It allows us to performa data lake implementation to handle/treat huge amounts of data, or what we call the "terrible bytes”.

This solution is a cheaper way for us to offload the otherwise expensive data. We can move data from outdated database versions, such as Oracle 10. It is now out of support, but still hosts some of our historical data. This solution has helped us move our data to the current version.

Previously, we had our data on more expensive platforms. Now, using this solution, it is much cheaper to have all of the data available for searching, not in real-time, but whenever there is a pending request.

Hortonworks has allowed my organization to increase the amount of data that we regularly store from sensor data and weblogs, which in turn gives us a greater scope of data to analyze.

It was for end to end data processing and data manipulations.

The Hadoop value proposition is in expanded functionality, linear scalability, and reduced software and infrastructure costs. Hadoop offers several generic frameworks for batch, real-time, and iterative processing, such as map-reduce, spark, and spark streaming. Additionally, these frameworks provide libraries for predictive analytics and machine learning. This type of expanded functionality is not easily achieved on any other single platform.

It is a different paradigm than standard relational databases. We can also process different tasks then just those related to the standard database world. That said, we are capable of processing various data science tasks, e.g. natural language processing or log processing.

• Maintenance of our own data lake in the enterprise-level
• Storage and analysis of server logs
• Applying Operational Intelligence in the enterprise-level based on the analysis of various department units data
• Semantic analysis based on the feeds from social networking data, clickstream data, etc.

It helps a lot in data in motion (ingestion and manage in real time). We are able to do 3rd-party data monetization of our data within a t+20 minute time frame to our end customers.

We're able to perform sentiment analysis on Twitter data.

It allows our organization to collect data from databases that are different, and where the data is similar, it allows for a detailed analysis from a single data store.

Data is now available.

It allows us to provide our customers with data insights that they previously were unable to obtain.

This product gives the possibility to the organization to easily and quickly install and configure a Hadoop cluster. With this cluster, the organization will be able to store and process their data and bring out some specificity on it. For example, unknown common points between their clients or key elements that will increase or decrease the churn of the client.

It's easy to deploy and we've used this distribution for some of our recommendation and trend analysis use cases.

From an overall perspective, Hortonworks support is crucial to our operations.

We've done multiple implementations of it. It enables customers to perform sentimental analysis from social media data to engineering analytics.

With it, we have faster processing times for our apps.

We have successfully ported a Microsoft SSIS product application into Hadoop, that saved millions of dollars for the company and, at the same time, they are getting better performance. Also, we implemented fraud detection, as quickly as possible, for the online orders. (Fraudulent orders became a big headache for our company. The early detection of fraud is saving the company a lot of money).

It has helped our organisation cater to clients who are using Big Data for data storage and analysis combined with our security product.