL and maintains incrementally updated materialized views. It was built on Timely Dataflow, a research-grade dataflow computation engine from the differential dataflow paper.

Materialize supports sources from Kafka, PostgreSQL (via CDC), and MySQL. Its SQL dialect is close to standard PostgreSQL, and it supports complex joins and subqueries within materialized views. Materialize Cloud is the primary deployment target; self-hosting is supported but less documented.

The positioning is closest to RisingWave in terms of product philosophy: a streaming database rather than a processing framework. The key differences are that Materialize does not have the same depth of Kafka connector support, and its open-source edition (under the BSL license since 2023) has restrictions on production use beyond a single-node deployment.

Feature Comparison

Side-by-Side SQL Examples

The fastest way to understand the difference between Flink and its SQL-native alternatives is to look at the same use case in each.

Use Case: Revenue by Region, Continuously Updated

Apache Flink SQL

Flink SQL requires a running cluster, a configured catalog, and a separate serving layer (HBase, Redis, or a JDBC sink) to make results queryable:

-- Flink SQL (requires cluster + external sink for serving)
CREATE TABLE orders (
  order_id   BIGINT,
  user_id    BIGINT,
  region     STRING,
  amount     DOUBLE,
  status     STRING,
  order_time TIMESTAMP(3),
  WATERMARK FOR order_time AS order_time - INTERVAL '5' SECOND
) WITH (
  'connector'                    = 'kafka',
  'topic'                        = 'orders',
  'properties.bootstrap.servers' = 'broker:9092',
  'properties.group.id'          = 'flink-group',
  'scan.startup.mode'            = 'earliest-offset',
  'format'                       = 'json'
);

-- Results must be pushed to an external sink
CREATE TABLE revenue_by_region (
  region        STRING,
  order_count   BIGINT,
  total_revenue DOUBLE,
  PRIMARY KEY (region) NOT ENFORCED
) WITH (
  'connector' = 'jdbc',
  'url'       = 'jdbc:postgresql://db:5432/analytics',
  'table-name'= 'revenue_by_region'
);

INSERT INTO revenue_by_region
SELECT
  region,
  COUNT(*)    AS order_count,
  SUM(amount) AS total_revenue
FROM orders
WHERE status = 'completed'
GROUP BY region;

RisingWave SQL (verified)

In RisingWave, the same pipeline is a single CREATE MATERIALIZED VIEW statement. Results are immediately queryable with a standard SELECT:

-- RisingWave: define source (Kafka in production; table used here for demo)
CREATE TABLE alt_orders (
    order_id    BIGINT,
    user_id     BIGINT,
    region      VARCHAR,
    amount      NUMERIC,
    status      VARCHAR,
    order_time  TIMESTAMPTZ
);

-- Define the streaming pipeline as a materialized view
CREATE MATERIALIZED VIEW alt_revenue_by_region AS
SELECT
    region,
    COUNT(*)        AS order_count,
    SUM(amount)     AS total_revenue,
    AVG(amount)     AS avg_order_value
FROM alt_orders
WHERE status = 'completed'
GROUP BY region;

-- Query results instantly, no separate serving layer needed
SELECT * FROM alt_revenue_by_region ORDER BY total_revenue DESC;

Result:

 region  | order_count | total_revenue | avg_order_value
---------+-------------+---------------+-----------------
 us-west |           2 |        450.00 |          225.00
 us-east |           1 |        200.00 |          200.00

The materialized view updates continuously as new rows arrive. No sink configuration, no external database, no job submission.

Use Case: Tumbling Window Aggregation

RisingWave supports TUMBLE and HOP window functions with a syntax that is clean and PostgreSQL-friendly:

-- Revenue per 5-minute tumbling window, by region
CREATE MATERIALIZED VIEW alt_revenue_per_window AS
SELECT
    window_start,
    window_end,
    region,
    SUM(amount) AS revenue
FROM TUMBLE(alt_orders, order_time, INTERVAL '5' MINUTE)
GROUP BY window_start, window_end, region;

Query result:

       window_start        |        window_end         | region  | revenue
---------------------------+---------------------------+---------+---------
 2026-04-02 07:20:00+00:00 | 2026-04-02 07:25:00+00:00 | eu-west |   75.00
 2026-04-02 07:20:00+00:00 | 2026-04-02 07:25:00+00:00 | us-east |  200.00
 2026-04-02 07:20:00+00:00 | 2026-04-02 07:25:00+00:00 | us-west |  450.00

Use Case: Fraud Detection Alert View

This pattern tracks users whose cumulative transaction amount exceeds a threshold. In Flink, this requires a stateful operator with a keyed process function. In RisingWave, it is a HAVING clause on a materialized view:

-- Flag users with total spend above $1,000
CREATE MATERIALIZED VIEW alt_fraud_alerts AS
SELECT
    user_id,
    COUNT(*)          AS txn_count,
    SUM(amount)       AS total_amount,
    MAX(amount)       AS max_single_txn,
    MAX(txn_time)     AS last_txn_time
FROM alt_transactions
GROUP BY user_id
HAVING SUM(amount) > 1000;

Any downstream service can query alt_fraud_alerts with a plain SELECT. New transactions trigger an immediate update to the view. No custom Java operator, no Flink keyed state, no RocksDB tuning.

When to Choose Each System

Choose RisingWave when:

• Your team works primarily in SQL and PostgreSQL tooling
• You want a single system for ingestion, transformation, and serving without an external database
• Your pipelines involve CDC from PostgreSQL, MySQL, or MongoDB
• You need disaggregated storage to avoid local disk pressure at scale
• You want to avoid the JVM and Java build toolchain entirely
• You want a fully open-source system (Apache 2.0) with no production restrictions

RisingWave is the strongest general-purpose choice among the three for teams migrating away from Flink. The Flink to RisingWave migration guide covers the common migration patterns in detail.

