Data Ingestion Framework enables data to be ingested from and any number of sources, without a need to develop independent ETL processes for each source.

This framework provides a set of ETL processes that are used to ingest data from any source into any target by leveraging the metadata-driven approach, therefore lessening the overall development time, maintenance requirements, and reducing the complexity by effective error handling mechanism with audit and control.

For typical data lake projects, organizations need to ingest data from several varied data sources to a landing zone to be integrated into downstream applications. In most cases, organizations build ETL or ELT jobs for each source, which creates challenges.

Accelerate your project with our Dynamic Ingestion Framework by

  • Eliminating the need to build data pipelines for each source and data type, therefore reducing huge development efforts
  • Establishing a standard framework for handling all data types and sources
  • Avoiding the complexity involved with code maintenance
  • Having ABC controls for ingested data, while keeping operational metrics and audit controls in one location
  • User friendly GUI for managing the data onboarding process and maintenance thus ensuring security and governance

Call to learn more about how we can save your time, money and improve standardization with our Dynamic Ingestion Framework.

Data Ingestion Framework High-Level Architecture

Artha's Data Ingestion Framework

To overcome traditional ETL process challenges with adding a new source, our team has developed a Big Data Ingestion Framework that helps reduce development costs by 50% – 60%, and directly increases IT team performance.

Need help with data ingestion?

Learn more about how to curb your data ingestion worries and dig deeper into Artha’s Data Ingestion Framework.

Key Aspects of Data Ingestion Framework

data ingestion framework aspects

Performance of Dynamic Ingestion Framework

Horizontal Scalable

Horizontally Scalable


ETL process run with better performance


Increase the performance of ETL by more than 200%


Case Study
Providing better, relevant, and real-time merchant offers to credit card customers
Case Study
Build Big Data Lake, integrate data sources into Snowflake for generating health care insights
Case Study
Retail industry - Inventory and revenue optimization

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Need help in data ingestion

Want to explore on how to streamline data ingestion worries, Know more about Artha’s Data Ingestion Framework