Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale. It is a broad field with applications in just about every industry. Organizations have the ability to collect massive amounts of data, and they need the right people and technology to ensure it is in a highly usable state by the time it reaches data scientists and analysts. Data must not only be comprehensive but coherent, and this is the task that data engineers set out to do.
Companies of all sizes have huge amounts of disparate data to comb through in order to answer critical business questions. Whether business teams are dealing with sales data or analyzing their lead life cycles, data is present every step of the way. Over the years, technological innovation has made a grand impact on the vitality of data. These innovations comprise cloud technology, open-source projects, and the growth of data in scale. The last bit especially stresses the importance of engineering skills when it comes to organizing huge amounts of data and delivering it in a comprehensive and coherent manner.
Data engineering is a vital aspect of company growth, network interactions, and predicting future trends. It’s designed to support the process, making it possible for data consumers, such as analysts, data scientists and executives, to reliably, quickly and securely inspect all of the available data.
The key to understanding what data engineering lies in the “engineering” part. Engineers design and build things. “Data” engineers design and build pipelines that transform information and by the time it reaches the Data Scientists or other end users, it is in a highly usable state. These pipelines must take data from many disparate sources and collect them into a single warehouse that represents the data uniformly as a single source of truth.
For example, consider all of the data a brand collects about its customers:
● One system contains information about billing and shipping.
● Another system maintains order history.
● And other systems store customer support, behavioral information and third-party data.
Together, this data provides a comprehensive view of the customer. However, these different datasets are independent, which makes answering certain questions — like what types of orders result in the highest customer support costs — very difficult. Data engineering unifies these data sets and lets you find answers to your questions quickly and efficiently.
A data engineer is a worker whose primary job is to prepare data for analytical or operational uses. These software engineers are typically responsible for building data pipelines to bring together information from different source systems. They integrate, consolidate and cleanse data and structure it for use in analytics applications. They aim to make data easily accessible and to optimize their organization’s big data ecosystem.
Data engineering is a developing field that bisects software engineering and data science. In order to perform their responsibilities efficiently and effectively, here are some of the necessary skills and knowledge that a successful data engineer should have:
It’s safe to say that companies need to be data-driven, right? Every business runs on data gathering, analyzing, and interpreting processes. And data engineers help you design and build systems that collect, store, and analyze data at scale. With data engineers organizations have the ability to collect massive amounts of data, and ensure that it is in a highly usable state by the time it reaches data scientists and analysts. Data engineers are responsible for designing and creating systems for storing and analyzing data. They work with data pipelines, big data storage and processing, and model ETL (Extract, Transform, Load), major processes in data driven companies.
Data engineers alone are not enough to successfully implement the organization’s data strategy but they will provide a major help in the process, working along with other data specialists, data consumers, users and product managers.
Before, data scientists were expected to create the pipelines and infrastructures for their work. However, those tasks weren’t really in the scope of their skillset. Now that data engineers have come to light; data scientists and analysts’ work is a whole lot easier! Having people with these special skill sets will help your company grow by delivering available information that brings forth innovative solutions to scale your business.
We need data to accelerate business growth and gain key insights about customers and collaborators as well. If your company lacks a fundamental data engineering strategy, the data that is collected is essentially useless, so adding a data engineering strategy is key to take advantage of your data.
Rupert Murdoch said it best: “The world is changing very fast. Big will not beat small anymore. It will be the fast beating the slow”. Data engineers help organizations structure and access their data with the speed and scalability they need and is a crucial part of successful data science and analytics implementation.
At Golabs we have the knowledge and expertise on how to transform your organization into a futuristic data-driven entity.
Golabs is a data engineering nearshore service provider with a team of highly qualified and skilled data engineers that work closely with our clients to help them achieve their business goals whilst saving time and resources, avoiding errors, applying best practices, and deploying high-performance data driven solutions.
We bring the data experts you need to be more productive and deliver results faster! Let’s talk and start transforming your business today.