ETL processes: extracting, transforming, and loading data

Historically, large companies stored their data in isolated systems.

Financial transactions were kept in one database, marketing information in another, and customer data in a third.

This approach makes comprehensive analysis impossible, complicates reporting and leads to errors. ETL processes let you collect data from various systems, normalize it to a single format and load it into a warehouse, where it becomes available for analysis, BI systems, reporting and machine learning.

Modern businesses use ETL to build data warehouses (DWH) and to work with big data, IoT platforms, cloud applications and enterprise reporting systems.

Without reliable ETL, clear forecasts and personalized customer outreach are impossible.

The first step is extracting data from sources: relational databases, CSV and Excel files, APIs, 1C systems, CRM, online stores and cloud services.

In the extraction stage, it's important to determine which data is needed and to plan the frequency of extraction.

For high-activity systems (such as online stores), data is extracted regularly or even in real time.

Full extract — a complete snapshot of the table.

Incremental — selecting only the records that have changed.

During extraction it is important to ensure data integrity: if table schemas change or new fields appear, the system must handle them correctly without losing information.

Raw data may be unstructured and contain gaps, duplicates and invalid characters.

That is why they are sometimes copied to intermediate storage, where preliminary checks are performed.

In the second stage, data is cleansed, enriched, and converted to the required format.

Here are the main transformation operations:

Normalizing dates, currencies, addresses and phone numbers to a single format. For example, January 1, 2025 and 2025-01-01 are converted to the ISO 8601 standard.

Removing duplicates, fixing typos, replacing missing values.

This is essential for high-quality analytics. Enrichment.

Adding external sources: reference data, geographic coordinates, currency rates.

Linking data from different systems by identifiers (for example, joining orders with bank-account debits) and computing summary metrics (total purchases, average order value).

Applying logic that reflects enterprise processes: cost reallocation, currency conversion at the transaction-date rate, fee calculation.

After transformation, the data is structured and ready to load. Depending on the architecture, this may happen in a staging area (intermediate storage) or directly in the target warehouse.

In the final stage, data is written to the target storage: this can be a classic DWH on a SQL server, a cloud database (such as Amazon Redshift or Yandex ClickHouse), distributed big-data storage (Hadoop, Spark), OLAP cubes, operational data marts, or even schemas for machine learning.

Full — rewriting tables completely.

Incremental — adding and updating only new or changed records.

For large datasets, an incremental approach is preferable: it reduces system load and shortens the maintenance window.

Logging plays a key role: you must record load times, data volumes, and errors to monitor the state of the process.

Some companies use a two-phase load: data is first written to a staging area for validation and quality control, then to analytics data marts.

This approach helps avoid errors in the main database.

Besides traditional ETL there is the ELT architecture (Extract, Load, Transform). In ETL, transformation happens in an intermediate system, after which data is moved to the warehouse. In ELT, data is first loaded into the target database and then transformed by the database or analytics platform itself.

Transformations run on a dedicated server, offloading the warehouse.

You can use specialized tools for cleansing.

Duplicating data in an intermediate storage layer.

Simplified architecture, fewer data transfer stages.

The ability to use the compute power of a modern analytical warehouse (for example, a columnar database).

A high-performance target cluster is required.

Not all DBMSs can perform complex transformations.

Data quality is harder to control.

The choice of approach depends on infrastructure, data volume, and the required processing speed.

Many modern platforms (Azure Synapse, BigQuery, Snowflake) support hybrid scenarios, letting you run transformations both before and after loading.

ETL processes operate in different modes:

Data is collected over a set period (an hour, a day, a week) and processed as one large job.

This simplifies resource planning and suits reporting where the full data volume matters.

The drawback is latency: yesterday's data only becomes available today.

Data is processed on the fly: new events are loaded into the warehouse immediately.

This mode requires a complex architecture: message brokers (Kafka, Pulsar), stream-processing systems (Spark Streaming, Flink) and detailed error-handling strategies.

But it delivers minimal latency and suits fraud detection, online retail and IoT.

A compromise between batch and stream processing: data is collected in short intervals (a few seconds or minutes) and processed in groups.

This lowers infrastructure requirements while also reducing latency.

The choice of mode depends on business needs: batch processing is enough for monthly reports and financial analysis; for online recommendations, you need streaming.

To make an ETL process reliable and scalable, follow these rules:

Define the structure of sources, formats, volumes, refresh frequency, control mechanisms and recovery strategies.

Maintain a data registry: document sources, fields, relationships, transformation rules, and change history.

Use incremental loads to transfer only the records that have changed.

Build unit tests for transformations and integration tests for the whole pipeline to catch errors before loading.

Encrypt sensitive data in transit and at rest, restrict access to source and target databases, and comply with personal data protection laws.

Set up a notification system for failures, overdue jobs, and threshold breaches.

Analyze logs regularly to optimize the process.

Document in detail why each transformation is used, so new employees can quickly grasp the processing logic.

Use parallelism. In ETL scenarios, stages can run simultaneously: while new data is being extracted, you can transform the previous batch and load already-processed records in parallel.

This speeds up processing but requires proper configuration of threads and locks.

As the business grows, data volume increases.

Use flexible infrastructure (clustering, distributed file systems) so the system doesn't hit a single bottleneck.

The company's internal data culture also plays a significant role.

Even with perfectly tuned ETL processes, efficiency will suffer if employees keep storing critical information in Excel files or sending it over email.

Rolling out ETL must come with staff training and building the habit of working with data centrally — only then does the system start delivering real value.

ETL processes continue to evolve.

The most notable trends: DataOps and CI/CD for data.

Pipelines are developed, tested and deployed as code; changes pass through version control and are automatically rolled out to production.

Smart automation and machine learning.

Products include smart data profiling, automatic field mapping and transformation-rule generation. Serverless ETL.

Cloud platforms let you run pipelines without managing servers, allocating resources automatically and cutting costs.

Hybrid warehouses combine the properties of a DWH and a Data Lake, storing data in its raw form while also providing data marts for analytics. ETL processes are restructured to load data in Parquet/ORC formats, create Delta layers and tabular views. Edge ETL.

Edge computing — where IoT devices and microservices partially transform data before sending it to the center.

ETL is the core of any analytics platform.

It unifies disparate sources, turns unstructured data into valuable information and provides the foundation for business intelligence, forecasting and digital services.

Organizing ETL processes properly requires not just choosing tools but also careful planning, discipline and culture.

If you plan to build a warehouse or scale existing ETL processes, start with an audit of your current data, identify critical sources and business needs, choose the right mode (batch, stream, micro-batch), evaluate tools, and plan for scaling. And remember: ETL is a living organism that must evolve along with your business.