Presented By O’Reilly and Cloudera
Make Data Work
21–22 May 2018: Training
22–24 May 2018: Tutorials & Conference
London, UK

The eAGLE accelerator: How to speed up migrations from legacy ETL to big data implementations

Enric Biosca Trias (everis), Ángel Valencia (everis)
16:3517:15 Wednesday, 23 May 2018
Data engineering and architecture
Location: Capital Suite 2/3 Level: Intermediate

Who is this presentation for?

  • Those working in business intelligence

Prerequisite knowledge

  • Familiarity with business intelligence, ETL, and graph technologies

What you'll learn

  • Explore the eAGLE accelerator, which speeds up migration processes from legacy ETL to big data implementations
  • Learn how graph and automatic translation technologies help companies reduce their migration times

Description

Migrating from a traditional legacy BI system is a challenge. ETL processes have historically consumed much of the effort and many of the resources of traditional BI projects. At the same time, organizations have developed many ETL systems over the years, which often have insufficient documentation and are difficult to manage.

Enric Biosca offers an overview of the eAGLE accelerator, which speeds up migration processes from legacy ETL to big data implementations by enabling auditing, lineage, and translation of legacy code for big data. Along the way, Enric demonstrates how graphing and automatic translation technologies help companies reduce their migration times.

The eAGLE methodology is based on four pillars:

  • Parsing the code through open grammars based on ANTLR
  • Modeling processes and objects using graph technology
  • Creating a metadata model that allows quantifying and qualifying of the code
  • Accelerating translation through an abstract model and a framework

eAGLE uses graph technologies to model and analyze ETL code, which enables the use of KPIs and metrics to measure and quantify ETL code and allows users to visualize the processes in an advanced way to recognize impact, dependencies, data lineage, etc., explore the code, and discover hidden relationships, bottlenecks, and other inefficiencies. Generating a metadata model of the objects of the ETLs allows you to improve your knowledge of your ETLs as well as the objects in your data warehouse (particularly their relationships). Volume and complexity metrics allow an analytical view of the processes.

By analyzing ETLs using these technologies and methodologies, companies can better manage their migration strategy and save time analyzing the code. Performing a classification and translation accelerates the migration as much as 30% in total dedicated effort.

Photo of Enric Biosca Trias

Enric Biosca Trias

everis

Enric Biosca is a data and analytics manager at the everis Data Innovation Center (eDIN). Enric is a computer engineer with more than 10 years of experience in data and information architecture. He holds a master’s degree in data science from the Universitat de Barcelona.

Photo of Ángel Valencia

Ángel Valencia

everis

14 years of professional experience in IT consulting specializing in the areas of Artificial Intelligence,Big Data, Cloud, Business Intelligence, Technical Architectures and IT Strategy. Knowledge of the life cycle of complex transformation projects from conception to implementation.

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Comments

Picture of Enric Biosca Trias
Enric Biosca Trias | DATA AND ANALYTICS MANAGER
23/05/2018 12:51 BST

Hello All,

Don’t hesitate to ask or comment anything about our session after or before.

Thanks all