Hadoop came to prominence when the web exploded with unstructured data. The use of unstructured data is common for web analytics, where flexibility is required for unknown or compound fields (arrays, nested objects, or just unknown). The popularity of Hadoop for these use-cases led to its adoption, also for structured use-cases. For these cases, SQL query engines have been bolted on Hadoop, and convert relational operations into map/reduce style operations.
The BI pipeline built on top of Hadoop — from HDFS to the multitude of SQL-on-Hadoop systems and down to the BI tool — has become strained and slow, resulting in three main problems:
- Tedious data preparation, requiring hours or days of coding
- Inflexible infrastructure that prohibits ad-hoc queries on large quantities of historical data
- Slow access to data, inaccurate results, and lengthy time-to-insight
These problems result in lost insight, troves of under-analyzed data, frustrated data teams, and ultimately, lost revenue.
This paper will explore the reasons behind these problems, and how companies can help alleviate data professionals’ struggles at the source, with a new approach to storing, preparing, and analyzing big data.
Download the new white paper from SQREAM that explores an approach to Hadoop that aims to help businesses reduce time-to-insight, increase productivity, empower data teams for better decision making, and increase revenue.
All information that you supply is protected by our privacy policy. By submitting your information you agree to our Terms of Use.
* All fields required.
