The importance of Hadoop data mining cannot be underestimated, especially with the fast growth of Hadoop, as well as the explosion in new and varied sources of data. In fact, Hadoop data mining is becoming a commodity for almost every data-driven company and organization. Extracting useful intelligence from data has always been a challenge, but as data gets larger and more varied, the techniques used to mine data must evolve as well.
Hadoop Data Mining
The Process of Hadoop Data Mining
- Start collecting and storing data. Even if the data is sitting in a traditional data warehouse, over time, it can be accessed with new tools for faster utilization.
- Create an architecture to catalogue and sift through the data. This is even more critical as the volume and variety of data sources continues to expand and explode. Hadoop can scale quickly, depending on the business needs. If a business has large amounts of data, it’s possible to increase the amount of commodity hardware to run clusters on.
- Visualizing the data. Hadoop data mining can be done with next-generation tools like Trifacta. Trifacta allow teams to visually discover, structure, clean, enrich, validate and publish the content of their data. We call this process data wrangling.
How PepsiCo Benefitted from Hadoop Data Mining
Businesses and organizations can realize and capitalize on the opportunities offered by Hadoop data mining to take their analysis and operations to a new level. While Excel might be a great tool to start with for Hadoop data mining, it can introduce errors at scale and interfere with collaboration. Designed from the ground up for Hadoop data mining, Trifacta Wrangler allows non-technical business users, data analysts and data scientists to benefit from its automated transformation and visualization capabilities. PepsiCo had several challenges with manually reporting and analyzing performance sales data for more than 10 of their retailers. They determined that using applications such as Excel and Access was slow, laborious, prone to mistakes and not scalable. PepsiCo’s retail forecasting team wrangled retail sales data using Trifacta and was able to reduce time to insight by 70% and build time has also reduced as much as 90%, as a side benefit, found that analysts working together on data problems yielded never before seen insights into big box retail inventory management.
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