Example Applications

Some companies build their business on Cloudera's Platform for Big Data. Others deploy it to alleviate a particular business need. Here are some of the most common applications we've seen across our customer base.


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360-Degree Customer View

Most organizations keep information on their customers spread across a number different disparate systems: CRM, financial, point-of-sale, marketing, customer support, and so on. Imagine the questions that could be asked if all of those disparate data sources could be combined into a single, 360-degree view of each customer. For example, if a customer support person receives a customer call and could immediately tell where you were most recently looking on the company's website, they'd be able to more effectively guide the conversation. A retail salesperson can offer more compelling products and promotions if he or she can tell which email promotions you've responded to most recently. Cloudera Enterprise makes it easier to bring together disparate data sources — it can ingest data in any structure and any format.

100% open source and engineered to run on industry-standard hardware, Hadoop scales virtually without limits and handles any type of data, no matter how encoded or formatted and whether structured or unstructured. And now with Cloudera's Platform for Big Data, Hadoop is enterprise-proven, already delivering on the promise of Big Data across numerous industries and hundreds of use cases.

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Business Process Optimization

Due to a heavy dependence on disparate data management systems, many business processes today involve data movement, duplication, and processing to get unstructured data into a structured format or to make it accessible to different users within the organization. 

Cloudera Enterprise helps companies optimize their business processes by eliminating extraneous data movement and simplifying the approach to data management. Complex logic and algorithms can be applied to data processing pipelines to automate decisions. Users across all business units can access their multi-structured data in the same place. There's no limit to the total data volume or to the types of data that can be captured.

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Building a Data Hub

For some organizations, it isn't practical to consolidate all data management systems into a single environment. Different users need access to specialized tools that only run on particular databases, or they don't have the bandwidth to learn how to work with new tools. Organizations in different pockets around the globe need their own local data mart to alleviate network constraints. 

It still makes sense to integrate these various data marts and warehouses. Cloudera Enterprise often serves as a data hub, allowing companies to maintain various data management platforms while integrating them into a single core that eliminates data redundancy and maintains and single source of truth across the organization.


Nokia relies on Cloudera Enterprise to serve as the company's enterprise-wide information core. Read the case study.
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Data Processing / ETL Offload

Data processing is critical to supporting organizations’ everyday operations such as generating reports for suppliers and customers, measuring internal metrics day to day and reporting quarterly financial results. Hadoop is rapidly becoming a mainstay in organizations due to its flexibility, scalability and low cost of storing and processing raw data. With an increased focus on improving operational efficiency, leading organizations across industries are moving mission-critical data processing and historical data storage to Cloudera Enterprise. This enables them to store raw data in its native format and develop and maintain complex data pipelines faster, and at significantly lower costs, than were possible using traditional systems.

Search, media and advertising company shifts the center of gravity for data management to Cloudera. Read the success story.
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Data Warehouse Offload or Replacement

Hadoop took the database management industry by storm through its revolutionary ability to process massive data volumes flexibly, quickly and at low cost. But organizations with intensive analytic performance demands still needed a data warehouse in place for speed-of-thought analytics — until now. With the introduction of Cloudera Enterprise RTQ, powered by Cloudera Impala, companies can leverage Hadoop for their complete data lifecycle needs: data storage, processing, analysis and serving. More and more industry leaders are migrating data marts or entire data warehouses from legacy relational database management systems over to Cloudera Enterprise to take advantage of its cost efficiency and flexibility for both data processing and analysis at scale. 

Learn more about Cloudera RTQ and Cloudera Impala.
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Predictive Modeling

Predictive models are complex and difficult to create. Historically, they've run using SAS software on small samples of data, otherwise known as "sandboxes," where data scientists can explore the data. By running analytic procedures closer to disk, the time to build and execute predictive models can be significantly reduced. Hadoop's ability to run analytics in parallel across massive volumes of raw data — instead of relying on samples — results in predictive models that are much more accurate. Further, the cost savings and productivity gains provided by Hadoop make it an easy choice to leverage Cloudera for predictive modeling.  
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Telemetry

Telemetry is a critical component to many businesses across industries — ranging from meteorology to oil and gas to motor racing to agriculture to medicine. Telemetry is often used to trigger a response or flag abnormalities, but it has traditionally been difficult to collect all of that detailed data over long periods of time, store it and make it readily available for exploration and analysis. Hadoop has made it possible to store these massive volumes of detailed data over time, but until recently that data, or portions of it, needed to be migrated into an external database or analytic tool in order to derive insights from it.

The introduction of Cloudera Enterprise RTQ, powered by Impala, has changed this. Now speed-of-thought analysis is possible over massive volumes of detailed telemetry data, allowing users to ask bigger questions.

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Time Series Analysis

Time series data is distinct from other data sets due to its temporal order. Traditionally, relational databases have been used as a general purpose tool to store sample time series data sets, increment counters, and to run analysis on those sample data sets. They are relatively easy to use but face challenges scaling from a storage, performance and cost perspective. Time series data is a natural big data use case: machine-generated data is steadily flowing into the data management platform and should be stored in its raw form. Cloudera Enterprise offers an ability to capture, store and analyze granular time-series data at massive scale and lower cost.

Featured Customer Story

Experian Marketing Services leaps forward in operational efficiency with Cloudera. Watch the video.
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Using Cloudera to Improve Data Processing Read the whitepaper.
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