BigQuery: A RESTful Web Service that CIOs can Trust
With Big Data prevalent in all sectors in today’s world everyone from system administrators, business analysts, and CIOs are looking to store, provide access and make sense of Big Data. In such a scenario, it should not surprise anyone that the term ‘BigQuery’ has captured the attention of one and all. A fully-managed and cloud-based interactive query service is the external implementation of one of Google’s core technologies’ code name Dremel. One can run SQL-like queries against very large data sets and get accurate results in a span of mere seconds with BigQuery.
Difference between Dremel and BigQuery:
BigQuery which was launched for general availability is merely the public implementation of Dremel. It provides the core set of features of Dremel to third party developers via a portfolio of REST Application Programming Interface (API), command line interface, web user interface, access control and more while maintaining Dremel’s unrivalled query performance. Dremel shares Google’s infrastructure in a bid to run a single query on tens of thousands of servers at the same time. On the other hand, Google’s cloud platform helps one realize the superior performance at a very low cost without even needing to pay for the supporting infrastructure.
Why Should a CIO prefer BigQuery?
With features like enabling users to access the tools required to deal with different interfaces that they need, BigQuery makes available the Google API’s console in restricting access at the project and dataset level. It also comes with the feature where users can poll their queries for status update, as these continue to run in the background in an unparalled manner. With the help of the Google cloud console, users can have access to the history of the queries along with other resources. BigQuery offers users full control and visibility of the data stored in it. It also enables them to transact the data easily and quickly for analysis with the help of high volume transaction recording or logging in at real time. Another advantage that BigQuery provides is that it allows all data to be brought together for analysis through SQL functions such as grouping and joining, as in traditional databases. This helps in managing time when it comes to extracting insights from a number of datasets.
How is BigQuery different?
Google’s BigQuery is unlike other big data technologies like MapReduce and data warehouse solutions, we can see this when we compare BigQuery with MapReduce, which Google used for big data processing. Contrary to BigQuery, MapReduce is merely a programming model that processes large datasets. With regard to having a quick response time and easy-to-use factor, MapReduce lacks behind BigQuery. As an overall package, BigQuery empowers a CIO with more options as compared to MapReduce.
Further, here is a citing of Crystalloids, an analytics firm that help businesses in improving their profitibality by analyzing massive amounts of data. As part of that, Crystalloids built a BigQuery-based application for ‘Center Parcs Europe’ to allow them accurately forecast number of vacationers, optimize prices and increase revenue. The application built using Google App Engine, Google BigQuery Service and other Google tools helped in turning the query results into charts and graphs via the Google Visualization API. This helped Center Parcs Europe save $150K per year in operational costs and another $800K for not needing to run the application locally as it was cloud-based. This example proves that BigQuery in the long run can be beneficial for organizations by making it easy to run their business.
There is no doubt that BigQuery can be a helpful tool for CIOs, as it helps crunching several terabytes of data within a few seconds. With benefits outweighing the limitations, it does the job of helping CIOs in enriching their businesses and getting the most out of their data while increasing profits, promoting growth and cutting costs.