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Handshake Uses Looker + Databricks to Turbocharge Online Recruiting
Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Machine Learning
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
- Education
- Professional Service
Applicable Functions
- Human Resources
- Sales & Marketing
Use Cases
- Predictive Maintenance
- Predictive Quality Analytics
- Supply Chain Visibility
Services
- Data Science Services
- Software Design & Engineering Services
The Challenge
Handshake is a platform that connects students, universities, and employers for job and internship opportunities. However, managing the complex user journeys in this dynamic three-sided marketplace is a challenge. The company needs to understand how students engage with employers, how employers discover and engage with students, and how universities assist their students. Furthermore, Handshake aims to democratize data, allowing users to have direct access and gain the insights they need. This requires managing a large amount of data, including click-stream data, application data, service data, and third-party tool data. As a startup, Handshake does not have the resources to build a complex software stack to handle this.
About The Customer
Handshake is a career network that unites universities, students, and employers. It was founded in 2014 by three Michigan Tech students who struggled to gain the attention of employers during their own recruiting experience. The platform connects 500 universities, nine million students and recent alumni, and more than 250,000 employers. Handshake aims to level the playing field by providing students at rural universities and HBCUs with the same access to job and internship opportunities as those at larger institutions. The company's mission is to democratize data, allowing users to have direct access and gain the insights they need.
The Solution
Handshake designed a new software stack that combines power, flexibility, and control. The company chose Spark as the platform, with Python as the main programming language. PySpark offers the machine learning libraries Handshake needs, and Spark SQL allows the team to create custom logic as needed, with support for different types of storage systems. Building on that foundation, the team added two key services: Databricks and Looker. Databricks offers interactive notebooks, a scheduler for batch processing and streaming, and the access control Handshake needs to protect sensitive student data. Looker, on the other hand, provides a range of powerful BI features and easy integration with Databricks. It allows cross-functional teams to have access to data and provides a layer of abstraction, both internally and externally.
Operational Impact
Quantitative Benefit
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