Data Analysts

You may have built a basic marketing data processing pipeline yourself, but this often leads to a time-consuming and complex journey. Each new step to increase data granularity can require exponentially more time.

Implementation of data pipelines and calculations using AI

• Describe the Data Processing Steps: Outline the specific data processing steps you need.
• AI-Generated Python Program: AI will generate a Python program tailored to your requirements, which will run on the Conduit server.
• ETL Data Pipelines: This program will create ETL (Extract, Transform, Load) data pipelines.
• Data Processing: It will process your data in the Data Lake.

Ad hoc reports

Even if you use a reporting solution like Google Looker or Microsoft BI, you can share the data from these reports with an AI chatbot, which will generate ad-hoc reports for business users.

Conduit Automates Routine ETL Operations

• Loading Source Data: Importing data from business applications.
• Data Consolidation: Combining data from multiple sources.
• Data Merging and Cleaning: Ensuring data accuracy and consistency.
• Postprocessing Reports: Finalizing and formatting reports.
• Report Distribution: Distributing reports to various clients and teams.