"Data is the new science. Big Data holds the answers." - Pat Gelsinger, the CEO of VMware, Inc. Organizations use their data to support and influence decisions and build data-intensive products and services, such as recommendation, prediction, and diagnostic systems. Apache Spark is a fast and general engine for large-scale data processing ,built around speed, ease of use, and sophisticated analytics. It was originally developed at UC Berkeley in 2009.Spark is a good fit for the Hadoop open-source community as its built on top of the Hadoop Distributed File System (HDFS).
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Balancing needed testing against the spiraling costs of the required infrastructure and challenges with the deployment of your code can be a real obstacle to even the best development and quality management teams. But with a little strategy and the right tools, it’s fairly easy to overcome these obstacles to delivering better software faster.
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When organizations need to protect their on-premises networks, IT departments typically turn to old, legacy hardware boxes. These hardware boxes are expensive, hard to manage, and slow. To complicate matters further, legacy approaches to connectivity (re: MPLS) and security (re: on-premises firewall and DDoS “scrubbing centers”) just don’t work for today’s distributed workforce and cloud-hosted applications
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Hybrid work and the shift to cloud applications and data have forced organizations to shift their network architectures rapidly, leaving gaps in security and access. As a result, ZTNA 1.0 is not enough.
Our partners at Palo Alto Networks introduced ZTNA 2.0 with Prisma Access which overcomes the limitations of legacy ZTNA solutions.
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