Today, data management is not only an important competency for organizations, but also a crucial differentiator that can determine market winners and losers. Web pioneers are innovating the way data is managed and Fortune 1000 companies and government bodies are adopting Big Data. When implementing Big Data, organizations are realizing that the strategy is more than a single technology, initiative or technique. Rather, it is a trend across many areas of business and technology.
The significance of Big Data in business processes and outcomes is changing every day. However, most organizations are still relatively new to Big Data technologies. Below are some key technologies your organizations can use to handle Big Data in a cost-effective manner:
Row-oriented databases store data in rows and are traditionally great for processing online transactions with high update speeds. However, they do not perform as well especially with queries, high volume data volume and unstructured data.
On the other hand, column-oriented databases store data with a focus on columns. This allows for huge data compression and very fast query times. The downside of column-oriented databases is that they generally only allow batch updates. Therefore, they have a slower update than row-oriented databases.
Schema-less Databases (NoSQL Databases)
There are a number of databases that fit the Schema-less database category. Examples include document stores and key-value stores, which focus on storage and retrieval of large volumes of structured, semi-structured or unstructured data. These databases achieve performance by eliminating some or all of the restrictions traditionally evident with conventional databases.
Critically, Schema-less databases are designed to take advantage of new cloud computing architectures that allow massive computations to be run efficiently and inexpensively. This makes operational Big Data workloads cheaper and easier to implement and manage.
Hadoop is an open source platform for handling Big Data that is flexible enough to work with multiple data sources. The platform can read data from a database in order to run processor-intensive machine learning jobs and even aggregate multiple data sources for large scale processing.
Hadoop has several applications but the most common use is with large volumes of constantly changing data such as machine-to-machine transactional data, web-based or social media data, or location-based data from traffic sensors or weather.
Hadoop is also used with MapReduce, a programming paradigm that allows massive jobs execution scalability against a cluster of servers. NoSQL and Hadoop work together to enables businesses capitalize on Big Data.
One of the major limitations of Hadoop is its low-level implementation of MapReduce. Developers need extensive knowledge to operate Hadoop. Between preparation, testing and running jobs, a full cycle can take hours and in turn eliminate the interactivity that users enjoyed with conventional databases. This limitation can be addressed by PLATFORA.
PLATFORA automatically turns a user’s queries into Hadoop jobs, thereby creating an abstraction layer that any person can exploit to organize and simplify the datasets stored in Hadoop.
WibiData is a combination of Hadoop and web analytics built on top of HBase. HBase itself is a database layer on top of Hadoop. WibiData allows websites to better explore and work with their user data in a better fashion. It enables real-time responses to user behavior such as serving personalized decisions, recommendations and content.
Implementing Big Data in Organizations
Implementing big data is a business decision and not an IT one. Organizations can benefit from analytics solutions when they approach them from a business perspective instead of from the Engineering/IT end. The above are some of the technologies that are key to successful implementation of a Big Data program.
For more information about Big Data and how Trace3 can help you leverage it for improved business, please contact us. We’d love to talk to you.