Big data means big business. So businesses and organizations need qualified professionals to transform data into usable applications. To tap into the power of vast amounts of information generated in the digital environment, organizations require a very special type of expert: the Big Data Engineer.
Keep reading to know what Big Data Engineers are, what they do, and how they are essential to improve business results.
By the end, you'll know why you'll probably need a Big Data Engineer in your team.
Table of Contents
What is Big Data
➤ The five V's of Big Data
➤ Big Data sources
Big Data Engineer definition
➤ What does a Big Data engineer do?
➤ Data knowledge
➤ Database management systems and SQL
➤ Cloud Management
➤ Soft skills
Better data = better business
Big Data Engineers and where to find them
Big Data is the massive amount of digital information generated every day by humans and devices, too large, too complex, and too fast to be processed by standard methods.
Data is constantly generated by actions, transactions, interactions, and connections between users, devices, infrastructures, systems. It originates in social networks, e-commerce, websites, apps, sensors, stored data, and smart equipment.
The uses for Big Data are almost infinite, but the most common is to predict user and consumer patterns. Other uses for Big Data are monitoring large-scale financial activities, epidemiological evolution, fraud detection, transport and energy services optimization, to name a few.
Governments, organizations, industries, and businesses rely on it to develop effective rulings, strategies, and products and adopt new relationships with citizens, users, and customers.
Doug Laney listed Big Data's main characteristics in the early 2000s in three V’s, which later became five:
Volume - The amount of available data is too large to handle by standard methods and growing. It is estimated that the volume of data created worldwide in 2021 will amount to 79 Zettabytes (or 79 billion Terabytes), a number that is expected to double in 2025.
Velocity - Data travels faster every day with smart devices, sensors, and apps generating information in real-time that needs to be handled quickly and effectively by organizations.
Variety - Data comes in many types and formats: structured, semi-structured, and unstructured:
Structured data encompasses all the data formatted into a model - think of spreadsheets or databases: MySQL works with structured data.
Semi-structured data is information that has some organizational properties without relying on a fixed format - emails, JSON queries, metadata;
Unstructured data doesn't have a specific format, with the qualitative traits being more important than the quantitative. Some examples of unstructured data are videos, quotes, log files.
The industry added two more V's to the original concept:
Veracity - Data must be accurate and trustworthy. The integrity of data is fundamental for effective analysis and strategy development.
Value - with all this information in hand, organizations, users, and devices can each make decisions and act towards their goal: promote a product, improve a personal plan, adapt to users' habits.
But where does all this data come from?
Not so long ago, data was mostly stored in paper records and was generated by humans. Nowadays, it seems almost everything can produce usable information.
Smart things - The Internet of Things is the name given to all the connected devices providing data to systems. It includes wearables, smart household appliances, smart cars, and many other devices streaming information, from the simplest sensor to the most complex industrial assembly line. They generate real-time data that can be organized and analyzed.
Humans still generate troves of information, most of it semi-structured or unstructured. Some data is deliberate, like social media posts, comments on websites, or multimedia content in image, sound, or text form. Other data is consequential, created through the devices they use that already generate parallel information like location.
Stored data, either from public or private origins, is made available every year. This data is kept in data lakes in cloud storage services and includes open-data portals, digital archives, or logs.
Big Data's complexity and sheer volume demand specialized professionals capable of harvesting, storing and organizing raw data to turn it into something useful.
Big Data Engineers design, build, integrate, maintain, test, and evaluate data processing systems capable of handling data on a very large scale.
Imagine Big Data as a violent river. The Big Data Engineer is in charge of planning, building, and optimizing a dam to harness power from it, turning chaos into energy. Which, with Big Data, means turning noise into insightful and actionable information.
A Big Data Engineer's role is to create and ensure a quality data-processing environment by designing and implementing the appropriate standards and methods, choosing the right tools and techniques, and defining data management processes. These actions must fulfill the organization's operational requirements and business or governance objectives.
