Powered by advancements in open source software, data management solutions are capable of storing, processing, and analyzing nearly any source of data, including streaming and real time. This level of technology represents both a paradigm shift in how enterprises can unlock new value from their data sources and significant changes in the infrastructure that serves as the foundation for an enterprise’s technology stack.
While enterprise data stores were previously bound by technologies that scaled vertically and had rigid schema and high levels of complexity, the open source transformation has paved the way for systems that can scale up and down with use, process any data source, and allow for optimization around value. The high-level benefits of using a modern data platform include reduced costs of data preparation and storage, increased productivity, and a direct increase in your ability to serve your customers better.
Historically, data solutions such as relational databases and data warehouses were implemented on a single, powerful server. If computing resources needed to be expanded, costs would increase, because it required specialized hardware to achieve the levels of resources needed. The modern data platform is built to be distributed and fault tolerant from the ground up by using commodity hardware, where a network of smaller computers combine their processing power in parallel.
While any single node on this system may be less powerful than a monolithic database server, the combined computing power is capable of scaling far beyond single servers. If more computing resources are needed, adding more commodity computers increases the power in a way that scales linearly: to double the computing power, you simply double the number of servers in the cluster.
This is a powerful and cost-saving method of scaling enterprise hardware. Specialized high-performance components are not needed, as many enterprises are modernizing their data platforms with tens or hundreds of nodes in the network. This approach is complemented by the latest advancements in cloud computing—namely the ability to scale up and scale down the number of nodes in the cluster based on current demands—the costs become optimized to the use cases. You don’t need a heavy investment or resource planning, and organizations that have shifted their usage to a modern data platform have realized significant savings.
The modern data platform offers benefits beyond the physical storage solution. Integrating a single environment for data storage, processing, and engineering dramatically increases productivity. Software and data engineers can code complex data operations pipelines that allow data scientists to focus their efforts directly on creating predictive models and advanced analytics.
In traditional systems, there’s generally a significant bottleneck where data scientists have to track down data sources and write tedious code to tie systems together. With systems like Apache Hadoop, there is no costly penalty for storing your data. And, with all the data and integration in a single environment, it delivers a significant time reduction in curating and pre-processing of data sets.
The distributed and parallel architecture of the modern data platform also offers performance increases. Production jobs that have been transferred from traditional data management solutions to modern data platforms generally experience significant improvements. The time to execute production jobs is decreased, and with best-in-class tooling and error handling, there’s also a decrease in production job failure. Engineers are able to code solutions instead of debugging failed jobs.
Beyond cost savings and productivity increases, the modern big data platform drives business value, and for many industries, that means a focus on the customer. The increases in processing power and storage solutions unlock new business opportunities that remain unrealized with older data management solutions.
For a retailer, a common, powerful use case is creating a single view of customers—condensing purchase, history, inventory systems, online behavior, in-store behavior, and more into a single profile that allows for real-time machine learning and personalization. For an insurer, it may be more personalized quotes and optimized pricing. A central theme across industries is harnessing the power of Hadoop to drive value for your customers, wherever they may be.
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