Azure Databricks

Azure Databricks is a powerful Cloud Computing platform designed to handle massive datasets and complex analytics with ease. It’s not just about having data; it’s about harnessing the power of that data through robust analytics and Machine Learning capabilities.

As Andrew Ng, a leading figure in Artificial Intelligence, once said, “Data is the new electricity.” In the same vein, Azure Databricks is the powerhouse that allows businesses to turn raw data into actionable insights. It is essential for any organisation that aims to thrive in the data-driven landscape of today’s world, enhancing their decision-making process and operational efficiency.

This blog will delve into Azure Databricks in more detail. We will explore the architecture, features, and benefits of Databricks in Azure that can empower your business to innovate and stay ahead of the curve. So, let’s embark on this journey of discovery and transformation!

Table of Contents

1) What is Azure Databricks?

2) Azure Databricks use cases

3) Features of Azure Databricks 

4) Pros and cons of Azure Databricks 

5) Conclusion

What is Azure Databricks?

Azure Databricks is an analytics and AI service that combines everything an organisation needs for Big Data processing, Machine Learning, and collaborative teams under a single, first-party subscription. Azure Databricks works smoothly with other Azure services, such as Azure Blob Storage, Azure SQL Data Warehouse, and Azure Machine Learning, when it comes to ingestion, storage, and training of models. It does this through collaborative features that allow more than one user to work together on a data project to share insights and team up in code development. Thus, Azure Databricks enables an organisation to derive actionable insights from data and expedite innovation in diverse domains. Databricks in Azure offers three platforms:  

1) Databricks SQL  

2) Databricks Data Science and Engineering  

3) Databricks Machine Learning


Azure Databricks use cases

Databricks SQL 

Databricks SQL allows you to run fast ad-hoc SQL queries directly over your Data Lake. To make your experience complete in leveraging everything on Azure with Databricks SQL Azure Active Directory comes into play. This enables the realisation of potentials from multiple Azure databases, among which are Azure Synapse Analytics, Azure Cosmos DB, Data Lake Store, and Blob Storage, serving a single centralised source of truth for effective and efficient storage of diverse data needs.

Integration of Databricks SQL with Power BI makes it an easy way for sharing discoveries and insights. This makes Power BI improve its user experience, connecting data directly from Power BI for quick reporting and visualisation. Additionally, Databricks SQL is flexible as it supports other popular BI tools, such as Tableau Software, in order to make sure that more hands could reach the data. Therefore, its usability be increased across different analysis and reporting functions.

Further, for full automation of the Databricks SQL objects and the streamlining process, the selection of interface will be the REST API. This API gives access for users to administer and configure Databricks SQL resources, hence allowing more automation and a high level of control over the data and queries.

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Databricks Data Science and Engineering

Databricks Data Science and Engineering is a unified analytics platform built on Apache Spark.

Databricks unites all these open-source Apache Spark cluster technologies and capabilities into one platform. Within the Spark tool in the Databricks Data Science and Engineering area, it contains the following key components:

a) Spark SQL and DataFrames: Spark SQL is a module of Apache Spark for structured data processing. DataFrames are a distributed collection of data grouped into named columns, much like tables in a relational database or data frames in R or Python.

b) Streaming: Its integration with HDFS, Flume, and Kafka supports streaming and real-time processing/analysis of data. Therefore, it is ideal for analytic applications and applications requiring interactivity.

c) MLlib (Machine Learning Library): MLlib is the standard toolkit of Machine Learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, and underlying optimisation primitives.

d) GraphX: GraphX is a graph computation engine tool that deals with graphs to serve from cognitive analytics to data exploration.

e) Spark Core API: It is a flexible core that can support a number of programming languages such as R, SQL, Python, Scala, and Java. It is best-aligned to handle almost any tasks concerning data processing and analysis.

Databricks Machine Learning 

Databricks Machine Learning is a unified, integrated platform purposely built to support end-to-end ML processes. The managed services offered by Databricks include experiment tracking, model training, feature development, and management.

In the critical areas of ML, it also provides feature and model serving. This is such a platform that's developed to ease and automate the work of setting up perfectly optimised clusters for ML tasks. That's it; the main features of Databricks Machine Learning are:

a) Managed cluster creation: The creation of clusters is basically headed by Databricks Machine Learning, and in a way, they do have optimizations meant for Machine Learning workloads. It comes pre-configured using popular Machine Learning libraries to offer a transparent environment for the task.

b) Library inclusions: Pre-packaged with Databricks Runtime are a variety of ML libraries. Starting from popular ones like TensorFlow, PyTorch, Keras, XGBoost, among others, it covers the best. It also includes indispensable distributed training libraries, such as Horovod, to handle large data volumes.

