Real-Time Data Analytics and Machine Learning Accelerate Business Growth for TripActions
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Real Time Analytics
- Education
- Equipment & Machinery
- Predictive Maintenance
- Real-Time Location System (RTLS)
- Data Science Services
TripActions, a corporate travel management organization, was facing a significant challenge with its existing infrastructure and data storage solution. The increasing volume of historical indexed data was straining the company’s infrastructure and primary storage solution, slowing down its performance and causing an ever-increasing cost of ownership. The historical data was never cleaned while analytical data was stored across various databases in different formats, creating multiple data silos and making data unavailable for analytics and machine learning. The company’s existing data solution was failing in terms of analytic capacity and scalability, which increased operational costs, slowed down onboarding of new clients, and stifled business growth. The initial architecture and data solution were based on Amazon Elasticsearch, which proved to be inefficient and expensive when data volumes increased. Data was schemaless, and there was no mechanism to join data from different databases. Partial data in Amazon S3 was stored in JSON format and synced with one-day lag, with no partitioning, which delayed TripActions’ reaction to issues or changes in data.
TripActions is a corporate travel management organization that helps control costs of business travel and incentivize employees via easily accessible business travel opportunities. The company was looking to design and build a new data streaming solution to accommodate the increasing amount of historical indexed data that was overstraining the company’s infrastructure and primary storage solution. The company’s existing data solution was failing in terms of analytic capacity and scalability, which increased operational costs, slowed down onboarding of new clients, and stifled business growth.
To address these challenges, TripActions reached out to Provectus to implement a new data streaming solution. Provectus delivered a real-time streaming solution on top of Apache Kafka for data ingestion, processing, enrichment, and transformation capable of accommodating the growing volumes of historical and analytical data on the platform. Data streams were consumed by real-time reporting and machine learning applications. All the data streams were designed in a way that they could be automatically stored in the data lake on top of Amazon S3 and AWS Glue. They were optimized for sub-second queries from Amazon Athena. The historical data from the existing platform was migrated from ElasticSearch, Redshift, RDS to a separate data lake to ensure data consistency and availability for analytics and machine learning. All Data Team-related services were moved to a separate Data VPC to improve the team’s productivity and gain better visibility into data.
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