How does Apache Spark fit into a modern data lakehouse?

Apache Spark fits into a modern data lakehouse as the primary large-scale processing engine, handling everything from SQL analytics and machine learning to streaming ingestion. It sits on top of open table formats and shared object storage, giving teams a single engine that can replace both a traditional data warehouse and a separate batch processing layer. The questions below unpack how that works in practice, including where Spark excels, where it struggles, and how the Stackable Data Platform (SDP) supports it in production.

What role does Apache Spark play in lakehouse architecture?

In a data lakehouse, Apache Spark serves as the general-purpose compute engine. It reads from and writes to open table formats stored in object storage, applies transformations at scale, and feeds downstream analytics, machine learning pipelines, and reporting layers. Spark doesn’t manage storage itself, it relies on the table format and storage layer to handle that.

The lakehouse model separates storage from compute. Storage is cheap and open, typically object storage like S3-compatible systems. Compute is elastic and pluggable. Spark fits naturally into that model because it’s stateless by design: you spin up a cluster, run a job, and release the resources. That makes it well-suited for workloads that vary significantly in scale from day to day.

Spark also integrates with a metastore, usually Apache Hive™ or a compatible catalog, which tracks table schemas and locations. That catalog layer is what allows multiple engines to share the same data without stepping on each other.

How does Spark interact with open table formats like Iceberg and Delta Lake?

Apache Spark™ reads and writes Apache Iceberg™ and Delta Lake tables through native connectors, treating them as first-class table sources. These formats add ACID transaction support, schema evolution, time travel, and partition pruning on top of raw Parquet or ORC files. Spark uses the format’s metadata layer to understand what files to read and write, without scanning the entire storage prefix.

With Apache Iceberg in particular, Spark can perform row-level deletes, upserts, and merge operations that were historically only possible in a traditional database. This is what makes the lakehouse model viable for use cases like CDC (change data capture) ingestion or GDPR-compliant data deletion, where you need to modify existing records rather than just append new ones.

The integration also supports snapshot isolation. Multiple Spark jobs can read a consistent view of a table while another job is writing to it, which removes a whole category of coordination problems that plagued earlier data lake architectures.

What’s the difference between using Spark in a data lake versus a data lakehouse?

In a traditional data lake, Spark operates on raw files with no transactional guarantees. In a data lakehouse, it operates on structured, versioned tables with ACID semantics. The underlying storage might look similar, but the behavior and reliability are fundamentally different.

In a data lake setup, a failed Spark job can leave partial files behind, corrupting the dataset. Concurrent writes from two jobs can produce inconsistent results. Schema changes require manual coordination. Reading performance degrades over time as small files accumulate. None of these problems have clean solutions at the storage layer alone.

The lakehouse model, with an open table format sitting between Spark and raw storage, addresses all of this. Failed writes are rolled back. Concurrent writes are serialized or rejected cleanly. Schema evolution is tracked in the table metadata. File compaction is a first-class operation. The result is that Spark in a lakehouse behaves more like Spark against a database than Spark against a file system, which is a meaningful reliability improvement for production workloads.

Can Apache Spark handle both batch and streaming workloads in a lakehouse?

Yes. Apache Spark™ supports both batch processing and structured streaming within the same engine and API. Structured Streaming treats a live data stream as an unbounded table, applying the same DataFrame and SQL operations you’d use in batch mode. Teams can reuse transformation logic across batch and streaming pipelines without maintaining two separate codebases.

In a lakehouse context, this is particularly useful for incremental ingestion. A Spark Structured Streaming job can read from Apache Kafka®, apply transformations, and write continuously to an Iceberg or Delta table, making data available for analytics within minutes rather than hours. Downstream batch jobs then read from the same table, seeing a consistent snapshot.

That said, Spark’s streaming model is micro-batch by default, not true event-at-a-time streaming. For workloads requiring very low latency, sub-second processing, or complex stateful event patterns, a dedicated streaming engine is probably a better fit. Spark streaming works well for near-real-time ingestion and transformation, not for latency-sensitive applications like fraud detection at millisecond scale.

How does Spark fit alongside other engines like Trino in a lakehouse?

Spark and Trino serve different purposes in a lakehouse and are commonly deployed together. Trino is a distributed SQL query engine optimized for fast, interactive queries across large datasets. Spark is a general-purpose processing engine optimized for complex transformations, machine learning, and large batch jobs. They share the same storage and table format, but they’re not interchangeable.

