Azure Databricks is a cloud based data engineering tool that is meant to process and transform expansive chunks of data. This industry-leading solution allows organizations of all types and sizes to achieve the full potential of data, but competently combining machine learning, ELT processes, and data. Darabricks is an Apache-Spark-based platform whose workloads are automatically split across discerning processors and can be seamlessly scaled up or down, based on the demand.
Collaborating on shared projects in an interactive workspace becomes quite simpler with Azure Databricks. It also would be the ideal system to leverage in order to unlock valuable insights from data, as well as develop artificial intelligence (AI) solutions. There are many reasons why you should consider utilizing the capabilities of Azure Databricks. Here are some of them:
- Familiar languages and environment: Even though Azure Databricks is based on Apache-Spark, it supports a number of commonly used programming languages, such as Python, Scala, R, Java, and SQL. Such languages are typically converted in the backend through APIs, in order to interact with Spark. This saves the data team from the hassle of learning some other programming language like Scala just for distributed analytics. People who aren’t proficient programmers get the advantage of switching between varying languages on Azure Databricks, which invariably makes their job a lot simpler.
- Facilitates machine learning: Through Azure Databricks, one can access advanced, automated machine learning capabilities with the usage of integrated Azure ML, and swiftly identify hyper-parameters and relevant algorithms. With this platform, it becomes immensely easy to simplify monitoring, management, and updating of ML models that have been deployed right from the cloud to the edge. Moreover, Azure Machine Learning can offer a central registry for experiments, ML models, and pipelines.
- High-performance modern data warehousing: Azure Databricks aids in combining data at any scale, while gaining valuable insights through operational reports and analytical dashboards. Data movement can easily be automated with Azure Data Factory, and ultimately loaded into the Azure Data Lake Storage. From there, data is transformed and cleaned through Azure Databricks, and made available for analytics with the usage of Azure Synapse Analytics. Companies can effectively modernize their data warehouse in the cloud with this platform, while enjoying a commendable degree of scalability and performance.
- Collaborative workspaces and version control: Azure Databricks manages to create an environment that offers workspaces for superior collaboration between business analytics, engineers, and data scientists. It also aids in deploying production jobs, which includes the use of a scheduler. The interactive workspaces made available to data teams are perfect for several members to collaborate for streamlined data extraction, model creation, and machine learning. Azure Databricks also comes with a built in version control feature which adds to the convenience of the users. Troubleshooting and monitoring can especially be made seamless and hassle-free with this addition.
Even though Azure Databricks is considered perfect for massive jobs, this platform can be just as easily used for any kind of smaller testing or development work. Its features essentially make it a one-stop-shop for all analytics work.