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 optimisation, to name a few.
Governments, organisations, 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 organisations.
Variety - Data comes in many types and formats: structured, semi-structured, and unstructured:
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.
Big Data's complexity and sheer volume demand specialized professionals capable of harvesting, storing and organising 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:
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 familiarised 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 analyse.
Machine learning skills, data mining, and predictive analysis are extremely useful for developing personalised 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 organisations, like management or marketing.
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But does your organisation need a Big Data Engineer? Probably, yes.
Companies and organisations worldwide are looking into their workflow and analysing the benefits of a Big Data strategy. Knowing how their products are being used, nearly in real-time, while reducing waste, optimising 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 organisations. 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, team extension 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.
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Content writer and digital media producer with an interest in the symbiotic relationship between tech and society. Books, music, and guitars are a constant.
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