How statistics are calculated
We count how many offers each candidate received and for what salary. For example, if a Data QA 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 QA tech & tools in 2024
Data QA
What is Data Quality
A data quality analyst maintains an organisation’s data so that they can have confidence in the accuracy, completeness, consistency, trustworthiness, and availability of their data. DQA teams are in charge of conducting audits, defining the data quality standards, spotting outliers, and fixing the flaws, and play a key role at all stages in the data lifecycle. Without DQA work, strategic plans will fail, operations will go awry, customers will leave, and organisations will face substantial financial losses, as well as a lack of customer trust and potential legal repercussions due to poor-quality data.
This is a job that has changed as much as the hidden infrastructure that transforms data into insight and then powers the apps that we all use. I mean, it’s changed a lot.
Data Correctness/Validation
This is the largest stream of all the tasks. When we talk about data correctness, we should be asking: what does correctness mean to you, for this dataset? Because it would be different for every dataset and every organisation. The commonsense interpretation is that it must be what your end user (or business) wants from the dataset. Or what would be an expected result of the dataset.
We can obtain this just by asking questions, or else reading through the list of requirements. Here are some of the tests we might run, in this stream:
Finding Duplicates — nobody wants this in their data.
– Your data contains unique/distinct values in that column/field. Will the returned value be a unique/distinct value in that column/field?
– Any value that can be found in your data is returned.
Data with KPIs – If data has any columns we can sum, min or max on it’s called a key performance indicator. So basically any models which are mostly numeric/int column. eg: Budget, Revenue, Sales etc. If there is data comparison between two datasets then below tests applies:
– Comparing counts between two datasets — get the difference in count
– Compare the unique/distinct values and counts for columns – find out which values are not present in either of the datasets.
– Compare the KPIs between two datasets and get the percentage difference between them.
– Replace missing values – missing in any one of the datasets with primary or composite primary key. This can be done in a data source that does not have primary key too.
– Perform the metrics by segment for the individual column value — that can help you determine what might be going wrong if the count of values in the Zoopla-side doesn’t match the count on the Rightmove-side or if some of the values are missing.
Data Freshness
This is an easy set. How do we know if the data is fresh?
An obvious indication here is to check if your dataset has a date column, in which case, you just check the max date. Another one is, when the data was pulled into a particular table, all of this can be converted into a very simple automated checks, which we might talk about in a later blog entry.
Data Completeness
This could be an intermediate step in addition to data correctness, but how do we know to get there if the space of answers is complete?
To do this test, check if any column has all values null in it perhaps that’s okay, but most of the time it’s bad news.
Another test would be one-valuedness: whether everywhere on the column all values are the same, probably in some cases that would be a fine result, but probably in other cases that would be something we’d rather look into.
What are Data Quality Tools and How are They Used?
Data quality tools are used to improve, or sometimes automate, many processes required to ensure that data stays fit for analytics, data science, and machine learning. For example, such tools enable teams to evaluate their existing data pipelines, identify bottlenecks in quality, and even automate many remediation steps. Examples of activities relating to guaranteeing data quality include data profiling, data lineage, and data cleansing. Data cleansing, data profiling, measurement, and visualization tools can be used by teams to ‘understand the shape and values of the data assets that have been acquired – and how they are being collected’. These tools will call outliers and mixed formats. In the data analytics pipeline, data profiling acts as a quality control gate. And each of these are data management chores.
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()