What are the essential data engineering concepts and principles that every data engineer should know?

In the dynamic realm of data, success hinges on structure. Introducing data models – the architects behind seamless information flow. ?? Imagine a puzzle where each piece fits perfectly, unlocking the bigger picture. That's what data models do! They're the conceptual designers, shaping how data intertwines, creating databases, tables, and rules that govern data behavior. ?? Collaboration is key! Data models empower us to bridge gaps between data engineers, analysts, scientists, and business gurus. They're the shared language that nurtures efficient teamwork, turning raw data into actionable insights. ????

Data Models should reflect the needs of business to a point. There are logical things you should do as a Data Engineer to create baked-in efficiency in your data model. (Co-locate most commonly used Data, Reduce repeated data in your model, Index and Partition on things people want to query. ) As a Data Engineer, this starts with customer desire and how they see themselves using the data. Sometimes Data Engineers get hung up on what model they want to use rather than truly asking the customer what they want.

1. By creating detailed data models data engineers ensures that info is organized logically and relationship are defined accurately. 2. It provides a clear and concise representation of the data domain, facilitating communication and understanding among stakeholders. 3. Data modelling improves data understanding, data consistency and integrity, efficient db design and development and accurate reporting and analysis and real world applications.

I think the point missed here, which is the most useful thing we should look for , is that what connects with what. The word for the above lines are relationship i.e relations , connection is more important to know and then preparing a data model are easy. There are 3 basic relationship which is used to connect one table to another and those are: 1. One to one 2. One to many 3. Many to many These will really help in your data modelling and creating a robust and reliable model. Learn about these too.

Data Model is the base and well designed data model results into an efficient, scalable and reliable data pipeline so in order to build that Data engineers need to spend time in building a logical model where they identify : 1. What they want to achieve based on business requirements. 2. How they want to achieve it e.g identification of schema , tables , relationships. 3. How often business requirements change and how scalable can you keep your model as often based on new requirements your old model fails and design is not scalable enough to embed new requirements. This would be the base to start with in my opinion once you are done with this you can move toward the physical data model and then towards the data pipelines architecture.

Think of data quality as something that's not just for the data engineers. It's like the foundation of a house, everyone involved in data, from analysts to business folks, should care about it. Good data quality means we can trust the information we're working with, which leads to better decisions and results for everyone.

Since both the producer and the consumer are involved in the data quality, it's wise to formalise and automate the process. Data contracts or schema evolution with change alerts enable early warnings to be sent to both the producer and consumer in case of mishaps, enabling fast collaboration and resolution.

Data accuracy is a key attribute of high-quality data. To avoid transaction processing problems in operational systems and faulty results in analytics applications, the data that's used must be correct. Inaccurate data needs to be identified, documented and fixed to ensure that business executives, data analysts and other end users are working with good information.

In my opinion, data quality is one of the core components of data engineering because bad data is worth nothing. Quite often it is under considered but, in fact, it requires a lot of implementation and architecting because the amount of data and their pipelines growing that makes data quality monitoring even more relevant and important.

I would suggest striving for Data Observability in the realm of data space. This approach takes a holistic view of data and aims to deliver its true value to your organization. In this context, Data Quality forms an important pillar under the umbrella of Data Observability.

In todays world, each geography is coming up with its own set of data protection laws like we have GDPR . Several industries have their own, like HIPAA in USA. For corporations to keep business running without paying hefty fines, data governance is no more a "good to have" thing. It is a mandate. There are several examples on internet which show hoe companies have had to face lawsuits for data breaches. Nowadays, all major enterprises hire specifically for such data governance specialists to ensure their data platform is compliant with both internal and external auditing. Needless to mention there are many 3rd party products which accelerate and augment this process.

Data governance is a vital framework ensuring responsible data management. It establishes policies, standards, and controls for data quality, security, and usage. Data stewards oversee assets, owners ensure accountability, and metadata captures attributes and relationships. It encompasses data security, compliance, and privacy. Lifecycle management, cataloging, and lineage tracking ensure data integrity. Governance committees set strategies, while training and communication educate employees. Continuous improvement maintains alignment with business goals, fostering trustworthy data-driven decisions.

