Big Data Analytics & Data Engineering
Work with authorized business data to deliver multi-source integration, cleansing and transformation, warehouse and metric modeling, BI reports, and analytics APIs. Shape delivery around business questions, data quality, and runtime requirements to build reusable foundations for analysis and cross-system collaboration.
Product and service scope
- Authorized source assessment, API integration, and data synchronization
- Data cleansing, transformation, quality checks, and field alignment
- Warehouse, analytical dataset, and business metric modeling
- BI reports, dashboards, and analytics API delivery
- Deployment, access controls, retention, and operations handover
Industries: Cross-industry · Retail & trade · Software & internet
These are product capabilities. Your selected plan or service agreement determines delivery scope, available features, and resource allowances.
Explore capabilities and workflowsBefore you purchase
- Business goals and service scope
- Deliverables, timeline, and acceptance criteria
- Quotation, payment arrangements, and support
From business data to usable analysis.
Does the data need to be uploaded to Runlume?
Central uploading is not a default requirement. The service can work with existing enterprise databases, warehouses, or private environments using the sources authorized for the project. Agree on purpose, access, transfers, and retention first; analysis does not automatically grant access to other tenants or systems.
What does the analytics service deliver?
The project scope defines delivery of integration and processing pipelines, quality rules, analytical datasets, metric definitions, reports, dashboards, or analytics APIs, with documentation and handover. Start with one business question and a set of sources. Agree separately on warehouse construction, scheduled updates, and ongoing operations.
How are pricing, acceptance, and use of the results agreed?
Estimate work from source count, data quality and volume, update frequency, deployment requirements, and deliverables, then agree on pricing and acceptance criteria. Consistent formats help prepare data, but business meanings and metrics still need validation. Conclusions depend on data quality, time periods, and methods, and do not guarantee business returns.