Data Engineering: Key Guide To Data Integration

It's difficult to think of an industry that hasn't been transformed by data science in the modern era. Although many people are unaware of the complexities of the data science discipline, they are aware that it is a growing field. People check their email for individualized discounts, use Siri to get instant answers to their questions and rely on their bank to detect and mitigate potential fraud activity.

 

While we enjoy the fruits of data science's laborers, other players are hard at work behind the scenes. These employees are in charge of building the data pipelines and warehouses that allow data scientists to write and maximize algorithms to improve our daily lives.

 

Introduction to Data Engineering

 

Data is all around us. With the increasing number of connected devices and their widespread use in recent years, the world has generated a massive amount of data that cannot be handled using traditional methods.

 

According to Statista, there will be 23.8 billion internet-connected devices by 2021, with 58% of those being IoT devices (smart home devices, connected self-driving cars, industrial devices, and so on) and the remaining 42% being non-IoT devices (laptops, smartphones, etc.). This means that data can now come from a variety of sources. Among these are:

 

1. User activities: Any user action generates data, and even data indicating where and how frequently we tap on the mobile applications we use is valuable. Every piece of data containing this type of information must be saved and distributed to the data centers of the applications.

 

2. "Internet of Things" devices: These devices generate massive amounts of data, such as sensor data generated in numerous locations. All of this data must be retrieved and sent to a data pool for analysis.

 

3. Program logs: Today's computer applications are made up of numerous components, each of which generates a program log. These logs are routed to a data engineering pipeline for further investigation.

 

The value of conclusions drawn from large datasets is proportional to the integrity of the data. Data scientists cannot make accurate predictions without an architecture that can structure and format growing and changing datasets. This is where data engineering can help.

 

The act of gathering, translating, and validating data for analysis is known as data engineering. Data engineers, in particular, create data warehouses to facilitate data-driven decisions. The foundation for real-world data science applications is laid by data engineering. When data engineers and data scientists collaborate, they can consistently deliver valuable insights.

 

Also Read | What is Data Wrangling? All you need to know

 

What do Data Engineers do?

 

Data engineers build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret in a variety of settings. Their ultimate goal is to make data accessible so that businesses can evaluate and optimize their performance.

 

In the world of AI and data, data engineering is a rapidly expanding field. But you might be wondering what a Data Engineer does. Based on information shared by industry coaches Nana Essuman and Femi Anthony during the Black and Brilliant AI Accelerator program, we'll shine a spotlight on the role of Data Engineer here. Condé Nast's Nana is the Director of Data Engineering, and Capital One's Femi Anthony is a Lead Data Engineer.

 

Data Engineers, at a high level, play an important role in assisting businesses in making data-driven decisions by collecting, transforming, and publishing data. Data Engineers create the databases that house a company's data behind the scenes. They create pipelines that transform raw data into formats that Data Scientists can use. They also build the infrastructure that automates model creation for machine learning and analytics.

 

A data engineer's role is as versatile as the project necessitates. It will be proportional to the overall complexity of a data platform. When you look at the Data Science Hierarchy of Needs, you can get a sense of a simple idea: the more advanced technologies involved, such as machine learning or artificial intelligence, the more complex and resource-intensive data platforms become.

 

Let's quickly outline some general architectural principles to give you an idea of what a data platform can be and which tools are used to process data. A data infrastructure serves three main purposes.

 

1. Data extraction:

 

Since the information is somewhere, we must first extract it. In terms of corporate data, the source can be a database, user interactions on a website, an internal ERP/CRM system, and so on. A sensor on an aircraft body could also be the source. Alternatively, the data could come from publicly available online sources.

 

2. Data storage/transition:

 

Storages are the primary architectural point in any data pipeline. We must save the extracted data somewhere. The concept of a data set is used in data engineering. The warehouse represents the ultimate storage location for all data collected for analytical purposes.

 

3. Transformation:

 

Due to the raw data being difficult to analyze, it may not make much sense to end users. Transformations aim to clean, structure, and format data sets to make them usable for processing or analysis. It can now be taken for further processing or queried from the reporting layer in this form.

 

A data pipeline's traditional architecture revolves around its central point, a warehouse. However, the presence of unified storage is not required, as analysts may use other instances for transformation/storage. They can also use no storage at all. As a result, the data pipeline architecture is defined by the number of instances that exist between the sources and data access tools.

