Why Do You Need Data Engineers And What Do They Do?
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Introduction:
As vital members of data analytics team in any firm, data engineers are responsible for overseeing, optimizing, monitoring and managing data storage, distribution, and retrieval throughout the company. They are responsible for developing new algorithms and finding the latest trends in data sets that can be used to extract new raw data useful for their company. This particular Information Technology role requires stronger technical skills, such as greater command on database design as well as several programming languages.
But how will the company CEOs gain insights from these large raw data chunks? Apart from this, data engineers are also responsible for communicating their technical knowledge across the departments thus data engineers need to possess strong communication skills.
To extract useful information from raw data, data engineers need to understand their clients’ goals and objectives because it is extremely important to understand business goals and aligning them with the data to handle complex and large datasets more practically.
Role of Data Engineer:
As a broad field of data, engineering requires you to master a long spectrum of the skills which is not possible for an individual data engineer. We have some specific roles and duties shortlisted that are mainly required by data engineers in different companies. Data engineers mainly fall into three roles in any firm. These roles are:
1. Database-Centric: Typically in bigger corporations, management of data flow is a full-time job and data engineers on this job focus entirely on analytics database. As database-centric data engineers, you will be required to work across multiple databases and develop table schemas in the data warehouse.
2. Pipeline-Centric: In medium-sized companies, data engineers usually work side by side with data scientists to utilize the collected data usefully. As a pipeline-centric data engineer, you will be responsible for working with computer science and distributed system thus in-depth knowledge for both these will be required.
3. Generalist: Smaller Teams and Companies often hire generalist data engineers to work on multiple tasks such as entire data process, from management to analysis. A general data engineer has to wear many hats which are data-focused but not very in-depth. This role is ideal for individuals transitioning from the field of data science to data engineering.
Why are Data Engineers So Important?
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Data engineering is a fundamental part of managing big data and automating workflows. The businesses of the future require data analysis for crafting their future actions aligning to the requirements of their target market — and that is impossible without access to data!
Data engineers ensure that the data collected is not only consistent and clean but is also of high-quality; so that it becomes useful for the firm or company implementing it. It is not always a visible part in the entire process of data science which can be very frustrating but without the use of data engineering, businesses cannot keep up with the constant input of infinite stream of data and expect to obtain the most accurate and reliable results which can form insightful analysis. Data engineering is very important since it shows the correct data to businesses of futures for them to stay relevant in the industry. Some of the most important reasons why data engineering is important are as follows:
1. Data Scientists Need Them: Data scientists need data engineers to clean, parse and evaluate data assets using different programming languages such as Python. This helps them with the buildup of data pipelines and warehouses which can be used to efficiently deliver clean data sets at a scale that data Science can produce big data products. The data engineers who are specialized in this field become a crucial company asset as they help the company success grow. A startup may only be able to hire one or two data scientists and no data engineer but doing so, their work takes 80% more time than the originally required time and this inefficiency cripples the company’s scale.
2. Data-Driven Decision Making: Data scientists can analyze major business issues and provide valuable insights however they require clean and accurate data to make search data-driven decisions and for that purpose, data engineers are required because they can parse and clean the data which is required by the data scientist. A company’s function grows much slower if data engineers are not there to offer services which result in the making of accurately data-driven decisions
3. Structuring Information: After an adequate amount of information is digested, the next role of a data engineer is to take this information and structure it so it is easily accessible. Data engineers are there to structure information to extract more useful insights for the company.
4. Timely Data: Stale data is not helpful — we need to make real-time decisions and for that, we need to accurately predict things such as customer retention, fraud, churn, etc. It wouldn’t be helpful to identify any fraudulent credit card activity after a long time, say 3 weeks later. So if we do not have timely data, the analysis upon it is useless and for that, data engineering comes in handy. They send timely data to the algorithms and dashboards to allow businesses to answer the questions when they need to.
5. Accurate Predictions: In this technological Era, well-managed data refers to accurate predictions. In this scenario lack of data is all about the lack of ability to manage this available data which inhabits many of our clients. We need good models, good Artificial Intelligence and good machine learning which is all impossible without a well-established data pipeline in place. This work is again done by the data engineers.
Conclusion
Data engineers are very skilled in a wide range of technologies. They are comfortable with deploying models on apps and websites, and understand how different data systems work. They also understand how to integrate APIs to extract key information.
In this increasingly complex technological time, big data means gaining useful insights which require the use of many algorithms and a basic understanding of each analytical principal. They now play an important role in the processes of the future where they will be able to develop and implement new technologies in the form of data-driven insights. Thus this field will make its way to a promising domain in Information Technology for the kids of the future.