Essential Skills For Data Engineering in 2023
Data engineering is a rapidly expanding field that’s fueled by a demand for data-driven decision making. This career path involves designing and building data pipelines to move data from source systems into data warehouses, data lakes, and other storage and processing solutions.
Data engineers need strong programming skills and a familiarity with big data technologies like Hadoop, Spark, and Kafka. They should also be knowledgeable about cloud computing and distributed systems.
1. Data Science
Data science is an increasingly important field as companies rely on large amounts of data to improve business processes. It also helps organizations manage financial risks, detect fraudulent transactions and prevent equipment breakdowns.
Data science requires a combination of technical skills, including data preparation, data mining, predictive modeling and machine learning. However, it also involves soft skills that include business knowledge, curiosity and critical thinking.
2. Machine Learning
Machine learning is a subset of artificial intelligence that gives computers the ability to learn from data and past experiences without programming. This enables machines to spot patterns and make predictions about future actions and behavior.
Organizations across a wide range of industries use machine learning techniques to tackle complex challenges. Some popular applications include customer relationship management (CRM), financial monitoring and fraud detection.
3. Big Data
Data is the lifeblood of businesses. It helps them improve operational efficiency, create personalized marketing campaigns and boost sales.
In 2023, companies will be challenged with massive amounts of varying types of data (volume) coming from multiple sources. This includes files, images, videos, sensors, system logs and more.
Managing and processing this information requires extensive data engineering skills. This means understanding a range of technologies, including NoSQL databases, Apache Spark systems and business intelligence (BI) platforms.
4. Data Visualization
Data visualization is the process of transforming raw data into visually-appealing presentations that can help analysts and executives understand information in an easier way.
Visualizations can also help to tell stories by removing noise and highlighting trends and anomalies in the data.
This skill is highly valued in the professional world, whether it’s used in a creative role or as part of a data analyst’s toolkit. It’s a great way to demonstrate your expertise in both analysis and storytelling.
5. Cloud Computing
In the world of cloud computing, software and IT services are accessed over the Internet. This allows organizations to scale up or down as they need them without purchasing servers, storage, or networking equipment.
Data engineers who master cloud computing can help businesses get more from their technology investments. This includes delivering analytics solutions at scale, ensuring business continuity and disaster recovery (BCDR), and reducing the cost of IT infrastructure.
6. Data Warehousing
Data warehousing is the process of collecting and storing current and historical data to support business intelligence (BI) applications. It provides access to data sets that may have previously been locked away in legacy systems, and facilitates reporting to business executives quickly and efficiently.
Data warehouses are an essential tool for a variety of industries. For example, the investment and healthcare sectors use them to analyze customer trends and market movements. They also help companies strategize and predict outcomes, generate patient treatment reports, and share data with tie-in insurance providers and medical aid services.
7. Automation
Automation is the process of using software or hardware to carry out a task that humans normally do. This can help cut costs and boost efficiency in a business.
Data engineers should have a strong understanding of automation, especially when it comes to designing and building data pipelines. This can include setting up triggers between tools to ensure everything is working smoothly.
8. Data Integration
Data integration is the ability to synchronize different digital tools and technologies into a single, accessible platform. It enables businesses to optimize their operations and provide actionable business intelligence.
It requires significant transformation and mapping of data from multiple sources to produce a single, unified view of it. In addition, it requires robust security and quality to ensure reliable insights.
9. Data Modeling
Data modeling is a critical first step in defining the structure of available data. It helps to ensure consistency in naming conventions, rules, semantics and security while ensuring the quality of data.
It is used in a variety of cases such as database design, system database architecture and software engineering. It is also a critical component of data architecture processes that document data assets, map how data moves through IT systems and create a conceptual data management framework.
10. Python
Python is a popular and powerful programming language that can be used for data science, machine learning and web development. It’s a language that is easy to learn and is highly scalable.
It is also portable and extendable to run on a wide range of hardware platforms. It also has a large community that can help new users quickly get to grips with the coding lingo.
