The document discusses HP's AppSystems for SAP HANA, which is part of HP's Next Gen Information Platform. This platform helps organizations harness the power of information through solutions like analytics and business insight, data management, collaboration, and driving transactions. It provides a better technology, economics and experience for organizations to generate insight from information and optimize their business processes.
SAP HANA Geçiş Sürecinde ve Sonrasında Microsoft Azure Neleri Kolaylaştırır?Core To Edge
SAP Cloud Forum 2017'de Microsoft ile birlikte yaptığımız "SAP HANA Geçiş Sürecinde ve Sonrasında Microsoft Azure Neleri Kolaylaştırır?" konulu sunumumuz.
GPUs used with Apache Spark are leveraged to speed up machine learning (ML) model training and inference. Data preparation stages are traditionally run on CPUs. The RAPIDS Accelerator for Apache Spark is a plugin jar that takes advantage of Apache Spark 3.x's ability to schedule on GPUs. The RAPIDS Accelerator replaces CPU expressions in a physical plan with GPU equivalents for dataframe operations. Code change is not required, making transition to GPUs seamless.
We'll give an overview of what the RAPIDS Accelerator is, how it works, and benefits from using the accelerator. We will discuss benchmarks showing the performance and cost benefits of leveraging GPUs for Spark ETL processing. We'll showcase a user tool that will help estimate speedups and cost savings.
GZIP data compression hardware has a critical role of increasing performance and smashing the monstrous costs of energy in big data systems. To demonstrate the benefits of using GZIP hardware, AHA assembled a web
server experiment to compare the performance of serving pages using:
• No GZIP
• CPU performing GZIP
• AHA372 Hardware Accelerators for GZIP
Using effective throughput, CPU utilization, and energy efficiency, these were some key results from the experiment:
• When the CPU simultaneously serves pages and performs GZIP, it consumes 5x more times energy and throughput drops by almost 5x.
• Hardware GZIP has a huge 18x throughput and 17x energy efficiency advantage over the CPU performing GZIP.
• With the energy costs of CPU GZIP, the break even point of GZIP hardware is between 10-22 days.
This paper shows that GZIP hardware offloading increases network I/O, optimizes workloads, and results in optimal system designs requiring significantly less capital and operational expenditures.
Analyzing IOT Data in Apache Spark Across Data Centers and Cloud with NetApp ...Databricks
This session will explain how NetApp simplifies the process of analyzing IoT data, using Apache Spark clusters across data centers and the cloud using NetApp Private Storage (NPS) for AWS/Azure, NetApp Data Fabric and NetApp Connectors for NFS and S3. IoT data originates at the edge in different geographical locations, and it can arrive at different data centers or the cloud depending on sensor location. The challenge is how to combine these different data streams across different datacenters to generate wider insights.
Learn how NetApp Data Fabric helps solve this challenge. In the Data Fabric architecture, the IoT data is ingested via Kafka into an Apache Spark cluster running in AWS/Azure, but the data is stored in NPS provisioned NFS share through NFS Connector. The IoT data in NPS can then be moved to on-prem datacenters, or on-prem IoT data can be moved to NPS or ONTAP Cloud for processing in AWS/Azure using NetApp SnapMirror Flex Clone or NFS Connector. We’ll also review how NetApp StorageGRID object storage maintains IoT data for archival purposes using S3 Target. The above options allow you to analyze IoT data from AWS, StorageGRID, HDFS or NFS, providing a feasible solution for deploying Spark clusters across datacenters.
Takeaways will include identifying Spark challenges that can be remedied by extending your Spark environment to take advantage of NPS; understanding how NPS and StorageGRID can provide a cost-effective alternative for dev/test, DR for Spark analytics; and understanding Spark architecture and deployment options that utilize data from multiple locations, including on-prem and cloud-based repositories.
Need For Speed- Using Flash Storage to optimise performance and reduce costs-...NetAppUK
Flash Storage technologies are opening up a wealth of new opportunities for improving the optimisation of applications, data and storage, as well as reducing costs. In this session, Peter Mason, NetApp Consulting Systems Engineer, shares his experiences and discusses the use and impact of different Flash technologies.
