Data Analyst (DA) Developer with Azure Databricks Salary in 2024

Azure Databricks is a unified, open analytics platform for building, deploying, sharing and operationalising your entire data, analytics and AI lifecycle at scale. The Databricks Data Intelligence Platform integrates with cloud storage and security in your cloud account and it manages and provisions cloud infrastructure on your behalf.

How does a data intelligence platform work?

By applying generative AI with your data lakehouse semantics, Azure Databricks automatically optimises performance and manages infrastructure in line with your business requirements.

Natural language processing learns the language of your business, so that you can search for and discover data by asking a question in human-sounding text. Natural language assistance can help you write code, diagnose potential issues and answer questions in the documentation.

Ultimately, it means that your data and your AI apps are governed securely – something you can control from an IT and data privacy point of view. APIs (eg, OpenAI’s) can be adopted without undermining data ownership and IP control. 

What is Azure Databricks used for?

Azure Databricks works with you to get through the integrations, to get data from where ever it is to all where it needs to be – to process, store, share, analyze, model and monetise (using solutions from BI to generative AI) on a single platform. 

Most data tasks, from exploring data to model training and deployment, can be done in the Azure Databricks workspace – view, run, and manage from one place, using the same tools. For example, Databricks Notebooks are the workbench for Python, Scala, SQL, R, Spark configuration, and everything in between:

• Data processing scheduling and management, in particular ETL
• Generating dashboards and visualizations
• Managing security, governance, high availability, and disaster recovery
• Data discovery, annotation, and exploration
• Machine learning (ML) modeling, tracking, and model serving
• Generative AI solutions

Managed integration with open source

Databricks has a strong commitment to the open source community. Updates of open source integrations in Databricks Runtime releases are managed by Databricks. The technologies listed below are open source projects initially developed by Databricks employees.

• Delta Lake and Delta Sharing
• MLflow
• Apache Spark and Structured Streaming
• Redash

Tools and programmatic access

Azure Databricks, for example, provides a selection of proprietary extensions that integrate our technologies and extend them with optimised convenience, allowing you to:

• Delta Live Tables
• Databricks SQL
• Photon compute clusters
• Workflows
• Unity Catalog

Alongside the workspace UI, you also interact with Azure Databricks programmatically with the following tools: 

• REST API
• CLI
• Terraform

How does Azure Databricks work with Azure?

The Azure Databricks platform architecture comprises two primary parts:

• The infrastructure with which Azure Databricks deploys, configures and manages the platform and relevant services.
• The customer-owned infrastructure managed in collaboration by Azure Databricks and your company.

Unlike many enterprise data companies, Azure Databricks doesn’t requite that you migrate your data into whatever proprietary storage system they construct around their platform – you can still use your data on your own servers. The way it works is that you configure an Azure Databricks workspace by configuring integrations that are secure between your Azure Databricks platform and your cloud account, and the Azure Databricks platform deploys cluster nodes in your account and uses your cloud resources to process and store your data in your object storage and other services that you control.

Unity Catalog takes this a step further and allows for permissions to access data to be managed via familiar SQL syntax within Azure Databricks. 

Azure Databricks workspaces satisfy demanding security and networking requirements of some of the world’s largest and most security-conscious companies. Azure Databricks – bringing a workbench-like experience to users, freeing them from many of the steps and questions associated with working with cloud infrastructure, without limiting the customisations and control that data, operations and security teams require.

What are common use cases for Azure Databricks?

Use cases for Azure Databricks are as diverse as the data that runs on the platform and the ever-growing number of personas of enterprise personnel and the data-skills they rely upon as their core job requirement. The following use cases list the various ways your enterprise, from top to bottom, can use Azure Databricks to help users perform critical tasks related to processing, storing, analysing and acting on the data that moves your business.

Build an enterprise data lakehouse

The data lakehouse brings together the strengths of enterprise data warehouses and data lakes to help accelerate, simplify and unify enterprise data solutions for data engineers, data scientists, analysts and production systems, where both streaming and batch data solutions will use the same data lakehouse as the system of record. This enables timely access to consistent data for all and reduces the complexity of building, maintaining and syncing many different isolated and often incompatible distributed data systems. What is a data lakehouse?.

ETL and data engineering

Whether you are building dashboards or powering artificial intelligence apps, data engineering lies at the heart of ‘data-powered’ companies by ensuring that data is accessible, clean and stored in models that can be easily discovered and leveraged. Azure Databricks leverages Apache Spark and Delta Lake with custom tools from the open-source community to provide a best-in-class ETL (extract, transform, load) experience. You can compose ETL logic in SQL, Python and Scala, and then orchestrate automatically deployed scheduled jobs with a few clicks.

Delta Live Tables makes ETL even easier, automatically managing dependencies across data sets, and continuously deploying and scaling infrastructure in production for timely and error-free data delivery according to your requirements. 

Azure Databricks offers several tools crafted for ingesting data, including Auto Loader, a highly performant and horizontally scalable tool to incrementally and idempotently load data from cloud object storage and data lakes into the data lakehouse.

Machine learning, AI, and data science

Azure Databricks machine learning builds upon this core functionality with a suite of built-in tools for data scientists and ML engineers, including MLflow and Databricks Runtime for Machine Learning.

Large language models and generative AI

Databricks Runtime for Machine Learning already makes it easy to use popular pre-trained models such as those from Hugging Face Transformers as part of your workflow, as a supplement to your model, or as part of a package or open-source module. Databricks MLflow integration makes it easy to use the MLflow tracking service for tracking and monitoring your transformer pipelines, models, and processing components. You can also invoke OpenAI models or those from partners such as John Snow Labs in your Databricks workflows.

For instance, directly on Azure Databricks, you can start with a LLM you choose and then train on your data, to work on whatever task you want to use it for. With the use of open source tooling like Hugging Face and DeepSpeed, it’s fairly easy to take a base LLM, start training it with your data, and get more accuracy for your workload or domain. 

Furthermore, Azure Databricks offers certain functions of AI that SQL data analysts can employ to interact directly with LLM models – like those from OpenAI – within their data pipelines and workflows. AI Functions on Azure Databricks. 

Data warehousing, analytics, and BI

Azure Databricks uses the wide array of UIs to make analytic queries run across these elastic compute resources and ultimately on the much cheaper, infinitely scalable, perpetually available storage that data lakes offer. Administration can set up these scalable compute clusters as SQL warehouses, while end users can simply point to those warehouses and run queries against data in the lakehouse without worrying about any of the complexities of working in the cloud. Users can input and run queries against lakehouse data using SQL query editors or notebooks. The latter run SQL, as well as Python, R and Scala, while also enabling them to embed under query cells the same types of visualisations available in legacy dashboards, along with links, images and commentary written in markdown.