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Businesses generate vast amounts of data, encompassing everything from customer feedback to sales performance and stock prices, all of which significantly influence company operations. However, deciphering the narratives embedded within this data isn’t always straightforward or intuitive, prompting many businesses to turn to the field of data engineering.
Data engineering encompasses the process of meticulously designing and constructing systems that facilitate the collection and analysis of raw data sourced from diverse formats and origins. These systems empower users to discern actionable insights from the data, thereby fostering business growth and innovation.
Companies, irrespective of their scale, are inundated with disparate data sets, necessitating thorough analysis to address pivotal business inquiries. Data engineering serves as a foundational pillar supporting this analytical endeavor, enabling data consumers such as analysts, data scientists, and executives to methodically, expeditiously, and securely navigate through the entirety of available data.
Data analysis presents challenges due to the heterogeneous nature of data management technologies and storage architectures. Complicating matters further, analytical tools often presume uniformity in data management and storage, posing significant obstacles for individuals seeking insights into business performance.
For instance, consider the diverse array of customer data collected by a brand:
While collectively offering a comprehensive customer profile, these disparate data sets operate independently, rendering the resolution of specific inquiries—such as identifying order types associated with elevated customer support costs—exceedingly challenging.
Data engineering endeavors to bridge these data silos, facilitating seamless integration and enabling prompt and efficient access to insights.
Data engineering is a skill that is increasingly in demand. Data engineers are tasked with designing systems that streamline data integration and facilitate navigation through complex datasets. Their roles encompass a variety of tasks, including:
Acquisition: Identifying and gathering diverse data sets scattered across the organization.
Cleansing: Identifying and rectifying errors within the data.
Conversion: Standardizing the format of all data to ensure consistency.
Disambiguation: Resolving any ambiguities in the interpretation of data.
Deduplication: Eliminating duplicate instances of data.
Following these processes, data is often stored in a central repository such as a data lake or data warehouse. Data engineers may also extract and transfer subsets of data to a data warehouse.
Data engineers play a pivotal role in designing, operating, and supporting the intricate environments that drive modern data analytics. Traditionally, data engineers meticulously structured data warehouse schemas, optimizing table structures and indexes to expedite query processing and ensure optimal performance. However, with the emergence of data lakes, data engineers now face the challenge of managing and delivering vast amounts of data to downstream consumers for analysis. Data stored in data lakes may lack structure and formatting, necessitating intervention from data engineers to unlock its value for the business.
Fortunately, once data sets have undergone comprehensive cleaning and formatting through data engineering, they become more accessible and interpretable. Given the constant influx of data generated by businesses, it is imperative to leverage software solutions capable of automating some of these processes.
The appropriate software stack can extract substantial insights and value from data, facilitating end-to-end data journeys known as “data pipelines.” As data traverses these pipelines, it may undergo multiple transformations, enrichments, and summarizations.
Data engineering and data science are complementary disciplines. Data engineers focus on ensuring data reliability and consistency for analysis, while data scientists leverage this prepared data for tasks such as machine learning, data exploration, and other analytical endeavors involving large datasets. Data scientists often rely on data engineers to locate and prepare data for their analysis.
Larkinapp streamlines data management for data engineers, providing a unified access point for all enterprise data for business intelligence and ad-hoc self-service analytics.