Flink vs Spark in the Lakehouse Era

Flink vs Spark in the Lakehouse Era | Nahraf

The Spark‑Flink Standoff Has Reached a Tipping Point

If you have been scrolling Hacker News or X this week, you have seen the same headline repeated: "Lakehouse is the future of data engineering". Companies are ripping out their old ETL pipelines and rebuilding on top of Delta Lake, Apache Iceberg, or Hudi. In that rush, a second debate is heating up behind the scenes – which execution engine will become the default for the lakehouse: Apache Spark or Apache Flink?

My hot take: Flink is already outpacing Spark for real‑time workloads, and the lakehouse community is quietly pivoting toward Flink because it solves the latency‑consistency paradox that Spark still wrestles with. If you keep betting on Spark alone, you are ignoring a wave that will reshape data engineering in the next 12‑18 months.

Why the Lakehouse Matters Right Now

A lakehouse combines the low‑cost storage of an object store (S3, GCS, Azure Blob) with the transactional guarantees of a warehouse. The key promise is one copy of truth for batch and streaming. Tools like Delta Lake, Iceberg, and Hudi provide ACID semantics, schema evolution, and time travel.

From a developer’s perspective, the lakehouse eliminates the classic "batch‑only" vs "stream‑only" silos. You can write a single pipeline that ingests clickstream data, enriches it, and writes back to the same table that your BI tools query.

The Core Technical Challenge

To make this promise real, the engine must deliver two things simultaneously:

Sub‑second end‑to‑end latency for streaming queries.

Exactly‑once semantics when reading and writing to the same lakehouse table.

Spark Structured Streaming achieved exactly‑once on write, but its micro‑batch model still adds a 5‑10 second tail latency. Flink, on the other hand, was built for true stream processing with a low‑latency, event‑time driven model.

The Spark Argument: Maturity and Ecosystem

Supporters of Spark point to its massive ecosystem – MLlib, GraphX, Spark SQL, and the fact that most data engineers already know PySpark or Scala Spark. The Spark community has also added Continuous Processing mode, claiming sub‑second latency.

However, Continuous Processing is still experimental, and many production teams avoid it because it requires a custom sink and lacks the rich connector catalog that Flink provides out of the box.

Real‑World Example: Netflix

Netflix runs a hybrid stack: Spark for nightly feature engineering and Flink for real‑time personalization. Their public talks repeatedly mention that Spark cannot meet the 100‑ms SLA for recommendation updates, so they off‑load that to Flink.

Flink’s Edge in the Lakehouse World

1. True Event‑Time Processing

Flink’s Watermark and State‑Backend architecture let you handle out‑of‑order events without the artificial batch windows Spark imposes. This means you can keep the lakehouse table up‑to‑date with true event time, not just processing time.

2. Unified Connectors for Iceberg, Delta, Hudi

The Flink community has shipped native connectors for Apache Iceberg, Delta Lake, and Apache Hudi. These connectors support read‑write semantics with exactly‑once guarantees via the Two‑Phase Commit protocol. Spark’s Iceberg connector is solid, but Flink’s implementation is more tightly integrated with its checkpointing model.

3. Stateful Stream Processing as First‑Class Citizen

With Flink you can keep per‑key state (e.g., user session aggregates) in a fault‑tolerant way and query that state directly from the lakehouse using Flink SQL. Spark only recently added support for stateful aggregations, and it still lags behind Flink’s performance.

Code Showdown: Writing to an Iceberg Table

Below are minimal examples of writing a simple transformation to an Iceberg table in both engines. Notice how Flink’s code stays fully streaming, while Spark falls back to a micro‑batch trigger.

scala
// Spark Structured Streaming (Scala)
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._

val spark = SparkSession.builder()
  .appName("spark-iceberg")
  .getOrCreate()

val source = spark.readStream
  .format("kafka")
  .option("kafka.bootstrap.servers", "kafka:9092")
  .option("subscribe", "events")
  .load()

val parsed = source.selectExpr("CAST(value AS STRING) as json")
  .select(from_json(col("json"), schema).as("data"))
  .select("data.*")

parsed.writeStream
  .format("iceberg")
  .option("path", "s3://lakehouse/events")
  .outputMode("append")
  .trigger(processingTime = "5 seconds")
  .start()
  .awaitTermination()

java
// Flink DataStream API (Java)
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.api.*;
import org.apache.flink.connector.file.sink.FileSink;
import org.apache.iceberg.flink.TableLoader;

public class FlinkIceberg {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);

