Aptitive is both an official Databricks partner and Microsoft Gold partner. Contact us today to learn how Azure Databricks can be used as a unified and Spark-based ETL processing engine, governed data lake, and machine learning platform.
With the quick rise and fall of technology buzzwords and trends (especially in the era of “big data” and AI), it can be difficult to distinguish which platforms are worth considering for your next project. One that will change the way you think about centralizing key organizational data is Microsoft’s Azure Databricks, a platform released in March of this year. While it offers role-specific benefits across the spectrum, outlined below are three primary differentiators showcasing how it could be an asset to any organization.
Azure Databricks is a fully managed, Azure PaaS-based offering of the collaborative, Spark-based advanced analytics platform Databricks.
1. Azure Databricks reduces management headaches.
The following features ensure that you spend your time analyzing and architecting your data rather than worrying about source control or monitoring computing performance. People on your team who would otherwise be managing these can now be tasked to business-focused goals rather than backend IT duties.
- One-click set up — This enables you to launch your Apache Spark environment through a point and click wizard, reducing confusion and saving you time.
- Auto-clustering — Machine learning adds or reduces computing capacity based on your needs. It reduces cost and complexity, allowing for a hands-off yet dynamic approach to monitoring performance.
- Convenient security settings — Azure Active Directory is integrated into Azure Databricks, ensuring that it’s secure. In addition, role-based permission settings can be dictated for each notebook.
- Built-in source control — Azure Databricks displays revision history logs in notebook editors and easily links to a variety of repositories.
- Collaboration capabilities — Notebooks can only be shared with those who have access to the workspace. Users can add comments without making direct changes to the code.
2. Azure Databricks integrates natively with existing Azure services and tools.
One of the best things about Azure Databricks is that you can implement Apache Spark analytics directly into your existing analytics platform in a variety of ways. Power BI shines here with Spark — no more waiting on scheduled refreshes or long-running queries hitting your database. Analytics is now real-time, all the time.
You can add jobs from Azure Databricks notebooks to your existing pipelines in Azure Data Factory to operationalize your ETL/ELT process. Additionally, you can use existing sources in your Azure environment and apply changes directly inside of the Databricks notebook. This process can be automated by setting it on a schedule and monitored through a log that displays any failures within a job. Aptitive’s Azure ETL framework and toolkit also integrates into Databricks for richer auditing and automation controls.
Similar to other Azure PaaS offerings, Azure Databricks can be easily integrated with Azure Blob Storage, Azure Data Lake Store, Cosmos DB, Azure Event and IoT Hubs, Azure SQL Data Warehouse, Power BI, and Snowflake. This allows you to apply Spark-enabled advanced analytics such as machine learning directly to your existing environment with extremely low setup overhead.
3. Azure Databricks is accessible to the entire organization across varying roles.
While knowledge of Scala or Python are needed for heavier development, many tasks can be accomplished with limited knowledge of SQL or R. Business users can easily query data directly in a notebook and select from a variety of visualizations, ranging from simple tables to geographic maps, to view their results (or use Power BI). The setup of a cluster is as simple as pointing and clicking through a wizard (not to mention that it is heavily documented and includes a wealth of tutorials). Pre-existing knowledge of Spark is not a requirement to start moving toward success.
The accessibility of Azure Databricks is expanded even further when the breadth of Azure integration capabilities is considered. As an example, structured streaming capabilities enable users to easily view data in real time, a task that may seem daunting to many of us. Azure Databricks benefits your business by streamlining a multitude of complex tasks and allowing those with more limited technological knowledge to actively participate in uncovering hidden insights through data exploration and downstream application integration.
Azure Databricks has implemented many features powerful enough to catapult your company into the world of advanced analytics and ensure that a wider variety of people can buy in to the results. Whether you decide to immediately implement solutions using Azure Databricks or plan to look around for other options, one thing is for sure: Azure Databricks has the substance necessary to outshine many of the over-hyped platforms in today’s fast-paced tech world.
If you’re interested in exploring Azure Databricks, but don’t know where to start, Aptitive’s data advisory team can help you find the best way to utilize this cutting-edge technology and integrate it into your ecosystem.
Faith Hemingway is a Data Management and Analytics Consultant at Aptitive. In her role, Faith enables companies to discover insights and access the information held by their data.