As technology evolves and we are able to collect more and more data, the scale of time-series data sets is continuously increasing. The rapidly expanding size of these data sets means that future-ready data historians and pricing models are essential for companies going forward.
Legacy systems often cannot keep up with the growth of data in modern usage scenarios and require constant hardware expansion, increasing your costs dramatically. At the same time, other purpose-built time-series data systems may not offer sufficient flexibility or integration, limiting their ability to keep TCO down. Moving your industrial data operations to TDengine — no matter whether you choose TDengine Cloud or an on-premises deployment — can significantly reduce your hard and soft costs.
Industry-Leading Performance
Many enterprises are still using general-purpose database management systems such as MySQL or MongoDB to process their time-series data sets. Unlike a purpose-built time series database (TSDB), these systems are not designed to handle or to make use of the characteristics of time-series data. As your data set becomes larger, it is necessary to purchase more and more hardware resources simply to provide acceptable performance.
TDengine is at least 10 times faster than general databases, and its data compression ratio is at least 5 times higher. With this performance increase, you can get superior results without adding hardware costs. In addition, according to a publicly available TSBS evaluation, TDengine significantly outperforms other time-series databases with up to 16 times faster ingestion and over 100 times higher query performance than InfluxDB or TimescaleDB while requiring fewer storage resources.
As data systems grow, hardware and cloud resource costs can quickly spiral out of control. Higher performance systems can significantly reduce these costs because they require fewer resources to deliver the same results. Because TDengine ingests data faster, stores data more efficiently, and responds to queries more quickly, it uses fewer CPU and storage resources and adds less to your bills.
For more information, see High Performance Time-Series Database.
Easy to Use with No Learning Costs
TDengine is easier to use than other time-series database solutions and does not require specialized training. The three main reasons are that it supports standard SQL, is easy to integrate with third-party tools, and comes with client libraries for various programming languages, including sample code.
Unlike competing products such as InfluxDB and Prometheus, TDengine has full support for standard SQL as its query language. SQL has been the industry standard query language for decades, and everyone from data science undergraduates to industry veterans is already familiar with it.
By supporting SQL, TDengine greatly reduces its learning curve and enables frontline staff, data analysts, database administrators, and others to get started quickly without having to learn a specialized language. In addition, SQL support means that it is easy to integrate TDengine with the rest of your database stack for analytics, visualization, and other operations, because these products generally support SQL out of the box.
In addition to SQL support, TDengine provides a variety of materials that make it easier to use than competing time-series database solutions. It includes client libraries for various programming languages, with sample code that can be used out of the box, so that you can quickly and easily develop applications that make use of your time-series data. Ingesting data from many different sources, such as Telegraf and Prometheus, and sending data to various analytics and visualization tools such as Grafana can all be performed in a matter of minutes, and the processes are fully documented on TDengine Docs.
For more information, see Easy Time-Series Data Platform.
Simplified, Fully Integrated Solution
TDengine is unique in that it includes three major components of a time-series data platform — stream processing, caching, and data subscription — built in to the system at no extra cost.
Generally speaking, incoming data from your data sources is first written to a message queue such as Apache Kafka. The messages in Kafka are then consumed by a caching product such as Redis, by a database, and potentially by a stream processing engine like Apache Spark. This means that you need to deploy and maintain Kafka, Redis, and Spark in addition to your database, requiring specialized personnel and making fault location more difficult.
By including stream processing, caching, and data subscription, TDengine eliminates the need to deploy third-party products just to process time-series data. Its components are simple, easy to use, and purpose-built to process time-series data. Non-tech companies in particular benefit from this setup as they generally do not have the technical staff to support multiple open-source components.
However, TDengine is an excellent choice even for companies that want to continue using these products thanks to its integration capabilities. For example, TDengine can easily be configured to consume messages from a Kafka deployment.
For more information, see Simplified Time-Series Data Solution.
Get Started
By registering for a TDengine Cloud account, you receive a free month of access so that you can perform a proof of concept (POC) and ensure that TDengine is a good fit for your business scenario before you pay a single cent. Afterward, you can choose from a variety of pricing plans based on compute resources. You can also use the open-source TDengine Community Edition on premises at no cost.
With its high performance, standard SQL support, component integration, and affordable pricing, TDengine can reduce your total cost of operations by 50%.