Back

Data Science Developer with Apache Spark Salary in 2024

Share this article
Total:
76
Median Salary Expectations:
$5,285
Proposals:
0.6

How statistics are calculated

We count how many offers each candidate received and for what salary. For example, if a Data Science with Apache Spark 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 Spark used?


Real-Time Fraud Detection Shenanigans



  • Spark spies on zillions of transactions faster than a caffeinated squirrel, sniffing out the fishy ones with its ML superpowers.



Streamlining the Streamers



  • It turbo-boosts Netflix's binge-predictor to keep you glued by churning through petabytes of "What to watch next?".



Traffic Jam Session Solver



  • This digital traffic cop manages road-choked cities, analyzing sensor data to clear the jam faster than you can honk.



Genomic Data Disco



  • Crunches more genes than a DNA smoothie maker, helping white coats find health insights hidden in our biological blueprints.

Apache Spark Alternatives


Apache Flink


Apache Flink is a distributed stream processing framework for high-throughput, fault-tolerant data streaming applications. Flink offers a streaming-first runtime that also supports batch processing.



  • Handles both batch and stream processing efficiently.

  • Offers exactly-once processing semantics.

  • Lower latency compared to Spark's micro-batching.

  • Not as mature as Spark, fewer community libraries.

  • Higher operational complexity for setup and tuning.

  • Less comprehensive documentation.




// Example Flink code for a simple stream transformation
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
DataStream text = env.fromElements("Flink", "Spark", "Hadoop");

DataStream transformed = text
.map(String::toLowerCase);

env.execute("Streaming Word Lowercase");


Apache Hadoop MapReduce


Hadoop MapReduce is a software framework for easily writing applications which process vast amounts of data (multi-terabyte datasets) in-parallel on large clusters.



  • Widely adopted, large ecosystem of tools and integrations.

  • Good for simple data transformations on large datasets.

  • Cost-effective on commodity hardware.

  • Slower performance for iterative algorithms.

  • Less efficient for streaming data processing.

  • Complexity in managing and tuning jobs.




// Hadoop MapReduce word count example
public static class TokenizerMapper
extends Mapper{

private final static IntWritable one = new IntWritable(1);
private Text word = new Text();

public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
}
}
}


Google Cloud Dataflow


Google Cloud Dataflow is a fully managed streaming analytics service that minimizes latency, processing time, and cost through autoscaling and batch processing.



  • Auto-scaling and fully managed service.

  • Integrated with Google's Big Data services.

  • Easy pipeline development with an intuitive API.

  • Less open source leverage, bound to GCP.

  • Can become expensive for large scale data.

  • Less control over infrastructure.




// Google Cloud Dataflow pipeline example
Pipeline p = Pipeline.create(
DataflowRunner.fromOptions(options));

p.apply("ReadLines", TextIO.read().from("gs://some/inputData.txt"))
.apply("ProcessLines", ParDo.of(new DoFn() {
@ProcessElement
public void processElement(ProcessContext c) {
c.output(c.element().toUpperCase());
}
}))
.apply("WriteResults", TextIO.write().to("gs://some/outputData"));
p.run();

Quick Facts about Apache Spark


A Spark Ignites in the Big Data World!


Did you hear about the time when data processing was slower than a snail on a leisure stroll? Enter 2009, when Matei Zaharia lit the proverbial match at UC Berkeley's AMPLab and gave the world Apache Spark. It's like he injected caffeine straight into the veins of data analytics!



Behold the Speedy Gonzales of Data!


Hold onto your hats cause Apache Spark zips through data processing at the speed of lightning – well, almost. It's known for running programs up to 100x faster in memory and 10x faster on disk than its big data brethren, Hadoop. Talk about a quick draw!



The Cool Kids of Spark Versions


Oh, the versions we've seen! Spark 1.0 dropped in 2014 like the hottest mixtape of the year, but it was just the beginning. Fast-forward to Spark 3.0 in June 2020 – it's like someone turned up the dial with features like Adaptive Query Execution that make it smarter than a whip!




