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Apache Spark and Akka are two different technologies with different use cases and functionalities. Here are some of their differences:

1. Purpose: Apache Spark is a distributed computing system designed for big data processing and analytics, while Akka is a toolkit and runtime environment for building distributed systems and applications.

2. Programming Languages: Apache Spark supports a range of programming languages, including Java, Scala, and Python, while Akka is primarily used with Scala and Java.

3. Application Architecture: Apache Spark follows a batch processing or streaming model, which means it processes data in batches or near real-time streams, while Akka follows an actor model, where computation is done by messages passed between actors.

4. Fault-Tolerance: Apache Spark provides built-in fault-tolerance mechanisms, while Akka offers configurable and customizable fault tolerance using supervisor hierarchies.

5. Data Processing: Apache Spark uses RDDs (Resilient Distributed Datasets) for data processing, while Akka uses actors and messages for parallel computation.

6. Scalability: Both technologies are designed to be scalable, with Apache Spark scaling horizontally by adding more nodes to the cluster, while Akka provides a flexible distributed architecture for building fault-tolerant and scalable applications.