Hi everyone in this blog I’m explaining about big data.
Big data is being generated by everything around us at all times. Every digital process and social media exchange produces it. Systems, sensors and mobile devices transmit it. Big data is arriving from multiple sources at an alarming velocity, volume and variety. To extract meaningful value from big data, you need optimal processing power, analytics capabilities and skills.
It is no secret that we are in the data age. Data comes at us from all directions, in all shapes and sizes. As technology leaders, we are tasked with answering complex questions like: How will we process all this information? How can it be more accessible? And how will users benefit from it?
RESURGENCE OF SQL AND RELATIONAL ARCHITECTURES:
With growth markets like the Internet of Things and mobile computing continuing unabated, enterprise data architectures require structured and semi-structured data to scale together.
As the lines between relational and non-relational database management systems blur, SQL continues to dominate as a preferred method for managing data for the following reasons:
Scale and performance - Preconceived notions that SQL lacks scale and flexibility have been dispelled by advances in in-memory computing and distributed system architectures. It is now entirely possible for relational databases to easily scale while providing the familiarity and stability of SQL. SQL also expresses incredibly complex queries with just a half dozen variations of the select command.
Everyone knows it - Decades of usage has made SQL a de facto data analysis language. With millions of SQL users and thousands of SQL tools readily available, making a change to some foreign query language is an uphill battle. The adoption of a SQL infrastructure inside a big data architecture can also lead to increased consumption of Hadoop by opening access to a greater population of users through SQL.
Stability - Relational database management systems support the SQL compatibility, transactional consistency, and enforced schema required by data-reliant enterprises.