In this article we explore the exciting concept of Big Data and SQL Server working in tandem
While in a layman’s term it can be defined as nomenclature for lots of data, but in definition, Big Data refers to extremely large and complex data sets which cannot be managed and processed with the help of conventional data management tools. It is heterogeneous data generated at high speed, requiring new tools, applications and frameworks for its processing and managing. An example of Big Data is data from social networking sites like Facebook, which is too much to be managed by a RDBMS. However with the coming of SQL Server 2016, there is a possibility of connecting Big Data to the RDBMS. How this is done in SQL 2016 will be discussed later in this article.
Big data has different meanings for different people. Some consider 5OGB as Big Data, some consider 500 GB as big data whereas for some 5TB is big data. It is difficult to quantify the qualitative Big data. Therefore some unified characteristics have been created to distinguish between any data and Big Data. These are called the three distinctive ‘V’s of Big data:
- Volume – This denotes the size of the data in use; it might be in Megabytes, Gigabytes, Terabytes or Petabytes and more. Data generation is no more limited to humans; machines can easily generate large amounts of data in a short span of time and that too more efficiently.
- Velocity – This refers to the speed of data generation, multiple applications demand diverse latency, there is however a widespread demand for quick information without any delays. An example of speedy data generation would include stock exchange data, face book shares and status updates and tweets.
- Variety – This refers to the variety of formats used to store data, different applications use different formats to store data. A lot of unstructured data in the form of videos, images and audios is released from structured data like spread sheets, relational database etc. All of this data is stored in different files and formats, and along with advancements made in Big Data technologies we have finally found a way to deal with all this data.
These are the characteristics using which you can identify Big Data. It is usually generated through multiple sources and does not have only one limited source, Social Media, Enterprise Data, Archived Data, Public Data, Transactional Data etc are all sources of Big Data. Coming back to the point we brought up in the beginning of this article; how can SQL 2016 help in connecting Big Data and RDBMS.
Microsoft through MS SQL server 2016 provides trusted data warehousing solutions for structured as well as unstructured data which can store heavy data, heavy enough to be measured in Terabytes and Petabytes along with providing real-time performance. A powerful platform is required to turn information into actionable insights, the In-Memory columnstore feature in the 2016 edition of SQL Server provides up to 100X improved query performance. with Polybase capabilities in APS, by Microsoft, and in Microsoft SQL Server 2016, user will be able to query across both relational and non-relational sources.
The SQL Server 2016 edition is not free from SQL crashes and it is better to keep a contingency plan in place
Despite its apparent sophistication, the 2016 edition of SQL Server is susceptible to SQL crashes. So it is wise to keep a contingency plan in place by investing in a sql server recovery tool like DataNumen SQL Recovery. Armed with an incisive recovery algorithm, this powerful tool can assuredly get back the data stored in the corrupted SQL file in quick time. Further this tool is your best bet if you are looking to recover a large SQL file running into several terabytes.
Alan Chen is President & Chairman of DataNumen, Inc., which is the world leader in data recovery technologies, including access recovery and sql recovery software products. For more information visit https://www.datanumen.com/