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Is Azure Databricks ‘Apache-Spark Based Platform’ Meant for Your Business?

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Azure Databricks is a Cloud-based data engineering application used to store, process, and transform large volumes of data. Apache spark developers exploring the massive quantities of data through machine learning models. The Apache-Spark-based platform allows companies to efficiently achieve the full potential of combining the data, machine learning, and ETL processes.

The recently added tool to Azure’s Cloud runs a distributed system, making the workloads split automatically across different processors while scaling up and down on-demand. It saves your time and operational costs and makes your business operations even more efficient. Now let’s see if Azure Databricks is meant for your business.

Azure Databricks = Apache Spark + Databricks + Enterprise Cloud

Once the businesses manage data at scale in the Cloud environment, it opens massive new opportunities for artificial intelligence, real-time applications, and predictive analytics. Spark makes it easy to run robust analytics algorithms in real-time to deliver accurate, quick business-insights. However, Spark remains challenging for enterprises with a large workforce and strong security requirements.

Databricks Makes Data Projects Easy…Easier Actually!

Familiar Languages and Environment

Databricks is Spark-based, but it uses SQL, Python, and R that IT teams are well familiar with. Backend APIs convert these languages to interact with Spark. Users do not have to learn another language to work with Databricks. However, you need to modify the package names for the languages in order to make them interact with Databricks. 

If you are not a proficient programmer, you can select to switch between various languages in Databricks. If your work involves interacting with functions from different languages, this is perhaps a useful feature. In the Azure Databricks, Notebooks allows you to view the output after every individual step.

High Production Deployment, Collaboration

Production deployment from Notebooks is easy and simple in Databricks. IT teams can deploy work from Notebooks into production simply by tuning the data sources and output directories. 

Azure Databricks creates an environment where data scientists, data engineers, and data analysts can collaborate effectively. In the Databricks environment, it’s relatively easy to deploy production jobs and enable multiple users to collaborate for machine learning, data extraction, and model creation.

Databricks provides simple monitoring and troubleshooting tasks. Its built-in automatic version control saves frequent changes and modifications done by users. 

Integrates Seamlessly with All Microsoft Stack

Databricks use Azure AD (Active Directory) security framework that allows users to utilize credentials authorization with corresponding security settings. Access and identity control are managed through the same environment. Azure AD integrates seamlessly with Azure stack, including Data Warehouse, Data Lake Storage, Azure Event Hub, and Blob Storage.

Databricks is considered the primary alternative to Azure Data Lake Analytics and Azure HDInsight.

Suitable for Small, Medium Jobs

We all know Azure Databricks is an excellent unified data analytics platform that is ideal for handling massive jobs. It also possesses the capability to efficiently handle and manage small to medium scale jobs. Databricks is considered one-stop-solutions for data analytics job due to its ability to manage big, medium, and small tasks efficiently. 

Extensive Documentation, Support Available

Though Databricks is the latest addition to Azure Cloud, it provides extensive documentation and support to help users understand and run it proficiently. Since Databricks is easier and extremely flexible to get started with, it simplifies the otherwise complex tasks associated with distributed analytics. The easiest way to understand Databricks is to move down the technology lane and take a look at Apache Spark, as it is the actual analytics engine in Databricks. 

If These Challenges Apply to Your Data Landscape, Databricks is FOR YOU.

Performance

Challenge

If you execute workloads serving analytics such as queries often, you will notice that it transforms data with each time the query runs.

Databricks Benefit

Databricks performance-based platform handles transform upon query model by utilizing various mechanisms that include Spark engine and storage such as Azure Data Lake Stores, Azure Blob. Moreover, it uses the latest Azure hardware with NvMe SSDs that facilitates faster I/O performance.

Time to Market

Challenge

Traditional data warehouse takes quite a long time to deliver business benefits.

Databricks Benefit

Databricks’ agile platform allows for unparalleled collaboration, which further results in enhanced responsiveness to change. The time that the data warehouse takes to deliver business benefits is reduced manifolds.

Scale Up & Down

Challenge

As the business expands, requirements changed. Sometimes, organizations fail to figure out exactly what they want to gain from tons of data stored in different formats at multiple locations. 

Databricks Benefits

Organizations’ business-critical data is generated and hosted in thousands of multiple formats, including XML, CSV, etc. Since analysts need access to this data for actionable insights, it’s important that the data is comprehensively stored in one single place.

Databricks allows extracting and transforming insights upon query from large data volumes stored in its native format in Blob Stores.

Databricks allows reading data directly from raw data files. It cleans, joins, and aggregates data using SQL queries; so, the term transform upon query is used. 

Transforming data with each query run means quick turnaround time and better responsiveness to change. 

Managed Platform

Challenge

The world is digitalizing and so do the businesses. With this, the data landscape becomes more and more complex, fragmented, and costly.

Databricks Benefits

Databricks is a managed platform that signifies that the IT teams don’t have to learn complex cluster management procedures, tedious maintenance tasks, or even new programming languages to derive benefits from Spark. With a simple interface, Databricks is impressively easy to use.

In short, Databricks reduces management tasks and help you get rid of analyzing, architecting, and maintaining data or managing computing resources. You can focus on business-expansion activities as Databricks will take care of your extended IT needs. 

If you want to explore the benefits of Azure Databricks, Aegis Softwares advisory team can assist you in finding the best way to implement and integrate it into your technology ecosystem. 

Summary: 

Azure Databricks is a fully-managed, collaborative Spark-based analytics platform that comes with a one-click setup, advanced security features, auto-clustering, built-in source control, and much more. Let’s understand the extensive benefits it offers to businesses- whether small, medium, or big.

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