In the modern business world that grows digital unabated, the competition between organizations is profound, as data engineering becomes more of a proactive growth enabler. Organizations driven by data are not only capable of delivering targeted, enhanced customer experience, but can also interpret and leverage new opportunities ahead of the competition. Technology leaders have already begun morphing their organizations into being data-driven to boost their digital transformation projects.
The Role of Data Engineering in Digital Transformation
The pre-eminence of data is recognized as the core of organizations for new possibilities in the world of digital transformation. The growing availability of analytics, business intelligence, and data warehouses have enabled data availability in forms that superficially benefit organizations from the perspective of their digital footprint. Organizations must re-adapt and make changes for new architectures while making integrations through investments in new processes and tools for understanding new technologies in isolation.
The essentiality of data engineering is measured in the following factors.
Fulfilling Statistical Inferences
Data is assumed to be plainly distributed and not biased while building simple confidence intervals. An important analysis in the linear regression analysis is that the variations in errors of a dependent outcome variable will be independent of the predicted variables. Also, most times, assumptions for statistical tests are based on the normal distribution of a model’s errors or values of measurements sampled from a set of tests.
Deriving Insights on Variables’ Relationships
It is not always the case that the variables’ relationship is linear, for example, comparing the income logs to another variable as the usefulness of that income reduces with higher income. Another prime example could be the multinomial monetary surge in a bank account, wherein the interest rate is compared to time. For deducing the relationship between variables, a linear relationship is required and organizations can do this by transforming the variables.
When multiple variables that are unevenly divided across parameters are visualized, organizations come up with data points that are close to those parameters. For improved visualization, transforming data for its even distribution across the graph is ideal while using a completely different scale on the axis of the graph.
Several data variables are never in the format required for specific queries, for example, the miles/gallon value for fuel consumption by vehicle manufacturers. However, for the comparison of vehicle models, the value prioritized is reciprocal, which is gallons/mile.
How is Data Engineering Changing?
The following are key transformations in data engineering over the recent past.
Data Management Using SQL
Over the past few years, MapReduce was being used for the management of big data, however, MapReduce needs sound knowledge of writing programs for the smallest of tasks. This was a key challenge faced by organizations. In the recent past, a big shift has been observed from MapReduce to programming languages such as SQL.
As part of the design of the data warehouses, Star Schema has been used for the typical modelling of data for analytical workloads. As computing and storage are affordable with this and as it helps to scale-out with the help of distributed computing and storage systems, several changes are occurring in the space of data modelling. These changes include dynamic schemas, blob storage, and higher denormalization.
There is a palpable shift to a more programmatic method from mere drag & drop ETL tools due to data complexity. Also, data generation has grown exponentially in the past couple of years, making traditional ETL tools obsolete as logic cannot be coded always. These are being replaced with more programmatic, generic tools that are configuration-driven and facilitate the management of task orchestrations.
Modern Data Engineering
Specialized skill sets are needed to perform data engineering tasks, however, modern data engineering involves technologies for supporting data professionals for performing data engineering tasks faster with improved efficiency. This further helps the data professionals streamline operations cost-effectively while keeping the workforce intervention at a bare minimum.
With digital transformation and data modernization on their mind, organizations often struggle with voluminous data while it gets generated faster and in a wide variety. Automating the processes by leveraging cutting-edge technologies becomes a must for making decisions driven by data. Organizations must focus on mastering data engineering for ensuring their infrastructure to be robust enough to operationalize data pipelines needed for performing analysis on the voluminous data.