Databricks Machine Learning can be used for the following applications:

a) Model training: It can be performed manually by the user, or they can make use of Automated Machine Learning (AutoML) techniques.

b) Experiment tracking: This platform allows tracking experiments of both parameters used in training and models. Trace easily from beginning to end with simple commands to check up on the progress of your ML experiments.

c) Feature Engineering: Databricks Machine Learning empowers the creation of feature tables and makes access to those features when training models and inference very convenient.

d) Model Management: Integrated Model Registry that allows easy sharing, management, and serving of ML models, hence better collaboration and improved model life cycle management.

a) Train models: Users have the flexibility to train ML models either manually or opt for Automated Machine Learning (AutoML) techniques.

b) Experiment tracking: The platform provides capabilities for tracking training parameters and models through the use of experiments. MLflow tracking ensures that users can efficiently monitor and manage the progress of their ML experiments.

c) Feature Engineering: Databricks Machine Learning allows the creation of feature tables and provides access to these features for model training and inference, simplifying the feature engineering process.

d) Model Management: Users can conveniently share, manage, and serve Machine Learning models through the integrated Model Registry, enhancing collaboration and model lifecycle management.

Features of Azure Databricks? 

The question is: "Why Azure Databricks and not any other Big Data Analytics Tool? What makes it versatile?" To know more about why you should use Databricks in Azure, let’s go through some of its features in detail: 

Features of Azure Databricks

Familiar with multiple languages and environments 

Despite being built on a Sparks-based platform, it allows its users to utilise commonly known programming languages such as Python, R, SQL, etc. These languages are further converted in the backend through Application Programming Interfaces (APIs) to interact with Spark.  

As a result, users do not need to learn new programming languages just to perform single-distributed analytics. 

Promotes collaboration and high productivity 

Azure Databricks has an integrated environment that offers workspaces for collaborations between Data Engineers, Business Analysts and Data Scientists. This environment can be hosted on Azure Virtual Machine, which provides scalable compute resources for efficiently running Databricks workloads within the Azure ecosystem. These collaborative workspaces provide multiple members to interact for prototyping, Machine Learning and extracting data. Thus, ensuring higher productivity in comparison to most analytic platforms. 

Hybrid and interactive 

Azure Databricks access and identify using the same secure and reliable production environment. This is done through Azure Active Directory which allows easy integration with the entire Azure store including Data Lake Storage, Data Warehouse, Blob Storage, and Azure event hub. 

Fit for small businesses too 

This is an easy-to-use platform for developing small-scale industries. Azure offers Databricks to be used as the one-stop shop for all analytical work. Thus, you no longer need to create separate workspaces and environments for development work. 

Broad list of data sources 

Azure Databricks can easily connect to on-premise (SQL servers, CSVs and JSONs) and other sources outside of the Azure library. like MongoDB, Avro files Couchbase, etc. 

Extensive documentation and support 

Using data bricks can provide us with extensive documentation and support for all Azure Databricks environments. This includes programming languages and Microsoft-specific documentation. 

It is a powerful and cheap platform in the current scenario. However, it continues to evolve with the requirements of time and technology. It is also flexible and easy to use making distributed analytics effortless. 

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Pros and cons of Azure Databricks 

Azure Databricks is a recent addition to the Azure cloud computing platform; therefore, with rapidly changing technological demands it is not always easy to work with. So, if you want to work with Databricks in Azure, first take a look at its pros and cons. 

Following are the advantages and disadvantages of Databricks in Azure: 


Here we will be discussing some advantages of Azure Databricks: 

1) A large amount of data can be processed using Databricks. Databricks in Azure contain data that is cloud-native. 

2) It is integrated with Azure Active Directory. 

3) Also, it supports various programming languages both on-premise and outside the library. 

4) The stacks are easy to start with and configure. 

5) It also has an Azure Synapse Analytics connector with the ability to connect to Azure databases. 


Now Let us discuss some disadvantages of Databricks in Azure: 

1) Presently, Azure Databricks can only support HDInsight and not Azure Batch or Azure Distributed Data Engineering Toolkit (AZTK). 

2) It cannot integrate with Git or any other transcoding tools. 

Thus, although there are several advantages of using Databricks, it cannot entirely fulfil the current technological needs.


Azure Databricks is an easy, fast, and collaborative Apache Spark-based analytics platform. This innovative platform has the ability to smoothly integrate various data-driven projects, such as Data Science, Data Engineering, and Business Analytics. Thus, advancing collaborations and improving the efficiency, security, scalability, and Azure compatibility of the Data Analytics Process. 

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Frequently Asked Questions

What impact does Azure Databricks have on time-to-market for business solutions? faq-arrow

Azure Databricks expedites time-to-market for business solutions through its unified analytics platform, simplifying data processing, analysis, and model deployment. This enables organisations to swiftly derive insights and implement data-driven decisions, gaining a competitive advantage in the market.

Does Azure Databricks assist in cost optimisation for businesses? faq-arrow

Yes, Azure Databricks aids in cost optimisation for businesses by offering scalable data processing capabilities, allowing organisations to pay only for the resources they use. Its efficient management of Spark clusters also helps minimise infrastructure costs and improve overall cost efficiency.

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The Knowledge Academy’s Knowledge Pass, a prepaid voucher, adds another layer of flexibility, allowing course bookings over a 12-month period. Join us on a journey where education knows no bounds.

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