A common pattern is to use Spark for data ingestion, transformation, and enrichment, and Trino for ad hoc analytics and BI tool queries. Spark writes data into Iceberg tables; Trino reads from those same tables to serve dashboards and analyst queries. Because both engines respect the open table format’s metadata, they see a consistent view of the data without any synchronization layer between them.

This multi-engine approach is one of the defining characteristics of a mature lakehouse. No single engine is best at everything, and the open table format acts as the interoperability layer that makes engine choice a technical decision rather than a vendor commitment. You can explore working demos that show how these engines interact in practice.

What are the main challenges of running Spark in a production lakehouse?

Running Apache Spark™ reliably in production comes with several recurring challenges: resource management, dependency isolation, job observability, and table maintenance. None of these resolve themselves automatically, each requires deliberate engineering effort.

Resource management and cost

Spark jobs are memory-intensive and can compete for cluster resources if not carefully configured. Executor sizing, shuffle partition counts, and memory fractions all affect whether a job succeeds or fails. In a shared environment, poorly tuned jobs can starve other workloads. Dynamic resource allocation helps, but it requires a properly configured external shuffle service or a shuffle-tracking mechanism.

Table maintenance and file sprawl

Open table formats introduce their own maintenance requirements. Iceberg and Delta tables accumulate small files over time, especially under streaming ingestion. Compaction jobs need to run regularly to keep query performance acceptable. Snapshot expiry and orphan file cleanup are also necessary to control storage costs. In most deployments, none of this happens automatically, it requires scheduled maintenance jobs, which adds operational overhead.

How does Kubernetes change how Spark is deployed in a lakehouse?

Kubernetes shifts Apache Spark™ deployment from a static cluster model to an on-demand, containerized one. Instead of maintaining a persistent Spark cluster, you submit jobs directly to Kubernetes, and the cluster scheduler provisions executor pods for the duration of the job and releases them when it finishes. This reduces idle resource consumption and aligns compute cost with actual usage.

Kubernetes also standardizes how Spark is packaged and deployed. Spark applications run as container images, which makes dependency management, versioning, and environment reproducibility much more tractable. The same image that runs in a development namespace can run in production without environment drift.

The tradeoff is operational complexity. Kubernetes adds its own layer of configuration: pod templates, resource quotas, service accounts, and network policies all need to be set up correctly for Spark to function. Dynamic provisioning of executor pods also introduces startup latency that a persistent cluster doesn’t have. For teams already operating Kubernetes infrastructure, these tradeoffs are generally favorable. For teams new to Kubernetes, the learning curve is real.

Spark on Kubernetes also integrates well with the broader lakehouse stack. Storage is accessed via S3-compatible APIs, catalogs run as services, and monitoring integrates with Prometheus and Grafana, all standard Kubernetes-native tooling. This makes the Kubernetes-native approach a natural fit for teams building a lakehouse on modern infrastructure.

How Stackable supports Apache Spark in a production lakehouse

The SDP provides a Kubernetes-native operator for Apache Spark™ that handles the operational complexity described above. Rather than managing Spark configurations manually, you define your Spark applications as Kubernetes custom resources, and the operator handles scheduling, lifecycle management, and integration with the rest of the platform.

Concretely, the SDP gives you:

A Spark operator that manages job submission, executor provisioning, and cleanup on Kubernetes
Integrated support for Apache Iceberg as the default open table format, with Trino available as a query engine alongside Spark
Monitoring out of the box via Prometheus and Grafana, so you have visibility into job performance without additional instrumentation
An infrastructure-as-code approach where your entire lakehouse stack, including Spark, Trino, Kafka, and the catalog, is defined declaratively and version-controlled
No vendor lock-in: the SDP runs on-premises, in any cloud, or in a hybrid environment, and all components are open source

If you’re building or migrating a data lakehouse and want to understand how this fits your specific setup, get in touch with the Stackable team to discuss your architecture.

Apache Kafka® is a registered trademark of The Apache Software Foundation. Apache Spark™, Apache Hive™, Apache Iceberg™, and Apache ZooKeeper™ are trademarks of The Apache Software Foundation. Trino is a trademark of the Trino Software Foundation. All other trademarks are the property of their respective owners.