Data engineering is a combination of technical expertise, business savvy and creativity with one goal: to help companies glean valuable insights from their information. The job is in high demand, according to a recent Dice study. The report predicted data engineering to be one of the fastest-growing jobs in technology, with a predicted 50% year-over-year growth in the number of open positions. The field requires a variety of skills such as consulting, database design, programming and statistical analysis. This article discusses the most in-demand data engineering skills you’ll need to get started in this rewarding career field.
1. Database Design, Implementation and Optimization
Database design refers to the process of designing database schemas and tables based on requirements or business rules. It involves deciding whether to use a relational or an object-oriented design, determining what type of database to use, and identifying the information elements that will be used.
Database design is a critical data engineering skill because databases underpin an organization’s information strategy. A properly planned database has the structure and functionality necessary to:
- Store information reliably
- Provide accurate information output for loading into other systems, such as BI tools
- Execute complex queries that load quickly
A poorly designed database can lead to many problems, including poor performance, information integrity issues and security issues. In effect, a poorly designed database renders a company’s information unusable.
Database implementation involves installing database software and performing configuration, customization and testing. The implementation also entails integrating the database with applications and loading initial data. The data could either be new data captured directly or existing data imported from another data source.
Optimization refers to strategies for reducing system response time. This is especially important as organizations collect massive volumes of data each day. The increased load could slow the database. To ensure the system runs at peak performance, the engineer must frequently monitor and optimize the system.
You may be wondering how all of this is different from a database administrator’s role. The key difference is that a database administrator focuses mostly on database functionality. An engineer needs to understand how the business plans to use the information. Understanding this information helps them determine the best technology and structure for the information.
2. Data Modeling
Data modeling is a process for analyzing and defining how a business plans to use its information. It is a valuable data engineering skill because it outlines which business processes depend on the information and where it comes from. Performing this process ensures the information meets the business requirements. Engineers use a process known as data modeling to outline this information.
Data models are representations of an organization’s information. It is also used to model and map the relationships between concepts or entities that exist in the company’s systems. Modeling can be categorized as conceptual, logical or physical models. Conceptual modeling helps identify how information should be organized for maximum usability. Logical modeling defines how the computer system should store the information. Physical modeling is the most detailed model. It is an actionable blueprint for those who need to build the database.
3. Extract, Transform, Load (ETL)
Most organizations’ information exists in silos and disparate systems. The engineer’s job is to figure out how to consolidate that information to meet business requirements. They do this through a process called Extract, Transform, Load (ET). ETL describes the stages that information goes through to be processed in a data warehouse.
Extract: Extract involves retrieving raw data from source systems such as relational information sources and information from unstructured sources such as social media posts or pdfs.
Transform: At this stage, the information must be converted to a standard format to meet the schema requirements of the target database. The level of transformation required depends solely on the information extracted and business requirements. The transform step includes validation and rejecting information that doesn’t meet requirements.
Load: Transferring the information to the destination system.
Data manipulation skills are important for this process. Often, the engineer needs to run queries to validate the information in the system. To do so, they must understand database programming languages such as SQL and NoSQL.
4. Programming and Scripting
Programming is the process of designing, writing, testing and maintaining instructions that tell a computer what to do. This data engineering skill is important because sometimes the engineer will need to write custom programs to meet business requirements. There may be times when a requirement can’t be met using existing technology. At which point, the engineer needs to create a solution.
5. Data Visualization
Business users don’t want raw information. They need to understand the information in plain terms and how they can use it to help with their business strategy. Data visualization is the process of representing information in a way that’s easy to understand. It is a great technique to communicate the findings to stakeholders. The most common types of visualizations are histograms, line graphs, bar graphs and scatter plots. They’re used to show how data has changed over time or how different variables relate to each other.
Data visualization tools are a type of application that collects and prepares information for stakeholders to review. These applications are sometimes referred to as business intelligence (BI) tools. Their primary function is to make sense out of volumes of raw information by providing insight through graphical representations. A few of the most common visualization tools include Tableau, Power BI, D3 and Plotly.
