No te pierdas Inspire 2024, que se llevará a cabo del 13 al 16 de mayo de 2024 en The Venetian, Las Vegas. Regístrate ahora.

Use Case

ETL Dashboarding

 

When you have access to ETL process performance, you can identify issues before users notice them downstream. Plus, understanding performance over time can boost total system optimization and scalability.

Bottom-Line Returns

With a full view of ETL process performance, drive ETL process strategy without investment in costly investigations

Risk Reduction

Reduce downstream impact of ETL issues

Efficiency Gains

Reduce time to resolution with ETL process issues

Business Problem

How many rows do your extract, transform, load (ETL) operations generate every day? How often does something go wrong? How long does it take before you can detect a problem, such as a load failure? When you extract thousands of records a day from multiple data sources, transform them with business rules, and load them into a data store, you hope nothing goes wrong. If something does go wrong, you hope you can correct it before someone down the line makes a decision that relies on the error. ETL saves you huge amounts of labor every day, but it shouldn’t be a black box that you continually hope is running right. And you shouldn’t need an entire IT project to implement tools that show you what’s happening inside that black box.

Analytics Solution

With analytics and workflows, you can orchestrate your own ETL processes and create dashboards and alerts. The dashboard gives database administrators and business intelligence managers high-level insight into the status of the many operations your data undergo on the way to the data store. It monitors how rows are filtered, sorted, aggregated, joined, cleaned, deduplicated, and validated, and sends alerts in case of load failures. Managers can specify the performance indicators they’re most interested in, such as total number of loads, rows written, total data stored, and load time. They can use analytics to generate reports that list the data fields with errant values, along with recommended corrections. To support user training and process improvement, they can design reports that identify trends in defects.

 

Additional Resources

 
 
Starter Kit for Tableau
Learn More
 
 
Starter Kit for Snowflake
Learn More
 
 
Starter Kit for Predictive Analytics
Learn More
 
 
Starter Kit for Microsoft
Learn More
 
 
Starter Kit for Data Blending
Learn More
 
 
Starter Kit for AWS
Learn More
 

Customer Success Stories

 
Customer Story
EY’s Global Platform Delivers Value With Artificial Intelligence
  • Finance
  • EMEA
  • English
Learn More
 
Customer Story
The IRS Improves Accountability and Transparency with Alteryx
A government agency leverages analytics to improve data accountability and transparency in procurement.
  • Data Prep and Analytics
  • Data Science and Machine Learning
  • Analytics Cloud Platform
Learn More