What is Big Data?
As one of the most “hyped” idea in the industry today, there is no consensus as to how to outline Big Data. The term is often used synonymously with an interrelated concept such as Business Intelligence ( BI) and data mining. Basically, all three terms are about analyzing data and in many cases advanced analytics. But Big Data idea is diverse from the two others when data volumes, number of transactions and the number of data sources are so big and multifaceted that they need special methods and skills in order to draw insight out of data (Basically, traditional data warehouse technologies may fall short when dealing with Big Data solutions).
Basically, Big Data is data that contains a larger variety arriving in growing volumes and with ever-higher velocity.
Challenges of Big Data
The first thing anyone, thinks of with Big Data is its size. Handling huge and quickly growing volumes of data, is problematic for many decades. Worldwide, 2.5 quintillion bytes of data are created every day, and with the growth of the Internet of Things (IoT) domain, that speed is increasing. 90 percent of the present data in the world is produced in the last two years alone.
With Big Data, it’s critical to be able to scale up and down on-demand. Many organizations fail to take into account how quickly a big data project can grow and change. Continually waiting for a project to add extra resources cuts into time for data analysis as well as, workloads of Big Data, also incline to be bursty, which makes it hard to forecast.
When humans consume information, a great deal of heterogeneity is easily tolerated. In fact, the nuance and richness of a natural language can provide cherished complexity. Though, machine analysis processes expect standardized data, and cannot comprehend nuance.
Quality of data is not a new concern, but the ability to store every piece of data a business produces in its original form complexes the problem. Dirty data costs businesses in the United States $600 billion every year. Moreover, common causes of dirty data that must be handled, include user input mistakes, duplicate information, and improper data linking. In addition to being careful at preserving and cleaning data, big data tools can help to clean data.
The privacy and Security of data is another vast worry and one that increases in the context of Big Data.
Specific challenges include:
- User authentication for everyone accessing the data.
- Limiting access based on a user’s requirement.
- Audit data access histories and meeting other compliance guidelines.
- Proper use of encryption.
Need Help with Big Data
Nub8’s strategy, experience and analytical expertise will help you build solutions based on Big Data. Apache Hadoop, HPCC, Storm, Qubole, Cassandra, Statwing, CouchDB, Pentaho, Flink, Cloudera, Openrefine, Rapidminer, DataCleaner, Kaggle and Hive are some of best tools for development of Big Data solutions.