Author: Siva Sivakumar – Senior Director, Data Center Solutions
Data scientists are constantly searching for newer techniques and methodologies that can unlock the value of big data and distill this data further to identify additional insights which could transform productivity and provide business differentiation.
One such area is Artificial Intelligence/Machine Learning (AI/ML), which has seen tremendous development with bringing in new frameworks and new forms of compute (CPU, GPU and FPGA) to work on data to provide key insights. While data lakes have historically been data intensive workloads, these advancements in technologies have led to a new growing demand of compute intensive workloads to operate on the same data.
While data scientists want to be able to use the latest and greatest advancements in AI/ML software and hardware technologies on their datasets, the IT team is also constantly looking at enabling these data scientists to be able to provide such a platform to a data lake. This has led to architecturally siloed implementations. When data, which is ingested, worked, and processed in a data lake, needs to be further operated by AI/ML frameworks, it often leaves the platform and has to be on-boarded to a different platform to be processed. This would be fine if this demand is seen only on a small percentage of workloads. However, AI/ML workloads working closely on the data in a data lake are seeing an increase in adoption. For instance, data lakes in customer environment are seeing deluge of data from new use cases such as IoT, autonomous driving, smart cities, genomics and financials, who are all seeing more and more demand of AI/ML processing of this data.
IT is demanding newer solutions to enable data scientists to operate on both a data lake and an AI/ML platform (or a compute farm) without worrying about the underlying infrastructure. IT also needs this to seamlessly grow to cloud scale while reducing the TCO of this infrastructure and without affecting utilization. Thus, driving a need to plan a data lake along with an AI/ML platform in a systemic fashion.
Seeing this increasing demand by IT, and also envisioning this as a natural extension of a data lake, we are happy to announce Cisco Data Intelligence Platform.
Cisco Data Intelligence Platform
Cisco Data Intelligence Platform (CDIP) is a cloud scale architecture which brings together big data, AI/compute farm, and storage tiers to work together as a single entity while also being able to scale independently to address the IT issues in the modern data center. This architecture allows for:
- Extremely fast data ingest and data engineering done at the data lake
- AI compute farm allowing for different types of AI frameworks and compute types (GPU, CPU, FPGA) to work on this data for further analytics
- A storage tier, allowing to gradually retire data which has been worked on to a storage dense system with a lower $/TB providing a better TCO
- Seamlessly scale the architecture to thousands of nodes with a single pane of glass management using Cisco Application Centric Infrastructure (ACI)
Cisco Data Intelligence Platform caters to the evolving architecture bringing together a fully scalable infrastructure with centralized management and fully supported software stack (in partnership with industry leaders in the space) to each of these three independently scalable components of the architecture including data lake, AI/ML and Object stores.
Fully supported and in partnership with ISVs
Cisco has developed numerous industry leading Cisco Validated Designs (reference architectures) in the area of Big Data (CVDs with Cloudera, Hortonworks and MapR), compute farm with Kubernetes (CVD with RedHat OpenShift) and Object store (SwiftStack).
This Cisco Data Intelligence Platform can be deployed in three variants:
- CDIP with Cloudera with Data Science Workbench (powered by Kubernetes) and Tiered Storage with Hadoop
- CDIP with Hortonworks with Apache Hadoop 3.1 and Data Science Workbench (powered by Kubernetes) and Tiered Storage with Hadoop
- CDIP with Hadoop, Kubernetes and Object Store
This architecture can start from a single rack and scale to thousands of nodes with a single pane of glass management with Cisco Application Centric Infrastructure (ACI)
Read our Solution Brief for more details.
Go to Source
Author: Francoise Rees
Powered by WPeMatico