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August 11, 2023

Databricks for Business: Use Cases. How Databricks Support Data-driven Culture?


Edwin Lisowski

CSO & Co-Founder

Reading time:

10 minutes

With cloud-based workloads accounting for 75% of the total workload in a fifth of all organizations, it pays to have a proper strategy in place to fully leverage cloud computing capabilities. [1]

With that in mind, the ideal strategy should support big data transformations and analytics. It should also enable you to combine data from different sources and integrate it into various ETL, ML, SQL, and other AI-driven applications. This way, you can gain insights and use various technologies to maximize its potential.

That’s where Databricks comes into play. With this revolutionary cloud-based engineering tool, you can run a complex combination of data processes, including data unification, transformation, and analysis. You can also incorporate the data into various ML and AI applications for greater operational efficiency.

This article will delve deep into Databricks processes and how they can be used in data engineering; exploring everything from what it is, how it works, its various Databricks use cases, and how it can help you build a data-driven culture in your organization.

What is Databricks?

Databricks is a unified, web-based, open data science analytics platform effective in building, deploying, maintaining, and sharing enterprise-grade data analytics and AI solutions at scale.

Essentially, Databricks integrates into your existing cloud infrastructure, making it easy to manage and deploy different applications in a faster, more efficient, and cost-effective way.

How does Databricks for business work?

One of the biggest challenges facing organizations working with huge volumes of data is figuring out the most effective way to bring it all together in a way that fulfills their goals. This is where Databricks for business comes in. Databricks can help in this regard by enabling you to clean, store, and visualize data from disparate sources.

Databricks provides a unified platform for a wide variety of tasks, ranging from basic ETL to business intelligence and ML. The Databricks platform achieves this by simplifying the creation of modern data warehouses, which, in turn, facilitates more manageable self-service analytics and machine learning model deployment with enterprise-grade performance, maintenance, and governance.

At its core, the Databricks platform is made up of four open-source tools wrapped up into a cohesive, enterprise-friendly bundle that can be delivered as a cloud-based service.

The individual components of the Databricks platform include:

Apache Spark

Apache Spark is the core of the Databricks platform. Right from inception, the open-source big-data processing engine has been instrumental in the development of the big-data industry. The engine’s success is primarily based on its ability to support large-scale distributed computational datasets with such efficiency not experienced with previous technologies. It’s difficult to imagine modern data science without Apache Spark.

apache spark features

Besides its incredible efficiency in working with large datasets, Apache Spark is also flexible and seamlessly scalable. It can unify both batch and streaming data, support SQL, and incorporate different processing models. These unique characteristics make it incredibly easy to use and highly accessible.

Read more about Apache Spark machine learning for predictive maintenance


DeltaLake is an open-source storage layer designed to operate on top of existing data lake infrastructures to boost their reliability, security, and performance in data science and data engineering. The platform adds an extra layer of intelligent governance and data management to open storage environments and is compatible with Apache Spark APIs. And, like Apache Spark, DeltaLake runs both streaming and batch operations. [2]

The DeltaLake platform takes a ‘lake house’ approach to data governance. Essentially, it combines the effective data management capabilities and high performance of a data warehouse, with the low-cost flexibility of data lakes.

It also broadens the scope of tools available to the organization. For instance, the ‘warehouse’ component of its working mechanism supports business intelligence, while the data lake aspect streamlines AI process utilization. This gives you the best of business intelligence and AI automation, all on a single platform.

It might be interesting for you: Delta Lake on Databricks – Reliable Data Lakes at Scale


MLFlow is an open-source tool used to manage the end-to-end lifecycle of machine learning platforms and applications. Data science processes in an organization are complex and involve a series of applications that range from training algorithms to deploying ML models in different environments, including integrating them into new applications – all the while utilizing different tools.

MLFlow effectively streamlines ML lifecycle management by linking together different tools and aspects in the pipeline into a cohesive structure. Ultimately, this helps ensure data integrity, reliable insights, and easy integration of ML workflows into the business model.


Any data scientist using Python programming language when working on the Apache Spark platform is about to encounter difficulties, particularly around compatibility. Apache Spark is not compatible with Python. It only comes with Java and Native Scala APIs, thus making it nearly impossible to perform analytics with programs written in Java.

That’s where Koalas comes in. It features Pandas APIs, which are some of the most popular analytics software in the Python library. This means that programmers conversant with Python can work effectively on the Apache Shark platform without having to learn a new programming language.

Exploring Databricks in action: Top business use cases

Databricks’ ability to enable seamless processing and transformation of huge datasets and analytical capabilities through integration with machine learning models gives it a large number of Databricks use cases that cut across various areas of business across diverse sectors. Here are some of the most notable Databricks use cases in business.

Predictive analytics

Databricks provides a scalable, collaborative environment for analysts and data scientists to build and deploy predictive models. Take the retail sector, for instance. By integrating machine learning algorithms into the Databricks platform, retailers can analyze vast customer data including browsing behavior, purchase history, and demographics.

Retailers can then use analytics derived from this data to predict customer preferences, personalize marketing campaigns, and optimize inventory.

Risk management in the banking sector

Banks rely on predictive analytics to assess their customers’ risk profiles. For instance, when you seek a loan from a bank, the bank uses AI-driven analytics and predictive software to assess your default probability and the amount they should give you.

