AI Services - Data Engineering

Make your data available for AI-driven analysis with automated data pipelines

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Centralize your data under one roof

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.

Enable real-time event streaming & analysis

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.

Monitor and secure data flow

Continuously monitor your data pipelines to control pipeline’s performance, detect unusual behaviours and prevent data delivery delays.

Efficient flow of data is critical for a successful AI-driven solution


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.

A complete process of setting up enterprise-grade data pipelines

Data workflow architecture design

Building effective and scalable data pipeline infrastructure requires in-depth understanding of your data challenges, technical expertise and practical experience.

Data connectors integration

Develop and configure integration points for ingestion of multiple structured and unstructured data sources.

Messaging queues and pub/sub systems development

High-throughput and low-latency messaging platform is a core component of a modern message-centric data flow model.

Stream processing applications development

Build microservice applications that respond to events in real-time and operate on data streams, enriching and transforming the data for further processing.

Data access layer development

Design and develop APIs and data interfaces to enable fast data access for machine learning algorithms, analytics tools and end-user applications.

Data pipeline monitoring and maintenance

Appropriate metrics needs to be identified and monitored to ensure continuous availability and uninterrupted flow of data through the pipeline.

Success story

Building scalable Fast Data platform for one of the leading banks in Singapore

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.