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Top 10 Best Practices of Data Engineering in 2024
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23rd Apr, 2024
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Data is, without a doubt, the king of all business domains these days. Every business unit, including marketing, production, and finance, uses data to make significant decisions and carry out its operations. That is why every organization works towards designing and building structures for proper data storage and analysis. This process of data management is called data engineering. Companies hire experts who are well-versed in data engineering best practices and keep their data management sorted with their help.
The demand for data engineers is high, and you can expect it to increase exponentially in the coming years. As per AIM (Analytics India Mag), the market is experiencing a huge projection in demand for data engineers.
Different types of data engineers are broadly categorized into BI engineers, analytical engineers, data scientists, or analysts. You can enroll in a Data Science and Machine Learning online course to grab the best professional opportunities after successful completion. In this course, applicants will learn about data engineering design tools and the data engineering strategy that can bring efficiency to work.
10 Best Practices of Data Engineering
Let us dig deeper and discuss the best practices for every data engineer. A successful data engineer should know all these practices by heart and use them to gain proficiency in their work.
1. Focus on Modularity
Modularity is a proven methodology to ensure quality data engineering. You would design one module focusing on a particular problem. As each issue gets defined dedicatedly, it will be convenient for every team member to read, reuse and test the code. Even if someone joins the project at a later stage, they can check the modules and get hold of everything. So, focus on enhancing modularity, and your data management will become utterly convenient. Every time the new datasets get extracted, make sure you segregate them into modules based on their use or category.
2. Automate Data Pipelines
Data pipelines are the data engineering architecture patterns through which the information travels. It is a method using which the data gathered from different sources get ported to a data warehouse. If your data pipelines are efficient and reliable, you can expect to derive meaningful insights from them. So, it is fruitful to automate the data pipelines to boost overall productivity. It also helps save a lot of time, as the people working on the project do not have to manually extract, transform or load data.
3. Follow general coding Rules
Avoid using dead code because you cannot utilize it under different coding environments. Instead, keep the code base clean by constantly cleaning the abandoned and traditional files. This practice helps keep the code fresh enough for the new-age developers to understand and start working on it. So, frame some coding principles that the developer working on the project would religiously follow. Moreover, always keep upgrading these principles based on the latest updates. This way, your team would keep up with the market trends, and you wouldn't have to face the repercussions of using age-old techniques.
4. Maintain repeatability
While working on data, you will see that the same problem keeps repeating itself multiple times. The ideal way to deal with it is to design a data design pattern that can offer a fitting solution to the repeatable issues. By creating these patterns, you will have tools and techniques that can help in fast data processing, speeding up your development process. Even the list of data engineering best practices by Databricks emphasizes maintaining repeatability for efficient and quick database management. So, identify the recurring issues, find the best-fit solution for them, and you can do faster and more efficient data processing.
5. Maintain Processing Efficiency
One of the skills that the best Data Engineering Bootcamp will teach you is data processing. While working as a data engineer, you will perform different operations on data sets. Multiple processes will run at the same time, and the efficiency of each will decide overall accuracy. Under the boot camp, the educators will teach you to write automated codes to identify the issues that might develop during data processing. Furthermore, you will learn the practices or activities that can help eliminate the risk of data processing issues. Moreover, these practices will also help save time, effort, and resources. Hence, this efficient data processing technique can help in fruitful data analysis.
6. Set a security policy for database management
It is crucial to track every action related to the data. Especially as intruders are always looking out for loopholes in data storage, the job of a data engineer becomes crucial. The data engineers should recognize the potential threats to data security and set rules for safer accessibility. To set a proven security policy, you can categorize data sensitivity issues and define solutions for each problem. Learn about data engineering documentation best practices. Use them to create a document that anyone joining the project can refer to ensure data safety.
7. Embrace DataOps
DataOps is a collection of data practices for efficient data analysis. It doesn’t have any coding language or architecture. However, it promotes the collaboration of different tools and techniques for quality data analysis. DataOps have a hold over the entire data lifecycle, from gathering it from various sources to performing successful analysis. It is one of the Azure data engineering best practices that data engineers use worldwide.
8. The right tool for data wrangling
As the data will keep growing with each passing day, it is significant to keep cleaning it and removing dead codes. This way, you can keep the analysis process sorted and save storage space. One of the power bi data sets best practices is to use data wrangling tools. These tools help you remove all the data inconsistencies and sort every data field in a sorted way to make it easier to use. Simply put, these tools help detect incorrect records and either correct them or remove them from the storage.
