1. The Databricks Data Intelligence Platform
Hello! Welcome to the Introduction to Databricks course. In this video, we will be discussing an overview of Databricks and why it has become a popular option for enterprise data architectures.
2. Architecture Options
Before we talk about Databricks specifically, let's start by reviewing some of the common platforms available for enterprise data. Historically, there have been two main options: the data warehouse and the data lake. To satisfy their needs, enterprises often would have two separate "stacks" in their architecture.
3. Birth of the Lakehouse
To satisfy their needs, enterprises often would have two separate "stacks" in their architecture. Data warehouses are great for structured, fairly stagnant data, but poorly fit for faster changing or advanced datasets.
4. Birth of the Lakehouse
Data lakes, on the other hand, are great for their flexibility and support of all data types, but historically don't have great performance and can often get messy. This leads to an architectural conundrum for most organizations.
5. Birth of the Lakehouse
This prompted the need for a new architectural design: the lakehouse.
6. Birth of the Lakehouse
The lakehouse builds on top of the data lake, giving your architecture flexibility for all data types and workloads while getting the performance and governance benefits from the data warehouse design.
7. The Databricks Lakehouse
Databricks provides a single platform that can deliver the lakehouse architecture simply and at scale, allowing data teams can deliver every use case on any dataset without having to worry about how to manage these different technology stacks.
8. The Databricks Data Intelligence Platform
Databricks is now the Data Intelligence Platform. This new paradigm is a natural evolution of the Lakehouse and is a response to the growing need for advanced AI capabilities, such as Generative AI.
At the core, the Data Intelligence Platform is still based on the Lakehouse architecture. On top of that architecture are two key innovations. Databricks maintains a Data Intelligence Engine, a set of built-in AI models to make your development quicker and smarter. Secondly, Databricks provides an end-to-end platform for creating custom AI applications.
9. Databricks Architecture Benefits
Databricks unifies your entire data stack to can handle use cases from AI to BI, and give you the benefits of both the data warehouse and data lake architectures in one.
The lakehouse is a multi-cloud technology that runs on all of the leading cloud platforms. This means you can bring compute to your data without feeling locked into a particular vendor.
10. Databricks Development Benefits
Databricks creates a collaborative platform for your data teams. Any data persona will find a user interface that aligns with how they work and can even work together on the same artifacts in real time.
Databricks is built on open-source technologies. In the platform, users can use any of the leading languages, such as Python and SQL, for data processing and analysis without any special configuration, and seamlessly leverage the scalable performance of Apache Spark.
11. Let's practice!
With that overview complete, let's review some of the key benefits of Databricks and the lakehouse architecture!