Enterprise standards for data and IT are usually strict with appropriate levels of governance and oversight at every step from procurement to usage. Big data technologies are increasingly being asked to deliver value supporting critical applications and workflows. Therefore, the same governance requirements apply to Big Data as well.
Kafka has become a center piece in modern enterprise data architectures. Hadoop serving the role of persistent historical store that supports a myriad of workloads. Moving data from Kafka to Hadoop is a common task in such architectures. Read to find out more about the considerations and how to realize critical data pipelines.
“Data science” is a popular term and one in the ascendancy in Gartner’s Hype Cycle for Emerging Technologies 2014. It has multiple meanings based on whom you ask. One way to deal with subjective interpretations is to crowdsource the answer and pick the popular interpretations, provided there is enough data. Recently, a data scientist (who else?) at LinkedIn attempted to define the term “data scientist” using data from profiles of people that have the phrase “data scientist” across
“Big Data" and "Data Science" are hyped terms while in reality organizations struggle to realize value. In fact, the ability to derive value from your data is influenced by the right choice of technologies and staffing with the right skill sets. However, a key strategic investment to make is having the right data culture in the organization.