Exploring How Cloud Makes Big Data Better

Information technology at one time was that exclusive club that allowed only the elite few such as very large organizations and government bodies etc. through its doors. The story is quite different today. The rise and adoption of technologies such as the cloud have led to the democratization of IT, increasing the reach of technology, enabling cost reductions, and providing a plethora of applications to choose from without making any heavy investment. The cloud has given the much-needed horsepower to make the world more software defined. It hardly comes as a surprise that the cloud ranks high up in the priority for organizations across the globe.

How Cloud Makes Big Data Better?

Along with the cloud, we have also witnessed the rising importance of Big Data. Big Data has moved from the 'nice to have' to a 'must have' initiative as we move deeper into the data economy. The promise of valuable insights to create competitive advantage, drive revenues and spark new innovations are reason enough to bring it on the agenda of all kinds of businesses. As the adoption of Big Data and Cloud continue to increase, we are witnessing a growing interdependence between these two technologies with the promise of phenomenal gains.

1. Convergence — is the name of the game:
   While big data and cloud evolved independently over time, today these two technologies are becoming increasingly intertwined. The growing volumes of data and the need for faster analytics have driven big data to the cloud. Organizations today are looking at new data models derived from structured and unstructured data sources, they need complex event processing applications, they need usage-based compute resources, and they demand greater computing power. With an on-premise data store, processing and analyzing these high volumes of data becomes hard to execute. And given the operational and management costs associated with these on-premise solutions, it does not present itself as an agile and cost-effective solution.
   The cloud, on the other hand, helps in alleviating the enterprise data load and offers not only greater computing power, increased storage, and data agility, it also makes it infinitely easier to analyze and derive faster data insights.
2. A conversation shift:
   With the conversation moving from 'where can we store all this data' to 'what can we do with all this data', organizations are moving towards an orientation that is more outcome-based. Clearly, cloud computing and big data are better together. With a growing dependence on data, enterprises are looking at greater effectiveness from big data platforms. With the greater integration of data from both structured and unstructured resources, the big data platform that we need must be highly scalable, elastic, and performance driven. And this can be achieved by leveraging the computing capabilities of the cloud.
3. The need for greater scalability:
   Performance issues such as latency have no place in the enterprise today. When it comes to analytics, latency can play havoc with performance. The lack of efficient data warehousing and an inability to access real-time BI to answer business queries is a challenge that can be navigated using the cloud. Latency can be brought down efficiently to almost single digit milliseconds using the cloud to create direct interconnections between the data and the analytics. The need for additional processing power can also be addressed with the cloud as it is always there for the taking.
4. The financial advantage:
   Cost is an obvious advantage of the cloud. On-premise big data storage and analytics can cause a huge drain on the IT budgets as the organization then becomes responsible towards maintaining the big data centers. The cloud, on the other hand, makes no such demand and gives the organizations the flexibility to maintain small and efficient data centers that can be scaled on-demand. The cloud also makes it much easier to gather external data, something that is growing exponentially today. It also enables data access anytime, anywhere without any additional infrastructure demands, thus making it more cost-effective.
5. Increased collaboration:
   Analytics is collaborative. Collaboration is also a driver of cloud adoption.
   BI and big data analytics work better in the cloud as the cloud provides ready access to data, BI, and processing applications. The cloud makes it possible to share visualizations, share data, and perform cross-organizational analysis. This makes the data analysis available to a distributed user base as well and makes information more accessible to a broader demographic.
6. Better maintenance and lesser complexity:
   Analytics platforms, like software products, need maintenance. They need frequent upgrades, redesigns, migrations'the list goes on. By moving the analytics platform to the cloud, organizations can ensure that everything remains up-to-date at all times. The cloud also takes away the cost burden of over-provisioning for peak consumption as organizations can access on-demand scalable resources. With the convergence of cloud and big data, today we have cloud-based analytics applications that move the analytics closer to the data. Cloud analytics platforms also take away the effort that goes into putting together a functioning analytics platform. With a ready-to-use data processing and analytics setups, organizations become capable of accessing real-time data-driven insights faster. The can hit the ground running, as it were.

Big Data is only useful when it is used for analytics. It is also clear that the data deluge is only going to increase. And organizations will be hungry to use this rising deluge to their advantage. The key insight from this post is that this will only be possible by multiplying the power of big data with the advantages of the cloud.