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Analytics

The 1, 2, 3, of cleansing data

Most organizations experience some level of data quality challenge. Solving data quality challenges and cleansing data can exist in three ways: Data at source: requires business owners and subject matter experts to ensure data quality at the point of entry. It becomes important to identify what data quality issues exist, and identify ways to ensure a certain level of quality before any ETL/ELT takes place.

Bluepi CEO Discusses Strategies for Implementing A Data-Driven Business Transformation

When companies begin the process of moving their data to the cloud, they enter a transformative business journey that can seem intimidating and overwhelming. That’s where BluePi Consulting comes in. With offices in India and Australia, BluePi has extensive experience helping companies make that journey to the cloud. In this episode of “Data Cloud Now,” host Gautam Srinivasan chats with Pronam Chatterjee, CEO of Bluepi, about the challenges of that journey, the strategies employed to navigate it successfully, and the critical role that Snowflake plays in making the journey as seamless and painless as possible.

Educating ChatGPT on Data Lakehouse

As the use of ChatGPT becomes more prevalent, I frequently encounter customers and data users citing ChatGPT’s responses in their discussions. I love the enthusiasm surrounding ChatGPT and the eagerness to learn about modern data architectures such as data lakehouses, data meshes, and data fabrics. ChatGPT is an excellent resource for gaining high-level insights and building awareness of any technology. However, caution is necessary when delving deeper into a particular technology.