The use and reach of data has grown exponentially across different business in the past decade and data engineering with all its aspects like transformation, transportation and storage has become more relevant than ever. Let’s have a look at everything Data engineering entails so you can make the best choice for your needs, whether you are an enthusiast, or a business in need for specific services.
The first step is identifying the data engineering services you are looking to achieve and then see what kind of functions can help achieve those goals. The final goal of data engineering is to offer a streamlined flow of data which helps in offering a clear work flow. This includes functions such as Exploratory data analysis, deploying appropriate machine learning models, outside data resulting in populating fields. While this data flow can be achieved in different ways, the skill set required along with the the specific tools and techniques, can give you varied outcomes. The data pipeline however remains to be a common factor. This is a framework that consists of individual programs that treat the collected data in different ways. This data can be acquired from different sources such as:
The responsibility of the smooth flow of the data pipeline is that of the Data Engineer. The team is responsible for the creation, design, maintenance, infrastructure and extension of data pipelines. The incoming data and the data model besides the stor5age of the data are also the responsibility of the data engineering team. Larger organizations usually have different teams to handle data at different levels. For instance, AI teams might be responsible for the splitting and labeling of the cleaned data. On the other hand, Data Science team might require the access on database level for the proper exploration of data. The Business Intelligence team might be responsible for aggregation and data visualization. What are the functions and responsibilities of a data engineering team? There are different approaches to achieving the different data functions, and for the same it is first essential to ensure that the system flows adeptly. ETL The extract, transform and load data pipeline is what is followed in a lot of cases while the data flow usually comes under the ‘extract” category. It’s essential here to ensure that the pipeline can withstand unforeseen errors and malformations in data. While the up-time is an essential aspect in the case of time sensitive or live nature of data. Modeling and Data normalization These processes may be made use of at different time frames. The data can stored in different locations such as the data warehouse or the data lake so as to help easy retrieval of the same as and when required. The process of Data Cleaning This is considered to be a sub-part of data cleaning. The only difference is that in data normalization the focus lies on fitting diverse data into a data model, there are a number of actions that comprise of data cleaning such as the removal of corrupt data, ensuring that the dates are in the correct format, completing missing fields and so on.
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AuthorStephen Foster is a seasoned software developer and professional writer. Based in San Francisco, he specializes in agile methodology, UX/UI design and software application development. |