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What is ELT? Build ELT pipelines with Peliqan

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Introduction to ELT Pipelines in Peliqan

Learn how Peliqan simplifies data integration and management with its powerful ELT pipelines. This video walks you through the core features of Peliqan’s data synchronization process, from initial connection to advanced pipeline management.

What you will learn:

– Broad Connector Support: Discover Peliqan’s 250+ connectors for sources like ERP, CRM, accounting systems, cloud storage, and databases.

– Automatic Data Sync: See how Peliqan automatically creates and runs pipelines to sync data from your sources into a built-in or custom data warehouse (Snowflake, Redshift, BigQuery, Azure).

– Pipeline Management: Understand how to manage scheduling, including changing sync frequency, and how Peliqan automatically handles reauthorization and API rate limiting.

– Incremental Sync & Troubleshooting: Learn about incremental data fetching, state management, and using the AI data engineer for a quick overview of pipeline runs.

– Advanced Features: Find out how to run pipelines for specific tables using the “PQ Run Pipeline” function in an app, perform a full resync for schema changes, and configure advanced settings like adding custom models in connectors (e.g., Odoo).

Streamline your data workflows and monitor your ELT processes with Peliqan!

FAQs

ELT (Extract, Load, Transform) in Peliqan automatically syncs data from 250+ sources into your data warehouse. Peliqan handles incremental fetching, customizable schedules, and API rate-limit handling so the pipeline runs reliably without manual intervention.

In ETL, data is transformed before loading into the warehouse. In ELT, raw data is loaded into the warehouse first, then transformed using SQL. ELT is faster and more flexible for modern cloud warehouses.

Peliqan supports over 250 data sources, including databases, SaaS apps, APIs, ERPs, CRMs, payment gateways, and marketing platforms.

Yes. Peliqan supports incremental fetching, so only new or changed records are synced after the initial load. This keeps pipelines fast and reduces API usage.

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