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Data Scientist Job Description – What Companies Look For in 2025

Pickl AI

Summary: In 2025, data scientists in India will be vital for data-driven decision-making across industries. It highlights the growing opportunities and challenges in India’s dynamic data science landscape. Key Takeaways Data scientists in India require strong programming and machine learning skills for diverse industries.

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The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. Role of Data Scientists Data Scientists are the architects of data analysis.

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Data Warehouse vs. Data Lake

Precisely

Processing speeds were considerably slower than they are today, so large volumes of data called for an approach in which data was staged in advance, often running ETL (extract, transform, load) processes overnight to enable next-day visibility to key performance indicators. It is often used as a foundation for enterprise data lakes.

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Discover the Most Important Fundamentals of Data Engineering

Pickl AI

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

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The Backbone of Data Engineering: 5 Key Architectural Patterns Explained

Mlearning.ai

ETL Design Pattern The ETL (Extract, Transform, Load) design pattern is a commonly used pattern in data engineering. It is used to extract data from various sources, transform the data to fit a specific data model or schema, and then load the transformed data into a target system such as a data warehouse or a database.