Big Data Engineers are responsible for infrastructure design, data processing methods, system maintenance and development, research, and management. They are expected to:
- Design and build a data processing system;
- Create highly scalable data mining, storage and processing systems;
- Select storage types: data warehouses, data lakes, data clouds;
- Choose database types and computing systems;
- Define operational procedures through adequate data transformation tools and techniques;
- Define automation for data delivery;
- Select data sources and data types;
- Mine and collect the selected data for storage;
- Transform raw data into structured data;
- Prepare data to be used;
- Select data analysis and management tools;
- Create data architecture suitable to the organization's needs;
- Analyze data patterns and lifecycle to evaluate and improve the data gathering and processing stages;
- Research and suggest new data acquisition methods;
- Ensure data quality, trustworthiness, and value;
Big Data Engineers are a rare breed with a broad understanding of data processing and storage. The complexity of the tasks involved in Big Data processing demand unique skills, versatility, and proficiency in a diverse set of tools and coding languages. But what should you be looking for?
First of all, Big Data Engineers must understand data. They must know where data is - databases, repositories - and how to retrieve it - APIs and scraping.
They also have to understand the different types of data sources (structured, unstructured, semi-structured) and work with their specificities.
Good knowledge of Data Models, Data Schema, and a taste for Database Architecture and Design is recommended.
Programming is a huge part of the job, so Big Data professionals should master programming and scripting languages. The most common languages required are Java, C++, and Python.
They should also feel comfortable working in Linux or Unix and development environments like GitHub.
Big Data Engineers should be familiarized with different types of DBMS: relational or SQL databases, and NoSQL databases.
Mastering tools like Hadoop and related components (HDFS, Pig, MapReduce, HBase, Hive), Kubernetes, MongoDB, Couchbase, Spark is essential since many of these are better equipped to deal with Big Data management.
Knowing how to set up and manage cloud clusters is another must-have skill since most of the information and the data processing results will live in outsourced storage. Besides being a versatile solution for data engineering, it makes large volumes of data easier to access and analyze.
Machine learning skills, data mining, and predictive analysis are extremely useful for developing personalized experiences in recommendation-based systems. Example: services like Spotify or Amazon that use recommendation engines based on user data.
Data affects people's lives. Looking past data and foreseeing how to apply it in a useful way is a great ability to have as a Big Data Engineer.
Good communication qualities and teamwork skills are always well appreciated, since Big Data Engineers work along with data architects, data analysts, data scientists, developers. They also connect with non-IT sectors of organizations, like management or marketing.
But does your organization need a Big Data Engineer? Probably, yes.
Companies and organizations worldwide are looking into their workflow and analyzing the benefits of a Big Data strategy. Knowing how their products are being used, nearly in real-time, while reducing waste, optimizing production, and increasing the quality of their products and services will provide them a competitive advantage.
Good data will benefit the decision-making process of organizations. Backed by data evidence, they can improve performance and the quality of operations. Data-driven companies are quicker to develop effective commercial strategies and production methods, becoming more reliable and profitable.
Insights from good data processing can create new business opportunities, revenue streams and focus on consumers' real needs. For example, data about users' sleeping habits can lead to varied applications like targeting ads for impulse buying during insomnia spells or energy-saving strategies.
This is a job suited for jacks-of-all-trades, so even developers who don't have a degree in Big Data are not excluded. Most Big Data Engineers have a professional background in some areas mentioned above, working as programmers or information architects, but acquired advanced technical skills suited to this job through certifications.
But raising a in-house Big Data Engineer is hard, and hiring one may be something your business is not ready for yet. If Big Data Integration is something new in your strategy, staff augmentation can the best option.
And we know just the place to find a solution for all your data needs. Imaginary Cloud provides award-winning AI and Data Science services, and has been taking businesses to the next level for more than a decade.
Are you looking to develop a solid data strategy and improve business results? Let's talk.