In a world of rapid digital transformation, big data is naturally more prevalent in our line-of-business computer systems than ever before.
By 2020, the accumulated volume of big data around the world will increase from 4.4 zettabytes to around 44 zettabytes - that’s 44 trillion gigabytes - and chances are you'll need the right data tools to find the gold beneath.
However, what we refer to as big data actually only amounts to 10 percent of the total data available to organisations; the remaining 90 percent is unstructured, massive and not easy to derive business value out of.
This is why big data analytics tools such as Apache Spark are essential, as they are designed to work across massive clusters of databases and servers to explore data in a more efficient way than previously possible.
Azure Databricks fits into the big data equation as the cloud-optimised version of Apache Spark. It is specifically integrated and optimised for Microsoft Azure, and it was also designed by the founders of Spark, making it one of the best analytics platforms currently available for businesses on the Azure Cloud looking for a big data solution.
What is Azure Databricks?
Azure Databricks is a data analytics solution built on top of Microsoft Azure and used for managing, parsing and processing large quantities of information for the purpose of developing and deploying models on said data to derive actionable insights, which is foundational to achieving innovation.
Databricks is entirely based on Apache Spark and as such is a great tool for those already familiar with the open-source cluster-computing framework. As a unified analytics engine, it’s designed specifically for big data processing and data scientists can take advantage of built-in core API for core languages like SQL, Java, Python, R, and Scala.
It also includes:
DataFrames with Spark SQL
GraphX for graphs, graph-parallel computation and data exploration
Machine Learning (ML) support via MLib
As a fully managed, Platform-as-a-Service (PaaS) offering, Azure Databricks leverages Microsoft Cloud to scale rapidly, host massive amounts of data effortlessly, and streamline workflows for better collaboration between business executives, data scientists and engineers.
Here’s four reasons why Azure Databricks is a great analytics toolset for your big data workloads.
1. Azure Databricks makes big data collaboration and integration easy
Like all other services that are a part of Azure Data Services, Azure Databricks has native integration with several useful data analysis and storage tools on the Microsoft Cloud platform via connectors.
Currently, Azure Databricks support includes but is not limited to:
Azure Blob Storage
Azure Cosmos DB
Azure Data Lake Storage (ADLS)
Azure SQL Data Warehouse (Azure SQL DW)
Azure Event Hub
Apache Kafka for HDInsight
Microsoft Power BI.
Why integration with these various services is a major advantage for your advanced data experts is it helps them deliver data that delivers actionable insights in a way that your non-data experts - business executives, marketers and sales staff - can understand. For example:
Data engineers can create, clone and edit clusters of complex, unstructured data, turn them into specific jobs and deliver them to data scientists and data analysts for review.
Data scientists can explore jobs for insights, or run different types of advanced analyses on the same cluster of data in one interface - all the while Databricks auto-scales with the cloud to minimise the total resources in use for optimised performance.
Any derived insights can be stored in Azure SQL Data Warehouse at petabyte scale, and the elastic nature of the cloud data warehouse allows organisations to load and process any type of data at scale for enterprise reporting with Power BI, which can visualise your findings into a easy-to-read dashboard, which is much more accessible and understandable for your company's non-data audiences.
The possibilities for data analysis are broad with Azure Databricks, as is storage; because you have native integration with Azure Blob Storage, Azure Data Lake, Azure SQL Data Warehouse, and Azure Cosmos DB, your data team can use it to clean, merge, and aggregate data regardless of where it rests before you begin exploring it.
While it's true that Apache Spark and Databricks were previously usable on Azure, Azure Databricks significantly streamlines the entire process via Azure Portal, and is the best option available if on Azure Cloud.
2. It’s a fully managed by Azure with all of Apache Spark’s features
Azure Databricks has all of the key features of Apache Spark, with many more advantages at the infrastructure level.
Everything is managed by Azure, your systems are preconfigured, there’s no maintenance required and you can scale up and down in quite literally a ‘drag and drop’ interface without having to do anything else - a major advantage of PaaS and a big reason to start thinking about the move away from legacy on-premises systems.
With Azure Databricks, your users can completely remove redundant Spark clusters whenever you don’t need them anymore, and you can even pre-set when a cluster should be terminated based on inactivity.
This level of control over each cluster means you can save a significant amount of money and resources in the development phase, versus more complex procedures by manually doing so with on-premises Apache Spark.
Essentially, clusters are ready-to-use with Azure Databricks, and its cloud-based perks allow your business to focus on application and business requirements and less on infrastructure.
3. Azure Databricks is protected and safe with Azure
Azure Databricks uses the enterprise-grade compliance and security available to all services on the Microsoft Azure platform, making it one of the safest big data analytics platforms available.
Azure Databricks is also integrated with Microsoft’s Azure Active Directory (AAD) security framework, with no custom configuration required. All users can log into their Azure Databricks workspace via the URL and log in with their regular AAD credentials with minimal fuss.
AAD integration means you handle all of your organisation’s identity management, role-based access and security with the same system and protect your corporate big data without interrupting the standard workflow of your users.
Admins of Azure Databricks workspaces can use the Admin Console to add, manage and delete and manage users. You can also invite users not in the same AAD for additional collaboration possibilities, as long as the user is registered to another AAD.
If you use Apache Spark on Azure HDInsight, you have to pay for AAD integration as it’s a premium feature - something Azure Databricks doesn't demand. There’s also Windows Server AD implementation for organisations running hybrid-cloud environments, integrating on-premise and Azure based AD for a secure workspace.
4. It’s fast and optimised for maximum performance
Apache Spark is well known for its speed and Azure Databricks improves upon its industry-praised performance greatly, offering significant processing efficiency gains - up to 8x the performance in caching, indexing and advanced querying in comparison to other big data SQL analytics platforms.
It’s also able to process terabytes of data in just minutes, as best illustrated on the official Databricks blog.
All data you explore, manage, share and aggregate with Azure Databricks are backed by the Microsoft Cloud’s Service Level Agreements (SLAs) for maximum connectivity and up-time.
Currently, that’s 99.5% availability guarantee, and if it ever drops below that number while you use Azure Databricks, your business may be eligible for service credit (up to 25%), though in our experience it’s never been an issue.
Azure Databricks: Next steps
Azure Databricks is an extremely versatile service that allows your data experts to analyse big data more efficiently.
With native integration with many more essential Azure Data Services such as Cosmos DB, Power BI and SQL Data Warehouse, now is the time to learn more about how it can potentially grow and modernise your data platform.