Automatically collect raw data from siloed data sources, push it through a sequence of processing steps and store in analytics databases and data warehouses for further processing.
Take actions on the incoming data series from time-based data sources such as IoT sensors, telemetry systems, payment processing systems and server or application logs, at the time the data is created.
Continuously monitor your data pipelines to control pipeline’s performance, detect unusual behaviours and prevent data delivery delays.
Building effective and scalable data pipeline infrastructure requires in-depth understanding of your data challenges, technical expertise and practical experience.
Develop and configure integration points for ingestion of multiple structured and unstructured data sources.
High-throughput and low-latency messaging platform is a core component of a modern message-centric data flow model.
Build microservice applications that respond to events in real-time and operate on data streams, enriching and transforming the data for further processing.
Design and develop APIs and data interfaces to enable fast data access for machine learning algorithms, analytics tools and end-user applications.
Appropriate metrics needs to be identified and monitored to ensure continuous availability and uninterrupted flow of data through the pipeline.
A global banking and financial services enterprise struggled with inefficient access to a tremendous data lake with terabytes of offline data.
Grape Up designed and built a scalable fast data platform based on a event-driven architecture with serverless access layer to make the data available for other systems and application through a unified set of APIs with a variety of interaction models.