Data Wrangling with Hadoop: Advanced Techniques

Related lectures (32)

Explores digital transformation opportunities, big data, analytics, and technology innovations in business and research.

Explores data modeling concepts, SQL implementations, and practical applications in handling missing data.

Introduces data warehousing fundamentals, challenges, and the innovative concept of a 'lakehouse'.

Explores data challenges and hardware evolution for real-time intelligence in the era of big data.

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Covers the introduction and challenges of data warehouses, including integrating data, managing metadata, and optimizing query performance.

Covers data wrangling stages, structuring techniques, and common issues in data preparation.

Introduces collaborative data science tools like Git and Docker, emphasizing teamwork and practical exercises for effective learning.

Explores water consumption data in Geneva, including charts on consumption and losses, available datasets, and data processing phases.

Introduces the fundamentals of handling data, emphasizing the importance of Pandas and data modeling for effective analysis.

Covers the process of data wrangling, focusing on transforming and preparing data for analysis.

Explores Big Data generation, storage, processing, and dimensions, along with challenges in data analytics, cloud computing elasticity, and security.

Page 2 of 2