However, due to the large nature of their datasets, banks require robust systems like Databricks to effectively assess your past banking data including transaction history, asset valuation, and occupation.

databricks for banking sector, money, credit card

Optimizing energy distribution and production

The energy sector isn’t just tasked with producing energy. It also has to ensure efficient distribution and reduce costs. With Databricks, energy companies can leverage data from various sources such as weather forecasts and IoT devices to optimize energy generation, enhance energy production, and improve grid efficiency.

Essentially, by harnessing the power of real-time big data analytics, Databricks enables companies in the energy sector to make data-driven decisions, which ultimately leads to a more reliable and sustainable energy ecosystem.

energy sector, wind turbines, electricity

Back-testing in the stock market

Back-testing is one of the most popular predictive methods for predicting the behavior of a certain stock. The method typically involves feeding predictive models with historical stock market data for effective stock price prediction.

Azure Databricks enables businesses trading in the stock market to build machine learning models over a vast amount of data using parallel processing engines like Spark. The result is often accurate stock price predictions, which enables them to maximize profits and reduce risks.

Streamlining supply chain management

Any organization operating in complex global markets needs efficient supply chain management. The powerful data processing capabilities Databricks provides can help organizations analyze supply chain data including logistics, inventory levels, and demand patterns.

This way, businesses can better optimize inventory management, identify potential bottlenecks or disruptions, streamline logistics operations, and improve efficiency and cost savings throughout their operations.

Improving cybersecurity with advanced threat protection

Since the beginning of 2023, 300,000 fresh malware instances have been generated daily. Considering the cost of ransomware recovery can hit upwards of $2 million, it becomes a vital necessity for any business to protect itself from malware breaches. [3]

Databricks can help organizations combat cybersecurity threats by leveraging real-time data analytics and machine learning algorithms. Essentially, it does this by analyzing log traffic, monitoring network traffic, and identifying patterns of malicious activities. This way, businesses are better able to enact proactive threat detection and rapid response mechanisms.

Personalized healthcare

Personalized healthcare, diagnostics, and therapeutics, were expected to grow by 40% in 2022, with the global market value currently expected to grow at a CAGR of 7.2% from 2023 to 2030. [4][5] Databricks has played a vital role in revolutionizing the healthcare sector by enabling efficient analysis of large-scale biomedical and genomics data.

databricks for business, healthcare, scientist, medical research, laptop

With Databricks, researchers and other medical professionals can identify genetic variations, study the progression of infection or disease, and develop personalized treatment plans. Eventually, this will lead to monumental advancements in precision medicine, which, in turn, will promote accurate diagnoses and improve patient outcomes.

Databricks and future business strategy

The capabilities of Databricks for business, coupled with its potential impact on business intelligence, AI, and cloud technologies, and data engineering are already revolutionizing most businesses’ workflows. In the near future, organizations will adopt more efficient strategies to fully leverage the technology. Here are some of the potential strategies many businesses will adopt in the near future:

Enhanced data-driven culture

The onset of AI and ML applications gave rise to the data-driven approach toward decision-making and other business processes. By providing a unified analytics platform, Databricks is already revolutionizing the approach by enhancing accessibility and efficient analytics.

As organizations learn to leverage Databricks capabilities, some may focus primarily on making decisions based on data-driven insights rather than intuition.

Widespread cloud adaptation and optimization

As a cloud-based architecture, Databricks allows businesses to scale operations as needed without a significant impact on their operational costs. Considering the cost and safety implications of in-house data architectures, more organizations will realize the potential of Databricks and implement strategies centered around embracing cloud computing.

cloud computing, cloud integration, cloud service, mobile

The resulting widespread adoption will reduce costs (both infrastructure and operational), facilitate seamless collaboration among teams and departments, and enhance optimized cloud usage through analytics.

Data monentization

The data monetization market is predicted to reach $9.1 billion by 2030[6]. This growth can be attributed to the capabilities of businesses to explore new revenue streams. These can be in the form of exploring ways to package and sell valuable data insights, analytics services, or AI-driven products to external customers or partners.

Elevate your business with rapid insights using Databricks Deployment Services – set up a PoC in days and unlock performance and scalability potential.

Final thoughts on Databricks for business

Databricks is the future of business intelligence. The cloud-based open analytics platform enables organizations to efficiently develop and deploy ML and other AI-driven applications, which are vital to business success in the current data-driven business atmosphere.

In the near future, Databricks applications will infiltrate numerous industries, leading to advancements in intelligence gathering, analytics, service delivery, and process automation. If you want to know how to utilize Databricks for business, feel free to reach out!


[1] 2022 Cloud Security Report. URL: Accessed August 7, 2023
[2] What is Delta Lake. URL: Accessed August 7, 2023
[3] Ransomware Recovery Cost Reaches Nearly $ 2 million More than Doubling in a Year. URL: Accessed August 7, 2023
[4] Personalized Medicine. URL: Accessed August 7, 2023
[5] Grandviewresearch, com. Personalized Medicine Market. URL: Accessed August 7, 2023
[6] Data Monetization Market. URL:, Accessed August 7, 2023


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