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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.
Ingesting data that originate in multiple, heterogenous and often siloed data sources including databases, business applications, system and infrastructure logs, files or IoT and telematics data.
The data is standardized, refined, enriched and validated in order to prepare it for further analysis and processing.
Prepared data is loaded into target destinations, such as object storage buckets, a data lakes and analytics databases.
Through APIs and data access layers data is fed into other data processing components such as ML models, external applications and services or can trigger webhooks in other systems.
Building effective and scalable data pipeline infrastructure requiresin-depth understanding of your data challenges, technical expertise and practical experience.
Develop and configure integration points for ingestion ofmultiple structured and unstructured datasources.
High-throughput and low-latency messagingplatform 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.