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Process Mining with Data Pipelines (ETL) Developer Salary in 2024

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Total:
4
Median Salary Expectations:
$5,250
Proposals:
0.5

How statistics are calculated

We count how many offers each candidate received and for what salary. For example, if a Process Mining with Data Pipelines (ETL) 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 Data Pipelines (ETL) used?

 

Feeding the Analytics Beast



    • Those number-munching analytics tools are hangry! A data pipeline is like a sushi conveyor belt, delivering fresh data sashimi straight to their algorithms.




Keepin' It Fresh on E-Commerce



    • Imagine a virtual store where the price tags play tag. ETL keeps them in check, refreshing prices so shoppers don't pay for yesterday's deals.




Data Migration Migration



    • When databases go on vacation, ETL packs up their data suitcases and ensures no byte-sized underwear gets left behind.




Unlocking the Chamber of Secrets



    • Inside huge company vaults, data pipelines are the master key, turning data scraps into treasure maps that lead straight to the insights treasure!

 

Data Pipelines (ETL) Alternatives

 

Stream Processing

 

Stream processing handles data in real time, processing continuous streams of data as they are generated. Examples: Apache Kafka, Apache Flink.

 


// Example with Apache Kafka
Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");

Producer<String, String> producer = new KafkaProducer<>(props);
producer.send(new ProducerRecord<String, String>("stream-topic", "key", "value"));
producer.close();



    • Enables real-time decision making

 

    • Challenging to manage state

 

    • Complex error handling




Data Lake

 

Data lakes store vast amounts of raw data in its native format. Data can be structured or unstructured. Examples: Amazon S3, Azure Data Lake Storage.

 


// Example with Amazon S3
AmazonS3 s3Client = AmazonS3ClientBuilder.standard()
.withRegion(Regions.US_EAST_1)
.build();
s3Client.putObject("data-lake-bucket", "path/to/file", new File("local/path/to/file"));



    • Highly scalable storage solution

 

    • Lacks transactional support

 

    • Potential for data swamps if not well-governed




Data Virtualization

 

Data Virtualization provides an abstraction layer to access and manage data from various sources without moving or replicating it. Examples: Denodo, Tibco Data Virtualization.

 


// Data virtualization is typically used through a GUI or vendor-specific language, not code samples



    • Reduces data redundancy

 

    • May have performance issues with large datasets

 

    • Potentially complex setup and maintenance

 

Quick Facts about Data Pipelines (ETL)

 

ETL's Groovy Grandparent: "Extract, Transform, Load"

 

Let's wind back the clock to the 1970s, the disco era, when bell-bottom jeans were hip, and so was the concept of ETL. It was all about getting data to boogie from one database to another with style. These moves weren't just for kicks; they were mission critical for businesses. Although no one person claims the fame for the ETL shuffle, it was the collective brainchild of database gurus who needed to streamline data for better decision-making or, you know, just to keep it from doing the hokey pokey.



ETL Software: The Rise of the Data Transformers

 

Jump to the '90s, and software vendors are saying "As if!" to manual data wrangling. They started churning out ETL tools faster than you can say "Be kind, rewind." Informatica hit the scene in 1993, making it the Fresh Prince of data integration. This wasn't just any software; it was like a Swiss Army knife for data, cutting and dicing information for analytics like a ninja.



Apache NiFi: The Cool New Kid

 

The year 2006 brought the new kid in town: Apache NiFi (originally developed by the NSA, yes, that NSA). Stepping into the open-source playground, this tool had a knack for flow-based programming and an interface as slick as a Spielberg movie. NiFi made data flow effortless, like a good game of Tetris, letting data pieces fall right into place. And version after version, it just keeps getting cooler, turning the ETL world upside down, Stranger-Things-style.




// Example of NiFi's smooth moves: Creating a data flow
Processor myProcessor = new GetFile()
.name("DataIngestion")
.addProperty("Input Directory", "/path/to/data")
.schedule(100, TimeUnit.SECONDS);

What is the difference between Junior, Middle, Senior and Expert Data Pipelines (ETL) developer?



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

    • Data cleaning and preparation

    • Maintaining simple ETL tasks

    • Learning to use ETL tools and systems

    • Assisting in debugging minor issues


Middle2-570,000-95,000

    • Designing and deploying moderate complexity ETL pipelines

    • Implementing data validation and testing

    • Optimizing data flow for efficiency

    • Collaborating with team for system improvements


Senior5-1095,000-130,000

    • Developing complex data pipelines

    • Leading ETL process improvements

    • Mentoring junior developers

    • Managing data pipeline lifecycles


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

    • Strategizing pipeline architecture

    • Overseeing entire ETL process

    • Stakeholder communication

    • Leading the development team


 

Top 10 Data Pipelines (ETL) Related Tech




    1. Python & PySpark


      Imagine Python as the swiss-army knife of programming languages - versatile, easy to use, and surprisingly powerful when it comes to crunching data. It's the seasoned chef in the ETL kitchen, where libraries, such as Pandas, help in prepping data like a sous-chef. PySpark enters when your data behaves like it's on a caffeine rush, large and fast, necessitating distributed computing. This is when your Python scripts wear a superhero cape to harness the power of Apache Spark, handling voluminous datasets without breaking a sweat.



