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

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

Data Engineering - Building data pipelines

Capture

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.

Transform

The data is standardized, refined, enriched and validated in order to prepare it for further analysis and processing.

Store

Prepared data is loaded into target destinations, such as object storage buckets, a data lakes and analytics databases.

Serve

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 requiresin-depth understanding of your data challenges, technical expertise and practical experience.

Data connectors
integration

Develop and configure integration points for ingestion ofmultiple structured and unstructured datasources.

Messaging queues and
pub/sub systems development

High-throughput and low-latency messagingplatform 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.

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.

One of the leading banks in Singapore

Make your data work for you

Contact us to learn more about data engineering services.

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