One of the most prominent roles in this industry is Hadoop developer and we’ll cover the necessary Hadoop developer skills you must develop to enter this field. But first, let’s uncover why you should pursue a career in this field:
Why Become a Hadoop Developer?
Hadoop is among the most popular big data technologies. Moreover, the amount of data we generate every day is also increasing as we make technology more accessible to everyone.
Growth of Big Data
Here are some important facts that highlight the amount of data we generate every day:
- People send 500 million tweets
- 4 petabytes of data are created on Facebook
- 5 billion searches are made
- And, 65 billion messages are sent on WhatsApp
All of this data is very useful and the best way to utilize it is through big data implementations. That’s why the demand for Hadoop developers is increasing rapidly. Organizations want professionals that can use Hadoop and its numerous components to manage big data projects.
Becoming a Hadoop developer will allow you to fulfil this need of companies and help them with using big data effectively.
In 2018, the global Big Data and business analytics market stood at $ 169 billion and by 2022, it is estimated to reach $ 274 billion. This shows that the scope of big data and Hadoop is very bright and as the market will grow, the demand for professionals with Hadoop skill sets will increase accordingly.
There’s also a huge shortage of data science professionals (including Hadoop developers) worldwide. In a survey by Quanthub, when they asked companies which skillset is the most difficult to find talent for, 35% of the respondents said it was data science and analytics.
The market has a shortage of talented professionals so now is the perfect time to enter this field.
Hadoop offers one of the most attractive job prospects in terms of pay and growth opportunities. The average salary of a fresher Hadoop developer ranges from INR 2.5 lakh per annum to INR 3.8 lakh per annum. Experienced Hadoop developers earn up to INR 50 lakh per annum.
As you can see, there are many benefits to becoming a Hadoop developer. Now that we have covered the reasons why you should pursue a career in this field, let’s discuss the necessary Hadoop developer skills.
Top Hadoop Developer Skills
1. Hadoop Basics
You must be familiar with the fundamentals of Hadoop. Understanding what Hadoop is and what are its various components is necessary and it’s the first skill you should work on. Hadoop is an open-source framework of big data solutions and you should know about the different solutions available in this framework.
Apart from the solutions present in the framework, you should also know about the technologies related to the framework. How they are all interconnected and what does what is imperative to start developing Hadoop skill sets.
HDFS stands for Hadoop Distributed File System and is the storage system available in Hadoop. HDFS is widely popular among organizations and enterprises because it allows them to store and process large quantities of data at a very low cost.
All the processing frameworks available in Hadoop operate on top of HDFS. This includes the likes of MapReduce and Apache Spark.
HBase is an open-source non-relational distributed database. It is just as important in your Hadoop developer skill sets as HDFS.
HBase runs on top of HDFS and offers many features. It gives you a fault-tolerant way of storing various sparse data sets which are quite common in numerous big data use cases.
HBase is similar to Google’s big table and offers real-time read or write access to data in HDFS.
As a Hadoop developer, you’ll be using Kafka for real-time streams of data and do real-time analysis. It also helps you collect large amounts of data and it’s primarily used with in-memory microservices for durability.
Kafka offers excellent replication characteristics and higher throughput, hence you can use it for tracking service calls or track IoT sensor data.
It works well with all the tools we have discussed in this list including Flume, HBase, and Spark.
With Apache Sqoop you can transfer data between HDFS and relational database servers like Teradata, MySQL, and Postgres. It can import data from relational databases to HDFS and export data from HDFS to relational databases.
Sqoop is highly efficient in transferring large amounts of data between Hadoop and external data storage solutions such as data warehouses and relational databases.
Apache Flume allows you to collect and transport huge quantities of streaming data such as emails, network traffic, log files, and much more. Flume is capable of capturing streaming data from multiple web servers to HDFS, which simplifies your tasks considerably.
As a Hadoop developer, Flume will be a crucial part of your toolkit as it offers a simple architecture for streaming data flows.
7. Spark SQL
Spark SQL is a Spark module to perform structured data processing. It has DataFrames, a programming abstraction and it integrates Spark’s functional programming with relational processing, increasing the speed of data querying tasks phenomenally.
It offers support for multiple data sources and allows you to weave SQL queries with code transformations. All of these reasons have made it one of the most sought-after Hadoop developer skills.
8. Apache Spark
Apache Spark is an open-source analytics engine used for large-scale data processing. It offers you an interface to program complete clusters with implicit fault tolerance and data parallelism.