 

Also Read | How are Big Data and AI Working Together in the Modern World?

 

Data Engineering Trends

 

Having a data-driven decision-making process was once reserved for multinational corporations. Companies of all sizes can generate and analyze massive amounts of data thanks to cloud computing and the ongoing democratization of technology.

 

Some of the upcoming places where Data Engineering is being used massively:

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Data Engineering Trends

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1. AI-Assisted Development:

 

The use of artificial intelligence to automate manual labor and repetitive tasks is growing, which is great news for data engineers. With this in mind, data engineers can use AI to handle repetitive tasks in the field of quality assurance. It will free them up to concentrate on their core competencies, such as software development and problem-solving.

 

Data engineers can use behavior-driven and test-driven development techniques to train AI in coding in addition to automating repetitive tasks. It will allow data engineers to concentrate on other aspects of their jobs while still ensuring that their code is of high quality.

 

2. Software creation:

 

Data Engineers are the new software engineering rockstars. They employ many of the same tools that software engineers employ for a variety of purposes. They face the same challenges as software engineers, such as building and executing data pipelines.

 

The primary distinction between Data Engineers and software engineers is that Data Engineers specialize in working with data. That is, they are frequently in charge of gathering and storing data from various sources, such as web protocols like HTTP/3 or blockchain technology.

 

They collaborate with internal and external systems to collect data that can be used to develop new products and services as well as improve existing ones.

 

3. Automation of Data Engineering:

 

Data engineering is a thriving industry, but it is still lagging behind the rate of change in the data landscape.

 

Agile Data Engineering tools are emerging to address the data pipeline's repetitive tasks. These tools automate much of what was previously done manually, allowing data scientists to focus on solving problems through automation and machine learning rather than spending the majority of their time on repetitive tasks.

 

DataOps tools contribute to this process by automating DevOps practices such as automation, continuous delivery, and agile development. The goal is to increase agility and reduce defects, ultimately increasing productivity across the board.

 

4. Observability of Data:

 

Data engineering is concerned with the creation, maintenance, and optimization of data pipelines, which are methods of moving data from its sources to the end state of the consumer. Although the workflow in these data pipelines is now standard (consisting of known steps to perform data extraction, transformation, and loading), this process is extremely sensitive to changes in the data, whether in its structure or values, and these changes can directly affect the availability of the data pipeline, causing failures and rendering it unavailable. So, this is where Data Observability comes in.

 

Data Engineers require a method to operationalize any instant detection that exists within our data pipelines. In other words, as data pipelines grow in size over time and we are constantly required to handle massive amounts of data, we require automated tools to prevent downstream failures. For example, if a data pipeline fails, we must identify the problem immediately and alert the Data Engineering teams to resolve the issue. As a result, Data Observability is one of the hottest topics in Data Engineering today.

 

5. Data Accuracy:

 

Data Quality is a method of assessing the accuracy and dependability of data used and generated within an organization's data pipelines. The Data Quality process is not immune to the control of activities carried out by a company, where process improvements are made regularly. Similarly, data must be kept under the domain areas' control and go through this cycle of constant evaluation and refinement.

 

When working with data, we must name priorities, which means determining which data are most critical and relevant to the business and which applications the data will be part of. This analysis will guide the processing and use of data in decision-making, allowing the process to be optimized by cleaning the data and separating the useful from the ones that can cause noise in the analyses.

 

Also Read | 8 Benefits of Blockchain in Big Data Transformation

 

Conclusion

 

The definition of Data Engineering is as follows: It is a terminology used for collecting and validating quality data so that Data Scientists can use it. It is a vast field that includes various modules and data steps such as data infrastructure, data mining, data crunching, data acquisition, data modeling, and data management.

 

As a result, a single Data Engineer cannot work across the entire spectrum of skills. In this blog, we will outline the specific roles that a Data Engineer performs based on the employer's requirements.

 

Scale and efficiency are central to data engineering. As a result, Data Engineers must constantly update their skill set to facilitate the process of leveraging the Data Analytics system. Data Engineers are often seen collaborating with Database Administrators, Data Scientists, and Data Architects due to their extensive knowledge.

 

Without a doubt, the demand for skilled Data Engineers is increasing at an alarming rate. If you get a kick out of the building and tweaking large-scale data systems, Data Engineering is the career path for you.