This IBM® Redpaper publication focuses on data orchestration in enterprise data pipelines. It provides details about data orchestration and how to address typical challenges that customers face when dealing with large and ever-growing amounts of data for data analytics. While the amount of data increases steadily, artificial intelligence (AI) workloads must speed up to deliver insights and business value in a timely manner. This paper provides a solution that addresses these needs: Data Accelerator for AI and Analytics (DAAA). A proof of concept (PoC) is described in detail. This paper focuses on the functions that are provided by the Data Accelerator for AI and Analytics solution, which simplifies the daily work of data scientists and system administrators. This solution helps increase the efficiency of storage systems and data processing to obtain results faster while eliminating unnecessary data copies and associated data management.

This IBM® Redpaper publication describes the architecture, installation procedure, and results for running a typical training application that works on an automotive data set in an orchestrated and secured environment that provides horizontal scalability of GPU resources across physical node boundaries for deep neural network (DNN) workloads. This paper is mostly relevant for systems engineers, system administrators, or system architects that are responsible for data center infrastructure management and typical day-to-day operations such as system monitoring, operational control, asset management, and security audits. This paper also describes IBM Spectrum® LSF® as a workload manager and IBM Spectrum Discover as a metadata search engine to find the right data for an inference job and automate the data science workflow. With the help of this solution, the data location, which may be on different storage systems, and time of availability for the AI job can be fully abstracted, which provides valuable information for data scientists.

The Swift High Latency Media project seeks to create a high-latency storage back end that makes it easier for users to perform bulk operations of data tiering within a Swift data ring. In today's world, data is produced at significantly higher rates than a decade ago. The storage and data management solutions of the past can no longer keep up with the data demands of today. The policies and structures that decide and execute how that data is used, discarded, or retained determines how efficiently the data is used. The need for intelligent data management and storage is more critical now than ever before. Traditional management approaches hide cost-effective, high-latency media (HLM) storage, such as tape or optical disk archive back ends, underneath a traditional file system. The lack of HLM-aware file system interfaces and software makes it difficult for users to understand and control data access on HLM storage. Coupled with data-access latency, this lack of understanding results in slow responses and potential time-outs that affect the user experience. The Swift HLM project addresses this challenge. Running OpenStack Swift on top of HLM storage allows you to cheaply store and efficiently access large amounts of infrequently used object data. Data that is stored on tape storage can be easily adopted to an Object Storage data interface. This IBM® Redpaper™ publication describes the Swift High Latency Media project and provides guidance for installation and configuration.