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Data engineers design and develop large-scale data collection, storage, and analysis systems. Organizations can gather vast volumes of data, but they need the proper people and technology to ensure that the data is in a useful shape by the time it hits data scientists and analysts.
Working as a data engineer can help you make a concrete difference in a world where we'll be producing 463 exabytes per day by 2025, besides making the life of data scientists easier. Data science will fail if there are no data engineers to analyze and channel the data.
In this article post, Mobiprep has explained what a data engineer is, their roles, skill sets, and overall job description.
What Is Data Engineering?
Engineers create and construct things. "Data" engineers develop and construct pipelines that modify and transmit data into a format that is highly useable by the time it reaches Data Scientists or other end users. These pipelines must collect data from a variety of sources and store it in a single warehouse that represents it consistently as a single source of truth.
Data engineering is meant to help with data management by allowing analysts and data scientists to deal with data in a secure, accurate, and timely manner.
What is a Data Engineer?
In the world of data, a data engineer is like to a Swiss army knife; they can take on a variety of tasks and responsibilities, often representing one or more of the core components of data engineering listed above.
A data engineer's job entails storing, extracting, transforming, loading, aggregating, and validating data. This entails:
Creating data pipelines and successfully storing data for tools that require it.
Analyse the data to ensure that it follows data governance requirements.
Evaluate the advantages and disadvantages of various data storage and query solutions.
For example, if your company uses Amazon Web Services (AWS) as a cloud provider and you need to store and query data from many systems, you can use AWS. The best solution will vary depending on whether your data is structured or unstructured (or even semi-structured), normalized or denormalized, and whether you need data in a row or columnar format.
How does a Data Engineer Work?
Data engineers design systems that collect, handle, and convert raw data into usable for data scientists and business analysts to comprehend in a range of scenarios. Their mission is to make data more accessible to businesses so that they may evaluate and improve their output.
Following are some work you will do as a Data Engineer:
Acquire datasets that are compatible with the needs of your company.
Create algorithms to convert data into actionable information.
Database pipeline architectures must be designed, tested, and monitored.
Collaborate to have a better understanding of the company's objectives.
It is necessary to build new data validation procedures and data analysis technologies.
Ensure that data governance and security policies are followed.
Why Choose Data Engineering as a Career?
There is so much data available that can be used to improve business prospects across the organization's workflows. Data-handling technologies are complicated, and managing them effectively requires a certain amount of competence.As data becomes increasingly complicated, new technologies develop to help extract exact value from large data volumes. A data engineer can help in this situation.
As long as there is data to organize, data engineers will be in a growing market. According to a report of Statista - “The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2019”.
Take up a job in data engineering if you want to bring value to an organization's success, provide easy access to data, and aid decision-makers by giving data in the format and at the time they want it. Large volumes of data are readily available now that most businesses have undergone digital transformations, and solutions such as IoT and AI are gaining traction, making the job of a data engineer more fun.
Data Engineering Roles:
A data engineer's job description is as varied as the project demands. Let's quickly go over some broad architectural concepts to give you an understanding of what a data platform is and which tools are needed to process data.
Extracting Data:
We must first retrieve the data because it is stored somewhere. It's possible to use a database, online user interactions, an internal ERP/CRM system, and other business data sources.
Storage/transition of Data:
Storages are the most important architectural point in every data pipeline. We'll need a place to keep the data we've extracted.A data warehouse is the final storage point for all data collected for analysis in data engineering.
Transformation:
End users may not understand raw data in this form since it is difficult to interpret. In this condition, it can be taken for further analysis or retrieved from the reporting layer. The tasks of a data engineer can be applied to the overall system or to each of its components individually.
General-role:
A data engineer would be in charge of every phase of the data flow in a small team of data experts. From identifying data sources to creating analytical tools, a general-role data engineer would plan, implement, and manage all of these platforms.
Warehouse-centric:
The data engineer was traditionally in charge of developing SQL databases for data storage. This is still true today, but warehouses have grown in complexity. As a result, perhaps there is a numerous data engineer, some of whom expertise in warehouse architecture.
Pipeline-centric:
Data integration solutions that integrate sources to a data warehouse would be handled by pipeline-centric data engineers. These tools can load data from one location to another or do more specialized functions.They could contain data staging areas where data is received before being altered, for example.
Data Engineer skills:
Any specialist's abilities are related to the obligations they are in charge of. Because data engineers can do a wide range of tasks, their skill set would vary. However, their work can be divided into three categories: engineering, data science, and databases and warehouses.
Coding:
Because this job requires knowledge of coding languages, you should consider enrolling in programs to learn and practice these abilities.Standard programming languages include SQL, NoSQL, Python, Java, R, and Scala.
Relational and non-relational databases:
Relational and non-relational databases are among the most widely used data storing technologies. Both relational and non-relational databases, as well as how they work, should be familiar to you.
ETL systems:
ETL stands for extracting data from databases and other sources and storing it in a single repository, such as a data warehouse. Some popular ELT tools are Xplenty, Stitch, Alooma, and Talend.
Data storage:
Not all forms of data, particularly large amounts of data, should be stored in the same way. As you create data alternative solutions for a corporate entity, you'll want to know when to use a data lake vs a data warehouse.
Automation and scripting:
Working with big data necessitates automation, simply because firms are able to collect so much data. You should be able to use scripts to automate processes.
Machine learning:
While data scientists are mainly worried with machine learning, having a rudimentary understanding of the concepts may assist you better comprehend the expectations placed on your team by data scientists.
Big data tools:
When using big data methodologies, data engineers don't merely work with traditional data. They're frequently entrusted with significant volumes of information. Hadoop, MongoDB, and Kafka are a few examples of prominent tools and technologies that are evolving and varying per company.
Cloud computing:
The term "cloud computing" refers to any method of offering hosted services over the internet. Because cloud services are increasingly replacing physical servers, you'll need to understand cloud storage and cloud computing. Beginners might check into classes offered by Amazon Web Services (AWS) or Google Cloud.
Data security:
While some businesses have designated data security teams, many data engineers are still responsible for safely handling and storing data to prevent loss or theft.
Soft Skills:
Communication skills
Presentation skills
Team skills
Collaboration
Data Engineering Salary Outlook:
The average data engineer compensation is $ 92,496 per year, according to PayScale. An entry-level Data Engineer with less than one year of experience may expect to make $77,300 on average. The average total income for an early career Data Engineer with 1-4 years of experience is $87,822. The average total income for a mid-career Data Engineer with 5-9 years of experience is $103,616. The average compensation for a senior data engineer with 10 to 19 years of experience is $117,902.The average total compensation for employees in their late careers (20 years or more) is $115,411 per year.
End Note:
As you can see from the aforementioned materials, being a data engineer is no easy task.You'll need a deep understanding of tools and procedures, as well as a strong work ethic, to become one. This position is in high demand in the sector due to the current data boom, and it will continue to be a rewarding career opportunity for anyone willing to pursue it.
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