Cloud Data Integration & Engineering

Why Best

• Enables ~50X faster processing, with zero data egress charges through SQL ELT for AWS, Azure, Google, Snowflake, ORACLE, and Databricks targets
• Support the broadest array of connectors with the best-in-class performance
• Support cloud data lake (CDL) to cloud data warehouse (CDW) and CDW to CDW use cases.

Solution Capabilities 

Easy:

• Simple onboarding for first-time users with guided experience to build full ELT pipelines
• Specialized experience to accelerate development for SQL ELT pipelines
  

Efficient:

• Improve developer productivity by 28% by designing mapping using the intuitive Informatica graphical mapping designer
  

Cost-Effective:

• Reduce latency by 63% and data egress cost by $50,000

Customer Value

Paycor partnered with Informatica to generate reliable data-driven insights across the company. Reduced data wrangling time by 90% and reporting cycle from months to weeks. Improve the productivity of data engineers with AI-powered intelligence and automation.

Experience

Agnostic ELT Experience

• Single experience for all execution patterns – ETL and ELT, and for all ecosystem and connectors
• Suited for workloads that can be re-purposed for different ecosystems or different execution patterns as needs change or evolve

Cloud Ecosystem focused Experience

• Purpose-built for SQL ELT execution only
• Suited for workloads designed from inception to run on a specific cloud ecosystem in ELT mode

Why Best

• Quickly identify & resolve data inconsistencies with integrated Cloud Data Quality
• Build enterprise-scale AI-ready data pipelines with embedded data quality rules
• Consistent experience and unified metadata across all cloud services.

Solution Capabilities

Easy:

• Embed enforcement of quality rules into data pipelines and business processes
• The frictionless user interface, including low-code support and inline testing, reduces rule development and implementation by 80%

Efficient:

• CLAIRE-based recommendation of Data Quality Rules into CDI mapping canvas.
• Automate application of data quality rules across sources in on-premises and multi-cloud hybrid environments.
  

Cost-Effective:

• Ensure consistency of data quality with centralized rules management and execution.