What is the data engineering process and what does it look like in practice?

There is a lot of data in business. According to Dice’s ¹, the number of data engineering job listings has increased by 15% between Q1 2021 to Q2 2021, up 50% from 2019. Also, data engineering can help scale your business and gain valuable insights.  

According to a Gartner, Inc. survey, 80% of executives think automation can be applied to any business decision. So, data engineering concepts are strongly associated with business. Moreover, survey shows that "enterprises are shifting away from a purely tactical approach to AI and beginning to apply AI more strategically," - Erick Brethenoux,  VP analyst at Gartner.  

In the 1980s, the term "information engineering" was used to describe database design and incorporate software engineering into data analysis. From the 1990s to the 2000s, after the rise of the Internet, "big data" emerged. However, at the time, his DBAs, SQL developers, and his IT professionals working in this field were not called "data engineers."

Nowadays, computers are essential for data management in business. You may not be aware that something is tracking new records of information about your particular activity. Therefore, it is possible to analyze the vast amount of data available to businesses in order to learn as much as possible and improve factors such as cost-related procedures, customer reviews, or global human health.

What is data engineering?

The process of creating and constructing systems that enable users to gather and evaluate unprocessed data from many sources and formats is known as data engineering. These technologies allow users to discover valuable data platforms for the business to thrive.  

There are typically several different forms of operations management software (such as ERP, CRM, production systems, etc.) within large organizations, all containing databases with various types of information. Data engineering aims to simplify the analysis and use of data for decision-making. Collecting data from different locations, arranging it in databases designed to store vast amounts of information, organizing the data for effective use, and presenting it in an easy-to-understand.

Today, the data engineer job is at the core of every business. Businesses learn data engineering to address issues with their operations, as they:  

• Aim to make data easily accessible and available for data scientists and business intelligence engineers (or anyone working with data)
• Optimize the big data system architecture of companies
• Design and configure databases
• Collaborate with teams of business intelligence engineers, data scientists, and data analysts
• Explore and transform data in order to perform all these tasks. Data engineers first need to set up ETL pipelines.

Many data engineering pipelines connect data in most organizations, and each system often employs a unique technology and has an individual owner inside the company. They take the information about customers and analyze data.  

Data engineers vs. data scientists?

At first glance, it may be thought that both names are just synonyms, but these are two different jobs with separate responsibilities. Data engineers play a role in technical activities like coding and data warehousing skills. On the other hand, data scientists do data analysis and need business intelligence skills.  

Data engineers must design and build the data architecture, systems, and procedures required to gather, store, process, and integrate significant amounts of data from many sources. They work with raw data which contain human, machine, and instrumental errors, and therefore usually, unformatted and written in specific code. Their responsibilities include improving the reliability, efficiency and quality of data. They need to know many tools and languages to marry the system together or extract new data from other systems and try to find ways for system-specific code for data scientists.  Strong knowledge of database technology, computer languages, and software development methodologies are essential for data engineers.  

Data scientists typically already have data that has gone through the first round of cleaning and manipulation. The data science team must be well-versed in machine learning, statistics, and data visualization. Data scientists are in charge of studying data to draw conclusions and address business issues. They employ statistical and machine learning algorithms to develop predictions and spot trends in the data. If data scientists have performed their analysis, they should present the results to key stakeholders. If the results are accepted, they should ensure that the work is automated. Then, the vision can be shared with business stakeholders.

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What about the data engineering process?  

The term "data engineering" refers to a set of procedures that transform a substantial amount of unstructured data into sound output that analysts, data scientists, machine learning engineers, and other professionals can use. The data processing makes up an end-to-end workflow in most cases.  

Data ingestion (acquisition) is transferring data from many sources, including SQL and NoSQL databases, IoT devices, websites, streaming services, etc., to a target system where it is transformed for additional analysis. Data can be both structured and unstructured and arrives in many different formats. Data transformation entails cleansing data of mistakes and duplications, normalizing it, and transforming it to the required format. What is more, Data serving provides converted data to end users, such as a data science team, dashboard, or BI platform.  

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Why are data engineering skills needed in the project?

First, we should look at the responsibilities of the data engineer job. This may help you estimate if it's necessary to hire data engineers.  

• Design and implement effective database solutions and models for storing and retrieving enterprise data.  
• Examine and identify structural database needs by evaluating client operations, applications, and programming.  
• Evaluate database implementation procedures to ensure compliance with internal and external regulations.
• Install and organize information systems to ensure the company's functioning.  
• Produce accurate database design and architecture reports for management.  
• Monitor data migration from legacy systems to new solutions.  
• Monitor system performance by performing regular testing, troubleshooting, and integrating new features.  
• Respond to system failures promptly and provide support.