The challenge around data integration is that the systems that store data do not use a common language to describe the data. So while data is easy to move around, the metadata describing the data is not - it usually has to be translated between applications. So you can think of data integration as two steps 1. Transport and loading 2. Normalisation, Typing, and converting the data to fit in the target system. Sometimes, the normalisation and typing are done upfront, sometimes they are done by the ingestion library, and sometimes they are done manually in sql. The best place to normalise the data is before loading, and automated, to prevent having to do this later in the database where the process is more time consuming and error prone.

Data integration is the art of orchestrating a harmonious symphony from diverse data sources. As a data scientist, I've witnessed its transformative power firsthand. From structured databases to intricate APIs and cloud platforms, data integration weaves a seamless tapestry. ETL processes, like artists, extract, transform, and load data, ensuring its journey is meaningful. Data integration tools like Informatica and Talend become the instruments of orchestration, connecting disparate notes. In my journey, data integration is both science and art, weaving connections and creating meaning. From raw data to refined insights, its orchestration shapes the symphony of decision-making.

For optimal results in data space, it is recommended to identify your ingestion patterns and adopt a configuration-driven framework approach instead of creating point-to-point solutions. This approach allows for better flexibility and scalability, as the framework can be customized to fit the specific needs of the project. Additionally, it reduces the need for manual coding and maintenance, resulting in a more efficient and streamlined development process.

Orchestration encompasses activities to coordinate workflows, their underlying infrastructure, processes, teams, and dataflow. It covers a broad spectrum of control and visibility. For instance, you can control that process A runs in a specific way at a given time, and if A finishes without errors, you want to run B; otherwise, you want to run C. Orchestrators usually provide you with visibility about what happened, when, why, and allow you to take action on specific events, such as sending alerts on failure and triggering downstream processes. More comprehensive solutions collect metadata to provide an overview of dependencies between datasets, external systems they interact with, and pipelines that coordinate the underlying processes.

Data orchestration is a critical component in scheduling your data pipelines. Most commonly used orchestration tool is Airflow which provides the capabilities to schedule both batch and streaming processes. The main advantage is it provides alerting and monitoring features to track the health of your pipelines.

This is what I recommend to anyone who reaches out to me to transition to: 1. DE in 2023 is moving more towards low code or no code approach. 2. Sql was, is and will remain a DEs best friend. 3. Without python one might be able to code in Scala and Java but for orchestration of complex pipelines Python is the king of this jungle! 4. All DE work mostly will happen in some public cloud. 5. No one beats Spark in processing really big data . Happy data engineering!

Other than the mentions aspects, Data Security and Privacy are vital in data engineering. Protecting data from unauthorized access and ensure regulatory compliance has become crucial. Maintaining audit trails and continuous monitoring of data access and usage help detect and respond to suspicious activities. These measures build trust with users and stakeholders, protecting the organization from legal and financial repercussions associated with data breaches.

I think what's missing here is a mention of documentation / maintaining a data dictionary. In my experience, maintaining a repository with explanations of what the data means, where it comes from, and what to consider when interpreting it is essential. It's essential because it helps drive adoption among business stakeholders and it prevents misinterpretation of the data.

In addition to above, some concepts and principles that go long way in Data engineering- 1. Knowing fundamentas Data profiling to understand nature of data, type of data, frequency of data change. 2. Clarity with handson practice for handling Slow changing Dimension types (SCD0, SCD1, SCD2 are good enough) 3. Grasp of patterns established for common types of pipelines: File to DB, DB to DB, Webscrape to DB, Realtime to DB, DB to any other source and vice versa etc 4. Process logging frameworks and how to work with them (manually as well incase your CI/ CD isn’t working) 5. Various transformation types - incremental, full, transactional

The core concepts are often left out, giving much focus on learning tools - especially in the "Self-taught" and non-core CS realm of Data Engineers. For this reason we a left with Enthusiasts who only know how to perform some operations with gazillions of tools and claim the Data Engineering title. Data Engineering (or Engineering at all) is beyond tools. It's your ability to think and solve problems. You can make use of Spark? Cool. But do you know when to use it? Do you understand its architecture, and when to switch to some other tool instead? The "When you shoulds..." and "Why you shoulds.." should come along with the "How to dos..." when it comes to Engineering.