How to Become a Big Data Developer?
Nowadays, it is common for companies to experience a massive influx of complex data faster. It is not a secret that traditional data processing techniques and tools used to work with small amounts of structured data fail to handle complex and voluminous data. One way to counter this challenge is to generate less data which will be a step backward since companies need more data. Another way is to come up with new techniques and tools for analyzing more data. Along with tools, companies need qualified professionals in the Big Data space to help them improve their business.
There is a good demand for Big Data developers and data experts throughout the world. It is the right time for aspirants to enter the domain. Good Big Data training online can help them achieve essential skills for starting a bright career in the growing field of Big Data.
What is Big Data?
In simple words, Big Data refers to vast amounts of data in terms of volume, variety, and velocity. Volume refers to the amount of data that is increasing as we are moving towards digital platforms. Variety may be interpreted as different data types as earlier companies handled structured data that is changed nowadays. Nowadays, most of the data comes in the form of images, audio, and videos, commonly known as unstructured data. Velocity refers to the speed at which data is received and required processing. Higher velocity may require processing data without being stored.
This vast data needs to be processed and converted into a form easily readable to reveal trends, patterns, relationships, outlier, and other meaningful aspects. All this exercise helps businesses to take data-backed decisions and solve critical issues to improve performance. An experienced and skilled professional, for example, a Big Data developer, is required to conduct such activity.
Who is a Big Data Developer?
A Big Data Developer has mastered the techniques required to analyze large amounts of data. The primary responsibility of Big Data developers is to deliver a variety of data-related services. Developers should be proficient in working on various Big Data tools, know about databases, and strong scripting skills. He should have analytical skills such as data interpretation, data integration, data mining, and strong debugging capabilities.
Becoming a Big Data Developer
Here are some of the skills you need to gain in order to become a big data developer:
Big Data Analytics Tools: Big Data developers need to have an in-depth knowledge of various Big Data analytics tools and frameworks. Hadoop is an open-source framework that allows distributed processing of vast data over clusters. It can be scaled up easily to several machines. Hadoop is the basic foundation over which other Big Data technologies work. Familiarity with the Hadoop ecosystem or Hadoop components like Distributed File System, MapReduce, Hive, Pig, Flume, Sqoop, and Yarn is mandatory for Big Data developers. All tools mentioned are used for various aspects of data analytics and add significant value towards mastering the Big Data ecosystem.
Real-time Data Processing: Speaking about Big Data development without the mention of Apache Spark is incomplete. It is a well-known tool that provides excellent performance and is extensively used for real-time processing. Real-time processing is handy in conjunction with recommendation systems or fraud-detection systems. Apache Spark is based on resilient distributed datasets (RDDs) model and faster compared to Hadoop. Apache Spark provides many libraries built on top of it for machine learning.
DataBases Technologies: Data analytics processes require storing/retrieving data frequently for further processing. Developers need to understand the various database technologies available for effectively storing/retrieving data.
- SQL: SQL or Structured Query Language is used to store, update, and retrieve structured data stored in relational databases. Relational databases work on fixed schemas, i.e., data is stored in multiple tables with rows and columns. To store or retrieve data, one may use a join method. SQL is essential to add to your portfolio for structured data analysis, and Big Data analytics is no different. Strong SQL knowledge is a must for you if planning to get into Big Data analytics.
- NoSQL: Most of the data generated nowadays falls into unstructured categories that cannot be stored in traditional databases. This data involves text, images, audio, videos, and much more. Analyzing and storing unstructured information is quite challenging due to its variety and volume. However, if it is stored and analyzed effectively, it can give important insights to companies. Some of the sources wherein we get the unstructured data include business documents, emails, customer feedback, webpages, etc. NoSQL technology is quite useful for storage, updating, and accessing data. Main features for NoSQL databases include flexible schema, horizontal scaling, and support of faster queries. NoSQL databases are of various types, such as key-value data stores, document stores, wide column stores, and graph stores. Some popular and widely used databases are MongoDb, ApacheHBase, Cassandra, etc.
Scripting Languages: All phases of data processing require a strong understanding of programming languages. Whether you are working on collecting, cleaning, integrating, storing, or processing data, programming language comes in handy at all phases. It is virtually impossible to stay relevant in the IT space without learning a programming language. There are many languages to choose from, such as Python, Java, R, Scala, etc. You need to master at least one language and have a basic understanding of others. It is often required to work on multiple languages, and it is beneficial to gain knowledge of numerous languages.
Data Visualization: The final step of any data analysis process is summarising the data in the simplest way to provide maximum value to the customer. Dashboard and dynamic reports add extra value to your research and help you present the outcomes effectively. Some of the popular tools such as Tableau, PowerBi, and Qlikview are extremely helpful for visualization. In-depth understanding and hands-on is highly recommended in at least one of the tools.
Knowledge of Operating Systems: Operating systems are necessary as you may need to work on the cloud for data handling. Windows, Linux, and Unix knowledge is critical for working in the data analytics field. Mastering the basics of one the operating system is essential.
Machine Learning: Machine learning is the future technology, and knowledge of machine learning is crucial. Machine learning helps classify data, identify trends, fit curves, make predictions, and provide recommendations. Almost all data analytics will eventually lead to machine learning due to its enormous potential.
Soft Skills and Domain Awareness: A Big Data developer usually work in a team and requires various soft skills such as communication skills, presentation skills, teamwork along with creativity, and critical thinking.
Earning of a Big Data Developer
The Big Data Developer in the US makes an average annual earning of USD 87,321. The earning may increase with experience level, certification, and various other factors.
No doubt that a Big Data developer is a highly stable, secure, and high-earning job. However, it depends on one’s passion and dedication to space. Gaining essential knowledge and hands-on experience in tools with adequate training is a wise step.