Apache Spark is playing a critical role in the adoption and evolution of Big Data technologies because it provides sophisticated ways for enterprises to leverage Big Data compared to Hadoop. The increasing amounts of data being analyzed and processed through the framework is massive and continues to push the boundaries of the engine.
Built on the premise that Apache Spark is the only unified analytics engine that combines large-scale data processing with state-of-the-art machine learning and AI algorithms, the 2019 Spark + AI Summit rolled into San Francisco last week. The event was billed as the largest data and machine learning conference in the world. The Pepperdata team was there to exhibit and meet customers and prospects, giving us an opportunity to learn more about customer wants and needs in the rapidly evolving Big Data application and infrastructure performance (APM / IPM) environment.
This was not a large-scale event, but it was very well attended. It’s clear that Big Data is playing an increasingly important role in business operations that rely on massive amounts of data to support revenue-generating online applications. Think always-on streaming services, on-demand applications, and business-critical transactional processing for retail, travel, banking, insurance, and healthcare services.
We saw a constant stream of visitors to the Pepperdata exhibit booth with many large enterprises being represented. Here are some of our impressions and takeaways from this event…
Although focused on Spark and AI, the common thread that ran through the summit was the adoption of and migration to the cloud. The big cloud service providers were in evidence, including the Big Four: AWS, Azure, Google Cloud and IBM Cloud. And of course, Databrick runs its unified analytics platform in the cloud. Almost every exhibitor had booth messaging that referenced the cloud, and many sessions at the event had a cloud theme. Up, up and away!
We spoke with a lot of people who are considering managed platforms, ephemeral clusters, or no cluster at all because they are frustrated with wrestling with large Hadoop platforms. Data science in the cloud is happening at scale, but cloud can be hard to manage. So, many enterprises are choosing to off-load cloud management to vendors like Databricks and Snowflake. We also had many visitors to our booth who were adopting a hybrid cloud strategy involving two or more cloud service providers; one as primary and the other as failover.
Attendees who stopped by the Pepperdata booth were interested to learn about our Big Data Cloud Migration Cost Assessment, which takes the stress and guesswork out of cloud migration by automating the task of profiling on-premises workloads and identifying cost- and performance-optimized cloud instances on any and all of The Big Four cloud service providers.
And if you’re a data scientist or Spark programmer, the many postings cluttering the job board indicated the high demand for your services. Three of the “exhibiting” vendors were at the event for the stated purpose of recruiting.
In summary, the 2019 edition of the Spark + AI summit in San Francisco proved to be a rich learning experience for attendees, including the Pepperdata team. We’re grateful to the many customers, prospects and friends who paid us a visit at the show! In spite of all the modern video and voice conferencing tools at our disposal, you simply can’t beat face-to-face, human interaction. That’s something that AI will never replace.
Other Things You Can Do
- Use Free Pepperdata Application Spotlight APM
- Start a Free Cloud Migration Cost Assessment
- Read the New Pepperdata White Paper: Cloud Migration – Opportunities and Risks for the Business Unit
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