Big Data Engineering
The amount of data produced across the globe has been increasing exponentially and will accelerate in future. Effectively analyzing these data set can increase the efficiency, productivity, add business value, create significant business results. To make use of data, companies need reliable, efficient way to store and manage data and effectively manage the data pipelines. The data flows need to be distributed and scaled well to manage the huge inflow for generating valuable insights.
Data Engineering is a serious topic about building scaled applications in IT world. As value of data decays over time, the need for application processing data in interactive and non-interactive manner is completely justified where data engineering breaks in transitioning questions into models into applications. Scaling has many implications for software architecture, designing for scale, big data systems are inherently distributed systems. The architecture must explicitly deal with issues of partial failures, unpredictable communications, latencies, concurrency, consistency and must include replication and recoveries in the system design. As the system grows to utilize thousands of processing nodes, disks, distributed geographically, the issues are worse. The applications must mandate that scalable applications treat failures as common events and must be handled gracefully ensuring the operations are not interrupted.
To mitigate the risks, challenges associated with scale and technology, a systematic, iterative approach must be considered during the system design for long term scalability. Because of scale, a well structured software engineering approach is approach is needed to frame the technical issues, identify the architecture decision criteria, and rapidly construct and execute relevant but focussed prototypes. Without a structured approach, it is easy to fail and get into the trap of chasing after a deep understanding of underlying technology instead of answering the key go or no go questions about a particular technologies. Getting the right decisions for the minimum cost should be the aim of this exercise.
Analytics is no more a top-down approach, with internet of everything era, data volume is growing rapidly and managing the data pipelines, understanding and finding the value from data is rapidly becoming a big challenge, and analysts may not be all time the decision makers as machines have started taking the roles thorough machine learning to make many adjustments and decisions based on the data generating with high requirements on computing. Companies with a comprehensive big data environment that processes data at rest and in motion will be far more productive and efficient, bringing better business value. In order to achieve this, it is very critical that companies should incorporate their architecture for real time ETL and address the time to value of data that is offering real time insight when needed.
It may happen that fairly simple idea can result into rather complex system of processing modules, interconnected with big data pipelines backed for Internet of everything. These can be achieved with proper data engineering practice that can be taken into next level to handle ever increasing amounts of data whether it is real time or data at rest, to handle every aspect of Internet of Everything data from storage to analytics and allowing us not only to provide quality services, better business results but also experiment with new technologies.