// A slice of code to highlight Spark's SQL prowess:
val sparkSession = SparkSession.builder.appName("Spark SQL magic").getOrCreate()
val dataframe = sparkSession.read.json("examples/src/main/resources/people.json")
dataframe.show()

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


































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

  • Assisting in the maintenance of Spark jobs

  • Learning the codebase and improving coding skills

  • Debugging and fixing minor bugs

  • Writing simple Spark queries and transformations

  • Participating in code reviews


Middle2-570,000 - 100,000

  • Developing complex Spark jobs

  • Optimizing data processing pipelines

  • Implementing Spark streaming tasks

  • Conducting moderate-level data analysis

  • Collaborating with cross-functional teams


Senior5-10100,000 - 150,000

  • Architecting scalable Spark solutions

  • Leading data model designs

  • Ensuring best practices in data processing

  • Mentoring junior developers

  • Reviewing code for critical systems


Expert/Team Lead10+150,000+

  • Setting the project's technical direction

  • Managing team and project timelines

  • Overseeing multiple projects simultaneously

  • Handling stakeholder communications

  • Driving innovation and best practices



Top 10 Apache Spark Related Tech



  1. Scala



    Imagine telling a gourmet chef to cook with a plastic spoon – that's coding Spark without Scala. Why? Because Spark is written in Scala, which means it fits together like peanut butter and jelly. With Scala, you handle big data transformations with the elegance of a ballroom dancer – smooth, efficient, and with a touch of class. Plus, functional programming paradigms in Scala make it a breadwinner for Spark's immutable datasets. Here's a little taste:


    val spark = SparkSession.builder().appName("Scala Spark Example").getOrCreate()
    val data = spark.read.json("hugs_from_grandma.json")
    data.show()




  2. Python



    While Scala is the native tongue of Spark, Python is like the friendly international student who's loved by everyone. It's inviting, readable, and versatile – perfect for data scientists who speak math more fluently than binary. Through PySpark, Pythonistas can orchestrate the data-crunching power of Spark using cozy Pythonic syntax. Python may sometimes be slower for tightly-packed computations, but its ease of use makes it a hotcake.


    from pyspark.sql import SparkSession
    spark = SparkSession.builder.appName("Python Spark Example").getOrCreate()
    df = spark.read.json("cat_memes.json")
    df.show()




  3. Java



    Remember the time when Java ruled the world? Well, it still has a fiefdom in Spark territories. Java's like that old knight that doesn't know when to quit – still armored in verbosity and ready to charge. But be prepared to type – a lot. Your fingers might be left feeling like they just ran a marathon, but Java's performance and portability often justify the workout.


    SparkSession spark = SparkSession.builder().appName("Java Spark Example").getOrCreate();
    Dataset data = spark.read().json("historical_tweets.json");
    data.show();




  4. Hadoop



    When talking about Spark, omitting Hadoop would be like baking a cake and forgetting sugar. Hadoop's file system, HDFS, is like the colossal warehouse where you store all your big data before Spark starts its magic show. Not to mention, YARN – Hadoop's resource manager – often acts as Spark's stage manager, making sure every act in the performance goes off without a hitch.




  5. SQL



    In the data world, talking SQL is like having the secret password to every speakeasy in town. Spark SQL lets you whisper sweet nothings (well, queries) to your data framed as tables. It's like playing database inside a big data environment – you get the familiar feel of querying with SQL, plus the thrill of Spark's lightning fast analytics.




  6. Docker



    In the glamorous world of software, Docker is the versatile costume designer. With Docker, you can package your Spark application along with its environment into neat, containerized wardrobes, ready to be deployed on any Linux machine like a runway model. It makes "It works on my machine" a love story instead of a breakup.




  7. Kubernetes



    Kubernetes, affectionately known as K8s, is like the shepherd that herds all your container sheep with a nifty whistle. It's the go-to for orchestrating a choir of Docker containers, making sure your Spark application scales gracefully and recovers from disasters like a phoenix rising from ashes – or in this case, nodes reinstantiating from failed pods.




  8. Apache Kafka



    Streaming data is like a river – to drink from it, you better have a good bucket. Kafka is the sturdiest bucket you could ask for in the Spark ecosystem. It allows you to sip streams of data in real-time, making sure your Spark application is always hydrated with the freshest and crispest data on tap.




  9. Apache Airflow



    Orchestrating a symphony of data pipelines without Airflow is like trying to conduct an orchestra with a rubber chicken. Airflow lets you author, schedule, and monitor workflows as breezily as if it's a walk in the park. Spark jobs can be just a beat in your opus, with Airflow's conductor baton making sure each section plays in perfect harmony.




  10. MLlib



    Spark's MLlib is like having a Swiss army knife for machine learning. Dive into big data like it's a ball pit, and come out with predictive insights, clustering, recommendations – you name it. MLlib has machine learning all sewn up, making it a playland for data scientists looking to spin cotton candy out of raw, unprocessed data.



Subscribe to Upstaff Insider
Join us in the journey towards business success through innovation, expertise and teamwork