Data Engineer with Apache Hadoop Salary in 2024
How statistics are calculated
We count how many offers each candidate received and for what salary. For example, if a Data Engineer developer 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.
Trending Data Engineer tech & tools in 2024
Data Engineer
What is a data engineer?
A data engineer is a person who manages data before it can be used for analysis or operational purposes. Common roles include designing and developing systems for collecting, storing and analysing data.
Data engineers tend to focus on building data pipelines to aggregate data from systems of record. They are software engineers who put together data and combine, consolid aspire to data accessibility and optimisation of their organisation’s big data landscape.
The extent of data an engineer has to deal with depends also on the organisation he or she works for, especially its size. Larger companies usually have a much more sophisticated analytics architecture which also means that the amount of data an engineer has to maintain will be proportionally increased. There are sectors that are more data-intensive; healthcare, retail and financial services, for example.
Data engineers carry out their efforts in collaboration with particular data science teams to make data more transparent so that businesses can make better decisions about their operations. They use their skills to make the connections between all the individual records until the database life cycle is complete.
The data engineer role
Cleaning up and organising data sets is the task for so‑called data engineers, who perform one of three overarching roles:
Generalists. Data engineers with a generalist focus work on smaller teams and can do end-to-end collection, ingestion and transformation of data, while likely having more skills than the majority of data engineers (but less knowledge of systems architecture). A data scientist moving into a data engineering role would be a natural fit for the generalist focus.
For example, a generalist data engineer could work on a project to create a dashboard for a small regional food delivery business that shows the number of deliveries made per day over the past month as well as predictions for the next month’s delivery volume.
Pipeline-focused data engineer. This type of data engineer tends to work on a data analytics team with more complex data science projects moving across distributed systems. Such a role is more likely to exist in midsize to large companies.
A specialised, regionally based food deliveries company could embark upon a pipeline-oriented project, building an analyst tool that allows data scientists to comb through metadata to retrieve information about deliveries. She could look at distances travelled and time spent driving to make deliveries in the past month, and then input those results into a predictive algorithm that forecasts what those results mean about how they should do business in the future.
Database centric engineers. The data engineer who comes on-board a larger company is responsible for implementations, maintenance and populating analytics databases. This role only comes into existence where data is spread across many databases. So, these engineers work with pipelines, they might tune databases for particular analysis, and they come up with table schema using extract, transform and load (ETL) to copy data from several sourced into a single destination system.
In the case of a database-centric project at a large, national food delivery service, this would include designing an analytics database. Beyond the creation of the database, the developer would also write code to get that data from where it’s collected (in the main application database) into the analytics database.
Data engineer responsibilities
Data engineers are frequently found inside an existing analytics team working alongside data scientists. Data engineers provide data in usable formats to the scientists that run queries over the data sets or algorithms for predictive analytics, machine learning and data mining type of operations. Data engineers also provide aggregated data to business executives, analysts and other business end‑users for analysis and implementation of such results to further improve business activities.
Data engineers tend to work with both structured data and unstructured data. Structured data is information categorised into an organised storage repository, such as a structured database. Unstructured data, such as text, images, audio and video files, doesn’t really fit into traditional data models. Data engineers must understand the classes of data architecture and applications to work with both types of data. Besides the ability to manipulate basic data types, the data engineer’s toolkit should also include a range of big data technologies: the data analysis pipeline, the cluster, the open source data ingestion and processing frameworks, and so on.
While exact duties vary by organisation, here are some common associated job descriptions for data engineers:
- Build, test and maintain database pipeline architectures.
- Create methods for data validation.
- Acquire data.
- Clean data.
- Develop data set processes.
- Improve data reliability and quality.
- Develop algorithms to make data usable.
- Prepare data for prescriptive and predictive modeling.
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)
println(result)
- 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 Name | Years of Experience | Average Salary (USD/year) | Responsibilities & Activities |
|---|---|---|---|
| Junior | 0-2 | 50,000 - 70,000 |
|
| Middle | 2-5 | 70,000 - 100,000 |
|
| Senior | 5-10 | 100,000 - 130,000 |
|
| Expert/Team Lead | 10+ | 130,000 - 160,000+ |
|
Top 10 Apache Hadoop Related Tech
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.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.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.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.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;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;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.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.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.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.