What is the most important data engineering concept to master?

Mastering data engineering requires expertise in: 1. Data Pipelines & ETL 2. Big Data Technologies 3. Data Warehousing 4. Database Systems 5. Data Modeling & Architecture 6. Data Governance & Security 7. Cloud Computing & Services 8. Strong Programming Skills These areas are pivotal for effective data handling, analysis, and security in data engineering careers.

- Data quality is a fundamental concern for data engineers👨💻, and they play a crucial role in designing systems and processes that uphold the integrity and reliability of the data used within an organization. - it involves ensuring that the data used for analysis, reporting, and other purposes is accurate, consistent, and fit for its intended use. - Data quality is a critical aspect of data engineering because poor data quality can lead to incorrect conclusions, flawed analytics, and unreliable business decisions.

Here are a couple of things to consider: 1. SQL (Structured Query Language): Proficiency in writing complex SQL queries is essential for interacting with and extracting valuable insights from databases. 2. ETL (Extract, Transform, Load) Processes: Mastering ETL processes is vital for efficiently moving and transforming data between systems, ensuring data quality and consistency. 3. Data Requirements Analysis: Being able to understand and analyze data requirements is fundamental for designing accurate and efficient data models that align with business objectives. 4. Data Quality Management : Understanding and implementing techniques for ensuring data quality is paramount.

As a Data Engineer I can say that Data Quality play a crucial role in our field. Let's discuss it with following points. 👇🏻 ⏩ Fundamental Concern: Data engineers hold a pivotal role in safeguarding the integrity and reliability of organizational data. ⏩Designing Robust Systems: They are instrumental in designing systems and processes dedicated to maintaining data quality. ⏩Accuracy and Consistency: The focus is on ensuring data used for analysis and reporting is accurate, better in quality, consistent, and aligns with its intended purpose. ⏩Unreliable Decisions: The consequences of inadequate data quality extend to unreliable business decisions, underscoring the importance of meticulous data engineering practices.

Data quality is derived of process understanding and business world; and need data governance and management control it in lifecycle activities like update, create, remove , propagate and so on.

According to my experiences😊, data quality is the foundation of good data engineering. - Poor data quality can lead to incorrect analyses and decisions, just like using salt instead of sugar while making a cake🍮. - It's about making sure that the data we work with is like high-quality, leading to better outcomes and success in our data-driven endeavors.

As a Data Engineer, we should always remember these points 👇🏻 ⏩ Good data engineering starts with having good data quality. ⏩If the data isn't right, our analysis and decisions might be wrong, just like using salt instead of sugar in a cold drink. ⏩ It's like making sure the data we use is well suited, so that we can do better and succeed in our data-driven projects.

Data quality is not only important for data engineering, but in general. In principle, everything depends on data quality. The difference as a data engineer is that you have a direct influence on data quality and are therefore in a position to improve it.

Data quality field define how use and value is raw data itself, help data integration to detect possible wrong data that you need to transform or manage with accountable team, keep sure you process generate high value results. Those points are addressed in data pipelines that is managed by data engineering team.

Data quality refers to data’s accuracy, completeness, consistency, and relevance. It’s a measure of data’s fitness for its intended purpose. Having trust in their data is crucial for organizations because it impacts the effectiveness and efficiency of data-driven systems and decision-making processes. Good data quality empowers data engineers to develop and maintain robust data pipelines that reliably deliver accurate and trustworthy data to end users. If the data used in these pipelines is of poor quality, the resulting insights and decisions could be incorrect or unreliable, leading to low-quality business decisions, reduced efficiency, and increased costs.

In my opinion, improving data quality always requires a systematic approach. Data profiling identifies issues, while cleansing corrects inaccuracies. Validation enforces the rules, ensuring integrity, and standardization ensures accurate data formats. Enriching data with additional information can also add depth. Continuous monitoring tracks and reports quality over time. This comprehensive strategy, from collection to consumption, transforms raw data into a reliable and valuable asset, providing a solid foundation for informed decision-making.

A good practice to automatic in you system; it's create data quality rule template to use to available in your system, and you need to fill this template it with required details of rules and manage it with permissions like you will do in security access controls.

To define and manage your data quality rules and metrics can use frameworks like Data quality model maturity (DQMM) or Total Data Quality Management (TDQM). Tools are meaning to automatic your data quality management; so first define management.

Since python is used worldwide and can be collaborated easily with any tools or any cloud services. I am suggesting Great Expectation Library of python to be used for data quality.

Like software testing; you can apply this concept in this system: create unit test for specific rules from specific attribute, then integration testing to check all rules of specific attributes work well together and the end with functional testing to check answer of violations rules. Remember test your data quality rules with several common and weird cases.

You can use great expectation library to set the various rules to raise the exception if that test case is violated, etc. Also, such testing are essentials for certain key columns which you don't want to get hamper in your production data

In my experience no matter how efficient and optimised data pipelines we might have built, if data quality checks are missing data engineers have to spend additional hours to find the issue and fix it. I prefer using unit tests and great expectations in python to add what i expect from data after transformations. It helps to catch data issues quickly. Moreover we can get data quality report on top of it. Do give it a try.

Data Quality has become of paramount importance these days because ML Engineers and Data Scientists have realized that their models are only as good the data they work upon. And the data they work upon is provided/supplied by Data Engineers. Hence, you now know who's going to manage the Quality part too: yes, Data Engineers. Data Engineers need to deepen their domain knowledge to understand key metrics and rules for managing Data Quality and define what Quality means within their system. Its an added responsibility but another exciting learning and career opportunity for Data Engineers to explore.

In a tech institute, mastering data engineering centered on the ETL process. Students delved into Extracting data from diverse sources, Transforming it to fit business needs, and Loading it into a unified repository. This fundamental concept not only formed the backbone of their data engineering knowledge but also empowered them to architect scalable data solutions. The story emphasizes how understanding the core ETL process is pivotal, serving as a gateway to mastering the intricate world of data engineering.

I agree data quality is one of the important areas to master in data engineering field. But as per my experience and learnings there are few more areas we should focus upon:- Data modelling - Converting business requirements to logical and physical models. Development of pipelines- As per architecture building the pipelines and following software engineering practices. Data warehousing - Efficient way to store the data, to make the retrieval easy and insightful. Data visualisation - Show casing the metrics with the help of reports.