Data Engineering: All you need to know

In the IT industry , you might have heard about the word “Data Engineer”. Today we are going to study everything about data engineering. Topics we are going to cover:

What is Data engineer?

Data Engineer is someone who collects, uses and analyze the data at scale, Every organization have large amount of data and they need some people and technology who can make a high and good use of that data, They aim to make data easily accessible and to optimize their organization’s big data ecosystem

The amount of data an engineer works on varies according to the organizations. The bigger the organizations, the more complex data will be there , some organizations are really sensitive about data for example healthcare and financial services.

Roles of Data Engineer

Pipeline-centric engineers: These data engineers mostly work on a midsize data analytics team and more complicated data science projects across distributed systems. This role is most likely used by mid size and large companies.
Generalists: Data engineers with a general focus typically work on small teams, performing end-to-end data collection, intake and processing. They may have more skills than data engineers, but less knowledge about systems architecture. A data scientist wants to become a data engineer will fit well into the generalist role.
Database-centric engineers: These data engineers are tasked with maintaining, implementing and populating analytics databases. This role mostly exists at larger companies where data is distributed across several databases. The engineers works with pipelines, tune databases for efficiency and create table schemas using extract, transform, load (ETL) methods. ETL is a process in which data is copied from various sources into a single destination.

Responsibilities of Data Engineer

Build data systems and pipelines
Interpret trends and patterns
Evaluate business needs and objectives
Prepare data for prescriptive and predictive modeling
Build algorithms and prototypes
Develop analytical tools and programs
Perform complex data analysis and report on results
Identify opportunities for data acquisition
Combine raw information from different sources
Explore ways to enhance quality and reliability of data
Collaborate with architects and data scientists on several projects

Skills Required to Become Data Engineer

It will be beneficial for Data engineers to have skills in programming languages such as C#, Java, Python, Ruby, R, Scala and SQL.
Engineers should have a good understanding of ETL tools and REST-oriented APIs for creating and managing data integration jobs. These skills also helps in providing simplified access to prepared data sets to data analysts and business users.
Data engineers must have the knowledge of data warehouses and data lakes and how they work. For instance, Hadoop data lakes which offload the processing and storage work of established enterprise and data warehouses support the big data analytics efforts on which data engineers work.
Data engineers must also have the knowledge NoSQL databases and Apache Spark systems, which are common components of data workflows. Data engineers should have a knowledge of relational database systems as well, such as PostgreSQL and MySQL. Another important component is Lambda architecture, which supports unified data pipelines for real-time processing.
Business intelligence (BI) platforms and the ability to configure them are another important skill for data engineers. They can establish connections between data warehouses, data lakes and other data sources with BI platform. Engineers should know how to work with the interactive dashboards which used by BI platforms
Lastly, understanding of Unix-based operating systems (OS) is important. Unix, Solaris and Linux provide root access and functionality that other Operating Systems such as Mac OS and Windows don’t. They give the user more and good control over the OS, which is useful for data engineers

How To Become Data Engineer

There are a lot of certification courses of Data Engineers on different learning platforms , but certification alone does not matter to land into your dream job. Experience also considered as necessary and one of the most important factors. Other ways to become a data engineer are following:

DP-203 Exam: Imagine getting certified by Microsoft, DP-203 is Data Engineering exam organized by Microsoft azure , If you perform well in it, you will be an Azure certified Data Engineer. Azure Data Engineers are responsible for integrating, transforming, and consolidating data from distinct structured and unstructured data systems into structures that are good enough for building analytics solutions. Azure Data Engineers help stakeholders apprehend the information thru exploration, and that they construct and keep at ease and compliant facts processing pipelines through the use of specific tools and strategies. these professionals use various Azure data offerings and languages to store and produce cleansed and better datasets for evaluation.

Note: You can refer to Exam DP-203: Data Engineering on Microsoft Azure

University degrees. Useful degrees for aspiring data engineers include bachelor’s degrees in applied physics, computer science, mathematics or engineering. Also, master’s degrees in computer science or computer engineering will be a plus point for candidates to set themselves apart.
Project-based learning: This is the more practical approach to learning data engineering skills, the first step is to set a project goal and then examine which skills are necessary to reach it. The project-based approach is a good and practical way to maintain a motivation and structure learning.

Data Engineer vs Data Scientists

Data engineers and data scientists works together. The data that companies have in databases and other formats is prepared and organized by the data engineers . Data pipelines are also build by them that make data available to the data scientists. This data is used by data scientists for analytics and other projects that improves the business operations and outcomes.

Data scientists and data engineers have a difference in their skillsets and focus. Data engineers do not necessarily have a specific focus; they tend to be competent in some areas and well-rounded in their knowledge and skills. By contrast, data scientists often have specialized areas of focus. They are concerned with the more exploratory data analysis. Data scientists tackle new and big-picture problems, while data engineers put the pieces in place to make that possible.

Frequently Asked Questions

Q1. What is Data Engineering?

Data Engineering is the term one uses when working with data. The main process of converting the raw data into useful information that can be used for different purposes is called Data Engineering. This involves the Data Engineer working with the data by performing research and data collection on the same.

Q2. What is Data Modelling?

Data modeling defines the simplification of complex software designs by breaking them into simple diagrams that are easy to understand, and it does not require any prerequisites. This provides many advantages as there is a simple visual representation between the data objects and the rules associated with them.

Q3. Who does a Data Engineer work with?

Data engineers works with Data Scientists to improve the accuracy and quality of the information, enabling the businesses to make more responsible business decisions. They also work with leaders across the organization to help and support business decisions.

Q4. What is Hadoop? Explain briefly

Hadoop is an open-source framework, which is used for data storage and data manipulation, also for running applications on units called clusters. Hadoop is considered gold standard of the day when it comes to handling and working with Big Data.

It also helps in provision of the huge amounts of space needed for data storage and a large amount of processing power to handle limitless jobs and tasks concurrently

Q5. What are the four V’s of Big Data?

Volume
Variety
Velocity
Veracity

Related References

Next Task For You

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