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October 19, 2023

Understanding Open Data Architecture and Time Series Data

Anais Dotis-Georgiou

(Blue Planet Studio/Shutterstock)

The easiest way to think about an open data architecture is that it’s the opposite of a vendor-locked-in, closed-system environment. A system designed with open data architectural principles supports seamless data flows between different applications, even if they’re seemingly unrelated, because all data file formats and standards meet the same requirements. Using this model affords developers and stakeholders the opportunity to handpick tools that best suit each part of the workflow.

An open data architecture eliminates silos and allows data teams to collaborate on the same data, deliver data reliability and easily manage data. This kind of architecture is beneficial for significant volumes of data, as is seen with time series data.

Time series data is data with a timestamp associated with it. It comes from a variety of sources including manufacturing, DevOps monitoring, FinTech, AgriTech, application monitoring and much more. Some common examples of time series data include stock prices, IoT data (wind speed, pressure, temperature, humidity, etc.), observability data (metrics, logs, and traces), cybersecurity, and server health data.

Consider a dataset extracted from a windmill within an open data architecture. A team of data scientists analyzes the dataset and uses machine learning (ML) tools to perform predictive analytics. At the same time, a factory operator monitors the model’s performance and windmill data within a real-time dashboard to ensure operational efficiency. This windmill data is most likely time series data.

Building an Open Data System

(corund/Shutterstock)

At first glance, a closed system can seem appealing. Vendors do a great job of putting all the tools you might need (and even some you don’t) in place. But as time goes on, limitations appear. With a closed system, it’s not as easy to add new technologies, as the architecture is locked in and if the vendor doesn’t add new capabilities, then the only options are to live within the limitations or consider a new vendor or an open data system.

While an open data architecture is daunting when compared to the former, there are blueprints to follow when getting started. The component required for seamless transitions between applications and tools already exists. There are technical infrastructures already in place that format and standardize data to meet baseline requirements for interoperability, scalability, and integration.

Intersection of Time Series Data and the Open Data Architecture

A deep understanding of the project’s scope is important when determining what kind of system will be the best fit. For example, building an open data design for time series presents its own challenges. The speed and velocity of time series ingestion and the high amount of data in storage need a time series-specific solution. Understanding an open data architecture, and where it intersects with time series data, not only paves the way for a smoother process but also unlocks countless benefits for developer teams.

Interoperability

Interoperability is arguably the most important aspect of the open data architecture. Interoperability refers to the seamless data exchange between applications, devices, and products. The Apache Foundation aims to provide technology to standardize data formats and transport protocols to facilitate interoperability across open data tools.

Arrow and Parquet are Apache’s open source columnar format tools. Arrow is a framework for defining in-memory columnar data that every processing language can use. It aims to be the language-agnostic standard that helps facilitate interoperability for data with a columnar memory orientation. Columnar storage organizes data by column rather than row. This storage organization benefits time series data because this data type typically generates a larger number of rows than it does columns. Columnar organization allows Parquet to apply compression and encoding to each column independently, which significantly reduces storage requirements.

Data Collection

Collecting time series data is more complex than just sending data to a database. Identifying data sources — which can include sensor data, network events, or even a time series database at the edge sending data to the cloud — is only part of the process. Defining the processes and mechanisms involved with this process is also critical. These may include cleaning, aggregating, and formatting data to ensure quality and consistency before it reaches its final storage place.

For example, Telegraf is an open source data collection agent that facilitates data collection for multiple sources. Telegraf is plugin-based, with over 300 plugins, and it’s open source so any developer can write a custom plugin if one doesn’t already exist.

(Yurchanka Siarhei/Shutterstock)

Storage

A secure, scalable, purpose-built time series database is the best place to store time series data. These kinds of platforms abstract away the complexity of building an open data ecosystemFor example, databases built on Apache’s open data system support large scale storage. Arrow and Parquet bring additional value because they integrate with other analytics, machine learning, and transformation tools. Time series databases with open data architecture are designed to optimize time-stamped data. This approach is tailored for time series data, reducing storage footprint and requirements while maintaining integretity.

Data Visualization

Data visualization helps users understand patterns, trends, and insights hidden within their data. Common visualization tools within the open data ecosystem include Grafana, Tableau, and Apache Superset. These open data tools often offer wide compatibility, allowing developers who monitor time series data to track and better understand their time series data in real time. Several companies leverage open time series data architecture to monitor, alert, and visualize their time series data. Companies also use time series databases as part of their architecture, but differ from one another with the remainder of their tech stacks.

The Bottom Line

Utilizing an open data system means benefiting from the freedom of interoperability and combining tools to create a custom solution for each unique use case. Data collection, storage, visualization, and data analytics serve as pieces of the puzzle making up the open data architecture.

About the author: Anais Dotis-Georgiou is a Developer Advocate for InfluxData with a passion for making data beautiful with the use of Data Analytics, AI, and Machine Learning. She takes the data that she collects, does a mix of research, exploration, and engineering to translate the data into something of function, value, and beauty. When she is not behind a screen, you can find her outside drawing, stretching, boarding, or chasing after a soccer ball.

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