It runs in Hadoop clusters through YARN or through its standalone mode to process data in Cassandra, HDFS, Hive, HBase, or any Hadoop InputFormat. Spark is necessary because it allows you to run applications in Hadoop clusters up to 100 times faster in memory. Without Spark, working with large amounts of data would be quite cumbersome.
MapReduce is a programming framework that lets you perform parallel and distributed processing on large data sets in a distributed environment. While HDFS allows you to store large amounts of data in a distributed system, MapReduce allows you to process the same data in such a system.
A MapReduce program has a mapping procedure and a reduce method. The mapping procedure performs sorting and filtering while the reduce method performs the summary operation.
10. Apache Oozie
Apache Oozie is a server-based workflow scheduling solution. It allows you to manage Hadoop jobs and the workflows in Oozie are collections of action nodes and control flows.
As a Hadoop developer, you’ll have to use Oozie to define job flows and automate the data loading process into Pig and HDFS.
Oozie is an integral component of the Hadoop stack and recruiters look for this skill in Hadoop developer skill sets.
GraphX is an Apache Spark’s API you can use to create graphs and perform graph-parallel computation. It combines the ETL (Extract, Transform and Load) process, iterative graph computation, and exploratory analysis in one solution, making it highly useful and versatile.
To use GraphX you must be familiar with Python, Java, and Scala. It only supports these three programming languages.
12. Apache Hive
Apache Hive is a data warehouse software project based on Apache Hadoop that provides data query and analysis. Its interface is quite similar to SQL for querying data stored in multiple databases and file systems that can integrate with Hadoop.
To be able to use Hive, you should be familiar with SQL because it is a SQL-based tool. With the help of this tool, you can process data very efficiently as it is fast and scalable. It also supports partitioning and bucketing to simplify data retrieval.
Apache Mahout is a project for producing free implementations of distributed or otherwise scalable machine learning algorithms. With it, you can organize documents and files in clusters with better accessibility.
Mahout is a recent addition to the Hadoop ecosystem but it is quickly becoming a sought-after skill. You can use it to extract recommendations from data sets with more simplicity.
As a Hadoop developer, you’ll be using Ambari for system administrators to manage, provision, and monitor Hadoop clusters. Ambari is an open-source administration tool and it helps you track the status of the various running applications. You can say that it’s a web-based management solution for Hadoop clusters. It also offers an interactive dashboard to visualize the progress of every application running over a Hadoop cluster.
Java is among the most popular programming languages on the planet. It allows you to develop Kafka queues and topics. You’ll have to use Java to design and implement MapReduce programs for distributed data processing.
As a Hadoop developer, you might have to develop Mapper and Reducer programs that meet the unique requirements of your clients. Learning this programming language is imperative to become a Hadoop developer.
Python is an easy to learn and highly versatile programming language. Python’s syntax is very simple so it won’t take much effort to learn this language. However, it has tons of applications in Hadoop.
You can develop MapReduce jobs, Spark applications, and scripting components by using Python.
How to Develop Hadoop Skill Sets?
Becoming a Hadoop developer can seem daunting. There are many skills and areas to cover that it can get overwhelming. You should start small and cover the basics first. Many of the technologies are related to each other so learning them at the same time will help you make progress faster.
Plan your studies and stick to a strict schedule to ensure you learn efficiently.
However, all of this can be very challenging. That’s why we recommend taking a big data course. A big data course would have a structured curriculum that teaches you all the necessary concepts in a step-by-step manner.
We at upGrad offer the following big data courses in partnership with the IIIT-B. They will teach you about Hadoop and all the related technologies you should be familiar with to become a Hadoop developer.
This 13-month course is perfect for students and working professionals who want to develop Hadoop developer skills. You will study through online sessions and live lectures during this program. It also offers 7+ projects and case studies so you can apply what you have learnt throughout the course. By the end of the project, you’d have learned 14 programming languages and tools.
This course lasts only for 7.5 months and offers more than 250 hours of learning. You must have a Bachelor’s degree with 50% or equivalent passing marks to be eligible for this course. However, note that you don’t need any coding experience to join this program. The course offers 1:1 personalised mentorship from big data industry experts and IIIT Bangalore alumni status like the previous course.
Both of these courses are online and give you access to upGrad’s Student Success Corner. There, you get personalized resume feedback, career counselling, placement support, and dedicated mentorship to help you kickstart your career.
Adding these skills to your Hadoop skill sets can seem quite challenging but with the right mindset, preparation, and resources, it becomes easy as a breeze.
Which skill do you think is the easiest to develop on our list? Which one is the most difficult? Share your answers in the comment section below.