Data Virtualization: SmartDataLake

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Related lectures (32)

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Introduction to Spark Runtime Architecture

Covers the Spark runtime architecture, including RDDs, transformations, actions, and caching for performance optimization.

Data Cleaning Challenges: Optimizing Error Detection

Addresses challenges in data cleaning for analysis, proposing optimizations to reduce processing time.

Data Wrangling: Structuring and Wrangling Issues

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

Data Issues in Research

Explores challenges in data assumptions, biases, and more in research, including incomplete write-ups and frustrations of newcomers.

Big Data Challenges: Scaling to Massive Data

Explores challenges of handling massive data in the era of big data, discussing solutions like MapReduce and Spark.

General Introduction to Big Data

Covers data science tools, Hadoop, Spark, data lake ecosystems, CAP theorem, batch vs. stream processing, HDFS, Hive, Parquet, ORC, and MapReduce architecture.

In Silico Neuroscience: Data Reproducibility and Reusability

Emphasizes data reproducibility and reusability in in silico neuroscience, focusing on neuroinformatics tools and methods.

Introduction to Spark Runtime Architecture

Introduces Apache Spark, covering its architecture, RDDs, transformations, actions, fault tolerance, deployment options, and practical exercises in Jupyter notebooks.

Data Analysis to AI and ML, Social Media

Explores the evolution from data analysis to AI and ML, emphasizing big data, machine learning, and social media interaction.

Data, big data, clouds and IoT

Explores data representation, databases, cloud computing, and challenges in the cloud environment.