Databricks is a software company founded by the developers of Apache Spark. The company has created many hit softwares that play an integral role in the tech stack of any developer playing around with data engineering, data science, and machine learning. Some of their widely acclaimed software include Delta Lake, MLflow, and Koalas. The main focus of Databricks is to develop web-based platforms that are integrated with Spark and provide automated cluster management and notebooks resembling the style of IPython.
Azure Databricks provides a unified, open platform for all your data. It empowers data scientists, engineers, and analysts with a simple collaborative environment to run interactive and scheduled data analysis workloads.
Azure Databricks
Azure Databricks provides an optimized and comprehensive analytics platform for the cloud services of Microsoft Azure. Azure Databricks provides three environments:
- Databricks Structured Query Language (SQL)
- Data science and engineering with Databricks
- Machine Learning with Databricks
Databricks SQL
Databricks SQL (DB SQL) is a serverless data warehouse on the Databricks Lakehouse Platform that lets you run all your SQL and BI applications. Because of enhanced optimizations, it provides you with upto 12x better price/performance. It provides a unified governance model, open formats and APIs, and your tools of choice. Hence there is no lock-in.
Databricks SQL provides a platform that is easy to use. As a result, this ease of use enables the analysts, who work with SQL queries, to query the Azure Delta Lake and hence create numerous visualizations and the ability to create and share dashboards.
Databricks Data Science and Engineering
Databricks data science and engineering provide an interactive working environment for data engineers, data scientists, and machine learning engineers. Users can use the following methods to send data from the big data pipeline:
- Infuse into Azure via Azure Data Factory in the form of batches
- Real-time streaming via Apache Kafka, Event Hubs, or IoT Hub
Databricks Machine Learning
Databricks Machine Learning is a complete machine-learning environment. It provides services for experimenting, tracking, training models, development of features, and management. Furthermore, it also includes model serving.
Advantages and Disadvantages of Azure Databricks
Let us now discuss the pros and cons of Azure Databricks.
Advantages
- The ability to share large amounts of data since Databricks is a part of Azure
- Ease of setting up and configuration of clusters
- The ability to connect with Azure DB via the Azure Synapse Analytics connector
- Integration with the Active Directory
- Support for multiple languages. Although Scala is the primary language, it works equally well with Python, SQL, and R.
Disadvantages
- Currently, it only supports HDInsight and not Azure Batch or AZTK
Reasons to Use Azure Databricks
Let us now discuss the top reasons to start using data bricks today.
Familiar Environment and Languages
Although Azure Databricks is Spark-based, it allows commonly used languages like Python, R, etc. Backend APIs convert these languages for interaction with Spark. This saves the user from learning new programming languages and the learning curve that comes with the process. Hence, this is the biggest motivational factor for any developer to use databricks.
More Productive and Collaborative Environment
Azure Databricks provides a conducive environment for more productivity and collaboration. Deployment from work notebooks in production can be instantly done by simply tweaking the data sources and the output directories.
A Databricks workspace is a software-as-a-service (SaaS) environment for accessing all your Databricks assets. The workspace organizes objects (notebooks, libraries, and experiments) into folders and provides access to data and computational resources, such as clusters and jobs. Databricks also provides workspaces for collaboration, deploys production jobs, and has an optimized engine for execution.
Extensive Data Sources
Besides the Azure-based sources, Databricks can easily connect to authorities, including on-premise SQL servers, CSV files, and JSON. Some other data sources include MongoDB, Avro files, and Couchbase. Because of its support for so many platforms, it a widely acclaimed platform in the Big Data industry.
Support for Small Jobs
Azure data bricks are known for their support for massive jobs. However, users can also use it for small-scale development and testing work. This eliminates the need to create separate environments or VMs for development work. As a result, this allows Databricks to be a one-stop solution for all analytics work.
Massive Documentation and Online Support
Although Datbricks is a new addition to Azure, it has, in reality, existed for many years. Users can easily find extensive documentation and support for all the aspects of Databricks, including the programming languages needed.
Conclusion
Azure Databricks is very powerful yet relatively cheap. As the digital revolution expands, big data technology will become necessary for organizations and businesses. Azure Databricks provides essential flexibility and is easy to use. Hence, this flexibility makes distributed analytics very easy to use. So make sure you migrate to it if it fulfills all the aforementioned criteria.
