With the recent boom in enterprise-level artificial intelligence (AI) adoption across a wide range of industry verticals, big data analytics has also advanced tremendously. Those developments are thanks to the amount of data that’s available, innovative and hybrid multicloud–based solutions that automate the data processing workflow, and techniques that process that data with modern computing power.
With those analytics advancements, businesses can extract value and insights from their data faster, more efficiently, and at a lower cost. This blog post explores modern analytics workloads in Apache Spark clusters with the NetApp® storage portfolio.
In Hybrid cloud solutions with Apache Spark and NetApp AI, I wrote about what Apache Spark is designed for, and what challenges it mitigates for customers who use Hadoop. In addition to being a fast analytics engine with machine learning (ML) libraries and enabling deep learning (DL) frameworks that function seamlessly with NetApp AI, Spark plays well with our modern data analytics portfolio. It works directly with the Hadoop Distributed File System (HDFS), NFS direct access, and object storage.
Before you decide to use Apache Spark with NetApp storage to overcome your large-scale distributed data processing and analytics challenges, you might need to answer questions such as:
This three-part blog series can help you answer those questions.
We understand the challenges that you face in modern analytics. Our comprehension is based on our findings from many proof-of-concept (POC) studies with large-scale customers in various industries, such as financial services, retail, healthcare, life sciences, manufacturing, and automotive. Some of the challenges include:
We have uncovered and tackled the analytics hurdles to provide solutions that use Apache Spark with NetApp storage. Stay tuned for my next blog post, where I discuss analytics workloads with a NetApp storage solution in detail.
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.