Latest Updates to the Silk Data Virtualization Platform

At Silk, we pride ourselves on our ability to deliver fast performance, improved manageability, increased resiliency, and greater cost efficiency for customers’ business-critical applications. Which is why we are always striving to take our platform to the next level: we want lightning-fast performance, the smoothest manageability, the strongest resiliency, and cost-efficiency that will have you diving into a pile of money from your cost-savings like you’re Scrooge McDuck.

We recently pushed live our latest updates to the Silk Data Virtualization Platform. This release includes some important improvements including Concurrent and Cascading Replication, a Read Acceleration Engine, higher Google Cloud performance, and additional tags in Google Cloud.

Concurrent and Cascading Replication

Let’s say you have data in Production that you want to leverage for Disaster Recovery (DR), Test, and Development purposes. Typically, you would need to replicate that data multiple times for each location – which can degrade the performance of Production as it works to complete each replication. On top of that, replicating data to multiple targets can be complex and challenging. We saw this and thought, “We can do replication better”.

Silk now supports replicating data to multiple destinations through both Concurrent and Cascading Replication. With Concurrent Replication, you can now replicate a volume group to multiple destinations in parallel. This makes it easier to share a common data set to more users for more use cases – such as the example above of sharing data simultaneously to DR, Test, and Dev environments.

Meanwhile, new Cascading Replication allows users to replicate a volume group to Destination A. Meanwhile, Cascading Replication now allows users to maintain performance while replicating to multiple destinations. You can replicate a volume group to Destination A. In turn, Destination A can instantly replicate the data to Destination B. By offloading replication efforts to Destination A, the various users can quickly access the same data without degradation to performance. By offloading replication efforts to Destination A, the various users can quickly access the same data with full performance.

Read Acceleration Engine

The Silk platform has always been ideal for workloads in heavy throughput environments, such as analytical workloads. But what about workloads, such as transactional ones, that are sensitive to latency and have heavy IOPS? To improve performance for these workloads, we are excited to announce our new Read Acceleration Engine. The engine improves the handling of frequently read data to minimize latency and increase the number of transactions. A single Silk Data Pod can now handle over 2 million IOPS!

This is an optional feature available in both Microsoft Azure and Google Cloud that will make it possible for Silk users to improve customer conversions, readily handle more transactions, and accelerate development and innovation… all while improving price-performance!

Faster Google Cloud Performance

As a data virtualization platform built on top of standard cloud infrastructure, Silk rapidly evolves our platform using the latest and greatest cloud resources to benefit our customers. To push our performance even further for our Google Cloud customers, we are now leveraging the n2-standard-64 Virtual Machine (VM) in the performance layer of our Silk architecture for Google Cloud. To push our performance even further for our Google Cloud customers, we now use the n2-standard-64 Virtual Machine (VM) in the performance layer of our Silk architecture for Google Cloud. By making this switch, Silk users will see their max throughput increase by over 20%, while max IOPS increases by over 45%! And this momentous performance increase comes with minimal additional cost for our users.  And this momentous performance increase comes with minimal additional infra cost for our users. Cha-ching!

Additional Tags in Google Cloud

Having the ability to gain deeper insight into your infrastructure is important for accurately identifying issues and alerts. Which is why we’re excited to announce new tags for Silk’s Google Cloud customers that use observability tools like DataDog and Dynatrace. These new tags will make it possible to map alerts and lifecycle events back to specific Silk instances for clearer status monitoring and alerting.

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If you’re ready to start experiencing the power of Silk for yourself, get in touch for a demo today!