Sumatra is a platform that provides self-service capabilities for data teams, enabling them to build real-time feature pipelines from event-based data.
The eLearning platform is a solution that allows teachers, tutors, and students to try a new study format or earn additional income.
Various professionals can share their knowledge and skills and make money from it. This is a tutoring marketplace with easy access for all types of users, helping them get the most out of studying with a helpful platform.
We were involved in the project from the start, participating in each significant process within full-cycle software development.
Our clients had a clear understanding of what they wanted to achieve and provided us with detailed requirements and a desired design. So, our software developers were responsible for the tech stack, IT architecture, and infrastructure creation, covering both front-end and back-end, as well as quality assurance and brief post-launch maintenance.
Ensuring Data Quality and Consistency
anomaly detection, deduplication, and lineage tracking into the pipeline management system
Supporting Diverse Data Sources and Formats
for popular sources and support of various serialization formats.
Avoiding Data Drift and Feature Staleness
of incoming data streams for schema drift, distribution changes.
This page lists materialization statuses and provides additional information for each. The materialization comprises timelines, event types, and features. They can be run multiple times in a row or deleted. If the materialization is in progress, it gets automatically updated in the table. This list uses GraphQL data caching, which avoids making requests for pages the user has already visited and instead retrieves them from the cache.
This panel displays all branches available to the user, along with their full modification history. On this page, users can get information about the plan, clone the branch, and start materialization. Using such an editor makes it easier to manage the entire service related to event-based pipelines.
Getting data about timelines is possible on this dashboard. Moreover, it tells the user when everything was last updated, who made the changes, and how many events are currently present. This is like a timeline backend by warehoused event data for feature engineering, replay, and backfill.
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