Business Intelligence (BI) Developer with Azure (Microsoft Azure) Salary in 2024
How statistics are calculated
We count how many offers each candidate received and for what salary. For example, if a Business Intelligence (BI) developer with Azure (Microsoft Azure) 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 Business Intelligence (BI) tech & tools in 2024
Business Intelligence (BI)
Business intelligence (BI) is the term used for analysis by SQL specialists, typically yielding status reports for the business. Data analytics grew from BI, partly because the need for reporting and analysis became more frequent and dynamic, but also because most company data now resides in the cloud – in a data warehouse and on a customer data platform (CDP) – and tools to administer these systems became easy to use by people other than SQL specialists, such as data analysts. Understanding the differences between data analytics and business intelligence is essential to operating a profitable business that deploys data in the 21st-century way.
Using both BI and data analytics should help you to better understand the day-to-day execution of your business, and improve your decision-making process.
What is business intelligence and new trends?
At its most basic, business intelligence is defined as the collection, storage, and analysis of input received from different operations in an organisation. Although the entire purpose of BI is to track the overall direction and movements of an organisation, as well as providing and suggesting more informed decisions from data, it does so by producing reports for managers that would help them in their decisions. For instance, these reports can give insights on what’s going on inside the business, but can also be solely about external aspects surrounding the business, for example, in creating an analysis of a market in which they have a desire of venturing into.
What tends to happen with BI is to provide explanations of why the business is in the state it is – as well as presenting some perspective on how operations have grown over time. BI uses facts from recorded business data to help interpret the past, which means that company officials can move ahead with a better grasp of the company’s journey and where it is heading. Business intelligence is often also required to ‘play out’ various scenarios to assist with business planning. For example: ‘What will happen to signups if we raise our prices?
In day-to-day business operations, a system that would produce such reports was a traditional system of what was then known as ‘business intelligence’. And because stakeholders would require such reports on a regular basis – every month, or every quarter – producing the same report over and over again was a tedious task for the so-called business intelligence analysts. Today’s Business Intelligence, however, relies largely on automated regular reports, which are often generated by in-house data analytics, so that in the modern sense data analytics is an integral part of business intelligence.
Behind Business Intelligence (BI)
Approach is a set of technologies which are helping companies to collect and analyze data from business operations, and following actionable insight, they are using such insight to make sustainable business decisions. With the ever-growing amounts of data, it can be highly beneficial for the procurement stream to acquire some kind of understanding in business intelligence tools in order to start forming its current strategy and future strategic decisions. Through this write up, I’m offering to cover the essence behind the term, along with some further explanation with examples to provide. I am also trying to cover the related and relevant topics, and most importantly I will try to answer any possible questions you may continue to have with regards to business intelligence.
The definition of Business Intelligence
Often confused with business analytics, business intelligence (BI) is an umbrella term for the processes, methods, and software that collects both internal and external data, structured and unstructured, and processes them for further analysis. Users are then able to draw conclusions from the data by means of reports, dashboards, and data visualization.
Formerly the preserve of data analysts, business intelligence software is spreading and becoming accessible to wider circles. Businesses are becoming truly ‘data driven’. The accelerating spread of the large-data revolution gives businesses everywhere a chance to squeeze the full potential of digital transformation, via enhanced operational advantages.
However, Business Intelligence (and related notions such as machine learning, artificial intelligence…) not only aims at best optimizing the processes or at increasing the performances of the entity, it also helps to guide, speed up and to improve the decisions made by the company and based on real-time actual metrics.
These applications are now referred to as essential tools for companies to get an overview of the business, to discover market trends and patterns, to track sales and financial performance, to set up key performance indicator monitoring, to boost performance and many other things. In other words, this data, if used well, is one of the main resources for gaining competitive advantages.
How does Business Intelligence work?
Business Intelligence is based on four stages which are: Data Collection , Data Storage , Data Distribution and Use.
- Collection: Initially, ETL (Extract, Transform, and Load) tools are used to collect, format, cleanse, and combine all the data, regardless of the source or form of appearance. This raw data comes from various sources, including company information system (ERP[2]), its customer relationship management (CRM) tool, marketing analysis, call center, etc.
