Apache Cassandra vs OSI PI Data Historian

Choosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of Apache Cassandra and OSI PI Data Historian so you can quickly see how they compare against each other.

The primary purpose of this article is to compare how Apache Cassandra and OSI PI Data Historian perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.

Apache Cassandra Overview

Apache Cassandra is a highly scalable, distributed, and decentralized NoSQL database designed to handle large amounts of data across many commodity servers. Originally created by Facebook, Cassandra is now an Apache Software Foundation project. Its primary focus is on providing high availability, fault tolerance, and linear scalability, making it a popular choice for applications with demanding workloads and low-latency requirements.

OSI PI Data Historian Overview

OSI PI, also known as OSIsoft PI System, is an enterprise-level data management and analytics platform specifically designed for handling time series data from industrial processes, sensors, and other sources. Developed by OSIsoft (acquired by AVEVA in 2021), the PI System has been widely used in various industries such as energy, manufacturing, utilities, and pharmaceuticals since its introduction in the 1980s. It provides the ability to collect, store, analyze, and visualize large volumes of time series data in real-time, allowing organizations to gain insights, optimize processes, and improve decision-making.

Apache Cassandra for Time Series Data

Cassandra can be used for handling time series data due to its distributed architecture and support for time-based partitioning. Time series data can be efficiently stored and retrieved using partition keys based on time ranges, ensuring quick access to data points.

OSI PI Data Historian for Time Series Data

OSI PI was created for storing time series data, making it an ideal choice for organizations that need to manage large volumes of sensor and process data. Its architecture and components are optimized for collecting, storing, and analyzing time series data with high efficiency and minimal latency. The PI System’s scalability and performance make it a suitable solution for organizations dealing with vast amounts of data generated by industrial processes, IoT devices, or other sources.

Apache Cassandra Key Concepts

• Column Family: Similar to a table in a relational database, a column family is a collection of rows, each consisting of a key-value pair.
• Partition Key: A unique identifier used to distribute data across multiple nodes in the cluster, ensuring even distribution and fast data retrieval.
• Replication Factor: The number of copies of data stored across different nodes in the cluster to provide fault tolerance and high availability.
• Consistency Level: A configurable parameter that determines the trade-off between read/write performance and data consistency across the cluster.

OSI PI Data Historian Key Concepts

• PI Server: The core component of the PI System, responsible for data collection, storage, and management.
• PI Interfaces and PI Connectors: Software components that collect data from various sources and send it to the PI Server.
• PI Asset Framework: A modeling framework that allows users to create a hierarchical structure of assets and their associated metadata, making it easier to understand and analyze data.
• PI DataLink: An add-in for Microsoft Excel that enables users to access and analyze PI System data directly from Excel.
• PI ProcessBook: A visualization tool for creating interactive, graphical displays of PI System data.

Apache Cassandra Architecture

Cassandra uses a masterless, peer-to-peer architecture, in which all nodes are equal, and there is no single point of failure. This design ensures high availability and fault tolerance. Cassandra’s data model is a hybrid between a key-value and column-oriented system, where data is partitioned across nodes based on partition keys and stored in column families. Cassandra supports tunable consistency, allowing users to adjust the balance between data consistency and performance based on their specific needs.

OSI PI Data Historian Architecture

OSI PI is a data management platform built around the PI Server, which is responsible for data collection, storage, and management. The PI System uses a highly efficient, proprietary time series database to store data. PI Interfaces and PI Connectors collect data from various sources and send it to the PI Server. The PI Asset Framework (AF) allows users to model their assets and their associated data in a hierarchical structure, making it easier to understand and analyze the data. Various client tools, such as PI DataLink and PI ProcessBook, enable users to access and visualize data stored in the PI System.