However, these columns have invalid data to be removed (NaN), multiple letter cases, acronyms, and special characters that are pre-processed in this phase. Data Pre-processing: As we can see in the data, there is not unique ID to join or search data, then texts within the columns name, country and suffix are used.Data Reading: reads from the data sources.Finally, when source_a_gender is not null set this value to gender, otherwise use the source_b_gender only if the probability is greater than 0.5.Īlso, some metrics like male percentage and female percentage are produced into the metrics storage system.Then, search the name on the genderSourceBExampleDSand add the column source_b_gender. If the suffix does not have the gender then search the name on the genderSourceAExampleDS and add a new column source_a_gender.When the suffix has an explicit gender, for example, Mr or Ms add the gender right away in the column gender.Then, the pipeline aim is to produce a dataset where the column gender is added to the usersSourceExampleDS as follow: UsersSourceExampleDS that contains users’ information. Let’s work on a basic example and define a pipeline structure. Thus, the pipeline structure should be defined after a process of data exploration that provides the phases need to produce the expected outputs from the inputs. The first and most important step (even more than code cleanliness) in every pipeline is the definition of its structure. Step 1: Define first your pipeline structure We will expose a process that contains a set of steps and patterns that will help you in creating better spark pipelines using a basic spark pipeline as an example. Thus, design principles that ensure that code is maintainable and extensible might be broken, leading to further problems in an environment where our products should be dynamic. Nevertheless, new tools like notebooks that allow easy scripting, sometimes are not well used and could cause a new problem: extensive data pipelines are written as simple SQL queries or scripts, neglecting important development concepts as writing clean and testable code. Creating data pipelines by writing spark jobs is nowadays easier due to the growth of new tools and data platforms that allow multiple data parties (analysts, engineers, scientists, etc.) to focus on understanding data and writing logic to get insights.
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