Baselines & Benchmarks –Making Open Source Big Data ETL Easy - Seguridad Mania.com - España y América Latina

Bringing heterogeneous data into a homogenous data warehouse environment is one of the most daunting aspects of any big data implementation. Extracting, Transforming, and Loading (ETL) your data can easily consume up to 80% of a data processing effort. Even though Apache Spark and HPCC Systems Thor can be thought of as complementary, there is interest in comparing their performance with ETL-related benchmarks. While Spark is gaining in popularity for use cases that relate to machine learning, it is not the ideal environment for performing ETL. In contrast, HPCC Systems Thor is built with ETL in mind. Join us to hear how HPCC Systems Thor's performance compares to Apache Spark utilizing standard benchmarking methodologies. Learn how these benchmarks and HPCC Systems can help you establish new ETL baselines that: 1) Improve the speed and accuracy of the ETL process 2) Enable efficient use of developer resources and development budgets 3) Facilitate the use of standard hardware, operating system, and protocols