Should the data warehouse be deployed on the cloud? IDC addresses this question on a regular basis. As adoption of cloud software increases, organizations of all sizes across industries and geographic regions are evaluating and assessing the opportunities and challenges of deploying software on the cloud. Data warehousing solutions are no exception to this trend.
With a traditional data warehouse powering big data, it’s not unusual for data loads and complex queries to run for days, which hinders the analytical process. Plus, these data warehouse environments are often designed to analyze structured data only, and not valuable unstructured data generated from new external sources such as social media and mobile computing.
This paper provides the definitive guide on the critical areas of importance to bring data lake organization, governance, and security to the forefront of the conversation.
This Checklist Report discusses what your enterprise should consider before diving into a data lake project, no matter if it’s your first or second or even third major data lake project. Presumably, adherence to these principles will become second nature to the data lake team and they will even improve upon them at some point.
This study was designed to document key perceptions, challenges, and successes by focusing on data organization, integration, security, and definitional clarification to address key areas of concern and interest in ongoing data lake adoption. The intent of the survey and this corresponding report is to understand and share the current and planned adoption of technologies in the Hadoop ecosystem, intended specifically for a data lake strategy, and to learn how adopting companies are addressing critical data lake success factors, including rethinking data for the long-term, establishing governance first, and tackling security needs upfront. The survey and report also identify emergent areas of concern and new areas of clarification needed for data lake maturity.
The biggest challenge for most organizations is to increase their analytical strength. This analytical strength can help the organizations to uncover new business insights. To make data science affordable for every organization and to deploy data science across the entire organization and not only by the high priests of data science, other disciplines must be involved.
Discover how Data Management Platforms are allowing marketers to merge data from advertising partners and their customer databases to power more individualized marketing.
With today’s needs for complicated data architecture systems and the business’s need to make sure that their data is on the most economical platform, moving away from EDW to platforms like Hadoop can be more than daunting to an organization. This whitepaper walks you through how Teradata customers have used the services team to help them migrate to new platforms seamlessly and why it’s important to have a strategic partner like Teradata when taking on this data movement project.
I recently caught up with Ravi Mayuram, SVP Products & Engineering at Couchbase, to discuss recent developments in the NoSQL database industry such as the relationship with Hadoop and Spark, container technology, security, and much more.
The EDW market continues to evolve as enterprise architecture pros recognize that improved scalability, better performance, and deeper integration with hadoop and NosQl platforms will address their top challenges.