Big data analytics present challenges to the compute and storage infrastructure like no other workloads. Petabytes of data feeding into an array of resource-intensive analytical processes requires data management tools that are up to the task.
This blog explores the challenges unique to big data analytics workloads, including six success stories of companies that make extensive use of analytics in the cloud with the help of NetApp’s Cloud Volumes ONTAP.
Jump down to the analytics customer success stories below:
- Siemens Healthineers Migrates to Azure and Lowers Costs by Tiering Data
- AstraZeneca: Hybrid Deployment on Google Cloud, Kubernetes Persistent Storage, and DR
- Semiconductor Chip Manufacturer Caches Data to the Cloud
- High Performance Cloud Computing on Google Cloud for a Multinational Investment Banking Firm
- Hedge Fund Division Mirrors On-Prem Data to Google Cloud
- Multinational Biotechnology Company Migrates an App to AWS
Big Data Analytics Challenges
Today’s organizations collect petabytes of data which streams in from machine-generated processes, application logs, IoT devices, and more, both structured and unstructured. Storing and managing this data is just the first of many challenges when running big data analytical workloads.
These challenges include the following:
- Performance: Analytics are always compute-intensive. When you need them to run on big data from multiple sources located both on-prem and in the cloud, your storage solution needs to serve the data fast where the workload is running to keep the analytic at top efficiency.
- Ease of management: When you have several analytics applications running multiple large data sets simultaneously, data management becomes a considerable challenge. Finding a way to orchestrate where the data is and moving it with ease between environments without juggling multiple interfaces is key.
- Lower costs: Analytics data sets are large and will incur costs both for the on-prem storage and when being used in the cloud. Your data management tools must have the ability to minimize your storage costs no matter where the data is stored.
- Data protection: Recovering from data loss or a disaster is non-negotiable when it comes to analytics workloads. All data must be easily recoverable to keep processes running. Data management tools must include robust disaster recovery capabilities.
- Automated Data Mobility: In addition to the above challenges, the same data will be used in development, staging, and production environments. Your data management solution needs to handle automated bulk movement of data between environments as quickly and cost-effectively as possible.
The success stories that follow show how Cloud Volumes ONTAP helped these companies overcome their big data analytics challenges.
Big Data Analytics Customer Success Stories with Cloud Volumes ONTAP
Siemens Healthineers Migrates to Azure and Lowers Costs by Tiering Data
Siemens Healthineers drives research into health technologies that are helping people live longer and healthier lives. This data-driven company has relied on NetApp for storage solutions in their data centers around the world. As part of a cloud-first strategy, one of their divisions in Japan moved its application to Azure. To migrate the data and file shares that application relied on, Siemens turned to Cloud Volumes ONTAP for Azure.
With Cloud Volumes ONTAP, Siemens Healthineers was able to:
- Reduce costs by leveraging data tiering. Cloud Volumes ONTAP automatically tiers 15 TB of archive data to lower-cost object storage on Azure Blob.
- Move to the cloud without changing application code.
- Create an environment on Azure with similar capabilities as their data centers, hosting a variety of workloads and enabling the fulfillment of their hybrid cloud strategy.
AstraZeneca: Hybrid Deployment on Google Cloud, Kubernetes Persistent Storage, and DR
AstraZeneca is one of the world’s leading pharmaceutical companies, with a portfolio of products for major disease areas. These innovative medicines provide relief to millions of patients worldwide. With over 60,000 employees, its annual revenues exceed $22 billion.
AstraZeneca leverages big data in its hybrid cloud environment to run analytic workloads to advance its research efforts. These efforts serve to improve patient outcomes and accelerate treatment timelines. With most of this data stored on-premises, the company relies on Cloud Volumes ONTAP to easily orchestrate moving it to Google Cloud, where the analytics jobs are run.
AstraZeneca uses Cloud Volumes ONTAP for a number of benefits:
- Hybrid cloud deployment so analytics workloads are easy to manage in any environment.
- Containerized Kubernetes workloads can provision persistent storage automatically with NetApp Trident.
- Disaster recovery for their analytics application ensures speedy recovery for their massive data estate should a disaster take place, and at low costs.
- Cloud Volumes ONTAP and SnapMirror® allow easy data replication between environments and regions.
