IBM and Cloudera, better together

Challenge: Building an enterprise Hadoop-based data lake can be the perfect solution for storing, exploring and managing today’s big data. The data lake allows for the ingestion of new semi- and unstructured data sources, including streaming audio and video, social media, sentiment and click-stream data.

The challenge for the enterprise is to build a data lake that has the proper level of security, data governance and the analytic tools needed by its data scientists to drive tasks such as reporting, visualization and machine learning.

Solution: IBM and Cloudera are offering an enterprise data platform with integrated products and services to speed time to value when collecting, managing, governing, accessing and exploring big data.

Challenge: Being able to accurately predict customer behavior, process machinery failures and detect fraudulent behavior using machine or deep learning is the basis for AI. To discover the patterns in data and generate the most accurate insights, all sources of data must be accessible.

The first challenge for the enterprise is accessing the data across the organization — from data marts, warehouses, hybrid and multiclouds. The second is having the data science tools and business analytics to empower data users to economically extract meaning from and interpret complex data sets.

Solution: IBM and Cloudera are driving AI with solutions that give organizations the ability to unlock the value of data in new ways, deploy and manage business models, predict future outcomes and automate processes for better data-driven decisions.

Challenge: Explosive growth of data has forced organizations to use their enterprise data warehouse (EDW) for purposes that it was never intended for — including running extract, transform, load (ETL) workloads and storing large volumes of unused data.

The challenge for the enterprise is to harness the new types of data, updated analytics practices and more efficient, cost-effective methods of storing and accessing data.

Solution: One of the most effective modernization approaches is offloading EDW data and ETL workloads to a flexible platform that provides economical storage, incorporates current technologies for machine learning and analytics and is optimized for the cloud.