Businesses, no matter the industry, produce vast amounts of data. Unfortunately, data can’t be utilized in its raw form. This is where data engineering comes in. But what is data engineering? Simply put, it’s the process of designing and building systems that collect and analyze huge amounts of data.
The big data industry is expected to reach $103 billion by 2027. Moreover, the US Bureau of Labor Statistics predicts that the job market for data administrators and architects will grow by 8% between 2022 and 2032. This growth is above average compared to other professions and makes data engineering one of the fastest-growing jobs of the decade!
Additionally, Glassdoor data indicates the average salary of a data engineer is $113,000. That’s $40,000 more compared to the previous year. Sounds interesting enough to build a career in this field? This article can be your stepping stone to help you get started by understanding the fundamentals and basic concepts of data engineering.
Fundamentals of Data Engineering
Let’s dive a little deeper into the fundamentals of data engineering.
What is data engineering?
Data engineering essentially involves setting up processes, like data modeling, data pipelines, and data integrity checks—to clean and validate raw data that’s received. These are some of the tasks a data engineer is expected to execute on a daily basis. Here’s a quick example for a better understanding. Let’s assume an e-commerce company is dealing with their customer data. They might have separate systems in place for the following data:
- Customer information for support and behavior pattern
- Order history
- Billing and shipping
This information put together creates comprehensive customer data. However, these data sets are stored independently. Data engineering unifies these data sets so that we can get answers to important questions like “which product has the highest demand in a particular season?”
A lot of companies are already proficient in using data engineering processes to generate insightful information that can be easily analyzed.
Where does data engineering fall in the data life cycle?
In order to extract all the information from the gathered data, it needs to be cleaned and streamlined. Once this is done, interpreting the data becomes easier and faster. This process is vital for data scientists to carry on with their processes smoothly. In essence, a data engineer is responsible for identifying and clearing out any hurdles that a data scientist might face due to bad data.
Typically, data goes through the following stages:
- Generation
- Collection
- Storage
- Processing
- Management
- Analysis
- Visualization and interpretation
- Deletion
Data engineering takes place around the processing stage when the data has been collected and stored. A data engineer works on it right before the data scientist needs to step in. This places all data engineers right in between software engineers, who collect and store the data, and data scientists, who generate valuable information from processed data.
Important Concepts of Data Engineering
So far we have looked at the fundamentals of data engineering. Now, it’s time to explore some commonly used jargon and concepts in this industry:
- ETL/ELT: Engineers use data pipelines to transfer data from one point to another. These pipelines are usually structured as Extract, Transform, and Load (ETL) or Extract, Load, and Transform (ELT)some text
- Extract: This step involves connecting to the data source. For example, this could be an API or a file store.
- Transform: In this step, the engineer will standardize and deduplicate the data.
- Load: This step is just loading the data into a table in your data warehouse.
- Data modeling: A data model is a representation of the structure of relationships between various data elements. This is done using diagrams and schemas to represent different entities and their relationships. It helps ensure data integrity and enhance data performance
- Integrity checks: These checks are crucial as they can prevent the wrong data from being uploaded. Sometimes, data being loaded improperly will not fail if it has the same data type. Regular integrity checks help keep this problem at a minimum and can catch abnormalities early on. Here are a few types of integrity checks that should be performed:
- Null checks: To check what percentage of a column is null
- Anomaly checks: To check any drastic changes in fields or row counts
- Category checks: To check if the field has data that only belongs to a particular category
- Uniqueness check: To check whether any wrong join has caused columns to duplicate unnecessarily
- Aggregate checks: To check if any undesired change or removal of data has occurred
- Data processing: There are two ways in which a data pipeline can be processed, streaming or batch processing. Batch processing can be used for data that needs to be processed at regular intervals. Streaming should be used for pipelines that process data which needs to be updated in real-time.
- Data warehouse: A data warehouse is a central storage for data which can be used to analyze and make decisions. Here the data is stored in a secure, organized, reliable, and easily retrievable manner. Tools like Amazon Redshift, Google BigQuery, and Snowflake can be used for data warehousing.
- Data lake: A data lake stores data in its raw form until the data is required for analysis. Also, data lakes are a bit more cost-effective compared to data warehouses. Some tools that can be used for this are Amazon S3 and Amazon Data Lakes.
