How to progress to the role of Data Engineer?

Data engineers are responsible for designing and maintaining an information systems architecture, which combines concepts ranging from analytical infrastructures to data warehousing. Here Coding compiler explains how one can progress towards the role of data engineer and what skills he should acquire to become data engineer. Let’s start reading.

How to become a Data Engineer?
Data Engineer

How to become a Data Engineer?

Table of contents:

  1. Data Engineer vs. Data Scientist
  2. What are Data Foundations or the infrastructure for creating and storing data?
  3. Data Architect vs. Data Engineer
  4. What is the difference between Data Architect and Data Engineer?
  5. Databases who take an active part in building the organizational data architecture
  6. Data Engineering Skills

The work of Data Engineers is extremely technical. They are responsible for designing and maintaining an information systems architecture, which combines concepts ranging from analytical infrastructures to data warehousing.

Data engineers need to have a deep understanding of common scripting languages ​​such as SQL and Python and are constantly improving the quality of data. They need to support increasing data volumes by leveraging and improving data analysis systems.

Data engineers are also responsible for creating the steps and processes used in modeling, mining, verification, and data acquisition. Data engineers work in a Big Data environment.

The demand for skilled data engineers has been growing rapidly in recent years.

In the modern world, businesses and organizations need and even require a robust data architecture to store and access data.

Demand for data engineers is increasing as the organization expands into the Data Science field. As a result, we have been hearing more and more about the demand for Data Engineering personnel.

How to progress to the role of Data Engineer
Data Engineer

An organization can assume that it can develop these skills and generate experience in data engineering while working on projects through projects.

From the experience of organizations that tried to advance in this way, this assumption is erroneous.

If data people in the organization do not bring practical experience and know-how in building a data pipeline, working with a data management system, analyzing data, and of course writing code that will make the data available and accessible, The organizations find that they erred in their assumptions, the processes do not run properly, and the same mistakes repeat themselves.

Data Engineer vs. Data Scientist

The skills and responsibilities of data scientists and data engineers often overlap, although both roles become increasingly separate roles.

  • Data Scientists tend to focus on translating Big Data into Business Intelligence, while Data Engineers focus much more on building a data creation infrastructure.
  • Data Engineers need Data Engineers to get the environment and infrastructure they work on and focus more on interacting with infrastructure rather than building and maintaining it, they take the responsibility of taking raw data to make it useful, understandable, and ready for analysis.
  • Data Scientists are internal data engineers and are assigned the task of leading and conducting high-level research to identify trends and changes by using a variety of sophisticated infrastructures, tools, techniques, and techniques to maximize the use of the data. In contrast, data engineers work to support data scientists and analysts, providing infrastructure and tools that can be used to provide end-to-end solutions to business problems.
  • Data engineers build a scalable, high-performance infrastructure to provide clear business insights from raw data sources; Implement complex analytical projects with an emphasis on collecting, managing, analyzing, and visualizing data; And of course to develop analytical solutions in real time.
  • Data Scientists work with Big Data, and Data Engineers work with the infrastructure to create and store these Data Foundations.
  • In addition to the knowledge and tools used, Data Scientists usually work with R, SPSS, Hadoop and Python, and have high analytical capabilities and knowledge in modeling. Data Engineers are familiar with tools such as SQL, MySQL, NoSQL, Cassandra, In both relational and non-SQL environments.
Data Engineer

What are Data Foundations or the infrastructure for creating and storing data?

Data Foundations is an environment / infrastructure that supports all types of reports and analysis. A data engineer’s goal is to provide reliable, organized, and up-to-date data for Analytics support and reporting.

Strong infrastructure offers organizations tremendous benefits, making them more effective in their behavior and decision-making.

The practical benefits include:

  • Improving organizational communication and sharing information in the field of statistics
  • One infrastructure for all organizational information
  • One version of the records is saved
  • Support for shared understanding of information in the organization

Once an organization does not implement a strong and efficient infrastructure, it increases the risks in information security and supports inefficiencies within the organization.

A poor data infrastructure can provide multiple answers to the same question and less support the smart business decision-making process.

Data Architect vs. Data Engineer

Data Architect and Data Engineer are working concurrently on building the concept of data, data flow, visualization, and then building an Enterprise Data Management Framework.

A data architect, like the role of a systems analyst, describes the complete environment and structure when the data engineer uses that product to build the environment. Data Architect has the ability to “make order in data chaos”. Without it, huge amounts of business data are useless.

The data architect defines information sources in the organization and how they are controlled. He is responsible for understanding business objectives on the one hand and the existing data infrastructure on the other; It defines data architecture principles and designs it to provide competitive advantages to the organization.

Data Architect

When a data architect designs the “work plan” for enterprise data management, each Data Science team will ask a data architect to visualize and prepare data in an environment that can be used to query the data.

Very often, these experts have academic degrees in computer science, years of experience in various systems or application / application development, and deep knowledge in information management.

Typically, early adopters will have to pave the way for data planning, data management, and data storage work before they can advance to the role of a data architect.

Data engineers help the data architect implement and build the program that has been created – to build an environment and a proper infrastructure for searching and retrieving data so that both scientists and analysts can use it later.

In most cases, data engineers acquire their skills through formal training and short, targeted courses for a specific technology. In the Big Data world, these engineers are responsible for building and maintaining Enterprise Data Architectures.

So what is the difference between the two roles?

  • Data Architect builds an environment and infrastructure concept for working with data; Data engineers build and maintain it.
  • A data architect guides teams of data scientists while data engineers provide a supportive environment for the proper functioning of enterprise data.
  • Once a data architect has played the role of data engineers; But starting about four years ago, it can be seen that data engineering as a field and a separate role is growing and there is a growing demand for people for this role.
  • Although both data architect and data engineer are experts on database management technologies, they still use their knowledge differently in their roles.

We will describe a number of roles in the field of data that take an active role in building organizational data architecture:

  • Data Architect – The  data architect describes a vision in the field of data according to the requirements of the organization, translates it to the technological requirements and defines data standards and principles.
  • Project Manager –  Leads the project that creates new data flow.
  • Solution Architect – An information systems designer to meet business requirements. This is usually the same person as the  Data Architect.
  • Cloud Architect or Data Center Engineer –  prepares the infrastructure on which information systems, including cloud storage solutions, will work and not only.
  • DBA or Data Engineer –  builds information systems, integrates with sources of information and is responsible for data quality.
  • Data Analyst –  are end users in the data architecture, used to create reports and manage ongoing data updates for the business.
  • Data Scientists  also use the data architecture and leverage it by using advanced techniques of data analysis for new insights.
Data Architect vs.  Data Engineer

Data Engineering Skills

In general, data engineers need a good understanding of database management, which includes in-depth knowledge of built-in query language – SQL language. They build infrastructures, tools, environments and services.

The most useful skills are:

  • Experience with Apache Hadoop, Hive, MapReduce, Hbase, and other NoSQL technologies.
  • High level of programming – Familiarity and experience with at least one of the top programming languages ​​such as Python, Java, Scala or other languages ​​can be very useful.
  • Working with Linux is very helpful, as relevant information systems often run on this operating system.
  • Knowledge and experience in building ETL – This experience is essential for the job. ETL is a data storage process used to retrieve data from source systems and then store it in a data warehouse. Getting familiar with ETL tools and data storage solutions is very valuable.
  • Machine Learning – This is the field of expertise of Data Scientists, but a good understanding of the domain and the role of Data Scientists – helps the work of a data engineer, since there is a close connection with data Big Data. Machine Learning processes are very effective in analyzing Big Data data and support many techniques for handling large data and drawing

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