A typical big data technology stack using Apache Spark might involve several components for data storage, processing, and analysis. Here's an example of a big data tech stack that incorporates Apache Spark:

1. Data storage:

   • Hadoop Distributed File System (HDFS): A distributed file system for storing large datasets across multiple nodes in a cluster. HDFS provides fault tolerance, high availability, and scalability.
   • Apache Cassandra: A highly scalable, distributed NoSQL database designed for handling large amounts of data across many nodes with no single point of failure.

2. Data processing:

   • Apache Spark: A fast, general-purpose cluster-computing system for big data processing and analytics. It can process data stored in HDFS, Cassandra, and other data storage systems.

3. Data ingestion and integration:

   • Apache Kafka: A distributed data streaming platform for building real-time data pipelines and streaming applications. Kafka is often used to ingest and process data from various sources into Spark for further processing and analysis.

4. Machine learning and advanced analytics:

   • Apache Spark MLlib: A machine learning library that comes with Spark, providing scalable and distributed implementations of various machine learning algorithms.
   • H2O.ai: An open-source machine learning and artificial intelligence platform that can integrate with Spark for large-scale data processing and model training.

5. Data visualization and reporting:

   • Apache Zeppelin: A web-based notebook for interactive data analytics and visualization, which supports Spark and other big data processing engines.
   • Tableau: A popular data visualization tool that can connect to Spark for analyzing and visualizing big data.

6. Cluster management and deployment:

   • Apache Mesos: A distributed systems kernel for managing resources and scheduling tasks across a cluster of machines.
   • Apache Hadoop YARN: A cluster management technology that is part of the Hadoop ecosystem and can be used to manage resources for Spark applications.

In this example tech stack, data is stored in HDFS and Cassandra, ingested and processed using Kafka, analyzed and processed using Apache Spark, and visualized with Apache Zeppelin or Tableau. Machine learning tasks can be performed using Spark MLlib or H2O.ai. Cluster management and deployment are handled by Apache Mesos or Hadoop YARN.

Keep in mind that this is just one example of a big data tech stack with Apache Spark. The actual components and configurations may vary depending on your specific use case and requirements.