Lecture

Data Wrangling with Hadoop: Advanced Techniques

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In course

COM-490: Large-scale data science for real-world data

This hands-on course covers tools and methods used by data scientists, from researching solutions to scaling prototypes on Spark clusters. Students engage with the full data engineering and data science pipeline, from data acquisition to extracting insight ...

Description

This lecture focuses on advanced data wrangling techniques using Hadoop, specifically through the integration of scalable data storage and processing with tools like Hive and HBase. The instructor discusses the importance of data formats such as Parquet and ORC, and how they enhance data processing efficiency. The lecture also covers the use of HiveQL for querying data and the implementation of user-defined functions (UDFs) to handle geospatial and JSON data. Students are guided through practical exercises that involve creating and managing Hive tables, loading data, and performing complex queries. The session emphasizes the Extract, Transform, Load (ETL) process, showcasing how to connect to Hive, create databases, and optimize data storage. Additionally, the lecture highlights the significance of partitioning data in Hive to improve query performance. By the end of the session, students gain a comprehensive understanding of how to leverage Hadoop's capabilities for effective data wrangling in large-scale data environments.

Instructors (3)

Olivier Verscheure

SWISS DATA SCIENCE CENTER Housed at both EPFL and ETH Zurich, the newly created Swiss Data Science Center (SDSC) will address the fragmentation within today's data and analytics landscapes. The SDSC will be composed of a distributed multi-disciplinary team of data scientists and experts in domains including personalized health and personalized medicine, transportation, earth and environmental science, social science and digital humanities, and economics. The center aims to federate data providers, data and

Sofiane Sarni

Teaching & PhD Courses Large-scale data science for real-world data COM-490 This hands-on course covers tools and methods used by data scientists, from researching solutions to scaling prototypes on Spark clusters. Students engage with the full data engineering and data science pipeline, from data acquisition to extracting insights, applied to real-world problems.

Pamela Isabel Delgado Borda

I am a PhD student in the School of Computer and Communication Sciences at EPFL. I am part of the Operating Systems Laboratory and my advisor is Prof. Willy Zwaenepoel. I received my Bachelor's degree in Systems Engineering from Universidad Catolica Boliviana, Bolivia in 2008 and Master's degree in Computer Science, specialization Foundations of Software from EPFL in 2012.

Official source

Related concepts (31)

Data integration

Data integration involves combining data residing in different sources and providing users with a unified view of them. This process becomes significant in a variety of situations, which include both commercial (such as when two similar companies need to merge their databases) and scientific (combining research results from different bioinformatics repositories, for example) domains. Data integration appears with increasing frequency as the volume (that is, big data) and the need to share existing data explodes.

Big data

Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Though used sometimes loosely partly because of a lack of formal definition, the interpretation that seems to best describe big data is the one associated with a large body of information that we could not comprehend when used only in smaller amounts.

Data processing

Data processing is the collection and manipulation of digital data to produce meaningful information. Data processing is a form of information processing, which is the modification (processing) of information in any manner detectable by an observer. The term "Data Processing", or "DP" has also been used to refer to a department within an organization responsible for the operation of data processing programs. Data processing may involve various processes, including: Validation – Ensuring that supplied data is correct and relevant.

Data cleansing

Data cleansing or data cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database and refers to identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data. Data cleansing may be performed interactively with data wrangling tools, or as batch processing through scripting or a data quality firewall. After cleansing, a data set should be consistent with other similar data sets in the system.

Data transformation (computing)

In computing, data transformation is the process of converting data from one format or structure into another format or structure. It is a fundamental aspect of most data integration and data management tasks such as data wrangling, data warehousing, data integration and application integration. Data transformation can be simple or complex based on the required changes to the data between the source (initial) data and the target (final) data. Data transformation is typically performed via a mixture of manual and automated steps.

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Big Data Best Practices and Guidelines

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Data Wrangling Techniques: HBase and Hive Integration

Covers data wrangling techniques using HBase and Hive, focusing on integration and practical applications.

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.

Big Data Ecosystems: Technologies and Challenges

Covers the fundamentals of big data ecosystems, focusing on technologies, challenges, and practical exercises with Hadoop's HDFS.

Advanced Spark Optimization Techniques: Managing Big Data