Building a Satellite Data Analytics Platform with Apache Spark, Apache Cassandra, and Apache Zeppelin

Introduction to Satellite Data Analytics

When dealing with large amounts of satellite data, traditional analytics tools often fall short. Last quarter, our team discovered the need for a scalable and efficient data analytics platform to handle the vast amounts of data generated by our satellite network. We chose to build our platform using Apache Spark 3.4 for data processing, Apache Cassandra 4.1 for data storage, and Apache Zeppelin 0.10.1 for data visualization and exploration.

The Problem with Traditional Analytics Tools

Traditional analytics tools are not designed to handle the scale and complexity of satellite data. They often become bottlenecked when dealing with large datasets, leading to slow processing times and poor performance. We needed a solution that could handle our data at scale and provide fast processing times.

Why Apache Spark, Apache Cassandra, and Apache Zeppelin?

Apache Spark 3.4 is a powerful data processing engine that can handle large-scale data processing. It provides high-level APIs in Java, Python, and Scala, as well as a highly optimized engine that supports general execution graphs. Apache Cassandra 4.1 is a highly scalable and fault-tolerant NoSQL database that can handle large amounts of data across many commodity servers with minimal latency. Apache Zeppelin 0.10.1 is a web-based notebook that enables data-driven, interactive analytics and collaborative documents with SQL, Scala, and Python.

Step-by-Step Implementation

To build our satellite data analytics platform, we followed these steps:

Setup Apache Spark 3.4: We started by setting up Apache Spark 3.4 on our cluster. This involved installing the necessary dependencies and configuring the Spark environment.