Lecture

Advanced Spark Optimizations and Partitioning

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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 covers advanced Spark optimizations and partitioning techniques, focusing on improving performance and efficiency in big data processing. Topics include Spark parallelization, RDDs, Spark units of work, handling big data, memory management, shuffle operations, memory optimizations, and data partitioning strategies. The instructor explains the importance of tuning partitions, minimizing data transfer, and optimizing memory usage. Practical demonstrations and exercises are provided to illustrate the concepts discussed, such as configuring partitions, repartitioning, coalescing, and custom partitioning. Students are encouraged to use Spark UI for task tuning and to understand the infrastructure for better utilization. The lecture also emphasizes optimization checklists and provides resources for further practice and exploration.

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 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.

Memory management

Memory management is a form of resource management applied to computer memory. The essential requirement of memory management is to provide ways to dynamically allocate portions of memory to programs at their request, and free it for reuse when no longer needed. This is critical to any advanced computer system where more than a single process might be underway at any time. Several methods have been devised that increase the effectiveness of memory management.

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.

Memory management unit

A memory management unit (MMU), sometimes called paged memory management unit (PMMU), is a computer hardware unit that examines all memory references on the memory bus, translating these requests, known as virtual memory addresses, into physical addresses in main memory. In modern systems, programs generally have addresses that access the theoretical maximum memory of the computer architecture, 32 or 64 bits. The MMU maps the addresses from each program into separate areas in physical memory, which is generally much smaller than the theoretical maximum.

Apache Spark

Apache Spark is an open-source unified analytics engine for large-scale data processing. Spark provides an interface for programming clusters with implicit data parallelism and fault tolerance. Originally developed at the University of California, Berkeley's AMPLab, the Spark codebase was later donated to the Apache Software Foundation, which has maintained it since. Apache Spark has its architectural foundation in the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way.

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