Data Science with Apache Hadoop Developer Salary in 2024

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Median Salary Expectations:

How statistics are calculated

We count how many offers each candidate received and for what salary. For example, if a Data Science with Apache Hadoop with a salary of $4,500 received 10 offers, then we would count him 10 times. If there were no offers, then he would not get into the statistics either.

The graph column is the total number of offers. This is not the number of vacancies, but an indicator of the level of demand. The more offers there are, the more companies try to hire such a specialist. 5k+ includes candidates with salaries >= $5,000 and < $5,500.

Median Salary Expectation – the weighted average of the market offer in the selected specialization, that is, the most frequent job offers for the selected specialization received by candidates. We do not count accepted or rejected offers.

Where is Apache Hadoop used?

Big Data Bees Buzzing in Hive

  • In the buzzing world of the bees, AKA data scientists, Hadoop is the queen bee organizing the hive of petabytes—making sense of chaotic data swarms.

The Library of Babel’s Digital Cousin

  • Imagine Borges' infinite library going digital. Hadoop is the nerdy librarian categorizing zettabytes of books without ever shushing you.

Taming the Digital Jumanji

  • Like a sturdy board game, Hadoop wrangles wild herds of unstructured data from the digital jungle, keeping geeks safe from data stampedes.

Data Lakes Over Flooded File Systems

  • When traditional file systems are drowning under data tsunamis, Hadoop builds vast data lakes where info-sea creatures can swim freely.

Apache Hadoop Alternatives

Apache Spark

Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs.

val conf = new SparkConf().setAppName("example").setMaster("local")
val sc = new SparkContext(conf)

val data = Array(1, 2, 3, 4, 5)
val distData = sc.parallelize(data)
val result = distData.reduce((a, b) => a + b)

  • Faster processing than Hadoop MapReduce.

  • More user-friendly API for complex computations.

  • High-level libraries for SQL, streaming, and machine learning.

  • Limited by memory, may not be ideal for very large datasets.

  • Can be more complex to tune due to in-memory caching.

  • Higher cost of operation due to required RAM.

Amazon Redshift

Amazon Redshift is a fully-managed, petabyte-scale data warehouse service in the cloud. It’s designed for large scale data set storage and analysis.

  • Fully managed, less maintenance overhead.

  • Seamless scalability with cloud resources.

  • Integration with other AWS services.

  • Cost can be high for heavy data operations.

  • Less open and flexible compared to Hadoop.

  • Data lock-in risk associated with cloud vendors.

Google BigQuery

Google BigQuery is a fully-managed, serverless data warehouse that enables scalable analysis over petabytes of data. It is a Platform as a Service (PaaS) that supports SQL and is optimized for running ad-hoc and complex queries.

  • Serverless and scales automatically to your data size.

  • Real-time analytics with high speed.

  • Fully managed and integrates with Google Cloud Platform.

  • Querying is cost-based, can get expensive for large datasets.

  • Less customization options than on-premises solutions.

  • May have limitations for certain types of complex joins.

Quick Facts about Apache Hadoop

Birthed from a Search Engine's Belly

Imagine a world where data is as vast as the ocean, and you're in just a small boat called 'No-Name Yacht'. That's what Yahoo must've felt like before Hadoop was born in 2006. Engineered by Doug Cutting and Mike Cafarella, Hadoop was the brainchild of necessity, grown from the womb of the Nutch project, an open-source search engine no one remembers. It's name? A homage to Doug's kid's toy elephant, proving geeks do have a sense of humor.

Hadoop Goes to the Gym: Version Updates

Like a bodybuilder on a protein shake frenzy, Hadoop buffed up with updates and flexed its muscles. Starting off as a scrawny framework, the 1.0 release in 2012 could barely lift a byte. But then came 2.0 in 2013, introducing YARN - Yet Another Resource Negotiator - which is as essential to Hadoop as coffee is to programmers. Fast forward to Hadoop 3.0 in 2017, and it's now deadlifting petabytes like a boss, with machine learning and cloud support.

Game-Changing Gimmicks of Hadoop

In the data circus, Hadoop is the clown juggling data blocks on a unicycle. The real pie-in-the-face was its game-changer, the Hadoop Distributed File System (HDFS). It slices and dices data, spreading it across a horde of computers like butter on bread, making sure if one piece flops, the sandwich doesn't fall apart. And let's not forget MapReduce - the twin-act that lets you process this data bread with a custom recipe, just like making a data smoothie.

