SOFTWARE SERVICES / SCRAPEGG
Data engineering & real-time pipelines
We build ingestion and processing pipelines that move information from its sources into reliable, searchable and usable data products.
Discuss your projectData engineering & real-time pipelines
- Ingestion & normalization
- Streaming pipelines
- Analytics foundations
- Operational visibility
THE APPROACH
Made for the way
your business works.
Data systems need to survive duplicates, late records and partial failures. We define the source contracts, normalization rules and processing states before adding parallelism. Deduplication, idempotent stages and replay strategies make the pipeline easier to operate as data volumes grow.
The founder’s experience spans enterprise data at HCL and GE Digital, product ingestion at Skoov, and large-scale social-content processing at Logically and Verideck. We apply that experience to batch ETL, event-driven processing, search indexes and analytics-ready datasets.
WHAT WE CAN BUILD
From requirement
to working capability.
Ingestion & normalization
API and file ingestion, schema handling, deduplication and consistent data models.
Streaming pipelines
Event-driven processing, queues and workers for data that arrives continuously.
Analytics foundations
Transformations, data quality checks and datasets suitable for reporting and search.
Operational visibility
Processing state, failure recovery, throughput monitoring and clear ownership.
PRACTICAL EXPERIENCE
Built on work
we’ve actually done.
At Logically, Rajkumar developed real-time ingestion and annotation pipelines. At Verideck, he delivered ingestion, enrichment and narrative analysis.
Read the Verideck engineering case studyGOOD QUESTIONS
Before we begin.
Do we need streaming or batch processing?
It depends on how quickly data must become useful. We choose the simplest approach that meets the latency and volume requirements.
Can you connect several different sources?
Yes. We define source-specific ingestion and normalize the results into a shared model where that is useful.
How do you handle failed processing?
We use explicit processing states, retries and replayable or idempotent stages so failures can be investigated and recovered.
HAVE SOMETHING IN MIND?
Let’s make
it happen.
A new product. A better workflow.
A problem worth solving.