Latest Big Data Whitepapers
Data lakes can store masses of structured or unstructured data in raw format until your enterprise needs that data for analytics. That’s why data lakes are now seen as an attractive alternative to traditional data warehouses. However, enterprises like yours struggle to realize the expected return on data lake investments because of unexpected data quality, data governance, and data immediacy challenges. This paper describes how to address these issues and prevent your pristine data lake from devolving into a useless data swamp.
Data lakes can store masses of structured or unstructured data in raw format until your enterprise needs that data for analytics. That’s why data lakes are now seen as an attractive alternative to traditional data warehouses. However, enterprises like yours struggle to realize the expected return on data lake investments because of unexpected data quality, data governance, and data immediacy challenges. This paper describes how to address these issues and prevent your pristine data lake from devolving into a useless data swamp.
Data lakes can store masses of structured or unstructured data in raw format until your enterprise needs that data for analytics. That’s why data lakes are now seen as an attractive alternative to traditional data warehouses. However, enterprises like yours struggle to realize the expected return on data lake investments because of unexpected data quality, data governance, and data immediacy challenges. This paper describes how to address these issues and prevent your pristine data lake from devolving into a useless data swamp.
Data lakes can store masses of structured or unstructured data in raw format until your enterprise needs that data for analytics. That’s why data lakes are now seen as an attractive alternative to traditional data warehouses. However, enterprises like yours struggle to realize the expected return on data lake investments because of unexpected data quality, data governance, and data immediacy challenges. This paper describes how to address these issues and prevent your pristine data lake from devolving into a useless data swamp.
Data lakes can store masses of structured or unstructured data in raw format until your enterprise needs that data for analytics. That’s why data lakes are now seen as an attractive alternative to traditional data warehouses. However, enterprises like yours struggle to realize the expected return on data lake investments because of unexpected data quality, data governance, and data immediacy challenges. This paper describes how to address these issues and prevent your pristine data lake from devolving into a useless data swamp.
One of the obstacles for large on-premise clusters are the requirements for high-end data centers in addition to the challenge of ever-increasing energy costs. Large cloud providers have increased their focus on HPC and added more specialized HPC services to their offerings. They leverage the success of their cloud computing ecosystem and are perceived by some to have the most agile and flexible solution available; allowing you to pay on a per-use basis and to adjust your capacity on the fly to meet your needs. But is this really a cost effective long-term strategy for running AI workloads or simulations that can benefit from capacity at scale?
Many analytics and business intelligence tools today reduce analytics to a visualisation exercise. Today’s challenges require more than self-service applications that only produce descriptive visualizations based on limited data. Yet when self-service tools focus on data visualisation, they minimize the importance of data governance and data integrity in enterprise settings. This limits our ability to gain true insights from our data and make meaningful business decisions.