Why ingest when you can Onboard Data?

Big Data

Written by Amar Arsikere - June 17, 2020 | Category: Big Data

Why ingest when you can Onboard Data?

Every data analytics project starts with the critical first step of creating and operationalizing healthy data lakes. A unified data lake is created by onboarding multiple data sources. Onboarding a data source is more than ingesting the data once. The data needs to be kept current, in sync and relevant throughout its usage, hence data governance also takes precedence. This complete process of data ingestion, data synchronization (CDC) and data governance constitutes Data onboarding.

The system needs to be able to keep track of the changes in the environment, changes in schema and any changes in the onboarding pipeline thus maintaining a trust-based relationship between users and the data. Also having a unified data lake would mean the system would need to be able to map different source data types to the data lake. Additionally, the system needs to create a data catalog with source information, usage information, data validation, data reconciliation with sources in an optimal integrated process – all of these steps on the whole determine the onboarding user experience. If the user needs to write code to pull together multiple data sources of different data types and is only offered fragmented frameworks to provide each feature, the onboarding time required shoots through the roof. Automation of the entire process would therefore be key to optimal scalable solution.

Here’s a snapshot from a use case that showcases the time that a non-automated approach could end up costing.

Infoworks DataFoundry is a solution that provides a fully automated data onboarding experience. It encompasses a complete host of capabilities and features for enterprise data operations, running natively on any cloud. Be it Databricks, Amazon EMR or Google DataProc – Infoworks DataFoundry is entirely agnostic of the infrastructure that it is deployed on and works seamlessly with them all. It takes away the hassles of worrying about where it will be deployed, the framework in use etc. with an enhanced user experience. It runs natively, automates record versioning, registers all onboarded data in the metadata store, automatically catalogs and provides a single trusted view of all data assets for a user. It also provides Data governance features enabling user collaboration across data sets accessible by them. With all of these features, Infoworks drastically reduces the time necessary to onboard data.

Thus, an automation driven data onboarding solution like Infoworks makes the lives of Data Scientists and Data Engineers hassle free by equipping them with all the necessary tools for efficient onboarding of data in one integrated solution. Find out more on how to rapidly onboard data to a cloud data lake in order to maximize analytical productivity with Infoworks from the following webinar.

About this Author
Amar Arsikere
Prior to co-founding Infoworks, Amar built the first data warehousing platform on Bigtable at Google, then built one of the world’s largest in-memory database infrastructures at Zynga. To deal with the complexity of these environments, Amar created an automation layer to simplify the entire data workflow. Amar started Infoworks to create a commercially available product based on his experiences. Infoworks presents the third time he has eliminated the complexity of big data.