As you can see, data engineers don't have a narrow focus; they have to be competent in several areas. Designing data requires being well-rounded in knowledge and skills. Data engineering has an essential role in the era of big data. Businesses have access to vast amounts of information from both the physical and digital worlds.  

What about CRISP DM?

The most popular methodology for data mining, analytics, and data science projects was published in 1999 to standardize data mining methods across industries. The Cross Industry Standard Process for Data Mining (CRISP-DM) is a process model that serves as the basis for the data science process. The best outcomes will probably be achieved by data science teams that combine a loose implementation of CRISP-DM with comprehensive team-based agile project management techniques.  

It has six sequential phases, which we'd like to describe in detail in the paragraph below:

6 CRISP DM Phases  

As you can see the process of processing data and preparing it for various purposes requires a lot of work and knowledge of the tools needed to perform such operations. There is a reliable and repeatable process that may simplify the data science background. In projects managing data science CRISP DM circle is a common solution used in the IT industry. In some stages data engineering is needed. There are two different data engineering roles within CRISP DM:

• Firstly, there are data engineers with cloud/platform experiences. Their focus is on managing and governing the data on an on-premise or cloud platform. In their range of responsibilities, platform security, configuration, and resource management is included.  
• Secondly, there are data engineers with data/AI product experiences. They are working closely with data scientists in the data product projects.

Before starting a project is essential to know the client's needs. This CRISP DM model can Encourage data miners to focus on their business goals to ensure that project outcomes will bring tangible benefits to the organization. However, analysts can forget about the ultimate business purpose of their analysis – the analysis can become an end in itself rather than a means to an end. So, the CRISP DM circle ensures that the business perspective is still in the project’s area.  

The Business Understanding phase focuses on recognizing the project's goals and specifications. When they focus on what the problem is, what model to apply, and begin gathering as much information from the dataset as possible, some data scientists may tend to neglect this element or this crucial step of learning about the business from a business perspective. Remember that understanding how to articulate a business challenge is a problem in and of itself. The business understanding phase has certain goals:  

1. Determine business objectives.  

2. Assess situation.  

3. Determine data mining purposes.  

4. Produce project plan.  

The next phase is data understanding. In addition to strengthening the basis of business understanding, it concentrates on finding, gathering, and analyzing data sets that might assist you in achieving project objectives. The data understanding, similar to the previous phase, has certain goals:

1. Collect initial data.  

2. Describe data.  

3. Explore data.  

4. Verify data quality.  

The third phase of CRISP DM circle is the data preparation phase and includes all actions used to create the final dataset from the initial raw data so that it can be processed further. The steps involved in data preparation will probably be taken more than once and not necessarily in that order:

1. Select data.

2. Clean data.  

3. Construct data.  

4. Integrate data.  

5. Format data.  

Another phase is modelling, which is responsible for choosing the modelling approach, such as regression, machine learning or time series analysis. In this step, the specialist defines the variables to attach to the model, specifies the model structure and parameters, and estimates or fits the model to the data. There are a few steps that should be considered in the modelling phase:

1. Select modelling techniques.  

2. Generate test design.  

3. Build model.  

4. Assess model.  

The penultimate phase in this diagram is evaluation. If you already choose and run the model, it's time to evaluate the result and check if this model works or not. Also, the evaluating phase can help define why the chosen model is appropriate: 

1. Evaluate results. (business success criteria)  

2. Review process data.  

3. Think about the next steps.  

The last phase is finally the deployment. Data Science Process Alliance states, "Depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data mining process across the enterprise." Some activities should be done to conduct the deployment phase:  

1. Plan deployment  

2. Plan monitoring and maintenance  

3. Final report and presentation  

4. Project review

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Final thoughts about data engineering

Data engineering fills this gap by offering the procedures and infrastructure required to gather, arrange, and convert unorganized data into a format that can be used. It entails developing and maintaining data integration systems, data warehouses, and pipelines that facilitate effective data storage, retrieval and analysis. Nowadays, companies and enterprises deal with growth in data volume, variety and velocity. Also, some of us are concerned as to whether their data and personal information are safe.  

1. https://marketing.dice.com/pdf/2021/Dice_Q2_Tech_Job_Report_Technologist.pdf