The Top 4 Benefits of Microsoft Databricks

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Published on: August 2, 2022
Written by: Roopak TJ

Choosing the right cloud data platform can be very challenging and there are many key considerations you should factor in when deciding which platform is the best for your organisation.

Factors such as ease of use, flexibility, integration with other business systems, scalability, cost, and features that add value to your organisation should all be considered when making the decision of which platform you should use. 

Microsoft Azure Databricks is winning hearts and minds with its unified platform message which caters to varied data and analytics use cases. With its unique features to bring data, developers and infrastructure to a single place, Databricks has empowered organisations to do more with data and unfold values. 

One platform for all 

The following benefits have contributed to the growth and development of Databricks: 

Every organisation has a preference for a data programming language. A data engineer and a data analyst would prefer to work on SQL whereas a Data Scientist would prefer Python or R. Choice of language influences the choice of tool/interface and this further involves a lot of data movement.  

With the introduction of Databricks, Data Engineers, Data Analysts and Data Scientists can collaborate at the same time on the same data with the language of their choice. It can be SQL, Python, R or Scala. Databricks uses a web-based interface called Notebook which can hold executable cells, visualisations and documentation.

It also enables collaborative working with version control with built-in commenting and sharing capabilities allowing the exchange of ideas between co-workers. Within the same notebook, different cells can execute different programming languages and developers can choose to run interactive or scheduled data workloads. 

Single data store - Introduction of data lakehouse 

In a typical data architecture, all the enterprise data (SQL, semi-SQL or NoSQL) first land on a data lake. Structured or sometimes semi-structured data is then moved to a data warehouse which is an OLAP platform, however, not all data is loaded to a data warehouse.

Data Analysts and Data Scientists usually work on processed data which becomes a bottleneck for them as they are dependent on Data Engineers for more data.  

Databricks has introduced Lakehouse which combines features of both Data Lake and Data Warehouse. Lakehouse has the flexibility, scale and cost-efficiency of Data Lake combined with the data management capability of Data Warehouse.

All the organisation data can now reside in a single place. The reasons why Lakehouse is gaining popularity will be explored in a subsequent blog. 

Enables modern analytics workloads with AI/machine learning  

Databricks Machine Learning is an end-to-end machine learning environment for developing, training, managing, and deploying production-level ML workflows. Users can now focus on the data as Databricks handles the heavy-lifting part of operational complexity with fully managed infrastructure. 

Coming from the original spark developers themselves, Databricks is designed to be faster than the standard Apache spark. In a recent performance test, Databricks ran faster than Apache Spark deployed on AWS EMR.

Along with the optimised spark engine, Databricks also brings to the plate a lot more features like Integrated workspace, types of runtimes, workflows, securities, integrations etc. Databricks is now available as a native service for Azure and AWS which helps in integration within the cloud pipelines and Machine Learning services.  

Suitable for all data sizes and cost-efficient 

Databricks use cases can vary from a single node driver for lightweight exploratory data analysis to a multi-node cluster for production-level workloads. Traditional data warehouses lacked this wide scaling option.

Databricks is very cost efficient if configured properly as it offers pay-as-you-go approach along with discounts if committed to certain usage over a period of time. Pricing is based on DBU (Databricks unit), similar to credit which is a normalised unit of processing power.

DBU rate depends on the service provider which is hosting the platform and pricing also depends on the selected runtime. Databricks provides different types of runtimes which can be selected based on our requirements and workload. For example, Machine Learning runtime that adds popular machine learning libraries, Photon runtime which runs faster SQL queries, and Databricks light for lightweight jobs. 

Databricks is a powerful data and analytics engine that makes large amounts of data easily distributable. With its continuously growing community and more frequent releases, it is not at all a surprise to see Databricks increasing its footprint within the data community. 

Contact us if you would like to know more about how Databricks can benefit your organisation. 

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