We all must agree that Big Big data has become the most prominent technology throughout the globe and most businesses are relying on this “Hottest Technology” of the era. Over the period of time, many changes and implementations have been made and that enabled some of the most exciting tools & technology into the mainstream. As for weather forecasting, and data visualization, a dedicated tool has been introduced (one or multiple tools), and so on.
You must be wondering what’s the need for big data in such an industry, well, here are some quick stats to support this,
- Forecasting itself is likely to cross 100 Zettabytes by the end of 2022 and will surpass 180 Zettabytes in the next 5 years (i.e. by 2025).
- There’s no surprise that the figures have drastically grown since the pandemic and more than 95% of the organizations have already started investing in big data and AI.
- A report also suggested that it nearly costs USD 3 Trillion annually to eliminate wasteful data and that’s where big data comes into action to prevent those harms, and the market is likely to cross USD 100 Billion by the end of 2023.
- As of now, Google (one of the giant search engines) holds around 83 percent of the market share, and above 40,000 queries are being pushed via this gigantic search engine. To handle those stats and figures requires a proficient specialist and that’s what makes their job (big data professionals) both demanding and interesting.
So, the primary objective is to get into Big Data and possess the best skills that can fit in any industry that you’ll be working in (and offcourse those skills that are highly in demand). In this article, we will discuss some of the best and most important skills that are required for big data jobs.
7 Most Have Skills for Big Data Jobs
First, come first, SQL is one of the most important skills that you must have. While using SQL, a programmer can have an advantage in working with multiple technologies (such as NoSQL).
SQL is the data-centered language that works as a base for the big data era.
Programmers use SQL for multiple operations such as adding, updating, deleting, or modifying any records or tables, and so on. Besides this, RDBM or Relational database management is a crucial part of the field of data science and a data scientist can only control, manipulate or define and query the DB using SQL commands.
Today, some of the modern big data systems (such as Hadoop and Spark) also use SQL only for maintaining the RDBMS (relational database systems) and processing structured data.
To know more about SQL, refer to this article: 30 Days of SQL – From Basic to Advanced
2. Apache Spark
Spark was first introduced by UC Berkeley in 2009 and since then it started gaining popularity in the field of data science. Today, Spark is capable enough to handle data (up to Petabytes) at a time and its data distribution happens across thousands of cluster cooperating servers (both physical and virtual). Spark also comes with an extensive range of libraries (and APIs) that can be commonly used by multiple programming languages (such as R, Scala, and Python).
Besides this, Apache uses Hadoop Distributed File System (HDFS) but can be integrated equally with other data storage systems. Developers prefer Spark often because it enables overlapping the complex technologies (such as MapReduce) and that’s why it is widely being used by data scientists and has been highly adopted by major organizations. People holding such skills possess to bag more lucrative packages than others.
Machine Learning, Artificial Intelligence, and Deep Learning are three hot fields of big data. Although the path is way beyond them they are the ones making a significant impact on the field.
Whether it’s your smartphone, car, laptop, home devices, etc. they all are now highly equipped with artificial intelligence that we’re using on daily basis. Whenever you pick your phone up and say aloud “Hey Siri”, it’s likely that you’re using these technologies. The point is, that AI, ML, and DL are everywhere in our surroundings today and data science is the interdisciplinary field of getting that knowledge as per requirement. These technologies are making a huge impact in our day-to-day lives and helping us in making a better future.
That’s why the professionals with the knowledge of machine learning, artificial intelligence, and deep learning are in huge demand irrespective of the business scale (from small to large) and the average payscale of entry-level professionals are somewhere around $110,100 per annum and makes to one of the most handsome jobs in the world.
4. Apache Hadoop
When it comes to handling any huge cluster of data, Hadoop is the answer all the time. Being one of the most popular big data platforms, it’s widely used for data operations that involve large-scale (unstructured) data. If you want to make your career in big data, you must understand the importance and knowledge of handling data on large scale.
Hadoop was first introduced by Doug Cutting and Mike Cafarella in 2005 and became public in late 2012. Ever since many implementations and development have been made. In today’s time, some of the most popular components that are widely used in Hadoop are Hive, Pig, HDFS, MapReduce, etc.
5. Programming Language
This is something that creates the base of your big data career and there are certain general-purpose programming languages that enables you to work in this field smoothly. Languages like Python, R, Java, C++, SQL, Scala, Julia, etc. are some of the most widely used languages and that can also remove the learning barricade from becoming a successful data analyst expert.
Top companies prefer to hire those candidates who possess knowledge of these programming languages. You need to learn Python, Java, or R programming language (at least for your initial career) and that’s where you will be able to start working on some of the most useful tools for data visualization, extraction, scraping, etc.
6. Data Visualization
Enabling the capabilities of displaying data visually is slightly more impactful than traditional methods. It helps people understand the latest trends, and patterns and help them in deciding the outcome (in many cases). That’s why data visualization is among the top skill sets that you must possess to get on board in the big data field.
Companies are willing to pay much lucrative salaries to those who possess the knowledge of the best data visualization tools such as QlikView, Tableau, etc. Hence, to give your career a headstart, it is important to know what Big Data skills you need to break into analytics and start working with data.
7. Statistical Analysis
It’s an important method of data analysis that helps in drawing meaningful outputs from any unstructured data. This method also helps in making fruitful business decisions based on data trends.
It can also be defined as a science of collecting and analyzing data to trace patterns and trends by involving numbers used in businesses. Being a data analyst will require you to possess this skill because it’s all about data now and companies look forward to those candidates that carry such skills. Some of the most important tools for statistical analysis are MATLAB, R, SAS, etc.
To read about the methods of Statistical Analysis, refer to this article: 5 Methods of Statistical Analysis.
It is always mandated for an individual to carry the ability of problem-solving skills (it could be for handling complex problems in creative methods) that help an individual to perform any task with perfection. Besides this, the implementation of big data techniques will require these qualities and will definitely help you to bag your dream job.
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