With more than thirty billion pins on the system, it is no surprise that Pinterest proves to be the most powerful big data team on the internet. Even though they have managed to build a comprehensive collection of interests online, they still have to go through several changes in order to meet data requirements each day. If you’re wondering how this is made possible, read on.
Big Data
Building a Self-Serve Platform
To deal with the extensive storage requirements of each new data, Pinterest has used Hadoop to process almost all of its data. Moreover, they also evolved their infrastructure to build big data applications and this could only be possible by building a self-serve platform. It is true that Hadoop is powerful in terms of storage and processing capabilities but it falls short as a self-serve platform because it does not have elastic or cloud computing.
Fortunately, Hadoop offers various applications, libraries and service providers to overcome these limitations. Before choosing the right solution, Pinterest assessed the setup requirements to make an effective decision.
Assessments
After assessing their needs, the team considered various open source solutions to meet each requirement. Pinterest orchestrated their operations including Cascading and Hadoop streaming so that they can keep their Hive Metastore consistent with the data present on their system. This made it possible for the data to be updated across workflows and multiple clusters without having to worry about incomplete or erroneous data.
Finding an Alternative Solution
Big data services are more complex than they seem. Given the circumstances and requirements, it was necessary for Pinterest and its team to come up with a viable solution to keep up with so much data at once. After careful considerations, the team migrated all of its Hadoop jobs to Qubole. Since it is an important player in the Hadoop service space platform, it performed exceptionally well and was stable throughout the transfer. The migration offered various advantages and served as a plug and play platform which is flexible and responsive with refined cluster and auto-scaling capabilities.
The move proved to be highly effective as it allowed Pinterest to free itself from the unnecessary overheads of operating Hadoop. Moreover, they were able to focus on engineering new efforts on big data applications. FrescoData is quite similar to Pinterest, but its primary purpose is to collect big data for marketing organizations. This makes it simpler for businesses to buy information rather than collect data on their own. If you are looking to collect information to build mailing lists for your next big email marketing campaign, you should instead consider buying them from the specialists.
