Is Data Engineering Used in Machine Learning?

90% of the world’s data was created in the last two years alone and by 2025 we shall be producing up to 463 exabytes of data every day. Big data has revolutionized the way we live our lives and conduct our business and live our lives. This kind of explosion demands capable technology that can process vast amounts of data in real-time to meet business needs and enable informed decision-making.

The demand gives data engineering a special role to become the fastest-growing field. All of a sudden enterprises have realized that they need data engineers to make the most of data as the demand for their skills spikes. Data engineers design and build technologies for collecting, processing, storing, and analyzing big data at scale using big data technologies and distributed systems. 

A data engineer typically has an undergraduate degree in science, mathematics, statistics, computer science, or a programming background in languages like Python, Scala, R, or Java, as well as a data engineering certification to demonstrate their knowledge and skills. Some will have years of experience and a well-packaged project portfolio highlighting their notable achievements and expertise. Data engineering has found its application in all if not most industries as organizations seek out the most suitable talent and technology to manage the data that they generate every day. 

On the other hand, machine learning engineering is a specialized type of data engineering that combines data science, machine learning, and data engineering fields. The ML engineer designs and builds AI systems that run and automate ML models. 

The role of a data engineer 

The role of a data engineer cuts across various industries. They are responsible for collecting, processing, and managing data by extracting valuable insights and information that enterprises require for strategic decision-making. Overall, data engineers make data easily accessible as and when needed by building systems that collect, process, and analyze data. 

Data engineers design, build, manage, and maintain data pipelines, infrastructure, and platforms as well as dataset processes that discover trends and hidden patterns in data. They will use prototype models designed by data scientists and scale them for production-level models that can be implemented for much larger volumes of batch and real-time data. 

How is data engineering useful in machine learning 

Whether it is the automation of processes for improved efficiency, image recognition during hiring, prediction for strategic decision-making, or personalized product recommendations for more conversions and a better customer experience, the power of machine learning cannot be underestimated. Machine learning is a powerful technique employed to leverage the vast amounts of data generated in enterprises to build models that can make accurate future predictions to give enterprises an edge in competitive markets. 

Data engineering is useful in the artificial intelligence and machine learning fields. As enterprises extend their capabilities to take advantage of AI and machine learning, data engineering will be the field that designs and delivers the frameworks, infrastructures, and architectures used for processing and modeling data. Data engineers and machine learning professionals certainly work collaboratively because like data scientists, machine learning professionals depend on data engineers to design and build the systems that they will use to facilitate ML processes. Data engineers implement machine learning algorithms recommended by ML professionals for the production environment. 

Some of the roles of data engineering in machine learning are:

■ Data engineers collaborate with machine learning engineers to develop data pipelines for processing and distributing data. Data pipelines streamline data flow from multiple data sources including NoSQL systems, cloud applications, internet and social media feeds, into data lakes or warehouses, to data analysis and modeling applications. 

■ They import the appropriate machine learning and statistical techniques that they will use to develop models used for predictive or descriptive purposes depending on business needs. 

■ They support the automation of tasks and processes to increase efficiency in service and product delivery. This is by building process automation infrastructure. 

■ They are responsible for ensuring high data quality is used. Thus, they are always finding ways to ensure data reliability and validity through techniques like cross-validation. 

■ Data engineers play an important role in feature engineering. This is the process of selecting and transforming the required variables (features) of raw data to be integrated into machine learning algorithms like predictive models to improve their performance and accuracy. Feature engineering involves four steps which are feature creation, transformation, extraction, and then selection. 

Top data engineering skills 

As their title suggests, data engineers are mostly concerned about the architecture, infrastructure, and frameworks for data processing and analysis. For this reason, their most important skills are those that border on data architecture and infrastructure. These include: 

■ Knowledge of database solutions like SQL and NoSQL and how they work 

■ Knowledge of data transformation tools that transform data from their raw state as obtained from various sources into required formats to ease analysis. Such tools as Talend, InfoSphere DataStage, and Hevo Data are popular data transformation tools. 

■ Knowledge of data warehousing and ETL tools like Hevo Data, AWS Glue, Stitch, and others. ETL (Extract, Transform, Load) processes data from multiple sources, converts it to the required format for analysis, and then loads it onto the data warehouse or data lake. 

■ Knowledge of various analytics techniques and Hadoop analytics tools such as Hive, MapReduce, and Hbase.   

■ Programming. Knowledge of at least one general programming language like Python, Java, C, or Scala. 

■ Machine learning knowledge is important for data engineers as the world warms up to the power of AI. Data engineers are depended upon to design and build customized solutions for various platforms and operating systems.

■ Knowledge of operating systems like Windows, UNIX, Mac OS, Linux, and Solaris. 

Conclusion 

In the coming years, the world is expected to produce even more data. Industries will demand even more robust real-time data processing solutions to make the most of the data available to them as the data engineer role becomes more central to business operations.

The AI, machine learning, and deep learning fields will not advance without data engineers to build scaling distribution systems and models through which the vast volumes of data will be channeled. This with the core objective of leveraging insights and hidden patterns in data for strategic decision-making. The future of machine learning is on IoT as one of the biggest sources of big data and engineers need to align with the current technology and data trends.

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