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How to scale data pipelines?

What is a data pipeline?

A data pipeline is a process to extract, transform, and load data from its source to a target system. The process is typically automated and scheduled to execute at some regular interval. The purpose of a data pipeline is to avail some data from its point of origin to some point of consumption. For instance, marketing teams who are seeking to analyze the performance of their campaigns rely on data pipelines to pul campaign impression data from its end point (which is write-intensive) to a data warehouse (which is read-intensive) where they run their reports.

One of the jobs of a data pipeline is to transform the data it processes. Often, when data is initially generated and persisted in its source system, it is not readily understood by its ultimate consumers. For various reasons, its shape and format cannot be easily processed by individuals. The data pipeline, therefore, will execute several jobs to transform the data, joining and reshaping it as required by the business. Data Standardization is one example of a data pipeline process that transforms the data.

Why are data pipelines difficult to scale?

Historically, businesses relied on only a few data sources, and those mostly required simple transformations. Traditional ETL tools effectively helped organizations build and manage the required data pipelines. In the last ten years, however, companies have been investing in data lakes, which rapidly increased the number of data sources that businesses maintained. With the availability of new data came new business requirements to transform and avail the data in many target systems. 

ETL tools were never designed to handle that level of data source and transformation complexity. Current solutions rely on procedural pipelines where developers must create a step by step flow data join and transformations. The more sources to onboard, the more complex the mappings, the more inter-connected these flows become. New solutions offer self-serve capabilities for business users in individual teams but, as sources grow and more and more people need to agree on complex definitions, these don’t scale.

So now, ETL engineers must spend significant amounts of time and energy to maintain their data pipelines. These efforts slow down the business's ability to access new data and generate new insights.

How to scale data pipelines?

Instead of using procedural tools to define transformations, businesses can augment their ETL tools with declarative transformation solutions, such as Lore IO. This new approach enables data analysts -- instead of ETL engineers -- to describe their desired reports. The declarative transformation solution automates the procedural logic, building and maintaining the data pipelines on its own. This enables teams to develop and test their data pipelines faster because they can test, validate, and change the data as it's being transformed.

Data owners are only asked for simple descriptions of the key semantics. Business users start to see results instantly.

Additional resources

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