The in-memory computing platform also provided an advantage when This means performing calculations. The key-value database required the data to be modeled based on access patterns where each numeric operation ? such as average/sum/min/max/group by/count required a key ? while This means the in-memory computing platform ran these common and custom aggregations natively on the server-side in a distributed manner and in extreme performance.
There was also a problem with accuracy
Complex performance queries on key-value databases do not dataset always provide accurate results. Since key-value stores are typically optimized for high throughput and low latency of single-record reads/writes ? ACID (Atomicity ? Consistency ? Isolation ? Durability) properties are guaranteed only at the single-record level. across two or more multi-record transactions can result in incorrect results.
The selection of a modern data platform solved the problem for the break down each chapter as you write. C02 calculator. The implemented solution delivered a 15-19 milliseconds query and analytics response time. The infrastructure footprint was reduced by a factor of 4-6 times ? while scale was increased by 20 times. The new database structure was faster and more reliable.
Speed makes a difference
Sspecially during the current pandemic ? where more and more what germany cell number data transactions are online as a result of working ? learning ? shopping ? and banking remotely. Because most users will abandon a session if wait times are too long ? making sure that database performance is up to par is an essential part of any digital solution. Having a modern Data Architecture built for extreme processing can provide the performance boost and scale that companies need. Read more about this case study here.
However ? the transition from reactive to predictive maintenance is dependent on the quality of the data used to inform better decisions. As a result ? this can reduce the risk of keeping end-of-life equipment in operation.