English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Hadoop clusters have established themselves as a foundation for various applications and experiments in the field of high-performance processing of large datasets. In this context, SQL-on-Hadoop emerged as trend that combines the popularity of SQL with the performance of Hadoop. In this work, we analyze the performance of SQL queries on Hadoop, using the Impala engine, comparing it with a RDBMS-based approach. The analysis focuses on a large set of electronic invoice data, representing an important application to support fiscal audit operations. The experiments performed included frequent queries in this context, which were implemented with and without data partitioning in both RDBMS and Impala/Hadoop. The results show speedups from 2.7 to 14x with Impala/Hadoop for the queries considered, on a lower cost hardware/software platform.

SQL Query Performance on Hadoop: An Analysis Focused on Large Databases of Brazilian Electronic Invoices