- Storage: Once aggregated, this data is then stored and centralized in a database, whether hosted on a server or in the cloud. This is called a data warehouse or a data mart.
- Distribution: The principle here is to distribute to the company’s internal partners everything that is created in the decision support platform. There are many new varieties of BI emerging, which use all of the characteristics of web 2.0 and therefore allow access to information used for decision-making to an even broader audience.
- Use: Various tools are used depending on the needs. For example, for multidimensional data analysis, there are OLAP (Online Analytical Processing) tools, for correlation search there are data mining tools, for performance communication there are reporting tools, for performance management there are dashboards and so on.
Business Intelligence technology to support procurement
But by giving procurement departments access to new Business Intelligence tools, they should be able to produce summary data that is accurate and relevant regarding both their corporate expenditure and their supplier base – such as actual and forecast turnover, contact and dispute histories, negotiated prices, the organization of contracts, and so on.
They can imagine and mine it quickly, and then communicate it in a digestible, understandable form to all, as well as use it as an input to inform business decisions as part of their sourcing strategy – to get better outcomes.
BI functionality allows them to give supplier performance benchmarks, score tenders, select suppliers according to multiple selection criteria in the application of Lean Procurement, etc.
In addition to this decision support, buyers also enjoy operational efficiency gains: procurement departments are notorious for lagging in terms of digitalization, and despite the benefits they could bring, buyers still spend almost three-quarters of their time on purely transactional or operational activities[2]. In this sense, such a solution makes total sense.
To take one example, the Itochu Corporation, a Japanese global trading company, says it has cut the time needed to produce its monthly reports by 92 per cent using BI tools[3]. That is a figure that any buyer today should sit up and take notice of.
Ultimately, such software makes communication between procurement departments and the wider company easier and more effective; armed with data and figures, they can work in tandem with other divisions, particularly finance, and also try to define their strategic footprint within the organization.
Resistance to BI
But such technology is not easy to develop. Two formidable challenges stand in the way.
- Complexity of use: At the beginning, the use of Business Intelligence implies profiles with technical skills, analysts, architects, or even developers specialized in BI. Nevertheless, the solutions in the market today are increasingly aimed at all staff in an organization, at the managerial and operational personnel. Easy both to use and interpret, they are now tuned so that the management tools can be tailored. The business user is beginning to see the rise of ‘self-service BI’.
- Quality, reliability, and usefulness of data: Second, the quality, relevance, and value of the data can themselves become a barrier, for instance, if the supplier selection process is not managed in a centralized way or not validated by procurement departments. It is thus essential that the collection be prepared and the databases organized before posing any queries.
Data is the 21st century gold, ie one of the most strategic resources for a company. No surprise then that, in addition to the logical quality, the era of Big Data is quickly turning into the era of Smart Data. In fact, towards a real Purchasing Intelligence approach. Business Intelligence programs can go even further by integrating predictive analytics, data, or text mining tools, etc., and thanks to BI capabilities, it’s up to the procurement function to aim for a Purchasing Intelligence approach in order to optimize the performance of the company.
Where is Microsoft Azure used?
Cloudy with a Chance of Streamlining
- Picture a world where code deployments fly up to the sky. Azure's DevOps services make this a breeze, chucking updates into the wild blue yonder with more automation than a robot butler.
Binge-Watching for Machines
- Azure's IoT services turn ordinary toasters into binge-watching data hogs, relentlessly streaming bytes like they're the latest hit TV series.
Save the Planet, One Virtual Server at a Time
- Deploy a legion of virtual servers without cutting down a single tree. Azure’s eco-friendly cloud keeps physical hardware to a minimum, giving a whole new meaning to virtual "green" space.
Fortune Telling with Data
- With Azure's AI and Machine Learning, you can predict the future like a techno-wizard, forecasting sales like reading tea leaves but with more graphs and less mess.
Microsoft Azure Alternatives
Amazon Web Services (AWS)
Cloud computing platform offering computing power, database storage, content delivery, and other functionality.