// Mock MapReduce code example, 'cause everyone loves pseudocode
public class MyMapper extends MapReduceBase implements Mapper {
public void map(LongWritable key, Text value, OutputCollector output, Reporter reporter) {
// Split your data sandwich into crumbs and munch on

public class MyReducer extends MapReduceBase implements Reducer {
public void reduce(Text key, Iterator values, OutputCollector output, Reporter reporter) {
// Take those crumbs and bake a new loaf of data bread

What is the difference between Junior, Middle, Senior and Expert Apache Hadoop developer?

Seniority NameYears of ExperienceAverage Salary (USD/year)Responsibilities & Activities
Junior0-250,000 - 70,000

  • Assist in the maintenance of Hadoop clusters

  • Support senior team members on Hadoop projects

  • Perform basic HDFS operations

  • Write simple MapReduce jobs

Middle2-570,000 - 100,000

  • Independently handle module-level Hadoop tasks

  • Optimize existing Hadoop queries for performance

  • Participate in design discussions for new Hadoop solutions

  • Contribute to cluster deployment and configuration

Senior5-10100,000 - 130,000

  • Architect Hadoop solutions and data workflows

  • Lead big data projects end to end

  • Mentor junior and middle developers

  • Ensure high availability and reliability of Hadoop clusters

Expert/Team Lead10+130,000 - 160,000+

  • Define strategic direction for Hadoop development

  • Oversee multiple big data projects

  • Drive innovation and best practices in the team

  • Represent the team in senior management meetings

Top 10 Apache Hadoop Related Tech

  1. Java

    Oh, Java, the granddaddy of Hadoop! If Hadoop were a donut, Java would be the dough – essential. It's like showing up to a knights’ joust with your trusty steed. Java is Hadoop's native language, and knowing your way around it is as necessary as a morning cup of joe for a coder. From MapReduce programs to the ins and outs of HDFS, Java is where you start, continue, and pretty much end. Brush up on your object-oriented thinking, and let the JVM be your trusty steed.

  2. Hadoop Distributed File System (HDFS)

    Picture a mammoth library, that's HDFS for you, a place where data is stored in gigantic chunks. It's like Tetris, but instead of blocks, you're organizing petabytes of data. You need to know how to play this data game—putting files in, getting them out, and not accidentally deleting your favorite cat videos while you're at it.

  3. MapReduce

    MapReduce is like a superhero team - one is good at mapping problems (the "Map" part), the other loves reducing them to smithereens (yep, "Reduce"). Developers who wield MapReduce craft intricate algorithms to process vast amounts of data across a truckload of servers, conjuring results like digital magicians. It's the old-school way Hadoop flexes its muscles.

  4. YARN (Yet Another Resource Negotiator)

    If Hadoop were a circus, YARN would be the ringmaster. This scheduler juggles jobs and resources with the flair of a showman. Not only does it maximize resource utilization, but it also ensures that your big data analytics don’t turn into an accidental game of bumper cars.

  5. Hive

    Imagine if SQL dressed up as a Hadoop component for Halloween - that's Hive! It allows SQL-versed folk to knock on Hadoop's door, run queries, and make sense of data without learning a whole new language. It's pretty much SQL's home away from home.

    SELECT count(*) FROM transactions WHERE amount > 1000;

  6. Pig

    Pig is like learning French to woo a Parisian - you write in Pig Latin, a scripty dialect, to charm the Hadoop system. Not as low-level as Java, not as high-level as Hive, it strikes a balance like a tightrope walker in a data circus, making data manipulation look almost graceful.

    A = LOAD 'data.txt' AS (name:chararray, age:int, gpa:float);
    DUMP A;

  7. Spark

    Picture a sleek sports car zipping through data processing tasks, that’s Spark in the world of Hadoop. With the need for speed, it handles batch processing and real-time analytics like a champ - we're talking Usain Bolt fast. It’s the shiny, newer toy for Hadoop aficionados chasing a performance thrill.

  8. HBase

    Meet HBase, the non-relational ninja of the Hadoop ecosystem, slicing and dicing large data sets in real-time. It's all about delivering the Big Data twist on the traditional database - imagine rows and columns but with enough steroids to handle billions of them at once. Use it when you need random, real-time read/write access to your Big Data wardrobe.

  9. Oozie

    Oozie is the ultimate data workflow choreographer, orchestrating every dance step of your Hadoop jobs. Imagine a conductor with the precision of a Swiss watch, making sure that every MapReduce symphony plays out without a hitch, on time and in tune.

  10. Ambari

    Lastly, Ambari waltzes in like a caring butler, managing and monitoring the life out of Hadoop clusters. With Ambari, peeking into the soul of your Hadoop setup becomes a walk in the park. You'll install, configure, and maintain these electronic beasts like you're sprinkling fairy dust on them.

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