Semiconductor Chip Manufacturer Caches Data to the Cloud
As an electronic design automation (EDA) company, this Cloud Volumes ONTAP user has extremely large data sets that need to be analyzed in order to perfect the chip design process. With every step of the chip design process producing enormous amounts of information and requiring simultaneous access from multiple users, reducing latency and increasing performance while keeping all the data in sync is key.
In this company’s case, data that is maintained on-prem has to be made available to users in AWS. That requires an easy way to move and orchestrate the data, something which Cloud Volumes ONTAP accomplishes with several features:
- Data caching with FlexCache® enables replication from on-prem to the cloud that is 95% faster, with both data sets kept continuously synced.
- Intelligent NVMe caching reduces storage latency
- Easy migration of hundreds of petabytes to the cloud with SnapMirror.
- Seamless hybrid deployment.
High Performance Cloud Computing on Google Cloud for a Multinational Investment Banking Firm
This multinational finance company is based in New York City and is one of the largest investment banks in the world. The company is aimed to deliver results with its extensive services portfolio, including investment and asset management, prime brokerage and securities underwriting.
The company’s demanding missing-critical workloads that analyze investments required increasing compute resources over time. This pushed interest within the IT department to form a cloud strategy that allows compute resources to scale up to the analytical demands.
Solution highlights and benefits:
- Automated functionality that would leverage a large number of compute nodes in Google Cloud to support its cloud strategy
- Remote caching with NetApp FlexCache scales storage performance
- Achieved storage framework automation for Cloud Volumes ONTAP as well as and Cloud Manager using Terraform
- Met the company’s strict security requirements by using Cloud Volumes ONTAP file sharing security features
- Reduced storage costs with NetApp storage efficiency features such as deduplication, compression, and thin provisioning.
Hedge Fund Division Mirrors On-Prem Data to Google Cloud
This company is a hedge fund that exists within a much larger multinational investment and financial services company. Using hi-tech research and automation that leverages Google Cloud, the hedge fund pinpoints risks in investments across the global financial markets for its clients.
This company turned to Cloud Volumes ONTAP to help burst a massive 80 TB of time series data in on-prem NetApp appliances to Google Cloud as part of their equity-trade simulation processes. Cloud Volumes ONTAP was the perfect solution to meet their needs and led to some major benefits:
- Elastic scalability and cloud bursting capabilities.
- Automation through the Cloud Manager API for key actions, a tool that integrates with their existing automation tools.
- Cloud Manager offered an easy, drag-and-drop interface and management for data between the existing clusters and the Google Cloud environment.
- SnapMirror seamlessly replicates data between the primary storage environment and Google Cloud with just the click of a button.
Multinational Biotechnology Company Migrates Apps to AWS
This multinational biotech company produces innovative therapeutic medicines. The discovery and development processes get a big assist from analytic workloads that continue to challenge its compute infrastructure. Cloud Volumes ONTAP was able to help.
The company leveraged Cloud Volumes ONTAP to migrate its Oracle-based eClinical application to AWS. The eClinical application manages the clinical trials they conduct and previous efforts to migrate it to the cloud did not succeed due to architectural and implementation struggles. Cloud Volumes ONTAP solved all of that and gave the company a number of other benefits:
- Cloud Volumes ONTAP enabled the lift and shift migration of their workloads to AWS without making changes to the application.
- Supported a single namespace for their application files using Cloud Volumes ONTAP’s FlexGroups.
- Reduced cloud storage costs significantly by automatically tiering infrequently used data from AWS EBS to AWS S3.
- Complied with regulatory requirements using Cloud Volumes ONTAP high availability configuration and added a secondary copy of the data in a separate geographical region for compliance with regulatory requirements.
- Achieved seamless administrative capabilities with NetApp Cloud Manager to manage all their environments within the same control plane.
Cloud Volumes ONTAP provides effective solutions to the challenges created by analytics applications. Cloud Volumes ONTAP enables high performance, reliable uptime and ease of management without compromising data protection and mobility in hybrid and multi-cloud environments.
Data analytics in the cloud will only grow in terms of requirements for storage management, and Cloud Volumes ONTAP can make your strategic investment in the cloud pay off even more.