We’ve seen how Big Data has risen to prominence in the IT industry. Even beyond the next few decades, Big Data will continue to govern the globe.
Being a big data developer is like finding a mango in a basket full of other fruits. You will be irreplaceable no matter how many technologies come and go.
However, most of us are unaware of the Big Data Developer’s roles and responsibilities. Also, to be Big Data developers, we must have the skills to be a master and advance our careers in the growing big data market.
In this blog, we will first look at who a big data developer is. What is Big Data Developer’s Role and Responsibilities? We will then look at the abilities that must be mastered in order to become a Big Data Developer.
Let’s begin with an overview of the Big Data Developer and its tasks and responsibilities.
Who is a Big Data Developer?
A Big Data Developer is the person in charge of creating Hadoop applications. It usually serves the big data demands of the organization in which he works and works to address big data problems and requirements.
A big data developer must be capable of managing the entire Hadoop solution lifecycle, which includes platform selection, technical architecture design, requirement analysis, application development and design, testing, and deployment. He is in charge of writing the code for Hadoop applications.
Roles and Responsibilities of Big Data Developer
The Big Data Developer, who is in charge of programming Hadoop applications in the Big Data area, has the following roles and responsibilities:
- Loading data from various data sets.
- Querying at lightning speed
- Make recommendations
- Your responsibility as a big data developer is to design, create, install, configure, and support Hadoop.
- He ensures data security and privacy.
- Hbase is managed and deployed by a Big Data developer.
- He analyses numerous data repositories and discovers insights.
- The Big Data Developer is in charge of Hadoop development and deployment.
- He is in charge of developing scalable and high-performance web services for data tracking.
- Big Data developers are responsible for translating difficult technical and functional requirements into precise designs.
- He suggests design adjustments and improvements to numerous processes and products.
Skills Required to Become Big Data Developer
- Knowledge of Big Data Frameworks and Hadoop-based technologies.
- Must know the Real-Time Processing Framework (Apache Spark).
- of SQL-based technology.
- Knowledge of NoSQL-based technologies such as MongoDB, Cassandra, and HBase.
- Knowledge of at least one programming language (Java/Python/R).
- Understanding visualization technologies such as Tableau, QlikView, and QlikSense.
- Knowledge of various data mining tools such as Rapidminer, KNIME, and others.
- Understanding of machine learning algorithms.
- Understanding of statistical and quantitative analysis.
- Freehand on Linux, Unix, Solaris, or Microsoft Windows.
- Problem-solving and creativity are must.
- Business knowledge is required.
Let’s talk about some of the skills that must need in big data development.
The advent of Big Data in the early twenty-first century gave rise to a new framework known as Hadoop. Full credit to Doug Cutting for creating a framework that stores and processes data in a distributed fashion while also allowing for parallel processing.
Hadoop triumphs as the foundation for other emerging Big Data technologies. The first step in becoming a successful Big Data Developer is to learn Hadoop. Hadoop is not a single phrase; rather, it is an entire ecosystem. The Hadoop ecosystem includes a variety of technologies that perform various functions.
The world requires real-time processing with speedy action. Every system, whether it is a fraud detection system or a recommendation system, requires real-time processing. It is critical for a big data developer to be conversant with the real-time processing framework.
Apache Spark is a real-time distributed processing framework that supports in-memory computing. As a result, Spark is the greatest alternative for big data engineers who want to be proficient in all the real-time processing frameworks.
Structured Query Language (SQL) is a data-centric language that is used to structure, manage, and process structured data stored in databases.
Knowing SQL is a distinct advantage for programmers working on big data technologies because it is the foundation of the big data era. In the industry, PL/SQL is also commonly employed.
Organizations are generating data at a breakneck pace. The amount of data has risen beyond our wildest dreams. Organizational requirements have shifted from organized to unstructured data.
NoSQL databases were introduced to satisfy the growing needs of enterprises. Large amounts of structured, semi-structured, and unstructured data can be stored and managed by the NoSQL database.
Data Visualization Too
Big data specialists must be able to analyze data by visualizing it. This necessitates a mathematical and scientific edge, as well as creativity and imagination, to quickly interpret very massive data sets.
Some well-known data visualization tools, such as QlikView, Tableau, and QlikSense, aid in comprehending the analysis conducted by various analytics tools. If you want to improve your data analytics and visualization skills, learning visualization tools will help you.
Machine Learning (ML)
Machine Learning is the hottest big data field. Its aids in the development of recommendation, personalization, and categorization systems. With technological breakthroughs, professionals with machine learning skills for predictive and prescriptive analysis are in short supply.
To be an effective data analyst, one must be proficient in machine learning methods.
Statistical And Quantitative Analysis
Big data is all about numbers. The most significant aspect of large data analysis is quantitative and statistical analysis.
Understanding key concepts such as probability distribution, summary statistics, random variables, and knowledge of statistics and mathematics aids the probability distribution. Knowledge of several tools such as R, SAS, SPSS, and others distinguishes you from others in the queue.
I hope these Big Data Developer abilities help you in determining the skills you should cultivate in order to become a big data developer.