Datadog vs OSI PI Data Historian
A detailed comparison
Compare Datadog and OSI PI Data Historian for time series and OLAP workloads
Learn About Time Series DatabasesChoosing 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 Datadog 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 Datadog 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.
Datadog vs OSI PI Data Historian Breakdown
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 |
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| Database Model | Cloud observability platform |
Time series database/data historian |
| Architecture | Cloud-based SaaS platform |
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 | Close source |
Closed source |
| Use Cases | Infrastructure monitoring, application performance monitoring, log management |
Industrial data management, real-time monitoring, asset health tracking, predictive maintenance, energy management |
| Scalability | Horizontally scalable with built-in support for multi-cloud and global deployments. |
Supports horizontal scaling through distributed architecture, data replication, and data federation for large-scale deployments |
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Datadog Overview
Datadog is a monitoring and analytics platform that integrates and automates infrastructure monitoring, application performance monitoring (APM), and log management to provide unified, real-time observability of an organization’s entire technology stack. Founded in 2010, Datadog has rapidly become a go-to solution for cloud-scale monitoring, offering SaaS-based capabilities that enable businesses to improve agility, increase efficiency, and provide end-to-end visibility across dynamic, high-scale infrastructures.
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.
Datadog for Time Series Data
Datadog excels in handling time series data through its metrics-based architecture. It is optimized for collecting and analyzing data points over time, such as CPU usage, memory consumption, or request latency. While Datadog is not a dedicated time series database, it integrates features like long-term data retention, aggregation, and visualization that make it well-suited for monitoring time-dependent metrics. However, it might not be the ideal choice for massive-scale, real-time analytics compared to specialized time series databases like InfluxDB.
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.
Datadog Key Concepts
- Datadog Agent: The Datadog Agent is a lightweight software installed on your servers, containers, or endpoints to collect and report metrics, logs, and traces. It acts as the primary bridge between your systems and the Datadog platform.
- Dashboards: Dashboards in Datadog provide a customizable interface to visualize metrics, logs, and traces. They support various widgets, including time-series graphs, gauges, and heat maps, to present data in a meaningful way.
- Integration : Datadog supports over 600 integrations to connect with various technologies, such as databases, cloud providers, and container orchestrators. Each integration collects relevant metrics, logs, and events and may require specific configuration via the Agent.
- Events: Events are data that are streamed to Datadog via Agents, integrations, or custom applications. They are streamed to Datadog and can be used for filtering and correlating what is happening in your application
- Tagging : Tags are metadata assigned to metrics, logs, and traces to group, filter, and search data. Effective use of tags, such as environment, region, or service, is crucial for organizing and analyzing data efficiently.
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.
Datadog Architecture
Datadog employs a SaaS (Software-as-a-Service) model with a highly distributed, cloud-based architecture. It uses agents to collect data from various sources, which are then processed and stored in Datadog’s cloud. The platform supports both structured and unstructured data, and its backend utilizes modern distributed systems principles to ensure scalability and reliability. Key components include the data ingestion pipeline, a metrics store, a logs processing system, and a query engine.
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.
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Datadog Features
Real-time dashboards
Datadog offers customizable, real-time dashboards that enable users to monitor a variety of metrics, traces, and logs all in one place. This centralized view aids in quick issue detection and resolution. These dashboards are interactive, enabling drilling down into granular details, facilitating precise troubleshooting and root cause analysis.
Automated alerts
Automated alerts in Datadog can notify teams of any issues or anomalies in real-time. These alerts can be fine-tuned to avoid noise and false positives, ensuring that only actionable insights get attention. They can also be integrated with third-party communication tools like Slack or PagerDuty for a seamless incident response.
Synthetic monitoring
Datadog’s synthetic monitoring allows users to simulate user transactions and monitor uptime, latency, and functionality of applications. This feature ensures that critical endpoints remain available and performant.
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.
Datadog Use Cases
Infrastructure monitoring
One of the primary use-cases for Datadog is real-time infrastructure monitoring. Businesses can keep tabs on servers, containers, databases, and more, all in one place. The comprehensive coverage helps teams quickly identify performance bottlenecks or availability issues, thereby minimizing downtime and enhancing system reliability.
Application performance monitoring
Datadog’s APM capabilities enable organizations to trace requests as they traverse through various services and components of an application. This is essential for microservices architectures where understanding the interactions between services can be complex. It helps in identifying slow services that could be affecting the application’s overall performance.
Security monitoring
Datadog assists organizations in monitoring security-related events by collecting logs and metrics from various sources. It helps in detecting unusual activities, unauthorized access, and potential threats. By correlating data across the stack, security teams can investigate incidents more effectively. Datadog’s compliance monitoring features support adherence to standards like PCI DSS, HIPAA, and GDPR.
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.
Datadog Pricing Model
Datadog uses a modular, usage-based pricing model where customers pay based on the specific products and volume of data they use. Pricing is typically divided among different products like Infrastructure Monitoring, APM, Logs, and more. Each product has its own pricing structure, often based on the number of hosts, instances, or data ingested. Datadog offers a Free tier with limited features and data caps, as well as Pro and Enterprise tiers that provide advanced features and higher limits.
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.
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