ETL processes: extracting, transforming, and loading data

Historically, large companies stored their data in isolated systems.

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

This approach makes comprehensive analysis impossible, complicates report preparation, and leads to errors. ETL processes make it possible to collect data from different systems, standardize it, and load it into a repository where it will be available for analysis, BI systems, reporting, and machine learning.

Modern businesses use ETL to build data warehouses (DWH), 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 files, Excel, APIs, 1C systems, CRM systems, 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. There are two extraction options:

Full load - a complete snapshot of the table.

Incremental - selecting only changed records.

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 final stage, data is written to the target store: this may be a classic DWH on SQL Server, a cloud database (for example, Amazon Redshift, Yandex ClickHouse), a distributed big data store (Hadoop, Spark), OLAP cubes, operational marts, or even machine learning schemas. Loading comes in two types:

Full - rewriting tables completely.

Incremental - inserting 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 loading technique: first, data is written to a staging area, where validation and quality control are performed, and then to analytics marts.

This approach helps avoid errors in the main database.

ETL processes run in different modes: Batch processing (batch).

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 will only be available today. Streaming processing.

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 provides minimal latency and is suitable for fraud detection, online retail, and IoT. Micro-batches.

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, while online recommendations require streaming.

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 features for intelligent data profiling, automatic field mapping, and transformation rule generation. Serverless ETL.

Cloud platforms make it possible to run pipelines without managing servers, automatically allocating resources and reducing cost. Integration with Data Lakehouse.

Hybrid storage combines the properties of a DWH and a Data Lake, keeping data in its original form while also providing analytics marts. ETL processes are reworked to load data in Parquet/ORC formats, create Delta layers, and build table views. Edge-ETL.

Edge computing is when IoT devices and microservices partially transform data before sending it to the central system. This reduces traffic.

ETL is the core of any analytics platform.

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

Proper ETL organization requires not only choosing tools, but also careful planning, discipline, and a data-driven culture.

If you plan to build a data warehouse or scale existing ETL processes, start by auditing your current data, identifying critical sources and business needs, choosing the right mode (batch, stream, micro-batch), evaluating tools, and planning for scaling. And remember that ETL is a living system that should evolve with your business.