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The standard redundant Redis solution is to run master/slave replication with
Sentinel managing the failover. This is expected to be followed up with either
a) Client support and use of Sentinel to discover the current master or b) A
TCP proxy in front of the Redis pod which is managed by Sentinel to point to
the master. The former is the ways Redis Sentinel is designed and the latter is
a growing trend - and the way ObjectRocket Redis is configured.
In this post I'm going to show how to quickly and easily create a Data Connector between your ObjectRocket MongoDB instance and your ObjectRocket Elasticsearch instance. Then I show you how to view your data within a custom Kibana dashboard.
Part of our mission statement here at ObjectRocket is that we enable customers to experience levels of data management ease and understanding never before seen. To that goal we are very excited to announce the early access release of our Data Connector for ObjectRocket MongoDB to ObjectRocket Elasticsearch.
Recently, Parse announced they'd be retiring their service entirely on January 28th, 2017. While this comes as a bit of a shock to their own customers and the community as a whole, they're working to ensure that the transition off their platform is a simple process. We at ObjectRocket are doing the same to help any customer find a good home for their MongoDB data! In this post I'll outline how easy it is to move over to the ObjectRocket family and how we can help. Let's jump right in.
Your mobile app's data is hosted on Parse and you just found out that Parse is shutting down. Now what? In this post, I'll cover how ObjectRocket can help support your transition with a fully managed MongoDB solution.
Today we are happy to announce the wider availablity of our fully managed Encryption-at-Rest for ObjectRocket MongoDB in Virginia (IAD), Dallas (DFW), Chicago (ORD), and London (LON). This optional premium feature is availiable on versions 2.6 and 3.0 (mmap or WiredTiger) in both dedicated and shared plans.
If you are looking to take advantage of the Spark's speed and ease of use, you owe it to yourself to try our Rackspace optimized Spark Stack. Spark combined with Tachyon and Zeppelin culminates into a very fast and easy-to-use option. In addition to this stand alone Spark Stack, we also have versions that are bundled with Hortonworks Data Platform, for those wishing to use Hadoop and Spark on the same cluster.
Today I am excited to announce we have added a new capability to our Managed Cloud Hadoop and Spark Service. In the past our users would provision their Hadoop or Spark clusters based on some pre-defined stacks we had created. While this worked for many of our users, there were others that wanted more flexibility and more control. To that end, we have been working for the last year on an entirely new provisioning system that would allow our customers to create fully customized stacks. This now means instead of settling for the default values for number or size of master services nodes, users now have the ability to define the individual node sized and how many of each. You want 3 name nodes instead of 2? No problem. You want 8GB of memory per name node instead of 4? No problem. You want to create a stack that has your ideal configuration of services, such as Spark with Kafka, and Zepplin, but not some of the other components that come by default? No problem.