Home How-To How to Become a Successful Big Data Analyst Or Hadoop Developer

How to Become a Successful Big Data Analyst Or Hadoop Developer

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How to Become a Successful Big Data Analyst Or Hadoop Developer

A big data Analyst Or Hadoop developer, therefore, is a software development professional who understands the challenges of big data and can leverage data systems and architecture such as Hadoop, to solve these challenges and make the system efficient. Data Analytics deals with the mechanical processes or algorithmic applications in developing insights from Big data.

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Almost all businesses use Data Analytics to improve decision making and verify or disprove prevailing models and approaches. Data Analytics centers on inference, and concentrates principally on the interpretation that mainly depends on the analyst’s knowledge.

HDFS (Hadoop Distributed File System) breaks down large files into small blocks of a specified size (default size per block is 128mb) and distributes them across different nodes.

A considerable confusion prevails with job seekers regarding data science, big data and data analytics for preferring a career role. Experts in data engineering, data science, data analytics, data mining and many related fields in data science often work side by side with respective individual functions, but mistakenly many people interchange functional roles in these fields.

Why should become a big data Hadoop developer

hadoop developer job

Opportunities in Hadoop are rife, and the jobs are rewarding. As seen in the example above from indeed.com, a professional Hadoop Developer can expect an average salary of $100,000 per annum.

Since its adoption, Hadoop has acquired a reputation for being scalable, making it a reliable platform for storing and processing data. Not only does adopting Hadoop make dealing with big data cost -effective, but its ease of integration with analytics software like Spark makes it an ideal tool for handling a large variety of workloads. As such, Hadoop is invaluable to enterprises in verticals such as insurance, banking, manufacturing, telecom, and online retail, that generate and use big data.

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The health sector, for instance, experiences a lot of information flow from Electronic Medical Records, wearables, and medical equipment, among others. Hadoop allows such institutions to store, map and process all this data and leverage it in decisions involving cure and treatment, all at a manageable cost. This, in turn, has created high demand for Hadoop developers.

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Google trends show that Hadoop has had stable growth over the last 5 years. It is also worth noting that at the same time, big data has experienced a similar trend, prompting the argument that both big data and Hadoop have a bright future.

big data & hadoop trend

Roles and Responsibilities

  • Creating and implementing Hadoop solutions
  • Working with SQL and NoSQL
  • Knowledge of data warehousing
  • Hadoop configuration and support
  • Designing web solutions for high-speed tracking and querying of data
  • Complete understanding of Hadoop and how to work within its ecosystem
  • Creating code with MapReduce
  • Managing and monitoring log files
  • Building Hadoop clusters
  • Using Pig and Hive to preprocess data
  • Analyzing large sets of data to uncover insights
  • Protecting the integrity, security, and privacy of data
  • Data loading
  • HBase deployment and management

Responsibilities will differ depending on the sector or domain you work in.

What skills do you need

  • Proficiency in Hadoop
  • Write reliable code in Python or Scala
  • Experience with Pig, Hive, and HBase
  • Know how to write MapReduce jobs and how to use PigLatin to write scripts
  • Understand data loading and tools used in the process, like Sqoop and Flume
  • Be an analytical thinker and a problem solver
  • Proficiency in back-end programming with java, OOAD, Node.js, and JS.
  • Understand database structures
  • Skillful in concurrency and multi-threading concepts
  • Understand workflows and schedules

Recommended courses for Training

  • The Hadoop Framework and how to deploy Hadoop in a cluster environment
  • Components such as Hive, Pig and Impala and how to use them to process data sets in HDFS
  • Spark algorithms, query processes using Spark SQL,
  • Parallel processing and data processing in real time using Spark
  • execute real-life projects with CloudLab
  • Working with HDFS to store and manage data
  • Characteristics and advanced concepts of MapReduce
  • Data ingestion with Flume and Sqoop
  • Using Hive and Impala to partition and create tables and databases
  • Working with HBase

This course, as with all other SimpliLearn courses, comes with a money back guarantee.

Certification: yes

Cost: $ 399

EdX Big data Fundamentals Training Course

This course will introduce you to the world of Big Data. You will learn how Big Data is driving organizational change. You will also learn about:

  • MapReduce and its applications
  • Fundamental techniques, such as data mining and stream processing
  • Designing algorithms for stream processing
  • A complete overview of the PageRank algorithm
  • the underlying random walk algorithms
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Coursera’s Hadoop Platform and Application Framework

Offered by UC San Diego, this course is for beginner-level programmers and other professionals who want to understand how to analyze big data.

The course is self-paced and only takes 5 weeks if you put in 1 to 2 hours of study every week. It will fit in your schedule if you are busy or need more time to understand complex areas. The individual modules will cover:

  • Insights into the challenges posed by big data
  • The components and basics of Hadoop ecosystem, its software stack and the execution environment.
  • The design of Hadoop Distributed File System (HDFS), its read/write processes and configurations for improving performance.
  • How to access data with HDFS
  • Understand the idea behind MapReduce, execute tasks in MapReduce and learn its trade-offs
  • Get introduced to the Spark framework and its characteristics and learn how it compares to MapReduce
  • To gain practical experience, you will be immersed in solving real-world data problems with Spark and Hadoop.

Though you will begin with no experience at all, the course guarantees that by the time you finish the curriculum, you will speak about Hadoop and big data with authority.

Certification: yes

Cost: Available upon enrolling

Cloudera Developer Training for Apache Spark™ and Hadoop

If you are a Python or Scala developer, this course will teach you concepts of big data processing and prepare you to become an expert in dealing with big data problems. Although you don’t need to have prior experience with Spark or Hadoop, you need to know programming with Python or Scala to take this course. You also need to know SQL and be familiar with the Linux command line.

You will learn:

  • How to use Apache Spark 2 to develop parallel applications.
  • How to write and execute Spark applications on clusters
  • Data storage and processing in a cluster
  • Data querying and processing with Spark SQL
  • Process streaming data from multiple sources with Spark Streaming
  • How to write applications that will accomplish ETL processing with core Spark
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You will also gain hands-on experience by practicing on live clusters in the cloud.

With your new skills, you will be able to improve the speed and quality of analysis and decisions in any industry and use case.

Certification: yes

Cost: Available upon enrolling

Conclusion

From the above, we can conclude that as long as companies continue to generate a lot of data, big data will continue to be relevant and along with it, applications like Hadoop that make data processing easy.

As such, as a big data Hadoop developer, you will continue to be pertinent to the processes involved in helping organizations use their data to solve business challenges.

Whether it’s fraud detection, selling more products, managing risk, or breaking into new markets, as long as data is involved, you will be at the center of it all. Considering you only need a few hundred dollars and 1 to 2 hours a week to get on a path to earning 6-figure income, the investment is worth your while

What are the difference between a Big Data Developer and a Data Analyst

Data Analysts generally operate under any business stream, such as operations, strategy, product, growth, sales, marketing, etc. Data Analysts link the data platform and the business stream. They use the shared data platform and help solve business issues managing data from the data platform. They need to manage a great harmony between business and technical skills to be successful in this role.

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Data Developer also known as Data Scientist, Data Engineer or Software Engineer generally operates under the Engineering wing. They set up the data programs that provide data to a data platform. They run programs like Hadoop, Spark, Custom Code, ETL tools, etc. to develop data pipelines for building and managing the data program. And to succeed in this task you need to be a strong technical chopper.

 

 

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