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Data Engineering Trends With Aswin & Ananth

Data Engineering Weekly

As we approach the end of 2023, it's an opportune time to reflect on the key trends and developments that have shaped the field of data engineering this year. In this article, we'll summarize the crucial points from a recent podcast featuring Ananth and Ashwin, two prominent voices in the data engineering community.

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5 Layers of Data Lakehouse Architecture Explained

Monte Carlo

Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake. The data lakehouse’s semantic layer also helps to simplify and open data access in an organization.

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Data Lakehouse Architecture Explained: 5 Layers

Monte Carlo

Data lakehouse architecture combines the benefits of data warehouses and data lakes, bringing together the structure and performance of a data warehouse with the flexibility of a data lake. The data lakehouse’s semantic layer also helps to simplify and open data access in an organization.

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Covid Data: An anomalous blip, or the new normal?

Cloudera

While it can be argued that digital transformation was already underway; it’s hard to dispute that it has accelerated in recent months. A recent McKinsey survey, cited in CRN , shows that worldwide, 58 percent of customer interactions were digital as of July 2020. This can be done at speed, and at scale.

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Claims Processing with Generative AI: Making Sense of the Data

Precisely

Yet experts warn that without proactive attention to data quality and data governance, AI projects could face considerable roadblocks. This article focuses on some fundamental prerequisites for the successful use of generative AI in the claims management process.

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Data Engineering Weekly #133

Data Engineering Weekly

link] LakeFs: How to Implement Write-Audit-Publish (WAP) I wrote extensively about the WAP pattern in my latest article, An Engineering Guide to Data Quality - A Data Contract Perspective. The author writes an exciting article about adopting unit testing in the data pipeline by producing sample tables during the development.

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Data Science vs Software Engineering - Significant Differences

Knowledge Hut

Although both Data Science and Software Engineering domains focus on math, code, data, etc., Is mastering data science beneficial or building software is a better career option? Data Science is a field of study that handles large volumes of data using technological and modern techniques.