Vasyl Soloshchuk
CEO at INSART
16 November 2021

Make the ETL Process Scalable and Fast Enough to Solve Modern Fintech Data Issues

Extract, transform, and load (ETL) is a data integration process referring to three distinct but interrelated steps. It is used to synthesize data from multiple sources multiple times to build a data warehouse or data lake.

  • You need the ETL process to make data analyzable.
  • The process provides a detailed historical perspective.
  • Recent technologies can extract data from network transactions, IoT, sensors, Wi-Fi, and other sources.

Read also: How to Improve Classical ETL in Fintech (guide)

Extract Phase

  • Text-based files contain information from third-party data providers that must be processed and loaded in a system (e.g., CSV, fixed-length, JSON).
  • API-based third-party data providers are used less often and generally provide much more fine-grained data because of API filters. However, such data may play two roles: a primary data source for ETL and a supplementary data source used on a transformation layer to join with and enrich the primary data source.
  • Internal data storage provides supplementary data to archive correct results in the next phases. Databases used separately or simultaneously will work as a target data source during the load phase.

Transformation Phase

  • This is the most business logic-oriented layer type.
  • The phase involves transforming different kinds of data from different data sources into unified, internally used, and data provider-unified types and sets of types.
  • Transformation into a unified format is crucial in the Fintech world, where in 99 percent of cases, a product doesn’t work independently in a vacuum but relies on many client data suppliers.
  • It also involves enriching and joining the main data source with data from supplementary data sources.
  • Finally, it involves filtering, splitting, joining, and deduplicating data.

Load Phase

  • This approach is used in RDBMS when it is hybrid storage that contains analytical data to build reports. It runs some AI tasks and other tasks, and it provides the same data middleware services that are presented to clients in real time (e.g., PostgreSQL, Oracle, MSSQL).
  • Data lakes (i.e., Snowflake, Google BigQuery, Redshift) and data warehouses are typically used in reporting and analytical engines that provide data to end clients and data scientists.
  • Data lakes and data warehouses also are used in layering a business intelligence or analytics tool on top of the warehouse and building machine-learning algorithms.

Faster Integration of New Data Sources

It’s worthwhile to discover and measure how much time it takes to integrate a new data provider into a developed ETL process. In modern Fintech, integrations generally take a lot of time, so new data source integration should be as smooth as possible.

Improvement is the development of unified internal data structures. If applicable, the idea is to develop processing logic that’s dependent on a single internal format and not develop it from scratch with the integration of a new data source.

There may be more improvements during the transformation phase. A classic approach to converting data from a data provider-specific format to an internal one requires code changes.

For more specific information and technical details, please read our guide, How to Improve Classical ETL in Fintech.

Why Invest in ETL?

When your business is growing, changing, and expanding to new markets, the data that must be processed is increasing and developing, too.

High-quality ETL solutions help with the following:

    • Managing all important information
    • Processing trustworthy analytics
    • Preventing data loss
    • Creating more integrations
    • Covering more system use cases
    • Saving time and money