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 AWS DynamoDB 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 AWS DynamoDB 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.

AWS DynamoDB vs OSI PI Data Historian Breakdown


 
Database Model

Key-value and document store

Time series database/data historian

Architecture

DynamoDB is a fully managed, serverless NoSQL database provided by Amazon Web Services (AWS). It uses a single-digit millisecond latency for high-performance use cases and supports both key-value and document data models. Data is partitioned and replicated across multiple availability zones within an AWS region, and DynamoDB supports eventual or strong consistency for read operations

OSIsoft PI System is a suite of software products designed for real-time data collection, storage, and analysis of time series data in industrial environments. The PI System is built around the PI Server, which stores, processes, and serves data to clients, and it can be deployed on-premises or in the cloud.

License

Closed source

Closed source

Use Cases

Serverless web applications, real-time bidding platforms, gaming leaderboards, IoT data management, high-velocity data processing

Industrial data management, real-time monitoring, asset health tracking, predictive maintenance, energy management

Scalability

Automatically scales to handle large amounts of read and write throughput, supports on-demand capacity and auto-scaling, global tables for multi-region replication

Supports horizontal scaling through distributed architecture, data replication, and data federation for large-scale deployments

Looking for the most efficient way to get started?

Whether you are looking for cost savings, lower management overhead, or open source, InfluxDB can help.

AWS DynamoDB Overview

Amazon DynamoDB is a managed NoSQL database service provided by AWS. It was first introduced in 2012, and it was designed to provide low-latency, high-throughput performance. DynamoDB is built on the principles of the Dynamo paper, which was published by Amazon engineers in 2007, and it aims to offer a highly available, scalable, and distributed key-value store.

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.


AWS DynamoDB for Time Series Data

DynamoDB can be used with time series data, although it may not be the most optimized solution compared to specialized time series databases. To store time series data in DynamoDB, you can use a composite primary key with a partition key for the entity identifier and a sort key for the timestamp. This allows you to efficiently query data for a specific entity and time range. However, DynamoDB’s main weakness when dealing with time series data is its lack of built-in support for data aggregation and downsampling, which are common requirements for time series analysis. You may need to perform these operations in your application or use additional services like AWS Lambda to process the data.

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.


AWS DynamoDB Key Concepts

Some of the key terms and concepts specific to DynamoDB include:

  • Tables: In DynamoDB, data is stored in tables, which are containers for items. Each table has a primary key that uniquely identifies each item in the table.
  • Items: Items are individual records in a DynamoDB table, and they consist of one or more attributes.
  • Attributes: Attributes are key-value pairs that make up an item in a table. DynamoDB supports scalar, document, and set data types for attributes.
  • Primary Key: The primary key uniquely identifies each item in a table, and it can be either a single-attribute partition key or a composite partition-sort key.

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.


AWS DynamoDB Architecture

DynamoDB is a NoSQL database that uses a key-value store and document data model. It is designed to provide high availability, durability, and scalability by automatically partitioning data across multiple servers and using replication to ensure fault tolerance. Some of the main components of DynamoDB include:

  • Partitioning: DynamoDB automatically partitions data based on the partition key, which ensures that data is evenly distributed across multiple storage nodes.
  • Replication: DynamoDB replicates data across multiple availability zones within an AWS region, providing high availability and durability.
  • Consistency: DynamoDB offers two consistency models: eventual consistency and strong consistency, allowing you to choose the appropriate level of consistency for your application.

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.

Free Time-Series Database Guide

Get a comprehensive review of alternatives and critical requirements for selecting yours.

AWS DynamoDB Features

Auto scaling

DynamoDB can automatically scale its read and write capacity based on the workload, allowing you to maintain consistent performance without over-provisioning resources.

Backup and restore

DynamoDB provides built-in support for point-in-time recovery, enabling you to restore your table to a previous state within the last 35 days.

Global tables

DynamoDB global tables enable you to replicate your table across multiple AWS regions, providing low-latency access and data redundancy for global applications.

Streams

DynamoDB Streams capture item-level modifications in your table and can be used to trigger AWS Lambda functions for real-time processing or to synchronize data with other AWS services.

OSI PI Data Historian Features

Data collection and storage

OSI PI’s PI Interfaces and PI Connectors enable seamless data collection from a wide variety of sources, while the PI Server efficiently stores and manages the data.

Scalability

The PI System is highly scalable, allowing organizations to handle large volumes of data and a growing number of data sources without compromising performance.

Asset modeling

The PI Asset Framework (AF) provides a powerful way to model assets and their associated data, making it easier to understand and analyze complex industrial processes.

Data visualization

Tools like PI DataLink and PI ProcessBook enable users to analyze and visualize data stored in the PI System, facilitating better decision-making and process optimization.


AWS DynamoDB Use Cases

Session management

DynamoDB can be used to store session data for web applications, providing fast and scalable access to session information.

Gaming

DynamoDB can be used to store player data, game state, and other game-related information for online games, providing low-latency and high-throughput performance.

Internet of Things

DynamoDB can be used to store and process sensor data from IoT devices, enabling real-time monitoring and analysis of device data.

OSI PI Data Historian Use Cases

Process optimization

OSI PI can help organizations identify inefficiencies, monitor performance, and optimize their industrial processes by providing real-time insights into time series data from sensors and other sources.

Predictive maintenance

By analyzing historical data and detecting patterns or anomalies, OSI PI enables organizations to implement predictive maintenance strategies, reducing equipment downtime and maintenance costs.

Energy management

OSI PI can be used to track energy consumption across various assets and processes, allowing organizations to identify areas for improvement and implement energy-saving measures.


AWS DynamoDB Pricing Model

DynamoDB offers two pricing options: provisioned capacity and on-demand capacity. With provisioned capacity, you specify the number of reads and writes per second that you expect your application to require, and you are charged based on the amount of provisioned capacity. This pricing model is suitable for applications with predictable traffic or gradually ramping traffic. You can use auto scaling to adjust your table’s capacity automatically based on the specified utilization rate, ensuring application performance while reducing costs.

On the other hand, with on-demand capacity, you pay per request for the data reads and writes your application performs on your tables. You do not need to specify how much read and write throughput you expect your application to perform, as DynamoDB instantly accommodates your workloads as they ramp up or down. This pricing model is suitable for applications with fluctuating or unpredictable traffic patterns.

OSI PI Data Historian Pricing Model

Pricing for OSI PI is typically based on a combination of factors such as the number of data sources, the number of users, and the level of support required. Pricing details are not publicly available, as they are provided on a quote basis depending on the specific needs of the organization.