Five challenges for Hadoop™ MapReduce in the Enterprise Lack of performance and scalability – Current implementations of the Hadoop MapReduce programming model do not provide a fast, scalable ...

They face many challenges on the way to centralizing Hadoop: Hadoop isn’t a thing: Hadoop is a word we use to mean “that big data stuff” like Spark, MapReduce, Hive, HBase, and so on. There ...

In this paper, we demonstrate a coded computing framework, named Coded Distributed Computing (CDC), which optimally trades extra computation resources for communication bandwidth in a MapReduce-type ...

Moreover, MapReduce didn’t magically eliminate the challenges with distributed, data-driven applications. The essential truth is that Hadoop was designed and optimized for the data needs of ...

For Hadoop to move forward into the next decade, the community must address one key but often overlooked thing: quality of service.

The Emergence of new programming frameworks to enable distributed computing on large data sets (for example, MapReduce). New data storage techniques (for example, file systems on commodity hardware, ...

As I work with larger enterprise clients, a few Hadoop themes have emerged. A common one is that most companies seem to be trying to avoid the pain they experienced in the heyday of JavaEE, SOA ...

In this paper, we demonstrate a coded computing framework, named Coded Distributed Computing (CDC), which optimally trades extra computation resources for communication bandwidth in a MapReduce-type ...