Data Engineer with Apache Spark Salary in 2024

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?

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.