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Building High-Quality and Trusted Data Products with Databricks


Building data products necessitates safety and reliability. Databricks provides a pathway to the creation of high-quality data products that adhere to these standards.

In this session, two domain experts, Karthik, and Pomerit, will elaborate on how to use Databricks to build data products.

Karthik is a professional architect for Databricks based in Germany. Primarily focused on the area of large scale data and AI, he has over 10 years of experience in the field of software engineering and architecture. Karthik has been with Databricks for over a year and a half and currently specializes in aspects related to data architecture, security, and governance.

On the other hand, Pomerit, a senior solution architect at Databricks, is tackling various initiatives from data warehousing to data science for the largest strategic clients. Prior to joining Databricks, Pomerit was an end user of data products, grappling with the challenge of identifying relevant datasets and securing their reliability.

Their collective experience offers insights into the process of building high-quality and reliable data products using Databricks. The conversation begins with their initiation of a discussion on what Databricks is and its main highlights.

Building High-quality, Reliable Data Products with Databricks

Karthik, a specialist architect at Databricks based in Germany, alongside his colleague Pomerit, a Senior Solutions Architect at Databricks, have shared their insights on building high-quality, reliable data products using Databricks. Karthik has concentrated on big data and AI, dedicating over a year and a half to software engineering and architecture, while Pomerit has been involved in diverse initiatives ranging from data warehousing for key strategic customers to data science.

The first step to building superior data products is securing a high-quality dataset. The challenge here lies in reliability, which largely depends on whether the user trusts the accuracy of the data. To achieve this, it's important to ensure the data's completeness and consistency, improving overall performance.

Data security and governance are equally vital. To maintain reliability, it's necessary to ensure that data products are adequately protected from inappropriate access or usage. Furthermore, data usage needs to be properly monitored, managed, and meet the requirements of relevant regulations.

Finally, the technological infrastructure to make all these possible is required. Databricks is a robust, scalable platform designed to meet these requirements, enabling the building of high-quality, reliable data products.

With these considerations in mind, as Karthik and Pomerit have already demonstrated, it is feasible to build excellent data products using Databricks. Keeping up with data quality, reliability, security, and governance may seem daunting, but with the right tools and strategies, it can be overcome.

While specific methods and best practices were discussed during the session, emphasis was also placed on the importance of each organization finding the best solutions based on their own goals and requirements. Using Databricks simplifies this process.


It's clear that quality, reliability, security, and governance are all critical in data products. Databricks can serve as a powerful platform to achieve these. Every organization must find the best solutions based on their own goals and requirements, and Databricks plays a crucial role in that process. By deeply exploring these aspects and addressing them accurately, it's possible to create high-quality, reliable data products.

About the special site during DAIS

This year, we have prepared a special site to report on the session contents and the situation from the DAIS site! We plan to update the blog every day during DAIS, so please take a look.