Choose ksqlDB when:

• Your entire data infrastructure is built around Confluent Platform or Confluent Cloud
• Every source and sink is a Kafka topic and you want to stay fully inside the Kafka ecosystem
• Your team already knows Kafka Streams internals and the ksqlDB SQL dialect
• You do not need to query results from external PostgreSQL clients

ksqlDB is a capable system within the Confluent ecosystem. Outside that ecosystem, the operational overhead and limited state management make it a harder sell compared to RisingWave.

Choose Materialize when:

• You are comfortable with the BSL license for production use above a single node
• You need complex multi-way joins on live data with low latency
• Your team has experience with Timely Dataflow concepts

Materialize's differential dataflow core handles complex join patterns well. The BSL licensing model is worth evaluating carefully before committing to a production deployment.

The Operational Difference in Practice

One of the most underappreciated advantages of RisingWave over Flink is the operational surface area.

With Flink, a typical production deployment involves: a Flink cluster with JobManager and TaskManagers, Kubernetes operators or YARN, a checkpoint store on S3 or HDFS, a separate metadata store (ZooKeeper or etcd depending on your Flink version), a state backend tuned for your workload (RocksDB configuration), and one or more external databases to serve the results of your streaming jobs. Each of these layers adds monitoring, alerting, on-call burden, and failure modes to manage.

With RisingWave, the operational surface is a database. You deploy it the same way you deploy PostgreSQL: as a managed cloud service (RisingWave Cloud), a Kubernetes operator, or Docker. Monitoring follows database conventions: connection counts, query latency, replication lag. Your data engineers can operate it without a distributed systems background.

For a detailed look at how total cost of ownership compares, see Flink vs RisingWave: Total Cost of Ownership.

SQL Syntax Differences at a Glance

Teams evaluating an alternative to Flink often want to know how much SQL rewriting is involved. The answer depends heavily on which alternative you choose.

For a deeper look at syntax differences between Flink SQL and RisingWave SQL, see Flink SQL vs RisingWave SQL: Syntax and Feature Comparison.

Getting Started with RisingWave

The quickest path from Flink to RisingWave is to run RisingWave locally and test your existing SQL logic:

# Start RisingWave in Docker
docker run -it --pull=always -p 4566:4566 -p 5691:5691 risingwavelabs/risingwave:latest playground

# Connect with psql (any PostgreSQL client works)
psql -h localhost -p 4566 -U root -d dev

From that point, you can translate your Flink SQL sources into RisingWave CREATE SOURCE statements and replace your Flink INSERT INTO sink jobs with CREATE MATERIALIZED VIEW definitions. Results are immediately queryable.

For CDC pipelines specifically, RisingWave connects directly to your upstream database without a Debezium intermediary:

-- Native PostgreSQL CDC source (no Debezium required)
CREATE SOURCE pg_orders WITH (
    connector = 'postgres-cdc',
    hostname  = 'db.internal',
    port      = '5432',
    username  = 'replicator',
    password  = 'secret',
    database.name = 'ecommerce',
    schema.name   = 'public',
    table.name    = 'orders'
);

See the PostgreSQL CDC streaming SQL tutorial for a full walkthrough.

FAQ

Q: Can RisingWave replace Flink completely, or are there cases where Flink is still needed?

RisingWave covers the majority of stream processing use cases that teams actually run in production: Kafka-to-serving pipelines, CDC materialization, real-time aggregations, windowed analytics, and multi-source joins. Flink remains the better choice when you need custom Java operators, the DataStream API for programmatic stream transformation logic, or MATCH_RECOGNIZE for complex event processing. If your workload fits in SQL, RisingWave covers it.

Q: Is RisingWave truly open source? Can I run it in production without a commercial license?

Yes. RisingWave is licensed under Apache 2.0. You can deploy it in production, modify it, and distribute it without any restriction. Materialize shifted to BSL 1.1 in 2023, which restricts production use beyond a single node without a commercial agreement. RisingWave has no such restriction.

Q: How does RisingWave handle exactly-once semantics?

RisingWave provides exactly-once processing guarantees through its checkpoint-based recovery model. State is checkpointed to object storage (S3 or equivalent) at configurable intervals. On failure, the system recovers from the last consistent checkpoint and resumes processing from the corresponding source offset. For Kafka sources, this means no duplicate processing and no data loss across restarts.

Q: What happens to my existing PostgreSQL dashboards and BI tools when I switch to RisingWave?

Nothing changes. RisingWave speaks the PostgreSQL wire protocol, so any tool that connects to PostgreSQL connects to RisingWave the same way. Grafana, Metabase, Tableau, Superset, and custom applications using JDBC or psycopg2 all work without modification. You point the connection string at RisingWave instead of a static PostgreSQL database and your dashboards start showing real-time results.

Q: How does the performance of RisingWave compare to Flink?

RisingWave has published Nexmark benchmark results showing that it outperforms Flink on 22 of 27 standard streaming benchmark queries. The benchmark covers a range of windowed aggregations, joins, and stateful operations. Because RisingWave's state lives in object storage rather than local RocksDB, it also scales to larger state sizes without the disk pressure that causes Flink job instability at scale.

Explore more: Apache Flink vs RisingWave: A Practical Comparison for 2026 | How to Process Kafka Streams Without Flink or Java | RisingWave on GitHub