In today’s data-filled world, managing and using data efficiently can be a puzzle. Enter Databricks Lakehouse Apps, a fresh way to handle data that makes it easier for businesses to get valuable insights.

Imagine having a single place where all types of data, from numbers to social media posts, can be stored and understood. Traditional methods like data warehouses are good with numbers but struggle with other types of data. Data lakes can hold everything but can get messy. Databricks saw this gap and introduced Lakehouse, combining the best of both.

Databricks Lakehouse Apps go even further. They’re like smart tools that help different teams – from tech experts to business folks – work with data easily. Data engineers can organize data from different places, data scientists can find patterns and make predictions, and business teams can create graphs and charts to see what’s happening.

In this article, we will explore the core components and benefits of Databricks Lakehouse Apps. Also, learn how this paradigm shift simplifies data architectures, accelerates time-to-insight, and empowers cross-functional collaboration.

What is Lakehouse Apps?

Lakehouse Apps introduces a fresh approach to building native applications for Databricks. These apps offer a super secure way to create, share, and run innovative data and AI applications directly within the Databricks Lakehouse Platform. This means they work right alongside your data while benefiting from Databricks’ top-notch security and governance features.

Why we need Databricks Lakehouse Apps

Building and deploying applications on data lakehouses can be challenging due to the following factors:

• Security: Data lakehouses are often heterogeneous environments that store data in a variety of formats and with different security permissions. This can make it difficult to securely build and deploy applications that access data in the lakehouse.
• Complexity: Data lakehouses can be complex to set up and manage, especially for developers who are not familiar with the underlying technologies. This can lead to errors and security vulnerabilities.
• Scalability: Data lakehouses can be scaled horizontally to handle large amounts of data, but this can also make it difficult to manage and secure the applications that run on them.
• Cost: Building and deploying applications on data lakehouses can be expensive, especially for large enterprises.

Databricks Lakehouse Apps addresses these challenges with a native, secure, no-compromise solution. Lakehouse Apps are built on the Databricks Lakehouse Platform, which provides a unified experience for data engineering, data science, and machine learning. Lakehouse Apps offer the following benefits:

• Security: Lakehouse Apps run directly on a customer’s Databricks instance, so data never leaves the customer’s environment. This ensures that data is always secure and compliant.
• Compute capacity and scale: Lakehouse Apps are fully serverless, so developers don’t have to worry about provisioning or managing compute resources. Apps are scaled automatically based on demand, so there are no upfront costs.
• Integration with security, management, and governance systems: Lakehouse Apps are fully integrated with Databricks’ security, management, and governance platform, Unity Catalog. This makes it easy for customers to control who can access apps, what data they can access, and how they can use it.
• Ease of development: Lakehouse Apps can be built using any programming language or framework. This makes it easy for developers to reuse their existing investments and build applications that meet their specific needs.
• Speed to market: Lakehouse Apps can be deployed in minutes, so organizations can quickly get their data and AI applications into production.

Benefits of Lakehouse apps

Here are some of the benefits of using lakehouse apps:

• Reduced time to market: Lakehouse apps can be built and deployed more quickly than traditional data warehouse applications. This is because Lakehouse apps can be built on a single platform that supports all data types, which eliminates the need to move data between different systems.
• Increased scalability and flexibility: Lakehouse apps can be deployed on a variety of cloud computing platforms, and they can be scaled up or down as needed.
• Improved cost-efficiency: Lakehouse apps can be deployed on a pay-as-you-go basis, and they do not require the same level of upfront investment as traditional data warehouse applications.
• Enhanced security and governance: Lakehouse apps can be deployed in a secure cloud environment, and they can be protected by a variety of security controls.

Some of the use cases for Databricks Lakehouse Apps include:

• Building data-driven applications for business users
• Developing machine learning models
• Creating dashboards and reports
• Automating data pipelines
• Integrating with other applications

Databricks Lakehouse Apps are still in preview, but they are a promising new way to build and deploy data and AI applications. If you are looking for a secure, scalable, and flexible way to build data-driven applications, then Databricks Lakehouse Apps are worth considering.

Here are some of the companies that are already using Databricks Lakehouse Apps:

• Walmart: Walmart is using Lakehouse Apps to build a data-driven platform for its retail operations.
• Capital One: Capital One is using Lakehouse Apps to develop machine learning models for fraud detection.
• Siemens: Siemens is using Lakehouse Apps to build a digital twin of its manufacturing operations.
• Netflix: Netflix is using Lakehouse Apps to improve its recommendation engine.
• Spotify: Spotify is using Lakehouse Apps to personalize its music streaming service.

Where to find Lakehouse Apps

Databricks Marketplace: Databricks Marketplace is a curated catalog of Databricks Lakehouse apps that have been built by Databricks partners. You can find apps for a variety of purposes, such as data visualization, machine learning, and business intelligence.

Conclusion

In wrapping up our journey through Databricks Lakehouse Apps, we find ourselves at the crossroads of a significant transformation in data handling. These apps signify a turning point, where the fusion of traditional data warehouses and modern data lakes reshapes how we approach and utilize data.