Top Big Data Hadoop Developer Skills | Trend Analysis 2022
As technology advances, businesses are increasingly relying on big data analytics to help them make better decisions. Hadoop is one of the most popular big data platforms and developers who are skilled in it are in high demand.
In this blog post, JobsPikr will give you a detailed look at the top Big Data Hadoop developer skills that are trending among Hadoop developers. Keep in mind that these skills can change depending on the specific needs of your business, so be sure to tailor your development team accordingly.
What is Hadoop?
Hadoop is a leading framework for storing and processing big data using a distributed computing architecture on commodity hardware. This open-source software library allows distributed processing of big-sized data sets across different clusters of computers using modern programming models. The Hadoop framework handles the infrastructure to store the data and is responsible for processing it in parallel across a cluster of computers.
The Hadoop framework is designed with a master/slave architecture that centers around a distributed file system called HDFS. This acronym stands for Hadoop Distributed File System, and it’s the primary way data is stored in the Hadoop cluster. HDFS takes care of distributing data across machines in a cluster to ensure each machine has a balanced workload and to minimize the impact of hardware failures.
What are the Benefits of Using Hadoop?
Hadoop is a free, open-source software framework designed to handle large data sets efficiently and reliably. Some benefits of using Hadoop are that it’s an open-source technology, provides reliability and scalability, is cost-effective, flexible to changing business needs, supports multiple data formats, can handle large amounts of data quickly. Some uses for Hadoop include real-time analysis, interactive data querying, data summarization, multi-user collaboration, and sharing of data with multiple users.
Hadoop is most commonly used by large corporations but can also be used in a smaller business or family enterprise. Some common uses of Hadoop are customer relationship management (CRM), human resources, finance, and accounting. Hadoop can be used in conjunction with several other open-source programs to help analyze large data sets more efficiently namely Apache Spark and Hive.
Hadoop runs on commodity hardware which allows businesses to avoid purchasing expensive hardware licenses or equipment. This factor also makes it cost-effective, as well as flexible to changing business needs. It is ideal for the storage of large quantities of unstructured data.
Big Data Hadoop Developer Skills
Hadoop was designed to scale up from a single server to thousands of machines, allowing businesses the ability to expand as their needs grow by adding more nodes to their Hadoop cluster. JobsPikr has identified the top 7 Big Data Hadoop developer skills that employers are looking for in order to hire a good Hadoop developer.
Pig Latin script has become very popular for querying data stored in Hadoop. Pig is a data flow language that makes it possible to analyze huge sets of data part by part rather than having to process the entire dataset at once. This scripting language is ideal for big data developers, who need to perform multiple jobs and processes on large datasets. So knowledge of pig Latin script and its libraries will definitely help you get a good Hadoop developer position with your preferred company or organization.
Hive is also another popular query language similar to Pig, but it uses SQL type commands which makes it easy for people familiar with traditional relational databases such as MySQL. The biggest benefit of using hive over other programming languages is that it has an interactive command-line interface (similar to Unix directory style) which makes it easy for developers to understand, code, and execute without any hassle.
Knowledge of Hbase will not only benefit you in getting Hadoop developer jobs but also give you an extra edge over other developers because being part of Apache foundation, it has become the core component of every other big data technology.
The main advantage of using Hbase is that, unlike traditional structured databases, NoSQL databases allow random access to data which means that instead of reading entire data first then searching for specific information, Hbase allows users to search for information exactly where it is stored.
Sqoop is used to import data from RDBMS databases such as MySQL, PostgreSQL, Teradata, etc. It uses MapReduce jobs to perform these operations which makes it very fast and efficient in transferring huge chunks of data into HDFS or other Hadoop file systems.
Knowledge of sqoop will help you fetch Hadoop developer jobs where companies prefer developers having advanced big data skills with knowledge about all the popular big data technologies.
Flume is a distributed real-time collection system for moving large volumes of log data from various sources into HDFS or Oracle. The main advantage of using a flume is that it can be easily used to transfer huge amounts of data from multiple sources while avoiding duplication of the data and maintaining the order in which events occurred.
A Hadoop cluster usually has multiple servers and there are chances that some of these nodes will go down for some reason or another. Since the reliability of a Hadoop cluster depends on how well these machines work together, a zookeeper was introduced as a centralized server that manages configuration information, naming registry, and synchronization service across all current members in a cluster.
All these functions performed by a single machine make it a very important part of any large-scale distributed system running over several machines.
Scalding is an open-source .NET framework used to write complex data processing jobs running over Hadoop, Pig, or Hive. It was built on top of C# and makes it easy for developers to use the full power of object-oriented programming along with map-reduce operations.
Since scalding uses specialized Scala syntax, learning it also means you will be able to write code for Apache Spark which is now considered as the core engine powering all other machine learning technologies like Graphlab, MLlib, etc.
This information should help you to identify which of the top big data Hadoop developer skills are trending in your industry. You can then use this new knowledge to make proactive decisions about what technologies and skillsets will be most beneficial for you to learn next.
Plus, if any of these trends do not align with your business objectives or job description, now is a great time to start planning for how you might pivot in order to take advantage of future hiring opportunities that may arise due to market demand. Contact us for more information.