Fundamentals of Data Engineering

Data engineering is critical to business success in the age of digital disruption.

In fact, according to Harvard Business Review, it’s estimated that over 463 exabytes of data will be created each day globally by 2025. Which, essentially, means that there will be an upsurge in the need for data engineering in the years to come.

As the backbone of any data-driven organisation, data engineering provides a solid foundation for data analysis, machine learning, and other data-related applications. 

Being specialists in the world of data engineering, we would like to take you through a few of the fundamentals:

  • Its definition, 
  • The role of data engineers in organisations, 
  • Key skills and qualities of successful data engineers, and 
  • An understanding of data pipelines and their components, as well as the types of data pipelines.

Defining Data Engineering

Data engineers are responsible for designing and implementing the systems and processes that make data accessible. 

For who? Data analysts, data scientists, and other stakeholders.

It’s essentially the process of designing, building, and maintaining the infrastructure required to handle large volumes of data. 

This includes creating data pipelines to extract, transform, and load (ETL) data from various sources into a data warehouse or data lake, as well as managing databases, data storage systems, and data processing frameworks.

Simply put, data engineers make your data work for you.

(More on data engineering, here)

The Role of Data Engineers in Organisations

Data engineers play a critical role in modern organisations. 

So critical, in fact, that according to IBM, 80% of work in analytics projects is on data engineering and 20% or less is spent on actually deriving insights and using data science tools and techniques. 

Why are they so important? It’s simple. Data engineers are responsible for building and maintaining the systems and processes that enable data-driven decision-making. 

You know, the big decisions that impact customer return, employee churn, and ROI.

Working closely with data analysts, data scientists, and other stakeholders, they’re goal is to understand their data requirements and design data pipelines that meet those requirements. 

This includes ensuring that data pipelines are:

  • Scalable, 
  • Efficient,
  • Secure, and 
  • Compliant with data governance and privacy regulations.

Key Skills and Qualities of Successful Data Engineers

When you’re on the lookout for successful data engineers, it’s important to pay close attention to a number of factors.

  • Data engineers that complement your organisation:
  • Possess a combination of technical and interpersonal skills. 
  • Have a deep understanding of data architecture, databases, and data processing frameworks. 
  • Are proficient in programming languages such as Python, SQL, and Java, and have experience working with big data technologies such as Hadoop, Spark, and Kafka. 

Besides the technical skills, a good data engineer has excellent communication and collaboration skills. Simply due to the fact that they work closely with cross-functional teams to design and implement data solutions.

Understanding Data Pipelines and Their Components

Data pipelines are a series of processes that extract data from various sources, transform it into a usable format, and load it into a target data store. 

1. Data pipelines consist of several components, including data sources, data processing, and data storage. 

2. Data sources can include databases, APIs, and file systems. 

3. Data processing involves manipulating, cleaning, and transforming data to make it usable for analysis. 

4. Data storage includes data warehouses, data lakes, and other storage systems.

Types of Data Pipelines

There are two types of data pipelines: batch and streaming. 

Batch pipelines process data in batches, usually on a schedule. These pipelines are well-suited for processing large volumes of data that do not require real-time processing.

Streaming pipelines process data in real-time, as it is generated. Streaming pipelines are ideal for applications that require real-time processing, such as fraud detection, stock trading, and social media monitoring.

Getting Started With Expert Data Engineering

These are the fundamentals of data engineering. Data engineers play a key role in designing and implementing the systems and processes that make data accessible and usable. 

And their ability to manipulate and manoeuvre data becomes a major differentiator in the world of business and digitisation. 

That’s why you need to hire the right talent or take advantage of external help.

Every organisation will need help designing, building, and maintaining the infrastructure required to handle large volumes of data. 

We can help.

Our team of expert data engineers has the technical expertise and industry knowledge to help your organisation succeed in this data-driven world. We specialise in designing and implementing scalable, efficient, and secure data pipelines that meet your specific data requirements. 

Don’t let the complexities of data engineering hold you back from realising the full potential of your AI. Contact us today.

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