Monte Carlo

AUGUST 31, 2023

Data is a priority for your CEO, as it often is for digital-first companies, and she is fluent in the latest and greatest business intelligence tools. What about a frantic email from your CTO about “duplicate data” in a business intelligence dashboard?

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Precisely

MARCH 14, 2023

And the desire to leverage those technologies for analytics, machine learning, or business intelligence (BI) has grown exponentially as well. We optimize these products for use cases and architectures that will remain business-critical for years to come. What does all this mean for your business?

Data Integration 52

Data Integration High Quality Data BI Data 52

Monte Carlo

AUGUST 17, 2021

Data informs every business decision, from customer support to feature development, and most recently, how to support pricing plans for organizations most affected during COVID-19. When migrating to Snowflake, PagerDuty wanted to understand the health of their data pipelines through fully automated data observability.

Insurance 40

Insurance Retail Data Pipeline Portfolio 40

Monte Carlo

MARCH 28, 2024

For the past few decades, most companies have kept data in an organizational silo. Analytics teams served business units, and even as data became more crucial to decision-making and product roadmaps, the teams in charge of data pipelines were treated more like plumbers and less like partners.

Data 52

Data High Quality Data Machine Learning Accessible 52

Monte Carlo

MAY 17, 2023

Data Warehouse (Or Lakehouse) Migration 34. Integrate Data Stacks Post Merger 35. Know When To Fix Vs. Refactor Data Pipelines Improve DataOps Processes 37. Analyze Data Incident Impact and Triage 39. Transition To A Data Mesh (Or Other Data Team Structure) 40. Prioritize Data Assets And Efforts 41.

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

Data warehouse (or Lakehouse) migration 34. Integrate Data Stacks Post Merger 35. Know When To Fix Vs. Refactor Data Pipelines Improve DataOps Processes 37. Analyze Data Incident Impact and Triage 39. Transition To A Data Mesh (Or Other Data Team Structure) 40. Prioritize Data Assets And Efforts 41.

Data Pipeline 52

Data Pipeline Data Data Engineering Data Engineer 52