In a data-driven world, more enterprises need servers that can handle large workloads, which is why many organizations are adopting Hadoop. According to a 2015 survey from TDWI, 60 percent of the enterprises that responded plan on having a Hadoop cluster in 2016, which is a 16 percent increase from the previous year. The founder of Hortonworks, the first Hadoop vendor to go public, predicted that 75 percent of the Fortune 2000 companies will run 1,000-node Hadoop clusters by 2020.
Despite the significance of big data and Hadoop, both are still relatively new concepts to many organizations and that unfamiliarity can lead to certain challenges. With the right knowledge, however, Hadoop can be an indispensable asset. If you are new to Hadoop or are looking for ways to get an optimal performance from your clusters, here are three tips for successful implementation:
1. Consider Your Business And Data Requirements
Thorough planning is an essential first step in Hadoop adoption. Before deciding to use Hadoop, you’ll need to gather, analyze and understand your business and data requirements and measure them against each other. As with any other big data project, it should align with your overall business goals. This means your determined business requirements should match up with the benefits of using Hadoop, otherwise it could be a huge waste of resources. To effectively make this decision, map out your business requirements before looking at how you can serve your data needs.
2. Start Out Small
When building your first cluster, it’s easy to get carried away and go too big, too fast. Starting off with something so complex can only lead to problems down the road. Bear in mind that one of the perks of using Hadoop is its scalability, and you can add nodes to your cluster as needed. Because it’s much easier to add than to take away in these situations, it’s better to start small. Run a small project as a proof-of-concept so you can develop as needed and give your infrastructure staff the time to learn about this new technology. Starting out small will also reduce implementation risks.
3. Combine Hadoop With a Data Warehouse
According to Philip Russom of TWDI, many data professionals see Hadoop as an extension of the data warehouse, as opposed to its replacement. In his report “Can Hadoop Replace a Data Warehouse,” Russom quoted one respondent who said that Hadoop can make data warehouses more of an economic value by migrating some data to Hadoop and reducing the “footprint on expensive relational databases.” This can make data warehouses more affordable and increase their growth capacity.
A combination use of a data warehouse and Hadoop can make a great impact on an organization. You can use a data warehouse to store important and structured data, while using Hadoop for any unstructured data. Because data warehouses are weaker in analytic processing compared to Hadoop, this combination can make a lot of sense for your needs. Adopting open source software like Hadoop can be an uncomfortable transition for organizations, so it’s not always best practice to jump right into it. Some organizations choose to migrate some of their data to Hadoop and use a multi-platform data architecture, which Russom called “one of the strongest trends in data architecture today.”