#240: Overcoming the challenges facing modern data engineering teams

Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science

This week on the Data Futurology podcast we host Paul Milinkovic, the APAC Regional Director for the leading data integration platform, StreamSets. Milinkovic joins us to share his insights into data engineers' challenges and the pipelines they manage and maintain.

One statistic really highlights just how challenging work environments have become for data engineers: 76 per cent of organisations have a pipeline break at least monthly and for 36 per cent, it's weekly. Rather than contributing strategically to their organisations, engineers split their time between diagnosis and repair, and building new pipelines. This costs the organisation, as half the time the engineer isn’t being used strategically. It also leads to cultures of over-working, burnout, and high levels of churn within the data engineering team.

Another challenge data teams struggle with is competing priorities. When multiple lines of business need pipelines developed, teams often need to triage to accommodate priority tasks, and this affects overall company outcomes. Being able to help organisations deliver a low or no-code environment that is highly visual and accessible to non-data specialists has been a critical benefit for organisations that have adopted StreamSets.

Milinkovic then shares two case studies where StreamSets has helped with overcoming these challenges. In one, a bank achieved a seemingly impossible task – becoming compliant with looming Consumer Data Act requirements within four months. Then, a second bank was able to leverage StreamSets to its data to detect and thwart $9 million in fraudulent activity in a single month.

For more deep insights into overcoming the challenges facing modern data engineering teams, tune into the podcast!

Links

Website: https://streamsets.com

Follow on LinkedIn: https://www.linkedin.com/company/streamsets/

Whitepapers:

https://go.streamsets.com/Whitepaper-Dollars_and_Sense_UGLP.html?utm_medium=website&utm_source=DataFuturology&utm_campaign=eg_dollars_and_sense_of_dataops

https://go.streamsets.com/Whitepaper-Dollars_and_Sense_UGLP.html?utm_medium=website&utm_source=DataFuturology&utm_campaign=eg_dollars_and_sense_of_dataops

https://go.streamsets.com/230214-lifting-the-lid-on-data-integration-UGLP.html?utm_me[…]turology&utm_campaign=eg_lifting_the_lid_on_data_integration

What we discussed:

00:00 Introduction

02:22: Felipe introduces Paul Milinkovic.

03:38: Milinkovic shares his background and his history with data at various levels and applications.

06:04: Milinkovic overviews StreamSets – when and why the company was founded, and what its core capabilities are.

09:04: What are the main issues that StreamSets helps data engineering teams solve?

12:57: How does StreamSets address traditional data pipeline design and build challenges?

12:33: What are the benefits of having a solution that is visual and accessible to non-technical users?

22:51: One of the common questions with the self-service approach to data is governance. How can that be handled while still allowing full flexibility?

26:46: Data engineers care a great deal about the quality and accuracy of data and the platforms that it sits on. Milinkovic explains why it is so important that they have the tools to be able to deliver that to the organisation.

31:24: What is the financial impact of data engineering teams spending as much time fixing pipelines as they are?

33:49: Milinkovic shares some case studies and use cases to highlight the value of StreamSets’ approach to data engineering.

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