In the world of Big Data, bare metal is king. Many companies are seeking an architecture that allows for full utilization of resources like I/O and throughput, but we often hear from you that when it comes to Big Data you are forced to trade the advantages of cloud (elastic, on-demand, flexible) for the consistency and predictability of bare metal. We don’t think you should have to sacrifice one for the other.
So you managed to survive the first post and are still hungering for more? Don’t worry, I got you covered. This time around, we’ll get into more of the peripheral, optional components that might be useful to you. The format will largely be the same as the first post, so let’s get right to it.
So you wanna learn you some Hadoop, eh? Well, get ready to drink from the firehose, because the Hadoop ecosystem is crammed full of software, much of which duplicates efforts, and much of which is named so similarly that it’s very confusing for newcomers.
Data processing platforms do not exist in siloes. That is why it is increasingly important for Rackspace to provide interoperability between datastores. This enables customers to choose the best technology for their data processing needs, without being restricted to a single tool.
The world of data platforms is forging forward with increasing velocity. To stay relevant in today’s Big Data conversation, technologies must implement features and enhancements at a swifter cadence than legacy technology. The only way this is possible is by orchestrating the worldwide execution of an open ecosystem of participants. Consider Apache Hadoop; this level of advancement would not be possible without a broad network of developers and engineers working together to rapidly innovate to solve new problems. In addition to just fixing the issues users have with Hadoop, the community is changing the perception of how users can leverage it. Once a go-to tool for large batch processing jobs, Hadoop is changing to address the needs of multiple workloads simultaneously such as streaming and interactive workloads all done at the same level of scale of the original batch jobs.
Last week, we hosted a live webinar, “Making Choices: What Kind of Relationship are you Seeking with your Database?,” in which we dug into the options available for the database tier of modern applications, particularly in the cloud.
At this year’s O’Reilly Strata event we will showcase our support of the newest genesis of the Hortonworks Data Platform (2.0), a release that we believe represents a paradigm shift in the perception of what you can use Hadoop to accomplish.
A few weeks ago we told you about our two Data Services for Hadoop-based applications, the Managed Big Data Platform service (in Unlimited Availability) and the Cloud Big Data Platform (in Early Access). Working hand in hand with Hortonworks, we are giving you a choice of architectures for your Hadoop applications, whether you need a custom-built Hadoop architecture based on specific dedicated hardware, or a dynamic, API-driven programmable Hadoop cluster in our public cloud.
As we close out another big year for data driven application adopters, the emphasis on delivering right business insights has never been greater. The companies that have embraced data analytics have quickly tapped into new revenue streams and innovated faster than their competitors. The conversation is no longer about when and why an organization should implement a robust data strategy, but which one they should focus on and how they will get valuable insights quicker. This may be best represented by the overwhelming attendance at this year’s fall Strata Conference focusing on the world of Apache HadoopTM, October 28 through October 30 in New York.