Companies now handle vast amounts of data on a daily basis which has led the the big data market to grow exponentially. It is estimated to grow from USD 28.65 Billion in 2016 to USD 66.79 Billion by 2021, at a Compound Annual Growth Rate (CAGR) of 18.45% according to “Markets & Markets”. Data is generated every hour, every minute, every second which is why enterprises need professionals to analyze and control this huge amount of data and utilize it to their benefit. Big Data related jobs are top notch in the market. However, building a career in big data is not an easy task. Apart from being a data savvy professional, you have to be an adept developer and an expert engineer.
What’s Big Data Developers?
A big data developer is someone who is responsible for the coding or programming of a big data application. Most of the time they are also involved in the design of big data solutions, because of the experience they have with Hadoop based technologies such as MapReduce, Hive MongoDB or Cassandra. A big data developer builds large-scale data processing systems, is an expert in data warehousing solutions, and should be able to work with the latest (NoSQL) database technologies.
Generally speaking, if you take Hadoop as an example, the big data developer designs, builds, installs, configure, and supports Hadoop’s development and implementation.
How To Become A Big Data Developer
1. Seek a degree in computer science, computer engineering or a related field.
A big data developer should have experience in software engineering before the move can be made to the field of big data. Experience with object-oriented design, coding and testing patterns as well as experience in engineering (commercial or open source) software platforms and large-scale data infrastructures is required. Big data engineers should also have the capability to architect highly salable distributed systems, using different open source tools. He or she should understand how algorithms work and have experience building high-performance algorithms.
So no matter who they are, to be a big data developer, all of them need to go with related professional big data architecture certification training. Getting master program training is the fastest and easiest way is to learn required skills of becoming a big data developer.
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2. Developing Following Skills You Need to Become a Big Data Developer
Problem Solving Aptitude
Big data is emerging and there are new technologies evolving everyday. As you dwell in the domain of big data, new technologies will come your way with every passing day. Therefore, to become a successful Big Data Developer, you should be a natural problem solver, and tinkering with different tools and techniques should be your forte.
Big data comes in various forms, e.g. unstructured, semi structured, which are tough to understand. Therefore, to draw insights from data you need to be very careful and should be extra cautious. Multivariate or logistic regression analysis may be useful for a small amount of data but the diversity and quantity of data generated for a business necessitates the use of data visualization tools like Tableau, d3.js, etc.
Data visualization tools help reveal hidden details that provide critical insights to drive business growth. Furthermore, as you progress in your career as a Big Data Developer, you grow up to become a Data Scientist or a Data Artist after being well-versed in one or more visualization tools is a practical requirement.
Computational processing of the growing volumes and varieties of available data via machine learning makes it cheaper and more powerful. The need to know machine learning is also essential to a Big Data Developer’s career, because it makes possible to rapidly and automatically produce models to analyze complex data and deliver faster and accurate results on a large scale. Building precise models provides organizations with a better chance of identifying profitable opportunities.
Data mining is a critical skill to be possessed by a Big Data Developer. Unstructured data comprise a huge amount of Big Data and data mining enables you to maneuver such data and derive insights. Data mining lets you sift through all the unnecessary and repetitive information in your data and determine what is relevant and then make use of that information to assess and predict outcomes.
Statistics is what big data is all about. If you are good in quantitative reasoning and have a background in mathematics or statistics, you are already close to become a Big Data Developer. Learn statistical tools like R, SAS, Matlab, SPSS, or Stata to add up to your skills and there is nothing that can stop you to become a good Big Data Developer.
SQL and NoSQL
Working with Big Data means working with databases. This mandates the knowledge of a database querying language. As a Big Data Developer, you should be aware of both SQL and NoSQL. Although, SQL is not used to solve all big data challenges today, the simplicity of the language makes it useful in many cases. Gradually, distributed, NoSQL databases like MongoDB and Cassandra are taking over Big Data jobs that were previously handled by SQL databases. Therefore, the ability to implement and use NoSQL databases is a must for a Big Data Developer.
General Purpose Programming
As a Big Data Developer, you need to code to conduct numerical and statistical analysis with massive data sets. It is essential to invest money and time to learn programming in languages like Java, C++, Python, Scala, etc. You do not need to master all of the languages. If you know one language well, you can easily grasp the rest.
Hadoop is an indispensable technology for Big Data. Many-a-times, Hadoop is mistaken to be synonymous to Big Data. It is essential to be a master in Hadoop to become a Big Data Developer. The knowledge and experience of core components of Hadoop and related technologies such as HDFS, MapReduce, Flume, Oozie, Hive, Pig, HBase, and YARN will render you high in demand.
Spark is also an important technology to consider for big data processing. It is an open source data processing framework developed around speed, ease of use, and sophisticated analytics. Of course, Spark is not a replacement of Hadoop rather it should be looked at as an alternative to Hadoop MapReduce. Spark runs on top of existing HDFS infrastructure to provide enhanced functionality and it also supports the deployment of Spark applications in an existing Hadoop v1 cluster (with SIMR or Spark-Inside-MapReduce) or Hadoop v2 YARN cluster or Apache Mesos.
Understanding of Business
After all, the main motive to analyse and process big data is to use the information for business growth. Hence, domain expertise empowers Big Data Developers to identify opportunities and threats relevant to the business and design deploy the solutions accordingly besides communicating the issues effectively with different stakeholders.
3. Take Data Architect Certification to Improve Your Ability
The top 14 data engineer and data architect certifications are following:
- Amazon Web Services (AWS) Certified Big Data – Specialty
- Cloudera Certified Associate (CCA) Spark and Hadoop Developer
- Cloudera Certified Professional (CCP): Data Engineer
- Google Professional Data Engineer
- HDP Apache Spark Developer
- HDP Certified Developer Big Data Hadoop
- Hortonworks Certified Associate (HCA)
- IBM Certified Data Architect – Big Data
- IBM Certified Data Engineer – Big Data
- MapR Certified Hadoop Developer 1.0
- MapR Certified Spark Developer 2.1
- Oracle Business Intelligence Foundation Suite 11 Certified Implementation Specialist
- SAS Certified Big Data Professional
- SAS Certified Data Scientist Using SAS 9
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Becoming a Big Data Developer requires proficiency in all the aforementioned skills. IT professionals may have an advantage in learning new programming languages and technologies but people from a statistical or mathematical background also have the advantage of an analytical mind.
Anyhow, the more effort you put into acquiring the skills, the better you will be rewarded with a higher pay package. So, invest in yourself and hone your skills with time.