Organizations spend a lot of time and effort building pipelines to consume and publish data coming from disparate sources within their Data Lake. Most of the time and effort in large data initiatives are consumed in data ingestion development. What’s more, with an increasing number of businesses migrating to the cloud, factors like breaking data silos and enhancing data discoverability of data environments have become a business priority. While Data Lake is the heart of data operations, one should carefully tie capabilities like data security, data quality, metadata-store, etc within the ecosystem. A common reusable framework is needed to reduce the time and effort in collecting and ingesting data. At Tiger Analytics, we are solving these problems by building a scalable platform within AWS using AWS’s native services and open-source tools. We’ve adopted a modular design and loosely coupled multi-layered architecture. Each layer provides a distinctive capability and communicates with each other via APIs, messages, and events. The platform abstracts complex processes in the backend and provides a simple easy-to-use UI for the stakeholders

● Self-service UI to quickly configure data workflows ● Configuration-based backend processing ● AWS cloud native and open-source technologies ● Data Provenance: data quality, data masking, lineage, recovery & replay audit trail, logging, notification