With the recent boom of enterprise-level AI adoption, deep learning (DL) has advanced thanks to the amount of data available and techniques for processing that data with modern computing power. This blog will explore DL applications for natural language processing (NLP) in Apache Spark clusters with the NetApp storage portfolio. And we'll point you to information about how the NetApp storage portfolio meets these challenges.
Apache Spark is a programming framework for writing Hadoop applications that work directly with the Hadoop Distributed File System (HDFS) and other file systems, such as NFS and object storage. It's a fast analytics engine with machine learning (ML) libraries designed for large-scale distributed data processing. It functions seamlessly with our NetApp® AI and modern data analytics portfolio. Its in-memory operations are more efficient than MapReduce for data pipelines, streaming, interactive analysis, and ML/DL algorithms. Apache Spark also mitigates the I/O operational challenges you might experience with Hadoop.
Should you use Apache Spark workloads with NetApp storage to implement large-scale distributed data processing and DL for NLP applications? You might need to answer the following questions:
We understand your DL challenges—we've conducted many proof-of-concept studies with large-scale financial services and automotive customers.
At NetApp, we've figured out how to address your DL challenges with Apache Spark workloads. For details, see this NetApp Community post, which discusses using Apache Spark DL workloads with NetApp storage solutions (AFF, E-Series, DataOps Toolkit), and Spark NLP sentiment analysis results and run-time comparisons.
For more technical information about Apache Spark DL NLP workloads with NetApp storage solutions, including commands and scripts used in testing and benefits, check out TR-4570: NetApp Storage Solutions for Apache Spark: Architecture, Use Cases, and Performance Results. See Hybrid Cloud Solution with Apache Spark and NetApp AI for hybrid cloud applications in distributed Spark clusters with the NetApp® storage portfolio.
Rick Huang is a Technical Marketing Engineer at NetApp AI Solutions. Having prior experience in ad-tech industry as a data engineer and then a technical solutions consultant, his expertise includes healthcare IT, conversational AI, Apache Spark workflows, and NetApp AI Partner Network joint program developments. Rick has published several technical reports since joining NetApp in 2019, presented at multiple GTCs, as well as NetApp Data Visionary Center for various customers on AI and Deep Learning.