10 reasons an enterprise should invest in a Self Serve Ingestion Platform

Heard a lot about Data Mesh and wondering what’s the next step for your Data Ingestion Platform ? Or wondering why your enterprise should go for a Self Serve Ingestion Platform when you already have a great team of Data Engineers taking care of a centralized Ingestion Platform?

Here is the answer - Let’s talk about top 10 incentives why an enterprise must go for a Self Serve Ingestion Platform.

1. Specialized Ownership to Decentralized Data Ownership

The idea is to move the ownership of Data Ingestion from centralized Data Engineering teams to decentralized source teams. Data producers have the expertise to control and fix data - making source teams accountable for Data ingestion results in quick fixes at source end. This also helps avoiding duplicate ingestion pipelines for the same source.?

Data owners be like?

2. Empower Data Stakeholders?

A Self Serve Ingestion Platform empowers data producers to ingest data with ease and at the same time it empowers data consumers to discover the existing pipelines and MetaData. Data stakeholders are not dependent on Data Engineers. This helps data workers in an organization to take data driven decisions without the burden of collaboration with Data Engineering to build ingestion pipelines.?

3. Easy and Simple

Self Serve provides a simple and easy? platform for non-technical users to ingest data from different sources to different destinations.?

4. Reduce time to ship for new pipelines

A reliable Self Serve system reduces time required to create a new pipeline resulting into faster time to ship data to destinations and faster business insights, in turn.

5. Enable teams to operate their data pipelines

With a no-code approach, a Self Serve User Interface provides a way for data producers to operate and maintain their pipelines - from changing the Database credentials to manage the number of nodes, to choose batch vs streaming ingestion, everything is possible for data owners now ! Great powers lead to great responsibilities, data producers are not responsible for the operational health of their data in lake.?

6. Governance, Compliance and Security

It's as simple as a data producer coming to the Self Serve platform and choosing the right options for their data wrt governance, compliance and security.

7. Data Accuracy

No more custom pipelines and no-code approach helps avoid manual errors leading to higher data accuracy.

8. Scalable Ingestion

Imagine if you need to ingest new tables to the lake everyday based on your analytics needs and you need to work with a limited number of Data Engineers to get the data into the lake. Removing the dependency on Data Engineers and providing a faster Ingestion approach, Self Serve provides a solution for scalable Ingestion.

The scale of data increasing exponentially like

9. Allow Data Engineers to invest their time wisely

With Self Serve, Data Engineers don’t need to create custom pipelines anymore. They don’t need to be worried about the source system, format of the data, credentials etc and they have enough time to build world-class data products. A win-win solution for any Organization.?

When Self Serve agrees to be your friend!?

10. Data Description

A fully functional Self Serve platform ensures no data ingestion without data description. The user interface should be designed in a way to ensure data producers must describe the data before ingestion and data consumers can explore that description of data to make the right call for exploratory data analysis. This saves everyone’s time in the organization.

By now, if you are convinced that Self Serve Ingestion is “the way” for your Organization, you might be wondering how you can get one built or how to start with that. Wait for the next blog post to get a quick start :)?

#IntuitTech #dataingestion #dataengineering #selfserve #datamesh

Team (one can learn Self Serve Ingestion from) : Athitya Kumar , Karandeep Singh , Shivanshu Gupta , Shivansh Maheshwari

Memes Credit : Thank You Athitya Kumar

Authors : Ritesh , Isha

Ritesh has 18+ years of experience building high performance and scalable systems. Over the years he has been involved in building and growing teams to deliver high paced product development. Early stage startup veteran, he excels at solving problems and designing scalable applications and platforms.

Isha is Group Engineering Manager at Intuit with a mission to Self Serve the data ingestion to lake in both real time and batch. As a leader, she is playing a key role to adopt Data Mesh at Intuit. She is working with DataBricks to achieve streaming materialization to support real time analytics and reporting use cases.