- Data lake house: This was introduced to merge the benefits of data warehouses and data lakes into a single solution. They provide ACID (Atomicity, Consistency, Isolation, and Durability) functionality like data warehouses while maintaining the flexibility and scaling features available in data lakes.
Differences and Commonalities Between Data Engineering and Data Science
The roles of a data engineer and a data scientist are distinct yet closely linked. Both of them are responsible for generating meaningful insights from the data while their roles, responsibilities, and skill sets used during this process are mostly different.
Responsibilities
Data engineering: A data engineer is supposed to develop, construct, test, and maintain architectures like databases and processing systems. The data this deals with is unformatted and can contain system-specific codes. Data engineers work on deciding and implementing a system that improves data quality, reliability, and efficiency to resolve data issues. For example, converting these system-specific codes into information that data scientists can process. Once all this is done, the engineer will set up processes for data modeling, mining, and production for passing on the data to data scientists.
Data science: A data scientist receives clean and formatted data. They then feed it to analytical programs and machine learning methods to prepare it for predictions and insights. Once all the insights are generated, the scientist is responsible for regularly presenting them to key stakeholders.
Tools and languages
Here are some tools used by data engineers on a regular basis:
- SAP
- Oracle
- Cassandra
- MySQL
- PostgreSQL
- MongoDB
- Hive
Conversely, below are a few tools a data scientist might use:
- SPSS
- R
- Python
- SAS
- Stata
- Tableau
- Excel
There’s also a few types of software used by both in some capacity:
- Scala
- Java
- C#
Education and skill set
Another thing that a data engineer and data scientist share is a background in computer science. Going deeper, a data scientist will have more knowledge about econometrics, statistics, mathematics, and research. Whereas, a data engineer usually comes from an engineering background. Their knowledge mainly constitutes computer engineering concepts and practices.
Overlap with data science
As we have seen throughout this article, the roles of a data engineer and a data scientist are quite distinct. However, they do work in collaboration. Data engineers create the structure and architecture for the quality data that scientists work on. Data scientists on the other hand, give feedback on the quality of data to help refine processes. Today, some people do possess hybrid skills and work in both roles for many companies.
Future of Data Engineering
Data engineering is mainly used in e-commerce and healthcare industries where data is indispensable. Here’s the future scope of this industry, if you choose to pursue a career in it.
Predicted demand for data engineers
The demand for data engineers is predicted to surge in the next couple of years. Data is here to stay and will always have crucial insights for companies to make the right decisions. Any business who wants to cater rightly to their customers must incorporate it as a part of their processes.
Data engineering implementations in the future
Here are a few important problems that can be solved by data engineering for a business:
- Performance & scalability: Implementing architectures and scalability strategies help the scaling demand for ever-increasing data.
- Data quality: The data validation and cleaning techniques employed during data engineering enhance data quality.
- Data privacy & security: Data engineering will also focus on implementing security protocols to ensure data safety.
Impact of AI on data engineering
Across the board, you will see that AI is disrupting many industries today. Consequently, it also has an impact on data engineering. However, it won’t be able to take over data engineering jobs due to some important reasons:
- AI cannot devise creative and imaginative solutions usually required in data engineering.
- It’s difficult for AI to comprehend the context behind several data sources which is an important factor in implementing data engineering processes.
- Data engineering sometimes requires complex decision-making, a task that still requires human expertise.
- It’s almost impossible for AI to incorporate the legal and ethical implications of data usage as they involve a certain level of judgment.
Data Engineering with Acceldata
Now, you probably have a good idea of how crucial data engineering is for any progressing business. Data engineering not only helps you get important insights from the data, it also helps keep your data secure and organized. To ensure this level of quality, all your data engineering requirements should be serviced by a trustworthy resource. Schedule an Acceldata demo today to address any of your data observability and engineering needs.
Summary
Data engineering is the process of designing and building systems to collect and analyze large amounts of data. It involves tasks like data modeling, data pipelines, and data integrity checks. Data engineers work closely with data scientists, ensuring data quality and preparing it for analysis. With the growing demand for data-driven insights, data engineering is a rapidly expanding field with promising career opportunities.
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