Next Generation BI with Databricks & Azure

The game-changing combination of Databricks and Azure is revolutionising BI, providing unmatched value across a whole host of industries.

Databricks, a unified data analytics platform built by the team behind Apache Spark, has become a go-to tool for businesses seeking actionable insights from their data. Databricks provides a near-unmatched toolset for processing, analysing, and visualising data at scale – all within a collaborative and intuitive environment.

Azure, Microsoft’s cloud computing platform, offers a comprehensive suite of services and tools that empower organisations to harness the true potential of their data.

In combination, Azure and Databricks constitute a powerful ecosystem for processing, analysing, and visualising data at scale; enabling businesses to leverage the power of the cloud and make data-driven decisions with confidence – all within a collaborative and intuitive environment.

The Azure Advantage

Microsoft are an industry leader in the Cloud tech space for good reason, and the team at Databricks have made the business-savvy decision to ensure their toolset works especially well on the Azure platform. Real attention has been paid when tweaking Databricks’ functionalities so users can leverage the unique strengths of Azure. This highly succesful specialist-deployment approach ultimately means Databricks’ Azure-native version turns all the standard benefits of the Databricks platform up to 11:


Databricks on Azure allows organisations to scale their data analytics infrastructure seamlessly. By leveraging the elasticity of Azure’s market-leading cloud infrastructure; businesses can handle massive datasets, process real-time data, and execute complex analytics tasks without compromising performance.

Secure Collaboration

Databricks facilitates collaboration among data scientists, data engineers, and business analysts through a user-friendly unified workspace.This empowers teams to seamlessly share code, notebooks, and visualisations – promoting knowledge sharing and accelerating innovation. Meanwhile, Azure’s secure access controls and user roles systems ensure that sensitive data remains protected without blocking collaboration across departments.

Advanced Analytics

Databricks leverages the power of Apache Spark to provide advanced analytics capabilities. With support for multiple programming languages like Python, R, and Scala, data professionals can explore, preprocess, and model their data with ease. Databricks also offers a rich library of machine learning algorithms and integrated tools for data visualisation, enabling users to gain valuable insights and drive informed decisionmaking using deep metrics unobtainable with traditional human-lead methods.

Real-Time Streaming

Azure’s robust event streaming capabilities, coupled with Databricks’ live processing capabilities, enables organisations to harness the potential of real-time data analytics. This system works by blending Azure’s Event Hubs & Stream Analytics services with Databricks’ Structured Spark Streaming ingestion platform, offering businesses as-it-happens analytics without sacrificing detail or affordability – uncovering hidden patterns and trends for immediate action.

Cost Efficiency

Azure’s pay-as-you-go model already helps businesses manage their costs more efficiently. Whe paired with Databricks’ efficient resource management, savings multiply to create one of the most competitively priced solutions on the market.

Organisations in the Azure Databricks ecosystem can even optimise this already low expenditure by autoscaling resource allocation for their variable data analytics workloads. By dynamically provisioning and deprovisioning resources based on demand, businesses can avoid overprovisioning and minimise unnecessary expenses, leading to significant cost savings.

The Power of Azure Cloud Elevates Databricks, Offering Businesses Powerful Real-Time Insights at Competition-Beating Prices

Life in the Lakehouse

So Databricks on Azure is good – but why should you care about Databricks at all? After all there are so many solutions out there! The answer is simple: ‘the Lakehouse‘.

In today’s data-driven world, organisations are constantly seeking innovative solutions to extract maximum value from their data and give them the edge over their competition. Lots of tools and platforms promise to do this – and with our extensive experience working in Data Engineering and Cloud Technology, we know that it is best to treat these promises with a healthy skepticism. But, the groundbreaking Databricks Lakehouse system however actually lives up to the hype. Plus, when discussing Databricks (the ‘Lakehouse Company’) it would obviously be remiss not to talk about the unique benefits of this proprietary Lakehouse storage system.

A Lakehouse functions exactly as it’s portmanteau naming convention suggests – combining the best aspects of data lakes and data warehouses to unify and centralise various datasets and use cases within a single platform. A Lakehouse confers a number of incredible benefits to analysts and data engineers, especially when deployed on Microsoft’s Azure cloud platform:

Unified Data Platform

A Databricks Lakehouse constitutes a unified data platform, bringing together data engineering, data science, and business analytics in a single environment. It eliminates the need for multiple tools and disparate systems, streamlining data operations and accelerating time-to-insights. This unified approach fosters collaboration among teams; enabling seamless data discovery, sharing, and governance.

