In a recent webinar, Functional (& Funny) Strategies for Modern Data Architecture, we combined comedy and practical strategies for migrating from Hadoop to AWS. Unravel Co-Founder and CTO Shivnath Babu moderated a discussion with AWS Principal Architect, Global Specialty Practice, Dipankar Ghosal and WANdisco CTO Paul Scott-Murphy. Here are some of the key takeaways from the event.
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Similar to the momentum that cloud technology has had in the business world, AI and machine learning are quickly becoming essential to the enterprise. 86% of companies now view AI as a “mainstream technology,” and corporate AI adoption rose 50% in 2021 from the year prior for initiatives such as service-operations optimization and product enhancement.
The benefits of moving your on-prem Spark Hadoop environment to Databricks are undeniable. A recent Forrester Total Economic Impact (TEI) study reveals that deploying Databricks can pay for itself in less than six months, with a 417% ROI from cost savings and increased revenue & productivity over three years. But without the right methodology and tools, such modernization/migration can be a daunting task.
The cloud is growing more and more popular each day. We are in an era where there is a prominent trend of companies migrating from traditional on-premise systems to more reliable and fast cloud-based systems. However, the conversion is still not rampant on a large scale, primarily due to the lack of awareness in the up-and-coming businesses about the cloud’s fundamentals. However, the cloud has proven to be a sound and worthy option time and time again.
Cloud-to-cloud migration can be an expensive, cumbersome, and overwhelming process with no guarantee of success. Yet, moving data from cloud to cloud is becoming more and more common in the public sector as operations are being migrated to the cloud more completely. Factors like complexities in environments, sheer volume of historical data, and application nuances can greatly impact the success or failure of a cloud migration effort.
