Technology Category
- Analytics & Modeling - Machine Learning
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Cement
- Construction & Infrastructure
Applicable Functions
- Maintenance
- Warehouse & Inventory Management
Use Cases
- Construction Management
- Time Sensitive Networking
Services
- Cloud Planning, Design & Implementation Services
- System Integration
About The Customer
Apester is a technology company based in Israel that provides tools for creating, distributing, and monetizing interactive visual content. The company helps publishers, advertisers, and businesses tell highly engaging online stories that are mobile friendly, seamlessly integrated with their sites, and can be distributed at scale. Since its launch in 2014, Apester’s comprehensive, easy-to-use creation tools have attracted approximately 100 million unique users per month. The company's content ranges from quizzes and polls to innovative, visual stories popularized on social media.
The Challenge
Apester, a company that provides tools for creating, distributing, and monetizing interactive visual content, was facing challenges with its business intelligence (BI) and data warehousing systems. The existing solution was adequate for small amounts of data, but as the company grew, attracting approximately 100 million unique users per month, it began to show signs of strain. The system also placed limitations on the kind of analytics Apester could run. The company wanted to capitalize on its growing customer base and gain as much insight as possible, without worrying about scale or cumbersome licensing fees. Additionally, Apester’s developers and data scientists wanted to use open source technology as much as possible to avoid over-reliance on any one vendor.
The Solution
Apester turned to Google Cloud for its new BI and data warehousing solution. The company began building its data solution around Cloud Dataflow, Cloud Dataproc, and Cloud Bigtable along with open source Apache Beam for its data processing and analytics needs. Over time, Apester explored Google’s options further and eventually settled on BigQuery as its main analytics solution. The company also migrated from a virtual machine-based architecture to one based on Kubernetes, improving the speed of Apester’s autoscaling without troubling the developers with server setup and maintenance demands. Kubernetes Engine became the backbone of the new infrastructure, while Cloud Pub/Sub became the message bus and Stackdriver helped take care of its logging and monitoring needs. Cloud Identity Access and Management (IAM) enabled Apester to give out permissions quickly and easily without compromising on security.
Operational Impact
Quantitative Benefit
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