Azure Databricks delivers Apache Spark-based analytics to the Microsoft Azure cloud. It is a unique collaboration between Microsoft and Databricks, forged to deliver Databricks’ Apache Spark-based analytics offering to the Microsoft Azure cloud.
Databricks was founded by the creators of Apache Spark with the goal of helping clients with cloud-based big data processing. Apache Spark is an open source cluster-computing framework running on top of Scala that provides an interface and foundation for programming entire clusters with integrated fault tolerance and parallelism. Databricks set the record in late 2014 for performance in large-scale sorting performance. It’s blazing fast. This collaboration and integration natively integrates Apache Spark’s performance and redundancy with Azure’s security and wide variety of product offerings for data storage, processing, analytics, and best-in-class Power BI analytics insights reporting.
Is Azure Databricks right for your company? Here are three reasons why it may be…
1. Get Started Quickly
With Azure Databricks, you can be developing your first solution within minutes. Once in the Azure Portal, you simply select Databricks under the Analytics heading and you’re ready to set up your first workspace, create a cluster and import Notebooks. Azure Databricks removes the difficulties and headaches from managing and configuring clusters. Utilizing Databricks Serverless and choosing Autoscaling takes that burden on itself–automatically–as your workloads and data sources need to scale. It’s easy to create and clone clusters to allow for branching of work efforts when the need arises. Everything from creation of your first cluster to security and billing is unified and as seamless as you’d expect from Microsoft Azure cloud platform.
2. Collaboration and Integration
Azure Databricks provides a single collaborative workspace for those involved in providing insights to customers and companies looking to gain a competitive edge on their competition, solve wide-ranging societal issues, or simply find ways to allow their companies to run more efficiently through data analysis, data science, artificial intelligence (AI) and machine learning (ML).
This Databricks workspace is where data engineers can transform static and streaming data from seamlessly integrated data sources.
This is the same workspace where data scientists can develop models for AI and ML against those transformations and where analysts can turn findings and models into mission-critical reports, charts, graphs and more in Power BI. They can also write their own SQL queries against Databricks notebooks to visualize Power BI or Tableau for self-service business intelligence.
Data sources are not limited to just Azure offerings, either. Azure Databricks can work with data sourced from Couchbase, Elasticsearch, CSV files, JSON files, Redis and more. A current list of applicable data sources is available here.
3. Universal Connectivity to Azure Storage Services
Azure Databricks seamlessly connects to Azure storage options.
This includes the ability to read and write to file-based storage, Blob storage and Azure Data Lake Store, as well as relational data stores, like Azure SQL Database/Data Warehouse, and NoSQL data stores, like Azure Cosmos DB. It also connects to streaming or event data sources in Azure, such as Event Hubs or Apache Kafka on HDInsight.
Azure Databricks utilizes Microsoft Azure’s Active Directory (AAD) security framework. AAD is a multi-tenant, cloud-based directory and identity management service that combines directory services, application access and identity protection into a single, core solution.
It is extensible to any Windows Server Active Directory implementation for hybrid environments that run both on-premise and in the Azure cloud. Four clicks is all it takes to integrate on-premise and Azure based AD creating a secure workspace for all your data professionals to collaborate.Security is of utmost concern to any enterprise processing or collecting data to mine valuable insights; a solid framework such as AAD backing your high-performing analytics platform is more of a requirement than it ever has been. Those with administrative access can quickly and easily grant and revoke access at a fine-grained level so your employees’ performance isn’t negatively impacted by a security bottleneck.