Developing Global Labor Market Intelligence at SkyHive Using Rockset and Databricks
公司规模
Large Corporate
国家
- United States
产品
- SkyHive Skill Passport
- SkyHive Enterprise
- Rockset
- Databricks
技术栈
- Databricks
- Rockset
- Spark ETL
- Delta Lake
实施规模
- Enterprise-wide Deployment
影响指标
- Productivity Improvements
- Customer Satisfaction
- Digital Expertise
技术
- 分析与建模 - 实时分析
- 平台即服务 (PaaS) - 数据管理平台
- 分析与建模 - 预测分析
适用行业
- Software
- Professional Service
适用功能
- 商业运营
- 产品研发
用例
- 实时定位系统 (RTLS)
- 远程资产管理
服务
- 数据科学服务
- 云规划/设计/实施服务
- 系统集成
关于客户
SkyHive is an innovative end-to-end reskilling platform that automates skills assessment, identifies future talent needs, and fills skill gaps through targeted learning recommendations and job opportunities. The company collaborates with industry leaders such as Accenture and Workday and has been recognized by Gartner as a cool vendor in human capital management. SkyHive has developed a comprehensive Labor Market Intelligence database that stores profiles of 800 million anonymized workers and 40 million companies, along with 1.6 billion job descriptions from 150 countries. The platform ingests 16 TB of data daily from job postings and paid streaming data feeds, utilizing complex analytics and machine learning to provide insights into global job trends. SkyHive is rapidly growing, adding 2-4 corporate customers daily, driven by its data-driven services and partnerships.
挑战
SkyHive faced significant challenges with MongoDB for analytical queries due to its slow performance in handling complex analytics involving data across jobs, resumes, courses, and different geographics. The query latency was high, and the system struggled with multidimensional queries and joins, making it impossible to provide the interactive performance required by users. Additionally, there were limitations on payload sizes and other hardcoded quirks, such as the inability to query certain countries like Great Britain. These issues hindered SkyHive's ability to deliver immediate results to customers, especially when expanding searches to non-English speaking countries, as data normalization across different languages was problematic.
解决方案
To address the challenges with MongoDB, SkyHive transitioned to a real-time data stack using Databricks and Rockset. Databricks was chosen for its compatibility with more tooling options and support for open data formats, enabling SkyHive to deploy a lakehouse architecture. This architecture processes data through three Delta Lake stages, refining and enriching data for efficient storage and processing. Rockset was selected as the new user-facing serving database, continuously synchronizing with the Gold layer data and building an index for multidimensional analytics. This setup allows SkyHive to serve pre-defined Query Lambdas and ad hoc free-text searches, providing real-time answers to complex queries. The integration of Databricks and Rockset has significantly improved SkyHive's ability to handle large datasets, run ML models, and support complex queries with low latency, enhancing both internal operations and customer satisfaction.
运营影响
数量效益
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