9. Incorporate extensibility
Another option on the data engineering pipeline best practices list is to incorporate extensibility. You can start with a basic setup and extend the features with time. You can do it with the pipeline automation policy, coding principles, or any other task associated with data engineering. This practice helps you upscale with changing technology trends. Moreover, it lets you utilize them to design a modern-day data management policy.
10. Maintain proper documentation
Lastly, the tip that tops the list of DBT snowflake best practices or AWS database practices is detailed data documentation. Keep a record of everything right from the time of data sourcing. It will allow everyone working on the project to understand the data management process. In addition, they can learn about the data pipelines, documentation, security policy, and everything else to get hold of the project from any point in time.
Top Skills of a Data Engineer
To become a successful data engineer, every professional needs to know the data engineering best practices of AWS. Furthermore, he must have the essential skills that help them stand out in the competition. So let us dig deeper and discuss a few skills every data engineer must possess.
Database Management
As the field is all about data, the professionals in this domain must be good at its management. They should know the tried and tested ways to fetch data from different resources and store it in a segregated form. Their skill is to use various tools that help in easy and smooth data collection and organization. They should do their work with utmost efficiency so that anyone on the team trying to access the data shouldn't feel any complications.
Analytical Skills
Another significant skill for data engineers is data mining, for which their analytical ability needs to be top-notch. It specifically involves extracting vital information from huge datasets, studying patterns, and more. There are multiple tools for data mining in the market, like Rapid Minder and APACHE, and the list is never-ending. A proficient data engineer should know how to work with this and get things done efficiently. Data analysis is the skill based on which most employers make the hiring. If you wish to grab the best opportunity, brush up on your analytical skills.
Database Design and Planning
Thorough knowledge of database design and planning is crucial for data engineers. The two databases widely used these days are SQL-based and non-SQL-based. A qualified data engineer should be well-versed in both. Moreover, he should know the tools that make working with these structures utterly convenient. His skills also lie in identifying the right tools based on the project requirements. Furthermore, he is responsible for designing an architecture that makes data handling easy and efficient.
Coding / Understanding of a Programing language
Any data engineer who knows how to code efficiently has a higher chance of getting the maximum salary package. It is a highly valued skill in the market that is a highlight to attract big employers. The programming languages that are much in demand these days are:
AWS
ETL
Python
Ruby
Perl
Azure
Java
There are other options as well that you can consider learning. However, according to the Cord, the skills and their demand in the data engineering domain are shown in the picture.
However, at least the basic knowledge of these programming languages is enough to establish yourself in this competitive domain. You can join professional classes or certification programs to learn these programming languages to an advanced level. Keep working hard to improve your coding skills, and you will keep scaling professionally.
Better Communication Skills
It is a fundamental skill that every professional should possess. As a data engineer, you will work on projects that have people from different teams. You will work with developers, testers, project managers, and other professionals on the same project. You would require strong communication skills to work with them cordially and collaborate to make the project successful. Moreover, as the world is connected digitally these days, you should be good at digital interactions. Be confident in person or while interacting through video, and ensure your body language works your best.
These are the fundamental skills that a data engineer must possess to grab the best professional opportunities. You can choose from KnowledgeHut’s best Data Engineer certifications and complete them to upgrade your skills and prove yourself as an asset to your organization.
Conclusion
Data engineering is much-in-demand, and you can expect it to grow exponentially soon. If you are inclined towards this domain, know your future is bright! You can provide staff with a decent salary package and rest assured that the amount you take home will keep increasing as you gain more experience. However, you must keep working on your skills to fetch the best job opportunities and keep scaling in this domain. Join the certification program that trains you in the latest tools and techniques. Try and work on live projects to get hands-on experience in the database domain. So, start from the fundamental knowledge of databases and their management and keep brushing up your skills to enjoy a secure and monetarily sound career.
Frequently Asked Questions (FAQs)
1. What are the responsibilities of a data engineer?
A data engineer is responsible for building data systems and pipelines to fetch and store data in a sorted manner. He also works to extract meaningful information from large datasets to study patterns and help other teams make significant decisions. Everything related to databases gets handled by the data engineer.
2. What are the essential qualities of a data engineer?
A data engineer should have a thorough knowledge of database management. In addition, he should have skills that help him do data mining and other operations with utmost ease and efficiency. At the personal level, a data engineer should be good at soft skills and confident enough to handle projects of any level.
3. How can I improve my data engineering?
You can do certification courses to improve your skills as a data engineer. There are many options in KnowledgeHut's best data engineer certifications. Choose one based on the skill you want to improve or the tools you wish to learn, and you can enjoy a fruitful career as a data engineer.
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