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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteData loading is the step that puts data into a target system, such as a database, data warehouse, or data lake. It is one part of a data-integration workflow—not another name for the whole workflow. What happens before or after the load depends on the approach: ETL transforms data before loading, while ELT loads it first and transforms it in the destination.
What data loading means
A data load transfers or inserts data into its destination. The source might be an application database or files, and the target might be a database, warehouse, or lake. Google Cloud describes the loading stage as “the process of inserting that formatted data into the target database, data store, data warehouse, or data lake” in its What is ETL? explainer.
Loading is distinct from extracting and transforming. A pipeline may extract data from a source, prepare it, and then load it into a target; the particular sequence depends on its design.
How loading fits into ETL and ELT
ETL: transform before loading
ETL stands for extract, transform, load. Data is extracted from its source, cleaned or reshaped, and then loaded into the destination. This can suit a workflow that already transforms data before it reaches the target, or one designed to limit transformation work performed by the target platform.
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ELT: load before transforming
ELT stands for extract, load, transform. Data is extracted and loaded into the destination first; transformations then run there, often using the target platform’s capabilities. Google Cloud generally recommends ELT for its BigQuery customers, while noting that ETL may make sense when an existing transformation process is in place or when reducing resource use in BigQuery is a goal. That is guidance specific to BigQuery, not a universal rule for every platform. See Google Cloud’s introduction to loading, transforming, and exporting data.
For example, a company could copy orders from an application database into an analytics warehouse. An ETL pipeline might standardize order fields before the copy reaches the warehouse. An ELT pipeline might load the source records first and standardize them with transformations in the warehouse.
Common ways to load data
Loading patterns describe when or how data arrives. They are separate from ETL versus ELT, which describes when transformation happens.
- Batch loading: Moves a group of records together, often on a schedule. It can be appropriate when data does not need to appear in the target immediately.
- Streaming: Delivers data continuously or in small increments to support near-real-time availability.
- Change data capture (CDC): Identifies changes made in a source database and replicates those changes to another system.
BigQuery documents batch loading, streaming, and CDC as distinct ways to load or access data. It also supports federated queries, which let users access some external data without physically loading it into BigQuery. Federation is therefore a way to access external data, not a data load. Details are in Google Cloud’s Introduction to loading data.
Full load versus incremental load
These terms describe how much data a load moves, not how frequently it runs or whether it uses ETL or ELT.
| Approach | What it moves | Common use |
|---|---|---|
| Full load | The source dataset | An initial import or a deliberate reload |
| Incremental load | Only new or changed data—the delta since a prior load | Ongoing updates after an initial import |
A typical pattern is to make an initial full load, then move changes incrementally. AWS explains full, incremental, batch, and streaming approaches in its ETL overview. The exact way a pipeline identifies changes depends on the source and destination.
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What to check before loading data
There is no single command, file format, or configuration that applies to every destination. Before setting up a load, check the target platform’s documentation and confirm:
- Source and interface: Whether the destination accepts the source through files, APIs, connectors, or another supported method.
- Format and schema: Which file formats and data types are supported, and whether the incoming fields match the destination schema.
- Permissions and security: Which identities can read the source and write to the destination, and where files are allowed to be read from.
- Character encoding: Whether text encoding and character-set settings match the data.
- Validation and recovery: How the workflow detects rejected or incomplete records, reports errors, and resumes or safely retries a failed load.
- Freshness and operations: How often data needs to arrive and how the job will be monitored.
These details are platform-specific. For example, BigQuery lists Avro, CSV, JSON, ORC, and Parquet for batch loads; that list is not universal. Snowflake provides its own data-loading documentation with destination-specific guides and considerations.
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A concrete file-load example
MySQL’s LOAD DATA statement reads rows from a text file into a table. Its behavior depends on options such as LOCAL, which changes whether the file is read from the client host rather than the server host. The MySQL manual also covers character-set and security considerations, including privileges. These are MySQL-specific details, not general rules for all database imports; consult the MySQL 8.0 LOAD DATA statement reference for the applicable requirements.
Choosing an approach
The appropriate design depends on the source, target, data volume, desired freshness, security needs, and available platform features. Consider the decisions separately:
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- How fresh must the data be? A scheduled batch may be enough for periodic reporting; a streaming or CDC approach may be worth considering when changes need to become available sooner.
- How much must move each time? A full load copies the dataset, while an incremental load moves identified changes after an initial copy.
- Where should transformation happen? ETL transforms before the destination; ELT transforms after loading in the destination.
- What does the destination support? Verify its accepted formats, source connections, commands, permissions, and schema requirements before choosing a design.
- What needs to happen when something fails? Plan how to validate the data, detect errors, monitor jobs, and recover without creating missing or duplicate records.
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