Incremental Load

What is Incremental Load?

Incremental Load is a data processing technique used to update a data lakehouse with new or modified data. It involves identifying and loading only the data that has changed since the last update, instead of processing the entire dataset. This saves time and resources by avoiding redundant processing of unchanged data.

How Incremental Load Works

Incremental Load works by comparing the incoming data with the existing data in the data lakehouse. It identifies new records that need to be added and modified records that need to be updated. These changes are then applied to the data lakehouse, ensuring that it stays up to date.

Why Incremental Load is Important

Incremental Load offers several benefits for businesses:

  • Improved Efficiency: By only processing and updating the changed data, Incremental Load reduces the time and resources required for data processing.
  • Faster Analytics: With Incremental Load, businesses can have more up-to-date data in their data lakehouse, enabling faster and more accurate data analysis and decision-making.
  • Cost Savings: By avoiding the need to process the entire dataset, Incremental Load helps reduce processing costs and optimize resource utilization.

Important Use Cases for Incremental Load

Incremental Load is particularly useful in the following scenarios:

  • Data Warehousing: Incremental Load enables the efficient updating of data warehouses, ensuring that the most recent data is available for reporting and analysis.
  • Data Integration: Incremental Load simplifies the process of integrating data from multiple sources by automatically identifying and loading the changes.
  • Data Streaming: Incremental Load can be used to continuously update data lakehouses with real-time data streaming, enabling near real-time analytics.

Related Technologies and Terms

Incremental Load is closely related to other data processing and integration techniques, such as:

  • Change Data Capture (CDC): CDC captures and tracks changes made to a database, which can be used as a basis for Incremental Load.
  • Extract, Transform, Load (ETL): ETL processes involve extracting data from various sources, transforming it to fit the target schema, and loading it into a data warehouse or lakehouse. Incremental Load can be part of an ETL workflow.
  • Data Replication: Data replication involves duplicating data from one database to another, usually for backup, availability, or analytical purposes. Incremental Load can be used to replicate only the changed data.

Why Dremio Users Would be Interested in Incremental Load

Dremio users can benefit from Incremental Load in the following ways:

  • Improved Performance: Incremental Load helps optimize data processing, resulting in faster query performance and analytics within Dremio.
  • Real-Time Analytics: By incorporating Incremental Load with real-time data streaming, Dremio users can achieve near real-time analytics on the latest data.
  • Cost Optimization: Incremental Load reduces the need for full data processing, leading to cost savings in terms of processing resources and storage.
get started

Get Started Free

No time limit - totally free - just the way you like it.

Sign Up Now
demo on demand

See Dremio in Action

Not ready to get started today? See the platform in action.

Watch Demo
talk expert

Talk to an Expert

Not sure where to start? Get your questions answered fast.

Contact Us

Ready to Get Started?

Bring your users closer to the data with organization-wide self-service analytics and lakehouse flexibility, scalability, and performance at a fraction of the cost. Run Dremio anywhere with self-managed software or Dremio Cloud.