        // Kafka source
        tEnv.executeSql("""
            CREATE TABLE kafka_events (
                user_id STRING,
                event_time TIMESTAMP(3),
                event_type STRING,
                payload MAP<STRING, STRING>
            ) WITH (
                'connector' = 'kafka',
                'topic' = 'events',
                'properties.bootstrap.servers' = 'kafka:9092',
                'format' = 'json',
                'scan.startup.mode' = 'earliest-offset'
            )
        """);

        // Iceberg sink with exactly-once
        tEnv.executeSql("""
            CREATE TABLE iceberg_events (
                user_id STRING,
                event_time TIMESTAMP(3),
                event_type STRING,
                payload MAP<STRING, STRING>
            ) WITH (
                'connector' = 'iceberg',
                'catalog-name' = 'my_catalog',
                'catalog-type' = 'hadoop',
                'warehouse' = 's3://lakehouse/',
                'format-version' = '2',
                'write.format.default' = 'parquet',
                'streaming' = 'true'
            )
        """);

        // Continuous insert
        tEnv.executeSql("INSERT INTO iceberg_events SELECT * FROM kafka_events");
        env.execute();
    }
}

Key takeaways:

Flink’s streaming = 'true' flag tells Iceberg to use the two‑phase commit protocol, guaranteeing exactly‑once without extra tricks.

Spark needs a trigger interval, which adds latency and can cause duplicate rows if a batch fails.

The Business Implications

Cost Efficiency – Flink’s low‑latency model means you can run smaller, more frequent jobs instead of large nightly batches. That translates to lower compute spend on spot instances.

Developer Velocity – With Flink SQL you can write streaming queries that look identical to batch SQL, reducing the learning curve for analysts.

Future‑Proofing – As more vendors (Starburst, Snowflake, Databricks) expose Flink‑compatible connectors, the skill set you invest in today will stay relevant.

Counter‑Arguments and When Spark Still Wins

Legacy Codebases – If your organization has 5+ years of PySpark jobs, rewriting everything in Flink may not be worth the immediate ROI.

Machine Learning Integration – Spark’s MLlib and seamless integration with Databricks notebooks still give it an edge for batch‑heavy ML pipelines.

Community Support – Spark’s documentation is broader, and you will find more StackOverflow answers for common pitfalls.

But note: even the biggest Spark proponents are now adding Flink to their stack for latency‑critical paths. The trend is hybrid, not zero‑sum.

The Verdict: Bet on Flink for the Lakehouse, Keep Spark for Heavy‑Lift Batch

If you are building a new data platform in 2024, start with Flink as the streaming backbone and use Spark for periodic batch enrichment or large‑scale ML training. This hybrid approach gives you the best of both worlds and positions you for the next wave of lakehouse innovations such as SQL‑based governance, real‑time materialized views, and universal catalog services.

Bottom line: The lakehouse is not a Spark‑only club. The industry is quietly shifting toward Flink for the real‑time core, and ignoring that shift will leave you stuck with higher latency, higher cost, and a less competitive data product.

Actionable Steps for Your Team

Evaluate your current latency SLAs. If you need sub‑second, spin up a small Flink cluster on Kubernetes (Flink operator) and run a pilot.

Add the Iceberg connector to Flink and migrate one high‑traffic streaming source.

Keep Spark for nightly feature engineering jobs, but plan to deprecate them over the next 12 months.

Invest in training: Flink SQL is similar to Spark SQL, so your analysts can adopt it quickly.

Monitor cost metrics; you will often see a 20‑30% reduction in compute spend after moving to true streaming.

The conversation is just beginning. Join the debate on X, share your experiences, and watch the lakehouse ecosystem evolve. The next big headline will likely read "Flink Powers the Real‑Time Lakehouse at Scale" – be ready to claim a front‑row seat.

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