Defining time series data
- Stock quote feed.
- Order history for a user in an online retailer.
- User activity in any application.
In an earlier blog on database consistency, we had a detailed discussion on the risks and challenges applications face in dealing with eventually consistent NoSQL databases. We also dispelled the myth that eventually consistent DBs perform better than strongly consistent DBs. In this blog, we will look more closely into how YugaByte DB provides strong consistency while outperforming an eventually consistent DB like Apache Cassandra.
YugaByte DB is a transactional, high performance, geo-distributed operational database that converges multiple NoSQL and SQL interfaces into an unified solution. The v0.9 public beta of YugaByte DB is compatible with Apache Cassandra (CQL) and Redis APIs, with PostgreSQL under development. A fundamental design goal for YugaByte DB has been to provide the same transactional,
Today, we are launching YugaByte out of stealth and announcing the availability of YugaByte DB’s first public beta release. YugaByte offers an open-source, cloud-native database for mission-critical applications. Yuga in Sanskrit represents an era or an epoch (about 4.32 million human years), a very long period of time. We picked the name YugaByte to signify data that lives forever without limits.
Image credit: https://loveforprogramming.quora.com
In the world of databases today, consistency is one of the most misunderstood concepts. It is also one of the big reasons NoSQL databases are difficult to reason about.
The CAP theorem states that “in the presence of a network partition, one has to choose between consistency and availability”.
Image credits: http://bigdatahadooppro.com/
During conversations offline, folks pointed out that my previous article talks about the problem gap that NoSQL should have solved for, but there is no clear definition of what NoSQL really is today. The definition has been stretched in various directions over time.
Ok, so what’s the big deal?
Came across the image above here and this made me smile. Not because of the implied complexity of choosing a database, but the reality with which this flow chart captures the state of the database world today in 2017. Of course, running whatever database you end up choosing in production is a whole another order of complexity.
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