Performance at Scale

Built on Apache Spark, Databricks Lakehouse offers exceptional scalability and performance even when not deployed on the Azure platform. Its distributed computing architecture enables processing of massive data volumes at lightning-fast speeds. This distributed analytics (!) system leverages powerful parallel processing capabilities, allowing organisations to effortlessly handle complex workloads and derive actionable insights in real-time. When working in the Azure Databricks ecosystem this efficiency is bolstered further by Azure’s hyperductile cluster management. Plus, delivery of high-quality analytics is guaranteed by Microsoft’s vast network of data centres & lifesaving disaster-recovery tools

Simplified Data Management

The Databricks Lakehouse system simplifies data management by providing robust features for data ingestion, cleansing, transformation, and enrichment. It supports a wide range of data formats, making it easy to integrate structured, semi-structured, and unstructured data from various sources. With built-in schema enforcement and data quality checks, businesses can ensure data consistency and reliability across the entire data pipeline. If you’re still fiddling around with complex transformations and data-frame work – why?! Azure Databricks solves this problem once and for all.

Governance and Security

Maintaining data governance and security is of paramount importance in today’s regulatory landscape. Databricks Lakehouse incorporates industry-leading security practices; including encryption, access controls, and auditing capabilities. By layering with Azure’s suite of advanced governance tools and proprietary SDL development approach, Databricks Lakehouse enables organisations to define incredibly granular access policies for data in incredibly secure storage, ensuring it is accessible only to authorised personnel. This marries nicely with data lineage and versioning features that offer enhanced traceability and compliance, making a Databricks Lakehouse on Azure one of the most secure storage & serving platforms in the marketplace today.


Databricks seamlessly integrates with a wide array of analytics and AI tools. This provides a unified environment for data scientists and analysts to explore, visualise, and gain insights from data using their preferred toolset. The system’s collaborative nature facilitates cross-functional analysis, leading to better decision-making and improved business outcomes.

A Lakehouse adds another layer to this – with native integrations for machine learning libraries & MLflow (more on that later), empowering organisations to develop and deploy these advanced models at scale via a low-cost, secure storage & serving solution.

The Lakehouse Platform puts Databricks Head & Shoulders above Competitors, offering a Unified Storage Solution for Dispersed & Disparate Datasets that Maximises the Breadth & Depth of BI Analytics


It’s cropped up a couple of times now, so lets be clear – Azure Databricks is a really great platform for developing AI. That’s largely because it features native integration with MLflow, a comprehensive open-source platform for managing the machine learning lifecycle. This integration offers a multitude of benefits for AI technicians which ultimately translate to more powerful, explainable models, faster – bringing businesses cutting-edge AI powered analytics without the fuss. MLflow’s most boundary-pushing features include:

Experiment Tracking and Reproducibility

MLflow used within Azure Databricks enables data scientists to track and compare experiments: capturing  parameters, code versions, and metrics. This promotes reproducibility, facilitates collaboration, and enhances transparency in model development. Models produced with these robust practices tend to be more reliable and explainable – offering consistent and trustworthy BI that can give businesses a competitive edge.

Model Packaging and Deployment

Azure Databricks’ storage and serving toolsets blend with MLflow’s model bundling capabilities to simplify packaging and deployment. Data teams can create reproducible workflows and seamlessly transition from experimentation to production, ensuring the scalability and reliability of their models. This means that analysts can expect AI-derived metrics to be consistent, meaningful, and thus usable for high-level business decisionmaking.

Model Registry and Governance

MLflow’s Model Registry within Azure Databricks provides a centralised repository for managing and versioning machine learning models. This enables organisations to maintain proper control over model versions and monitor performance – thereby governing the model lifecycle effectively.

Beyond ensuring robust, ethical, and trustworthy AI; this approach offers major efficiency benefits – helping AI teams to deliver insight-supercharging models at unmatched speeds without getting bogged down by overcomplicated admin and repo-management.

MLflow keeps Track of the Details to help AI Specialists 'Be Like Water' and Construct Optimised Models without Friction

Elephant in the Room

If you’re a tech guy, you might be rolling your eyes at this point and thinking – what about Hadoop?

For those not in the know: Hadoop is an open-source distributed computing framework designed to handle and process large amounts of data, and it certainly shares some functionality with Databricks. It even originates from the same Apache team, and has the most adorable yellow elephant for a logo.

