Migrating business-critical data to the cloud is not an option in today’s challenging economy
In recent months, the advantages of moving your big data to the cloud have become obvious. Organizations provided their workforce with remote capabilities and access to data from locations around the globe. Many companies had to shift their business models quickly to cater to the changing business climate. Today’s new normal has validated the importance enterprise driving their digital transformation programs with more urgency to weather the economic storm. As workers are more distributed so will be their data. Real-time access to accurate and consistent data sets to make strategic decisions is more important now than ever. These new requirements shine a light on the legacy on premise Hadoop data lake model. The only way to run machine learning analytics effectively is on large consolidated datasets in the cloud. However, moving petabytes of business-critical data under active change to the cloud is complex and challenging without the proper migration strategy.
Cloud analytics has become an essential service model to analyze big data and deliver insights for organizations to adapt to changing business needs. Big data cloud analytics provides greater functionality at a lower cost over native Hadoop offerings and simplifies the complexities for organizations having to manage their Hadoop implementations. Organizations can run big data analysis in the cloud without significant expenditure of resources and gain the benefits needed to adapt to changing business needs quickly. It’s no surprise that the cloud analytics market is projected to rise to 24.3% CAGR until 2026. 1
As technical teams plan their big data migration strategy to take advantage of machine learning and cloud analytics, they are wrestling with the complex challenge of moving exabytes of data to the cloud with minimal to zero business disruption and without the risk of data loss. Your migration strategy and tools must be evaluated carefully to ensure project success with minimal impact to your business. Legacy open source tools and scripts on the market like DistCP are manual, and often lead to lengthy or even failed manual migrations. A recent primary survey conducted with 220 cloud and data architects showed that 57% indicated zero or only hours of total downtime is acceptable for cloud data migrations.2
How can you move petabytes of big data to the cloud with business continuity and accelerate time to value? Using WANdisco’s patented, consensus blockchain automated approach, technical teams can migrate petabyte or exabyte-scale Hadoop on premise data lakes to the cloud with no application downtime during migration, and no risk of data loss, even when data sets are under active change. It’s business as usual during automated migration. Teams can use their on-premise systems as well as cloud instances without fear of data loss. Continuous replication ensures that geographically dispersed data remains consistent between on-premises, hybrid and multi-cloud environments to provide the flexibility needed for business agility.
In today’s new normal, real time access to accurate business critical customer data and insights is critical for the remote workforce to collaborate and business decisions, for developers to write code, and for businesses to navigate today’s economic rollercoaster. As organizations incorporate cloud analytics, flexibility is necessary to run advanced data science and machine learning algorithms in the cloud while users access existing on-premises workloads.
Learn more about migrating business-critical data to the cloud from industry leaders at WANdisco and Databricks in this 25-min webisode. Move petabyte scale data to the cloud with zero downtime and business continuity and leverage Spark-based analytics while migrating.
1 “Cloud Analytics Market Analysis – 2026,” Fortune Business Insights, February 2020
2 “Benchmark report,” WANdisco, April 2020