From the past few years, we are continuously listening to the word Big Data. We have seen how Big Data becomes the king in the IT world. Big Data will continue to rule the world even after the next few decades.
Being a big data developer is like a mango in a basket full of fruits. No matter how many technologies may come or go, you will remain irreplaceable.
But most of us are not aware of the roles and responsibilities of the Big Data Developer. Also, for being a Big Data developer we should know the skills to be a master and boost our career with the rising big data market.
In this article, we will first see who is a big data developer? What is the role and responsibilities of a Big data developer? We will then see the skills to be master to become the Big Data Developer.
Let us first start with the introduction to the Big Data Developer and its roles and responsibilities.
Who is a Big Data Developer?
A Big Data Developer is the one who is responsible for developing Hadoop applications. It typically serves the big data needs of an organization he is working in and works to solve the big data problems and requirements.
A big data developer must be skilled enough to manage the complete Hadoop solution lifecycle, including platform selection, designing technical architecture, requirement analysis, application development and design, testing, and deployment. He is responsible for the actual coding of Hadoop applications.
Roles and Responsibilities of Big Data Developer
The roles and responsibilities of the Big Data Developer who is responsible for programming Hadoop applications in the Big Data domain are:
- Loading the data from disparate data sets.
- High-speed querying
- Propose best practices and standards.
- As a big data developer, your role is to design, build, install, configure and support Hadoop.
- He maintains security and data privacy.
- Big Data developer manages and deploys HBase.
- He performs an analysis of the vast number of data stores and uncovers insights.
- Big Data Developer is responsible for Hadoop development and implementation.
- He is responsible for creating scalable and high-performance web services for tracking data.
- Big Data developer translates complex technical and functional requirements into detailed designs.
- He proposes design changes and suggestions to various processes and products.
Let us now see the skills required to become a big data developer.
Skills Required to Become Big Data Developer
The most important skills to be master in order to become a successful Big Data Developer include:
1. Knowledge of Big Data Frameworks or Hadoop-based technologies.
2. Knowledge of Real-time processing framework (Apache Spark).
3. Knowledge of SQL based technologies.
4. Knowledge of NoSQL based technologies like MongoDB, Cassandra, HBase.
5. Knowledge of any of the one programming language (Java/Python/R).
6. Knowledge of visualization tools like Tableau, QlikView, QlikSense.
7. Knowledge of different Data Mining tools like Rapidminer, KNIME, etc.
8. Knowledge of Machine learning algorithms.
9. Knowledge of Statistical & quantitative analysis.
10. Freehand on Linux or Unix or Solaris or MS-Windows.
11. Must possess problem-solving and creative thinking ability.
12. Must have Business Knowledge.
Let us now explore each of these in detail.
1. Hadoop-based technologies
The rise of Big Data in the early 21 century gave birth to a new framework called Hadoop. All the credit goes to Doug Cutting for introducing a framework that stores and processes data in a distributed manner and performs parallel processing.
Hadoop prevails to be the foundation of other rising Big Data technologies. Learning Hadoop is the first step towards becoming a successful Big Data Developer. Hadoop is not a single term, instead, it is a complete ecosystem. The Hadoop ecosystem contains a number of tools that serve different purposes.
For boosting your career as a big data developer, mastering these big data tools are a must.
Big Data tools which you need to master are:
1. HDFS (Hadoop Distributed File System): HDFS is the storage layer in Hadoop. It stores data across a cluster of commodity hardware. Before learning Hadoop, one should have knowledge of Hadoop HDFS as it is one of the core components of the Hadoop framework.
2. YARN: Yet Another Resource Negotiator (YARN) is responsible for managing resources amongst applications running in the Hadoop cluster. It performs resource allocation and job scheduling in the Hadoop cluster. The introduction of YARN makes Hadoop more flexible, efficient & scalable.
3. MapReduce: MapReduce is the heart of the Hadoop framework. It is a parallel processing framework that allows data to be processed parallely across clusters of inexpensive hardware.
4. Hive: Hive is an open-source data warehousing tool built on top of Hadoop. With Hive, developers can perform queries on the vast amount of data stored in Hadoop HDFS.
5. Pig: It is a high-level scripting language used for data transformation on the top of Hadoop. It is used by researchers for programming.
6. Flume: Flume is a reliable, distributed tool for importing large amounts of streaming data such as events, log data, etc from different web servers to the Hadoop HDFS.
