How statistics are calculated
We count how many offers each candidate received and for what salary. For example, if a Data Engineer developer with PostgreSQL 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 PostgreSQL used?
The Hidden Prowess of PostgreSQL
- Under the hood of your favorite Insta-food pics, PostgreSQL is the chef curating and serving your feed without a hiccup.
- Got a retail therapy bot? Thank Postgres for not buying 12 pairs of socks when you just wanted one.
- In the gaming realm, Postgres is the stealthy NPC keeping score while you're out there claiming virtual glory.
- Imagine the world's biggest library with a grumpy librarian; that's PostgreSQL in charge of healthcare records, except it never shushes you.
PostgreSQL Alternatives
MySQL
MySQL is an open-source relational database management system. It's known for its ease of use and speed, and it's often used for web applications.
- Widely used and supported
- Flexible and easy to set up
- Performs well in web applications
- Limited functionalities compared to PostgreSQL
- Less advanced security features
- Performance can degrade with complex queries
-- MySQL
SELECT * FROM users WHERE age > 25;
-- PostgreSQL
SELECT * FROM users WHERE age > 25;
MongoDB
MongoDB is a NoSQL database that stores data in JSON-like documents. It excels in applications that need quick iteration and flexible data models.
- Highly scalable
- Flexible document schemas
- Agile and developer-friendly
- Transactions are less robust than SQL
- Joins and complex queries can be challenging
- Data consistency can be an issue
// MongoDB
db.users.find({ age: { $gt: 25 } });
// PostgreSQL
SELECT * FROM users WHERE age > 25;
SQLite
SQLite is a self-contained, serverless, zero-configuration, transactional SQL database engine. It's perfect for mobile and lightweight applications.
- Lightweight and self-contained
- Zero-configuration necessary
- Good for embedded applications
- Not suited for high concurrency
- Lacks advanced features
- Write operations are serialized
-- SQLite
SELECT * FROM users WHERE age > 25;
-- PostgreSQL
SELECT * FROM users WHERE age > 25;
Quick Facts about PostgreSQL
Back in Time: PostgreSQL’s Baby Steps
Imagine it’s 1986, and the computer world is buzzing with neon leg warmers and side-ponytails. Michael Stonebraker, a dude with a vision from the University of California at Berkeley, kicks off the PostgreSQL journey with a project named POSTGRES. This project was meant to evolve the groundbreaking Ingres database into something even cooler, like trading in a Walkman for an iPod.
From SQL Whispers to Roars
By 1995, POSTGRES was teaching itself a new trick: speaking SQL. Yes, that's like learning French in Paris! The addition of SQL transformed POSTGRES into PostgreSQL, giving it a language everyone at the data party understands. It's like going from using Morse code to hosting a slick podcast.
Shapeshifting Through Versions: The PostgreSQL Chameleon
PostgreSQL has been through more costume changes than a pop star. From the vintage 6.0 release in 1997, which was like the 8-track tape of databases, to the fresh-as-avocado-toast PostgreSQL 14 in 2021, PostgreSQL is the Madonna of databases. With each release, it struts out new features like data types faster than a quick-change act!
// Let's say it's the '90s and you're adding 'Hello World' to your database:
INSERT INTO table_of_cool (message) VALUES ('Hello World');
What is the difference between Junior, Middle, Senior and Expert PostgreSQL developer?
Seniority Name | Years of Experience | Average Salary (USD/year) | Responsibilities & Activities |
---|---|---|---|
Junior PostgreSQL Developer | 0-2 | 50,000-70,000 |
|
Middle PostgreSQL Developer | 2-5 | 70,000-100,000 |
|
Senior PostgreSQL Developer | 5-10 | 100,000-130,000 |
|
Expert/Team Lead PostgreSQL Developer | 10+ | 130,000+ |
|
Top 10 PostgreSQL Related Tech
SQL Prowess: "Speak the 'Postgres' Lingo"
Every PostgreSQL developer must be fluent in the native tongue of databases: SQL (Structured Query Language). It's like the Esperanto for data manipulation and retrieval – a must-have in your polyglot programming utility belt. You ought to know how to craft queries that can summon data like magical incantations – from simple SELECT statements to complex JOIN operations capable of bending relations to your will.
SELECT name, job_title FROM wizards WHERE wand_power > 9000;PL/pgSQL: "Postgres' Secret Spellbook"
PL/pgSQL stands for Procedural Language/PostgreSQL SQL, the go-to tool for enchanting PostgreSQL with business logic directly in the database through user-defined functions and stored procedures. Think of it as writing little data-manipulating wizards locked up in your database, ready to perform complex tasks on your command.
CREATE FUNCTION raise_hp(target_id INT, hp_boost INT) RETURNS VOID AS $$
BEGIN
UPDATE adventurers SET hp = hp + hp_boost WHERE id = target_id;
END;
$$ LANGUAGE plpgsql;pgAdmin: "The Crystal Ball of PostgreSQL"
pgAdmin is like the trusty sidekick for every database sorcerer, a graphical interface to gaze into the depths of your PostgreSQL databases. Through this mystical pane, you can poke around your data, run spells...err...queries, and maintain the health of your database realms without whispering a single command line incantation.
PostGIS: "The Cartographer's Tool"
For those who need to navigate the mystical lands of geospatial data, PostGIS is the compass that turns PostgreSQL into a spatial database with superpowers. It allows you to conjure up location-based queries and build maps that can reveal hidden patterns like a treasure map x-marks-the-spot.
pgBouncer: "The Bouncer at the Data Tavern"
pgBouncer stands guard at the entrance to your database, managing the flow of client connections like a burly doorman at a club. It maintains a pool of connections so your database doesn't get trampled by overzealous application servers trying to party all at once.
pgBackRest: "The Keeper of the Scrolls"
No quest is without risk, and losing your data is akin to letting the evil lord win. pgBackRest is your guardian, ensuring you can always resurrect your data kingdom with backup and restore abilities so powerful they feel like time travel.
Patroni: "The Database Watchtower"
Patroni stands vigilant, ensuring your PostgreSQL deployment's high availability. It's like having a wise old wizard perched atop the tallest tower, ready to cast failover spells and switch masters like a pro if the current one kicks the cauldron.
Logical Replication: "The Copycat Charm"
Logical replication allows for copying selected data from one database to another – think of it as creating a clone army of your data. This selective replication is like having doppelgangers for your data that can take up arms in another castle if the fort is about to fall.
PEM (PostgreSQL Enterprise Manager): "The Overseer's Gaze"
The PostgreSQL Enterprise Manager is like the Eye of Sauron for your databases (but in a less malevolent way). It provides a bird's-eye view of your database landscape, monitoring performance, and alerting you to potential disturbances in the force…err…performance metrics.
Python and psycopg2: "The Alchemist's Mix"
Mixing Python with psycopg2 is like the alchemy of database interaction. Python's versatility combined with psycopg2's PostgreSQL-specific features let you transmute your queries and data into golden applications with ease.
import psycopg2
conn = psycopg2.connect("dbname=spellbook user=magician")
cur = conn.cursor()
cur.execute("SELECT * FROM potions WHERE effectiveness > 90")
potent_potions = cur.fetchall()