Let’s say you’re the Chief Technology Officer of a bank or retailer struggling to infuse AI that aims to improve customer experiences. You likely face three main challenges:
Data sprawl: Your customer data is currently on multiple clouds, including on-premises and a cloud data lake storage
Hurricane season is upon us and the US is facing its seventh hurricane this season already. No matter how severe or mild, hurricanes and other national disasters are a concern for both individuals and businesses who operate in these areas.
So what happens now when we go beyond the frontiers of the data warehouse and into the world of the data lake? – the world of Hadoop, of NoSQL, the world of schema on read, of discovering the data as is? For many organizations, the holy grail is to reap the benefits of the data lake while retaining
The data lake may be all about Apache Hadoop, but integrating operational data can be a challenge. Learn how to deliver real-time feeds of transactional data from mainframes and distributed environments directly into Hadoop clusters and make constantly changing data more available.
It is said that more data has been created in the past two years than in the entire preceding history of mankind. It would be interesting to find out how much of this data has been analyzed and put to good use. Analyzing and harnessing big data is undoubtedly the major challenge of the day for all