This might make it sound like Hadoop is a viable Databricks alternative (and in some use cases this is definitely true), but ultimately for the vast majority of businesses Databricks is going to be a superior choice. Here are the key reasons why:

Simplified Deployment

Azure Databricks completely eliminates the complexities associated with deploying and managing Hadoop clusters. With serverless architecture, organisations can provision resources on-demand, reducing administrative overhead and enabling rapid deployment of data analytics workloads. This accelerates development time, not only saving on costs but also winning you the undying affection of your tech teams. Hadoop definitely has a place, but the dreams of many old-school data engineers are still haunted by a laughing yellow elephant…

Seamless Integration with Azure Services

Azure Databricks, as the name might suggest, seamlessly integrates with various Azure services – most notably Blob Storage, Data Lake Storage, SQL Database, and Event Hubs. This integration allows organisations to leverage existing Azure resources alongside their Databricks counterparts – enhancing data ingestion, storage, and processing capabilities to unlock the full potential of a data ecosystem.

This functionality is just not there in Hadoop. The closest one can get is a Hadoop-Type cluster deployed on HDInsight, which doesn’t really match up. As such, interactions & transfers between data in Microsoft’s cloud infrastructure and processing stacks built in Hadoop has to be set-up manually, exaggerating development time and introducing uneccessary complexity (and thus fragility) to the data & analytics tech stack.

Autoscaling and Cost Optimisation

Azure Databricks automatically scales resources based on workload demands, ensuring optimal performance and cost efficiency. By dynamically allocating resources as needed, businesses can avoid overprovisioning and only pay for the compute and storage resources they require, resulting in significant cost savings.

In Hadoop this kind of management is all fairly manual, and can be quite laborious depending on the complexity of the rules that govern provisioning. Most businesses prioritise cost, speed, and performance – rendering Hadoop an obsolete solution without this automatic cost control system. 

For some use cases, the Elephant remains King! But, for Most Businesses, the Simplicity & Efficiency of Databricks Wins Out

Snow Go!

So if you’re a business seeking analytics, a Hadoop solution is probably out. Is there anything at all that can compete with Azure Databricks?! In the halcyon days of Azure Databricks, the answer would be a resounding no – but nowadays things are a little more complicated.

Snowflake is a cloud-based data warehousing platform that allows organizations to store, manage, and analyse large amounts of structured and semi-structured data. It is known for its unique architecture and scalability, which makes it easy to use and highly efficient. Once lacking an Azure-native application and a multicloud collaboration solution, Snowflake now possesses both, bringing its features closer in line with those of Databricks.

Ultimately, different platform priorities mean each is suited for different use cases: Snowflake for ETL & SQL operations, and Databricks for Data Science, ML, & Analytics. So for BI, you’re probably looking at a Databricks deployment. Plus, there are still a couple of ways that Azure Databricks outstrips Snowflake’s toolset for any application:

Integrated Approach

Snowflake focuses primarily on data warehousing, with a separate analytics layer interacting with stored data. Via the Lakehouse system, Azure Databricks instead provides a unified platform for both data storage and advanced analytics. This distinction might sound muddy, but from an ease-of-use and efficiency perspective it makes a huge difference –  allowing businesses who choose Azure Databricks frictionless  data preprocessing, exploratory analysis, and advanced machine learning within a single environment.

Collaborative Workspace

Although cloud platforms engender collaboration through filesharing, and this has been extended in Snowflake via multicloud support, Azure Databricks brings a much deeper form of coworking to the table. 

Right out of the box, Databricks users can access a truly collaborative space where data scientists, data engineers, and business analysts can work together. Interactive notebooks enable seamless code sharing, documentation, and visualisation. If you’ve ever used the Google Workspace Suite (GSuite) this live collab process will be very familiar and its benefits self explanatory. If not – the idea is to foster both live and asynchronous collaboration to meaningfully accelerate innovation across teams. This really works, and Distributed Analytics its one of our favourite Databricks features!

Snowflake Definitely has its Place, but for a BI Use Case Don't Get Left out in the Cold! Stick with Databricks for the Most Powerful Insights

Summing Up

Azure Databricks is a powerful and unified data analytics platform that surpasses traditional tools like Hadoop and Snowflake. Simplified deployment, seamless integration with Azure services, and native support for MLflow set this duo aside from the competition. Microsoft & Apache’s tools work in tandem to serve truly next-generation Bi – empowering organisations to unlock the full potential of their data, pursue innovation, and make data-driven decisions with confidence.

At Distributed Analytics, we see the transformative power of Azure Databricks every day – using this powerful platform to help our clients level up their data tech stacks and access deeper insights. It’s not always the right tool for the job, but more often that not we find that Databricks has what it takes to handle any data pain point. We’re committed to transparency and ending the bloat that plagues tech, so we don’t recommend platforms lightly – but Databricks on Azure really is a gamechanger.

If you’re interested in learning more, check out our other Databricks posts here. Alternatively, if you’d like to find out how Databricks could help your business meet its BI & data needs, reach out using the button below. 

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