Have you ever heard of a zettabyte? If you work in IT, you’ll be hearing more and more about zettabytes, exabytes, and petabytes while the data terms we think are big, such as terabytes and gigabytes wane away from our vocabulary. Right now, we are growing our data stores by 50% year-over-year, and its only accelerating.
In 2010, we crossed the barrier of the zettabyte (ZB) across all online data. This year, we will produce 4 ZB of data worldwide. In 2016, global IP traffic will reach 1.3 ZB.
While data volumes are skyrocketing, the type of data is also becoming more difficult for traditional databases to handle. Over 80% of it will be unstructured file based data that does not work well with block-based data storage typical of your typical relational databases (RDBMS). So, even if hardware innovations could keep up to support greater volume, the kinds of data we are now storing break traditional RDBMS at today’s speeds.
The bottom line is the volume and types of data being stored is unrealistic for a single, monolithic, structured RDBMS data store. They need to be broken apart and re-architected to survive the Information Explosion we are experiencing today.
We are living in the age of the Information Explosion. Data growth is accelerating at 50% year over year. In fact, more data will be generated in the next 4 years than in the history of the world. And people are addicted to information, staying connected for up to 12 hours a day and seeing more than 34 billion bits of information every day—the equivalent of reading two books.
Earlier this year, VMware CTO Richard McDougall stated in his 2013 predictions on big data that this year will be the year that ‘delete’ becomes forbidden. As a result, IT is challenged to scale a volume of data that is a moving target, with the only promise that it will continue to grow exponentially.
While vFabric Postgres 9.2 is already known to have a significant performance gains, the ability to create master-slave clusters adds more power to the architecture mix. Master-slave clusters provide extra assurances for data backups and failover, but also they can be used to distribute the workload for queries and to help applications scale.
Below, we will explain how set up and test a master-slave cluster in Postgres 9.2.
In this blog post I’d like to introduce you to one of our latest developments: the RabbitMQ Simulator.
This simulator was born out of a need—we wanted a better tool to teach RabbitMQ concepts to people that were new to the message broker and its AMQP protocol.
If you know me, you are probably aware that I have given quite a few RabbitMQ presentations over the year at several technical conferences. After explaining AMQP concepts many times with static images, I decided the time had come for a better tool—something more graphic and visual, something with more life and motion. So, I started working on this RabbitMQ Simulator project. Continue reading