Scott Koegler

Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief

Big Data - What Can Go Wrong?

Tech experts are heaping all types of praises on big data, some even saying it is perhaps the next big thing after the advent of the internet.  Although stating that big data is the greatest invention after the internet is arguable, it is cannot be disputed that it has changed many things in modern organizations. It allows employees and business owners to sift through data intelligently, use data to learn through machine learning and employ analytics tools to inform decision-making processes. While it is a source of success to many organizations, people must understand that this technology is a double-edged sword that can help or wreak havoc if not used properly. Building the right data culture is what can drive a positive change in an organization. Therefore understanding what may go wrong when using big data is critical. Here are areas that may go wrong in big data:

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Who Should Manage Your Hadoop Environment?

Hadoop is the leading open source technology for the management of data. In any discussion about big data and its distributions, you will never fail to come across Hadoop in the middle of the talks. As outlined in a technical paper by Google in 2006, it was designed as a distributed processing framework back in 2006. It was first adopted by Yahoo in the same year, followed by other tech giants such as Facebook, Twitter, and LinkedIn.  During this time, Hadoop evolved significantly into one of the most complex big data infrastructure known today.

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What is Important in Data Transformation?

Data has become one of the critical components of any business in the modern era. It is for this reason that you keep hearing some conversations around big data and big data analytics. With this, data transformation has also gained fame. It is the process where data is analyzed, reviewed and converted by data scientists from a given format to another. This process is essential for organizations, especially at a time when data integration is required for an effective running of operations and security. It might involve the conversion of large amounts of data and data types, elimination of duplicate data, enriching data and aggregating it. Here is how data is transformed:

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