Databricks are very much more relevant than ever. All business choices in firms now rely on data-driven decision-making. These decisions are made using data analysis and access to large quantities of data within the organization.
Large amounts of data flow from many source systems to the data warehouse or any analytics tool users may use to extract insights. Most companies need a workspace for data engineers, data analysts, and data scientists that is quick, dependable, scalable, and simple to use. Azure DataBricks can help with this.
DataBricks is a Cloud-based Data Engineering platform that is extensively used by businesses to analyze, manipulate, and examine enormous amounts of data.
Thus, it can be an excellent tool for business intelligence. This guide will explore everything you need to know about the basics of Azure DataBricks and how it can affect your BI tech stack.
Let’s start by defining exactly what this is.
Azure DataBricks is a data analytics platform designed specifically for Microsoft Azure cloud services. DataBricks SQL, DataBricks Data Science & Engineering, and DataBricks Machine Learning are the three environments available for creating data-intensive applications in Azure DataBricks.
For analysts who wish to perform SQL queries on their data lake, generate several visualization types to analyze query results from diverse viewpoints, and build and share dashboards, DataBricks SQL provides an easy-to-use platform.
DataBricks Data Science & Engineering is a collaborative workspace that allows data engineers, data scientists, and machine learning engineers to work together. The data (raw or structured) is imported into Azure in batches using Azure Data Factory or streamed near real-time using Apache Kafka, Event Hub, or IoT Hub for a big data pipeline.
This data is stored in Azure Blob Storage or Azure Data Lake Storage in a data lake for long-term sustained storage. Utilize Azure DataBricks to analyze data from many data sources and transform it into useful insights using Spark as part of your analytics process.
DataBricks Machine Learning is a managed end-to-end machine learning environment that includes experiment tracking, model training, feature development and administration, and model and feature serving.
Said, DataBricks is an Industry AI cloud data platform that is especially beneficial for executing complex data science projects in the enterprise, such as artificial intelligence and machine learning.
Now that we’ve broken down what DataBricks is, you’ll want to know why it’s being touted as a huge deal for business intelligence. Their platform comprises four open-source technologies that work together to deliver a variety of useful BI services via the cloud.
These services are bundled into a single SaaS interface for easy access. Consequently, the platform is well-rounded, with a wide variety of data capabilities. The following are some of the services provided by them:
These layers combine to create a unified data management platform that makes it easy for a data scientist within an organization to manage data properly. All of these basic tools are wrapped in their cloud-native service. Thus, it’s a great complementary solution for cloud-based data management and overall business intelligence.
There are so many benefits to using DataBricks as part of a BI tech stack, including the following:
No platform is perfect, and they are no exception. Consider a few downsides before integrating this tool into your BI tech stack:
So far, we’ve learned a lot about what DataBricks is, what it does, what it is designed to do, and its pros and cons. But what exactly is the use case for DataBricks in the context of business intelligence? To put it simply, Users can use it with other business intelligence tools, such as SQL endpoints or data clusters in your environment.
Suppose you wish to work with DataBricks using your favorite business intelligence tool. You may link your account to DataBricks clusters (located in the DataBricks workspace) or any SQL endpoints (such as a DataBricks SQL computation source that lets you run SQL commands on data objects within the platform’s environment). Your platform administrator will most likely do this for you, but you can also integrate the platform with your BI tools.
It is certainly a popular tool for those involved in business intelligence, and the tool itself is quite complimentary to BI tools and overall processes. However, it is better as a complementary component in a BI tech stack than a standalone or all-in-one solution.
Yurbi operates at the visualization layer. So, while the data analysts can use Databricks to build an extremely valuable data source for you, Yurbi helps securely communicate that data to the customers and end-users that need it.
In the context of embedded analytics, Yurbi provides:
In addition to all of those benefits, Yurbi was designed for business users (with the power needed for enterprises). This is important for any embedded use case where you want users to do more than look at a static chart, but also important for keeping the cost of operations and maintenance down by reducing the time needed by your development team.
Databricks can populate and manage your data. Yurbi can be the front-end to securely communicate that data with those who need it.
Reach out to us now, and let’s discuss how Yurbi can help you maximize DataBricks for embedded analytics, business intelligence, or both. Schedule a free live demo session to see how we work on the ground. These are not high-pressured sales pitches, as we want to see how we can help you out with your needs.