Peliqan

What is Peliqan?

Peliqan is an all-in-one data platform designed to unify data access and management for business teams, data engineers, and developers. It enables real-time data connectivity to databases, data warehouses, and SaaS applications, simplifying ETL pipelines with one-click setup and zero maintenance. The platform combines SQL querying with low-code Python scripting to facilitate data transformation, interactive app building, machine learning tasks, and reverse ETL workflows. Peliqan supports both a built-in data warehouse and integration with popular external warehouses like Snowflake, BigQuery, and Redshift, allowing flexible deployment and efficient data activation across organizations.

Key Features

• Real-Time Federated Queries

  Access and query data instantly across multiple sources using a federated query engine, enabling seamless data virtualization.

• Built-in and Bring-Your-Own Data Warehouse

  Use Peliqan’s integrated Postgres warehouse or connect your own data warehouse such as Snowflake, BigQuery, Redshift, or SQL Server.

• Low-Code Python Scripting

  Develop interactive data apps, automate workflows, run machine learning models, and implement reverse ETL with minimal coding.

• One-Click ETL Pipelines

  Automatically create and maintain ETL pipelines from 100+ data sources without manual intervention.

• Spreadsheet-Style Data Interface

  Intuitive spreadsheet UI for business users to view, filter, sort, and combine data from multiple sources without technical expertise.

• Extensive Integration and Marketplace

  Connect to various databases, SaaS applications, and BI tools; deploy popular open-source tools like Metabase and Superset via marketplace.

Use Cases

• Data Integration and ETL : Streamline data ingestion from diverse sources into a unified warehouse with automated pipelines and real-time access.
• Business Data Exploration : Empower business teams to explore, transform, and analyze data directly using spreadsheet-like interfaces without relying on data engineers.
• Interactive Data Applications : Build custom internal apps for data monitoring, reporting, and workflow automation using low-code Python scripting.
• Machine Learning Preparation and Execution : Prepare datasets, label data, and run ML models such as predictions and anomaly detection within the platform.
• Reverse ETL and Data Activation : Synchronize and write back data to SaaS applications or databases to keep operational systems up to date.
• Data Governance and Lineage : Track data provenance, dependencies, and impact analysis to ensure data quality and compliance.

FAQs