IT Engineer are high-level IT personnel who design, install, and maintain a company's computer systems. They are responsible for testing, configuring, and troubleshooting hardware, software, and networking systems to meet the needs of the employer.
SAP HANA Geçiş Sürecinde ve Sonrasında Microsoft Azure Neleri Kolaylaştırır?Core To Edge
SAP Cloud Forum 2017'de Microsoft ile birlikte yaptığımız "SAP HANA Geçiş Sürecinde ve Sonrasında Microsoft Azure Neleri Kolaylaştırır?" konulu sunumumuz.
GPUs used with Apache Spark are leveraged to speed up machine learning (ML) model training and inference. Data preparation stages are traditionally run on CPUs. The RAPIDS Accelerator for Apache Spark is a plugin jar that takes advantage of Apache Spark 3.x's ability to schedule on GPUs. The RAPIDS Accelerator replaces CPU expressions in a physical plan with GPU equivalents for dataframe operations. Code change is not required, making transition to GPUs seamless.
We'll give an overview of what the RAPIDS Accelerator is, how it works, and benefits from using the accelerator. We will discuss benchmarks showing the performance and cost benefits of leveraging GPUs for Spark ETL processing. We'll showcase a user tool that will help estimate speedups and cost savings.
GZIP data compression hardware has a critical role of increasing performance and smashing the monstrous costs of energy in big data systems. To demonstrate the benefits of using GZIP hardware, AHA assembled a web
server experiment to compare the performance of serving pages using:
• No GZIP
• CPU performing GZIP
• AHA372 Hardware Accelerators for GZIP
Using effective throughput, CPU utilization, and energy efficiency, these were some key results from the experiment:
• When the CPU simultaneously serves pages and performs GZIP, it consumes 5x more times energy and throughput drops by almost 5x.
• Hardware GZIP has a huge 18x throughput and 17x energy efficiency advantage over the CPU performing GZIP.
• With the energy costs of CPU GZIP, the break even point of GZIP hardware is between 10-22 days.
This paper shows that GZIP hardware offloading increases network I/O, optimizes workloads, and results in optimal system designs requiring significantly less capital and operational expenditures.
Analyzing IOT Data in Apache Spark Across Data Centers and Cloud with NetApp ...Databricks
This session will explain how NetApp simplifies the process of analyzing IoT data, using Apache Spark clusters across data centers and the cloud using NetApp Private Storage (NPS) for AWS/Azure, NetApp Data Fabric and NetApp Connectors for NFS and S3. IoT data originates at the edge in different geographical locations, and it can arrive at different data centers or the cloud depending on sensor location. The challenge is how to combine these different data streams across different datacenters to generate wider insights.
Learn how NetApp Data Fabric helps solve this challenge. In the Data Fabric architecture, the IoT data is ingested via Kafka into an Apache Spark cluster running in AWS/Azure, but the data is stored in NPS provisioned NFS share through NFS Connector. The IoT data in NPS can then be moved to on-prem datacenters, or on-prem IoT data can be moved to NPS or ONTAP Cloud for processing in AWS/Azure using NetApp SnapMirror Flex Clone or NFS Connector. We’ll also review how NetApp StorageGRID object storage maintains IoT data for archival purposes using S3 Target. The above options allow you to analyze IoT data from AWS, StorageGRID, HDFS or NFS, providing a feasible solution for deploying Spark clusters across datacenters.
Takeaways will include identifying Spark challenges that can be remedied by extending your Spark environment to take advantage of NPS; understanding how NPS and StorageGRID can provide a cost-effective alternative for dev/test, DR for Spark analytics; and understanding Spark architecture and deployment options that utilize data from multiple locations, including on-prem and cloud-based repositories.
Need For Speed- Using Flash Storage to optimise performance and reduce costs-...NetAppUK
Flash Storage technologies are opening up a wealth of new opportunities for improving the optimisation of applications, data and storage, as well as reducing costs. In this session, Peter Mason, NetApp Consulting Systems Engineer, shares his experiences and discusses the use and impact of different Flash technologies.
IT Engineer are high-level IT personnel who design, install, and maintain a company's computer systems. They are responsible for testing, configuring, and troubleshooting hardware, software, and networking systems to meet the needs of the employer.