So, you have made a major investment in your data warehouse. However, squeezing out marginal performance gains requires you to constantly invest in more hardware and services. There has to be a better, more cost-effective solution.
Increasingly, organizations are supplementing or replacing their enterprise data warehouse with Hadoop based platform for these reasons:
Enterprises are struggling to derive business value from the onslaught of structured, semi-structured, and unstructured data. Legacy data warehousing and analytics platforms don’t deliver the speed and scale necessary in today’s big data world.
The Hadoop Distributed File System (HDFS), in turn, is an open-source distributed file system that can serve as an effective storage ground for large amounts of data. Hadoop is well suited for batch processing where immediate interactive analytics are not required. HP Vertica Analytics Platform consists of a massively parallel database and an extensible analytics framework optimized for real time analytics of data scaling from gigabytes to petabytes. HP Vertica and Hadoop are complementary analytics platforms purpose-built for big data. Both are modern, scalable, massively parallel processing (MPP) systems built for commodity hardware at considerably lower total cost of ownership.
Here are some real-world examples on how enterprises are using the HP Vertica Analytics Platform to help accelerate their Hadoop environment.
Organizations everywhere are grappling with how to manage their growing big data sets from ERP and e-commerce systems, log files, sensor data, social media and more. Apache Hadoop provides a cost-effective enterprise data hub (EDH) to store, transform, cleanse, filter, analyze and gain new value from all kinds of data.
Specific uses cases include:
Companies are continually looking for ways to maximize productivity and profitability. Even when operations have been analyzed and optimized, subtle changes in environments make room for further, significant improvement. Taking a wide variety of granular measurements from sensors – on vehicles, equipment, consumer products, smart meters, etc. – lets businesses track patterns in operations to find new optimization opportunities.
Specific use cases include:
Customer analytics is vital for just about any industry or market segment. By understanding how your customers (or constituents) behave and interact with your organization, you are able to better serve, and profit, from the interactions.
The proliferation in the number of channels by which customers communicate has resulted in marketers drowning in data and struggling to better understand their customers. Marketers have to continually deliver above-market growth and show measurable results.
Specific uses cases include:
In the coming years, data generated by sensors and machine-to-machine (M2M) communication, or the Internet of Things, will fill the storage systems of data centers across a wide range of industries. In fact, IDC forecasts that machine-generated data will increase to 42% of all data by 2020, up from 11% in 2005.
For years, people have been talking about the potential impacts of this data deluge from the Internet of Things. Today, the talk is shifting to the opportunity to use sensor data to drive new business models. Data-driven business models are already happening across a wide range of industries—from insurance and banking to manufacturing and medical products. The future will bring much more of this.
Across a wide range of industries, organizations are running analytics on sensor data to enable new value-added products, services, and business processes. All