      # Python snippet for data manipulation using Pandas
      import pandas as pd
      data = pd.read_csv('unruly_data.csv')
      clean_data = data.dropna(how='all')




    1. SQL & NoSQL Databases


      SQL is like that old friend who has seen it all, from tiny datasets to big enterprise-level data warehouses. It's your go-to for structured querying, almost like playing chess with your data, moving pieces (tables) around to make the next big strategic move (query). NoSQL waltzes in when your data has a rebellious streak, defying structure and favoring the free-flowing charm of documents or key-values. It's like jazz to SQL's classical music, both essential in the modern data symphony.



      -- SQL snippet for selecting all rows from a "users" table
      SELECT * FROM users;




    1. Apache Kafka


      Think of Apache Kafka as the town gossip, disseminating information rapidly and reliably. It's a messaging system that thrives on high-throughput data scenarios. Kafka acts like a post office for your data streams, making sure every byte of data is delivered to the right application without dropping any packets along the way.



      // Kafka Producer example in Java
      Properties props = new Properties();
      props.put("bootstrap.servers", "localhost:9092");
      props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
      props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");

      Producer<String, String> producer = new KafkaProducer<>(props);
      producer.send(new ProducerRecord<String, String>("topic", "key", "value"));
      producer.close();




    1. Apache Airflow


      In the world of scheduling and orchestrating ETL tasks, Apache Airflow is the adept conductor of an orchestra, ensuring each section comes in at the right time. It brings order and rhythm to the complex symphonies of data pipelines, with its intricate workflows represented as directed acyclic graphs (DAGs). Each task is a musician waiting for the conductor's cue to play their part in the data processing concerto.



      # Apache Airflow DAG example
      from airflow import DAG
      from airflow.operators.python_operator import PythonOperator
      from datetime import datetime, timedelta

      default_args = {
      'owner': 'airflow',
      'depends_on_past': False,
      'start_date': datetime(2023, 4, 1),
      'email': ['email@example.com'],
      'email_on_failure': False,
      'email_on_retry': False,
      'retries': 1,
      'retry_delay': timedelta(minutes=5),
      }

      dag = DAG('etl_workflow', default_args=default_args, schedule_interval=timedelta(days=1))

      def etl_task():
      # ETL logic goes here
      pass

      t1 = PythonOperator(
      task_id='etl_task',
      python_callable=etl_task,
      dag=dag)




    1. Amazon Redshift


      Amazon Redshift stars as the fortified castle holding the treasure trove of your data. It's a petabyte-scale warehouse service that makes data analysts drool with its speed and power, thanks to columnar storage and massively parallel processing. Picture Redshift as a high-speed train carrying variegated data to its destination, with a promise of security, comfort, and the thrill of optimization.




    1. Google BigQuery


      Elastic and cloud-native, Google BigQuery is like a trampoline for data - it scales to the height of your analytical ambitions without breaking a sweat. BigQuery is the joining of a NoSQL database's flexibility with traditional SQL's querying prowess, resulting in a serverless wonder that lets you query oceans of data with the ease of skipping stones across a lake.




    1. Microsoft Azure Data Factory


      Imagine a data-driven theme park, and Microsoft Azure Data Factory is your fast pass to all the exciting rides. It's a cloud ETL service that facilitates creating, scheduling, and orchestrating data flows with ease. With Azure Data Factory, you're the maestro of data movement, leveraging the cloud's power to compose harmonious data pipelines.




    1. Informatica PowerCenter


      Informatica PowerCenter is like a venerable wizard in the alchemy of data ETL processes. It transforms raw, crude data into golden insights with a sweep of its transformation wand. This trusty veteran in the world of data integration brings to the table endless customizability and a rich library of components for those who seek to master the arcane arts of data wizardry.




    1. Talend Open Studio


      Talend Open Studio stands proud as the Swiss Army knife in the realm of data integration. Talend is open-source, beckoning with the allure of a toolkit that's as flexible as a gymnast. It's a favorite among data journeymen for building ETL processes, with the added bragging rights of boosting community-driven innovation.




    1. StreamSets


      StreamSets is like a nimble courier service for your data, delivering packets of bytes across the most convoluted of data topographies with a can-do attitude. This spry newcomer doesn't shy away from the complexity of continuous data flows, embracing them with an intuitive design and a clear focus on maintainability and performance.

 

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