We have far more devices in the world than we had a decade back. Over time the technology around us became cheaper and more accessible. Taking a look back, it is imperative to realize that the world has seen far more technologies emerge in the last decade than ever. We have advanced smartphones, voice assistants, wearables, smartwatches, home automation devices and many more. The surprise is that all of these are successfully using the technology, which was only a sparkly concept a few years back. While e might not have heard about artificial intelligence, machine learning, big data, data analytics, cloud, etc. around the 2000s, we are now more than familiar with them. It wouldn’t be incorrect to say that we live in their age.
If you take a close look, almost every other company and organization is jumping in to harness these fresh technologies. No matter which product or service we look at, they appear to be powered by artificial intelligence and analytics. All of this only points to a single direction- today we have far more data than we’ve ever had in the history of mankind. And as this data increases it is paving a way for many new technologies with the potential to harness it. As a simple rule, the more the number of devices increases, the more will we have data.
Be it artificial intelligence, machine learning or business intelligence, every other technology and concept is harnessing the abundant data in its unique way. And the best part is that the more data it works on, the better results it yields. As a result, today, every innovation, research, product or service that is being launched is powered by the magic of big data. Researchers with the help of the latest technologies utilize big data to come up with relevant facts and insights that are helpful in solving a particular problem or laying the foundation of a particular theory. Furthermore, researchers are also able to collaborate with each other in a far more efficient manner today. Thanks to cloud technology, sharing data and other information is as easy as doing it in person.
As we advance into the future, the role of big data and its impact on us will increase more than ever. The number of devices around us will increase too. Statistics suggest that by 2020, we will have more than 20 billion devices in the world, all connected to each other. That will be the magic of yet another powerful technology called the Internet of Things. But, Big data Engineers India will ultimately lead to lot of opportunities in the coming years. We already are witnessing some of these in the form of personalized technology and services. E-commerce websites these days analyze your browsing history along with your buying trends and suggest new products in the form of product recommendations. And this is just one example of how powerful big data can be.
With 2020 drawing upon us, experts suggest that we will officially enter the age of decisions, innovations and everything else in between being drawn by big data. There are no doubts about the fact that researchers will have to put in a lot of extra efforts to utilize the data, but that’s how the future is going to be. Whether you realize it or not big data on its own doesn’t make any sense. It’s like lying on a huge heap of cotton, without knowing what to do with it. Big data has raw data from almost all walks of life and consists of everything from elements that totally help innovate to those that must be thrown into the trash. So, the question is how to make sense of this data that can be put to use for any organization or task? Hint: It involves engineering!
Welcome to the world if big data engineering- the next-gen technology that will transform businesses and help them develop products and services that are personalized to the needs of every customer. Brace yourself as the next year draws because big data engineering is all set to rule it.
What is Big Data Engineering?
Data engineering is basically a process that enables users across various enterprises to avail clean and quality data. This ensures that they have data that can be trusted and used to derive better insights and actions in business. Data engineering has arrived on account of the disruption caused by big data. The industry on a whole is moving towards data management that helps in delivering insights through technologies like artificial intelligence, machine learning and more.
But the fact of the matter is that the amount of big data available is still huge, while these technologies that are used to manage it aren’t quite prepared for it. And what happens when IoT completely takes over and leaves the world with even more abundant data through its devices. While the cloud will still be used to agility, we would require a far more advanced system for making sense of this data.
Gartner defines data technology as the practice of making the appropriate data accessible and available to various data customers that have data scientists, enterprises, business analytics, and even users. As a discipline, bigs data engineering involves collaboration between the business and corresponding information technology.
Capabilities of Big Data Engineering in 2020
Organizations must not only stitch together fragments of data but instead develop an end to end solution by leveraging artificial intelligence. This platform must be able to support all factors that have led to the emergence of data engineering for big data. These include cloud, which makes it easy to scale and tune servers by separating storage and compute; Spark, which can be 100x faster than another platform like Hadoop for large scale data processing; Kafka that can handle trillions of events a day. Based on these necessities, data engineering platforms can have the following capabilities-
- Data catalog can help in discovering the right dataset
- Bring the apt data to the ML environment
- Operationalize data pipelines
- Process real-time data at scale
- Utilize data masking to desensitize confidential information
- Ensure trusted data is available for insights
- Simplify data prep and enable collaboration
Conclusion
Big Data engineering is critical to artificial intelligence and analytics success. In fact, a research by Databricks’ suggests that only a handful of projects in artificial intelligence are successful, while others fail mainly due to the lack of data. In spite of investing huge amounts of money, organizations are not able to derive profitable results from big data. Data engineering, on the other hand, can help bring many such fresh ideas from organizations and researchers into production.