Modern businesses are experiencing the problem of aligning real-time and chronological data. Several firms have introduced the concept of source technologies that have been successful to some extent. Besides, the balance between real-time and historical data offers various opportunities for modern business applications to function efficiently. Organizations are looking for ways to facilitate operational data that will help them trace a specific data structure. Besides, experts believe that operational data gives better control of data functions and allows software developers to devise an effective cloud infrastructure. In his article for Datanami, Alex Woodie shares how operational data can help you enhance real-time and historical data processes.
How Operational Data Streamlines Business Functions
Venkatesh, one of the co-founders of Macrometa, shares how the usage of operational data allows them to combine existing data patterns per the market standards. He mentions, “We’re taking the data mesh, breaking it apart, and bundling it and making it available in 175 locations around the world, roughly 50 milliseconds away from 90% of devices in the world that can act on the Internet.” The sole purpose of creating Macrometa is to improve the core data concepts while enhancing real-time applications simultaneously.
How Macrometa Uses Operational Data
Most of the Macrometa infrastructure is proprietary except for the RocksDB storage engine. It uses a proprietary system to evaluate and assess the future data volumes that occur at the rate of trillions of events per second. Casual data consistency is considered to be one of the significant breakthroughs in the realm of data architecture. Data experts that use Macrometa have stated that their main goal is to generalize distinct JSON types. It helps them build an effective database with a data engine on top of it to monitor the process.
The Layers of Macrometa
Woodie shares that Macrometa primarily has three layers. It has a data fabric to move the data, a computer layer that facilitates the data process, and a data governance layer. The data governance layers provide a set of guidelines to users regarding data privacy laws. The goal of Macrometa is to deliver a database similar to the pub/sub system and a complex event processing system like Flink.
Click on the link to read the original article: https://www.datanami.com/2022/09/23/has-macrometa-cracked-the-code-for-global-real-time-data/
