---
title: "Data Backfilling"
slug: "data-backfilling"
description: "Fill in gaps in your data easily with Dataddo's data backfilling feature. Manual data load, historical manual data load possible in just a few simple steps."
tags: ["Data flow", "Data backfilling"]
updated: 2025-03-23T16:48:22Z
published: 2025-03-23T16:48:22Z
canonical: "docs.dataddo.com/data-backfilling"
---

> ## Documentation Index
> Fetch the complete documentation index at: https://docs.dataddo.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Data Backfilling

**Data backfilling** is crucial for maintaining a complete and continuous set of data records. It involves importing historical data or addressing gaps that may have occurred outside of regular data load schedules. This process ensures that your data sets are comprehensive and accurate.

To perform data backfilling, initiate **one or multiple manual data loads**, with each covering specific time periods. This step-by-step approach helps preserve the integrity and continuity of your data history.

:::(Info) (**DATADDO TIP**)
See [Full Data Re-Sync](/docs/full-data-re-sync) to load all your historical data at once.
:::

**Here's how you can start backfilling data, depending on your target destination:**
* [Data Backfilling to Dashboarding Apps](/docs/data-backfilling-to-dashboarding-apps){target="_blank"}: Enhance your dashboards on PowerBI, Looker Studio, Tableau, and more by filling in missing historical data for a complete dataset.
* [Data Backfilling to Storages](/docs/data-backfilling-to-storages){target="_blank"}: Ensure your storage solutions, including BigQuery, Snowflake, or Databricks, house comprehensive datasets, enhancing reliability for data retrieval and analysis.
* [Data Backfilling for Database Replication](/docs/data-backfilling-for-database-replication){target="_blank"}: Seamlessly migrate complete datasets from traditional databases like MySQL, PostgreSQL, or SQL Server to advanced cloud storages such as Snowflake, BigQuery, Redshift, or Databricks. Full historical data replication enhances your cloud database's utility and accuracy.

Backfilling ensures that your historical records are as complete and accurate as possible, forming a solid basis for robust, reliable analytics and insights.