If you’re thinking about moving from a traditional relational database management system (RDBMS), you should consider Apache™ Hadoop®—because your competitors probably are. According to Gartner, Hadoop joined the mainstream in 2016. And Allied Research says the Hadoop market will likely reach $84.6 billion in revenue by 2021, with a CAGR (compound annual growth rate) of 63.4% from 2016 to 2021.
Why is Hadoop so popular?
Hadoop has many similarities to NoSQL databases, and many of the same benefits we discussed in our Top 3 Reasons to Migrate to NoSQL eBook: distributed architecture, reduced cost enabled by running on commodity hardware, high performance and open source availability. The big difference—and Hadoop’s big advantage—is its focus on super-scale processing of huge quantities of data. In addition, Hadoop’s ecosystem of tools makes it better suited than NoSQL for big data analytics.
Hadoop enables a range of big data storage and compute-intensive applications for enterprises, integrating both structured and unstructured data from multiple sources like email, data from the internet, video and machine-generated data.[bctt tweet=”Hadoop enables a range of big data storage and compute-intensive applications for enterprises, integrating both structured and unstructured data from multiple sources like email, data from the internet, video and machine-generated data.” via=”no”]
3 Hadoop Use Cases
As part of your big data strategy, use Hadoop:
- For data warehouse offload
- As an enterprise data lake
- As a platform for advanced analytics
Data warehouse offload
Maybe your existing data warehouse is experiencing performance issues. Or your storage costs are increasing as you continually add data to your warehouse. Perhaps you want to consolidate multiple databases or introduce new types of data into the warehouse. For any—or all—of these situations, Hadoop may be the answer.
Enterprise data lake
As an enterprise data lake, Hadoop can bring unrelated collections of raw data together for analysis: structured data, semi-structured data and unstructured data. As an alternative to your RDBMS, the benefits include cost savings, easier data consolidation, higher availability and high performance.
Platform for advanced analytics
Hadoop provides the scalability required to process and analyze large amounts of data. As your storage and processing needs grow, you can easily increase the capacity of your cluster by adding new servers incrementally.
Hadoop is also a powerful platform to analyze large amounts of data. The Hadoop ecosystem provides tools and libraries for machine learning, statistical modeling and analysis. With these, you can derive actionable insights. Read our Sonos case study to learn how one customer improved the end-user experience with Hadoop and help from Pythian.
To learn more about using Hadoop in your big data strategy, read our eBook.