// Example of launching an EC2 instance with AWS SDK for JavaScript
const AWS = require('aws-sdk');
AWS.config.update({region: 'us-west-2'});
const ec2 = new AWS.EC2({apiVersion: '2016-11-15'});
const params = {
ImageId: 'ami-0abcdef1234567890',
InstanceType: 't2.micro',
MinCount: 1,
MaxCount: 1
};
ec2.runInstances(params, function(err, data) {
if (err) console.log("Could not create instance", err);
else console.log("Created instance", data.Instances[0].InstanceId);
});
- Most extensive global cloud infrastructure
- Comprehensive set of services and tools
- Per-minute billing for better cost management
- Can be overwhelming for new users
- Occasional service disruptions
- Pricing complexity may lead to unexpected costs
Google Cloud Platform (GCP)
Offers services in all major spheres including compute, networking, storage, machine learning, and IoT.
// Example of creating a Compute Engine instance with Google Cloud SDK for Python
from google.cloud import compute_v1
project = 'your-project-id'
zone = 'us-central1-a'
machine_type = 'zones/{}/machineTypes/n1-standard-1'.format(zone)
compute = compute_v1.InstancesClient()
instance = compute_v1.Instance()
instance.name = 'instance-1'
instance.machine_type = machine_type
# Define the disk
disk = compute_v1.AttachedDisk()
disk.initialize_params.source_image = 'projects/debian-cloud/global/images/family/debian-9'
disk.auto_delete = True
disk.boot = True
instance.disks = [disk]
# Create the instance
response = compute.insert(project=project, zone=zone, instance_resource=instance)
print(response.result())
- Seamless integration with Google's suite (Analytics, Ads, etc.)
- Data and analytics are robust
- Friendly to Kubernetes and open-source projects
- Smaller network compared to AWS
- Billing is complex
- Limited enterprise support compared to AWS and Azure
IBM Cloud
Hybrid cloud platform offering a suite of AI, data, analytics, and IoT services.
// Code for authenticating and starting a service with IBM Cloud SDK for Node.js
const { IamAuthenticator } = require('ibm-cloud-sdk-core');
const CloudantV1 = require('@ibm-cloud/cloudant');
const authenticator = new IamAuthenticator({
apikey: 'your-apikey',
});
const cloudant = CloudantV1.newInstance({
authenticator: authenticator
});
cloudant.setServiceUrl('https://your-cloudant-url');
const createDb = async () => {
try {
await cloudant.putDatabase({ db: 'my-database' });
console.log('Database created');
} catch (error) {
console.error('Error creating database', error);
}
};
createDb();
- Strong focus on AI and machine learning with Watson
- Secure and reliable
- Great choice for hybrid cloud deployments
- Complex pricing tiers
- User experience is less intuitive
- Smaller market share and community
Quick Facts about Microsoft Azure
Azure's Celestial Birth
Once upon a time in the cosmic realm of 2008, the tech wizards at Microsoft conjured up a mystical cloud creature named 'Project Red Dog.' A year later, it metamorphosed into what mortals now revere as Microsoft Azure, a colossal digital canvas for creative coders and IT magicians to sculpt their cloud-based dreams.
Evolution of the Azure Species
Azure is like the Darwin’s finches of cloud computing—constantly evolving. It started with a simple set of services, and now it's an ever-growing ecosystem with over 200 products and services. From virtual machines that shape-shift to your needs, to AI services that foresee the future like an oracle, Azure's versatility is its superpower!
Groundbreaking Azure Incantations
The magicians at Azure didn't stop with pulling rabbits out of hats. They conjured up the Azure DevOps platform, transmuting mere muggles into agile wizards, able to cast CI/CD (Continuous Integration/Continuous Deployment) spells with ease.