6. Communication and Consulting
The engineer’s role is not solely technical. As experts, they play a critical role in helping companies get the most value from their information. As such, they need to serve as consultants. Their role as consultants involves evaluating the business requirements to:
- Determine if the requirements can be met
- Determine how best to meet those requirements
- Negotiate with stakeholders to prioritize requirements
- Help stakeholders understand the risks involved in the approach
Once the engineer makes their recommendations, they need to present those options to stakeholders. The engineer needs to communicate with stakeholders who may not be familiar with the technology. This is an important data engineering skill because the engineer must clearly and patiently explain how their solution meets the requirements.
7. Statistical Modeling
Statistical modeling is the process of constructing a mathematical function that describes an observed set of data. The engineer uses this model for predictive analytics.
Predictive analytics is the process of using information from past events to predict future outcomes. This is especially helpful for modeling human behavior based on previous transactions or interactions with other humans. It relies heavily on probability theory, machine learning techniques such as:
- Decision trees and random forests
- Linear regression
- Time series analysis
- Hidden Markov models
- Bayesian networks
- Clustering algorithms
One of the most common use cases for statistical modeling and predictive analytics is market analysis. Businesses use statistical analysis and predictive modeling to glean insights about how their markets are changing, such as where the most promising opportunities lie. Using information gathered from sales records and other sources, they can predict likely future business outcomes; this is called forecasting. Businesses may also use analytics or predictive models to find patterns in the historical behavior of customers that will help them predict what those same customers might want in the future. For example, by analyzing purchasing habits through retailers’ websites a company can determine new products to offer.
8. AI and Machine Learning
Artificial Intelligence is a computer science term. Artificial intelligence refers to systems that can do things without human input or independently of humans. This can include tasks such as learning, decision-making, and problem-solving.
Machine Learning (ML) is the process of building a computer program that can learn from, analyze and make predictions about data. Machine learning techniques include gathering information to create accurate models to recognize patterns in data. There are two types of ML.
The first type is supervised machine learning. This form of ML takes a set of sample data and tries to find an output rule that matches it. Unsupervised machine learning is used when there isn’t a clear target in mind but instead seeks patterns within raw information through techniques like clustering and outlier detection. A few use cases for AI and ML include:
- Predicting how much of a price increase the market can tolerate
- Predict the likelihood a customer may be late on their next payment
- Predict the customers most likely to leave
9. Cloud Computing
Engineers work with massive amounts of information. Companies need a cost-effective system to store this information. It can be expensive to purchase the hardware and software to support their information storage requirements. A more cost-effective solution is cloud computing.
Cloud computing refers to the delivery of computing resources over the internet. Using cloud computing, companies can rent physical servers, storage and databases from cloud providers. This lets companies quickly add more computing resources as needed. Typically within minutes, as opposed to the days it would take to provision a physical server. Providers charge a pay-per-usage model, so companies won’t waste money on resources that aren’t being used.
- Infrastructure as a service (IaaS): IaaS refers to the renting of IT infrastructure, including servers, virtual machines, storage, networks and operating systems.
- Platform as a service (PaaS): PaaS provides an environment for developing and managing web or mobile software applications.
- Software as a service (SaaS): SaaS involves supplying software applications on-demand over the internet.
DataOps (data operations) is a data engineering skill that involves collaboration between the DevOps team, engineers and scientists to automate and streamline data flows within an organization. The DataOps Manifesto is a set of best practices for achieving these goals. Three of the most critical principles are:
Value Working Analytics:
We believe the primary measure of data analytics performance is the degree to which insightful analytics are delivered, incorporating accurate data, atop robust frameworks and systems.
The beginning-to-end orchestration of data, tools, code, environments and the analytic team’s work is a key driver of analytic success.
Make It Reproducible:
Reproducible results are required and therefore we version everything: data, low-level hardware and software configurations, and the code and configuration specific to each tool in the toolchain.
Data engineering is an in-demand career field. Breaking into this field requires broad knowledge in data and Information Technology. Mastering the above data engineering skills will set you up for success as you begin your journey into this field.
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