Data is becoming increasingly important in all aspects of life, making data scientists an essential component of progress. However, a data scientist can only do their job well if they can access high-quality data. Many businesses nowadays maintain their data on multiple platforms and formats. This is where data engineers are needed!
Data engineers create pipelines that convert massive amounts of data into a format that data scientists can work with. Even though they don’t get the spotlight that data scientists and analysts get, they are just as crucial to any company’s success. By enrolling in data engineering training Bhubaneswar, you can get the skills necessary to become a valuable asset to any data analytics team.
Most people do not have a clear idea of what data engineering is, which has led to many misconceptions. Let’s talk about the common misconceptions around data engineering.
Myth 1: Data Engineers And Data Scientists Are The Same
The most common misconception regarding data engineering is that it is often mistaken for data science. However, they differ greatly in their roles. Data engineers concentrate more on creating data and developing pipelines for the company’s next level, whereas data scientists primarily understand and analyze data.
Myth 2: Data Engineers Do Value Extracting
Collecting data and extracting knowledge are two very different processes. The main task of data engineers is to transform raw data into a format that scientists can use for analysis and research. They don’t do anything with the data; in reality, they just hand it over to data scientists in a presentable manner, who are responsible for finding its value.
Myth 3: Data Engineers Only Extract, Transform, And Load
People often think that ETL methods are the only thing data engineering is about. Although the ETL process is important, it is just one aspect of data engineering. They are in charge of a lot more than that. Not only do they create and maintain ETL methods, but also data infrastructure, such as storage and clusters. A data engineer’s job involves a lot of different tasks. You can learn everything about it by signing up for data engineering training Bhubaneswar.
Myth 4: Data Engineers Must Ensure Data Accuracy
The role of a data engineer is to process a steady flow of incoming data. If this data is not cleansed and used soon, it will become irrelevant and outdated. That being said, data engineers don’t go out to perfect every piece of data. They use both the current data and any additional data that is relevant to the issue at hand. Full dataset cleaning will take months. By then, they won’t be helpful anymore.
Myth 5: Data Engineers Use Ready-made Tools To Get Clean, Usable Data
Maintaining a sharp mind is essential for data engineers, just as for software, mechanical, and chemical engineers. Data engineers must constantly shape algorithms to suit their instances, as this field has no one-size-fits-all strategy. To guarantee peak performance, they must be up-to-date on all the newest practices in their field.
Myth 6: Data Engineering Work With Big Data
Data engineering is always solution-focused, no matter how big or small the data set is. Data engineers can handle data of any size, regardless of its scale.
Myth 7: Data Engineering Requires Advanced Degrees
Data engineers are responsible for transforming and migrating data from various sources, which requires collecting and analyzing such data statistically to arrive at conclusions. Getting analytics data into Salesforce is something no college degree will teach you. On-the-job training is the key to success for data engineers. Nonetheless, data engineering courses Bhubaneswar can prepare you to get job-ready.
Myth 8: Data Engineering Is An Old-Fashioned IT Job
Nothing about data engineering involves the traditional tasks of managing expenses, removing Ethernet wires, or changing passwords. It has evolved into a more contemporary DevOps function integrating data science, operations, and programming. Data engineers create customized data infrastructure that isn’t commercially available, execute maintenance routines regularly, refine table schemas, and design data monitoring infrastructure to show the pipeline’s condition. Version Control Systems, environment management, and testing infrastructure used to be very bad on data teams. These are now simplified and taken care of by data engineers.
Final Thoughts
Nowadays, data is kept in different formats and platforms. The ETL pipelines that data engineers build make it possible for data scientists to work with data. Behind the stunning visualizations and machine learning results produced by data scientists are data engineers whose efforts go unnoticed. If you want to be a part of this exciting industry, join data engineering training Bhubaneswar.
Data engineering is a field that demands hands-on expertise for success. You will benefit from solid guidance even if there isn’t a set formula for success as a data engineer. AVD Group provides courses by expert trainers to help you become an expert data engineer. Check our website to learn and get started!