// A snippet of an Azure DevOps spell for Pipeline conjuring
trigger:
- master
pool:
vmImage: 'ubuntu-latest'
steps:
- script: echo "The alchemy of CI/CD begins!"
displayName: 'Invoke Spell'
What is the difference between Junior, Middle, Senior and Expert Microsoft Azure developer?
| Seniority Name | Years of Experience | Average Salary (USD/year) | Responsibilities & Activities | Quality |
|---|---|---|---|---|
| Junior Azure Developer | 0-2 | 50,000-70,000 |
| Learning and developing competency |
| Middle Azure Developer | 2-5 | 70,000-100,000 |
| Competent, able to work independently on most tasks |
| Senior Azure Developer | 5-10 | 100,000-140,000 |
| High-quality work; role model for lower levels |
| Expert/Team Lead Azure Developer | 10+ | 140,000-180,000 |
| Exceptional quality; leads and improves team performance |
Top 10 Microsoft Azure Related Tech
Azure SDKs & Command-Line Tools
Imagine arming a wizard with a calculator; that's what Azure SDKs and CLIs do for developers! These tools let devs cast spells in their preferred programming language, conjuring up resources within Azure faster than a caffeine-fueled coder at a hackathon. Whether you're a Python charmer, JavaScript juggler, or a .NET necromancer, these SDKs are your wands for cloud wizardry!
# Deploy an Azure VM using Azure CLI
az vm create \
--resource-group MyResourceGroup \
--name MyVm \
--image UbuntuLTS \
--generate-ssh-keys
Azure DevOps & GitHub
Azure DevOps and GitHub are like peanut butter and jelly for your continuous integration sandwich. With pipelines more bendy than a contortionist, these services will automate your build-test-deploy cycle smoother than a Tesla on autopilot. Merge requests and version control will seem as easy as stealing candy from a baby - though please don't do that.
trigger:
- main
pool:
vmImage: ubuntu-latest
steps:
- script: echo "Hello, world!"
Azure Functions
Azure Functions are like little minions of the cloud, diligently running background tasks or reacting to events. These serverless sidekicks can scale like Ant-Man and save you the hassle of server management, leaving you free to binge-watch your favorite series while they handle the grunt work. Just be sure not to feed them after midnight!
module.exports = async function (context, req) {
context.log('JavaScript HTTP trigger function processed a request.');
// Function logic goes here.
};
Azure Cosmos DB
Think of Azure Cosmos DB as the Swiss Army knife of databases: multi-model, globally distributed, and more scalable than a rock climber hopped up on energy drinks. With turnkey global distribution, you can cater to your users around the world as if you had Santa's sleigh - and no, reindeer are not included!
// Query using Azure Cosmos DB SQL API
SELECT * FROM c WHERE c.username = "codingninja"
Azure Kubernetes Service (AKS)
Azure Kubernetes Service rolls up like a gangsta in the cloud container orchestration neighborhood. AKS makes managing your containerized applications look like a walk in the park - though, in reality, you may feel like a ringmaster juggling with Docker images. Fear not, for AKS will keep your containers in line like well-behaved poodles!
# Create an AKS cluster
az aks create \
--resource-group myResourceGroup \
--name myAKSCluster \
--node-count 3 \
--enable-addons monitoring \
--generate-ssh-keys
Visual Studio & VS Code
Visual Studio and VS Code are like Batman and Robin for Azure development. These superhero IDEs come packed with IntelliSense, debugging powers, and direct integration with Azure - allowing you to save the day (code) with elegance and efficiency. Watch out Joker (bugs), the dynamic dev duo is here to thwart your nefarious schemes!
Azure Logic Apps
Logic Apps are basically if-this-then-that for grownups: a visual designer for automating workflows without writing a single line of code. They hook up with hundreds of services faster than teenagers at a prom. Just don't let their convenience make you lazy, or you might find yourself using a Logic App to fetch your coffee!
Azure SQL Database
Azure SQL Database is like a butler for your database management, serving up high availability, automated backups, and performance tuning on a silver platter. It allows you to focus on writing queries like Shakespeare rather than fiddling with database knobs and dials - unless that's your jam, of course!
Azure Machine Learning
If Tony Stark's J.A.R.V.I.S. did machine learning, it would be using Azure ML. This suite trains models faster than a barista on his first day, and with less confusion. Whether your data is pictures of cats or the secrets of the universe, Azure ML helps you mine wisdom from the chaos - while you take all the credit!
Azure Active Directory (AAD)
Azure Active Directory is the bouncer at the club of your application, deciding who gets the VIP treatment and who's left out in the cold. AAD's single sign-on and multi-factor authentication ensure that only invited guests party in your app, keeping the party crashers (aka hackers) at bay.