7. Sqoop: Sqoop is a big data tool used for importing and exporting data from relational databases such as MySQL, Oracle, etc to Hadoop HDFS or vice versa.
8. ZooKeeper: It is a distributed coordination service that acts as a coordinator amongst the distributed services running in the Hadoop cluster. It is responsible for managing and coordinating a large cluster of machines.
9. Oozie: Oozie is a workflow scheduler for managing Hadoop jobs. It binds multiple jobs into a single unit of work and helps in accomplishing a complete task.
2. Apache Spark
Real-time processing with rapid action is the need of the world. Whether it is a fraud detection system or recommendation system, every one of them requires real-time processing. For a big data developer, it is very important to be familiar with the real-time processing framework.
Apache Spark is a real-time distributed processing framework with in-memory computing capabilities. So Spark is the best choice for big data developers to be skilled in any of the one real-time processing frameworks.
Structure Query Language (SQL) is the data-centered language used to structure, manage and process the structured data stored in databases.
Since SQL is the base of the big data era, so the knowledge of SQL is an added advantage to the programmers while working on big data technologies. PL/SQL is also widely used in the industry.
The organizations are generating data at rapid speeds. The amount of data has grown beyond our imaginations. The requirements of the organizations are now extended from structured to unstructured data.
To meet the increasing requirements of the organizations, NoSQL databases were introduced. The NoSQL database can store and manage large amounts of structures, semi-structured, and unstructured data.
Some of the prominently used NoSQL databases are:
1. Cassandra: Cassandra is a NoSQL database that provides scalability and high availability without compromising performance. It is a perfect platform for mission-critical data. Cassandra provides fast and random read/writes. It provides Availability and Partitioning out of CAP.
2. HBase: HBase is a column-oriented NoSQL database built on top of Hadoop HDFS. It provides quick random real-time read or writes access to the data stored in the Hadoop File System. HBase provides Consistency and Partitioning out of CAP (Consistency, Availability, and Partitioning).
3. MongoDB: MongoDB is a general-purpose document-oriented NoSQL database. It is a NoSQL database that provides high availability, scalability, and high performance. MongoDB provides Consistency and Partitioning out of CAP.
A professional with knowledge of NoSQL databases will never go out of fashion.
5. Programming Language
For being a Big Data developer, you must have good hands in coding. You must have knowledge of data structures, algorithms, and at least one programming language.
There are various programming languages like Java, R, Python, Scala, etc. that caters to the same purposes. All the programming languages have different syntax but the logic remains the same.
For a beginner, I suggest that you should go with Python as it is simple to learn and is a statistical language.
6. Data Visualization tools
The big data professionals must have the ability to interpret the data by visualizing it. This requires mathematic and science edge to easily understand complex large data with creativity and imagination.
There are some prominent data visualization tools like QlikView, Tableau, QlikSense that help in understanding the analysis performed by various analytics tools. Learning the visualization tools adds an advantage for you if you want to boost your data analytics and visualization skills.
7. Data Mining
Data mining is very important when we talk about extracting, storing, and processing vast amounts of data. For working with big data technologies, you must be familiar with data mining tools like Apache Mahout, Rapid Miner, KNIME, etc.
8. Machine Learning
Machine Learning is the hottest field of big data. ML helps in developing a recommendation, personalization, and classification systems.With the advancements in technologies, professionals with machine learning skills for predictive and prescriptive analysis are sparse.
For being a successful data analyst one should have a good hand in the machine learning algorithms.
9. Statistical & quantitative analysis
Big data is all about numeric digits. Quantitative and Statistical analysis is the most important part of big data analysis.
The knowledge in statistics and mathematics helps in understanding core concepts such as probability distribution, summary statistics, random variables, probability distribution. The knowledge of different tools like R, SAS, SPSS, etc. makes you different from others standing in the queue.
10. Linux or Unix or Solaris or MS-Windows
Various operating systems are used by a wide range of industries. Unix and Linux are the most used operating systems. The big data developer needs to master at least one of them.
11. Creativity & problem solving
You must have the problem-solving ability and creative mind while working in any domain. The implementation of different big data techniques for efficient solutions needed both of these qualities in the professionals.
12. Business knowledge
For working in a domain, one of the most important skills is the knowledge of the domain he is working in. To analyze any data or develop any application, one should have the business knowledge to make analyses or development profitable.
I hope this Big Data Developer Skills article helps you in finding out the right skills that you should develop in yourself to become a big data developer.
The article helps you in figuring out the tools and the skills which you need to master to become a successful big data developer.