Big Data on Azure: Best Practices
Organizations all over the world are trying to use big data solutions to turn raw data into meaningful information. With 2.5 quintillion bytes generated on a daily basis, organizations need to set up their data solution wisely. Microsoft Azure offers a suite of data services, some work for big data, while others would be better for other purposes.
In this article, you’ll find a review of big data, Microsoft Azure services, and best practices for setting up your big data solution on Azure.
What Is Big Data?
Gartner defines big data as a “high-volume, and high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”
Big data solutions provide administration controls for huge amounts of data, including data capture, data storage, data backup, data analysis, and data visualization. Data administrators use big data systems to introduce efficiency into a complex data infrastructure. Big data solutions enable the use of advanced capabilities, such as AI-powered searching, sharing and collaboration features, and security for large volumes of data at rest, in use, and in motion.
What Is Microsoft Azure?
Microsoft Azure offers enterprise-grade cloud computing services, such as:
- Infrastructure as a service (IaaS)—provides remote virtualized computing resources, such as networking and servers.
- Software as a Service (SaaS)—provides remote software development resources, such as execution environments and operating systems.
- Platform as a Service (PaaS)—provides specific licenses for the remote use of software.
Azure offers robust features that promote scalable workflows, including a fast virtual machine (VM) service, Azure backup and storage solutions, Azure functions for serverless architecture models, and Azure Kubernetes Services (AKS) for simplifying the deployment of containers.
Why Azure Works Well for Big Data
Azure provides high-end enterprise-ready cloud computing services, including built-in backup and security features. Azure’s focus on hybrid cloud networks simplifies the migration process, by enabling simple on-premise-to-cloud migration strategies that allow organizations to keep some of their resources on on-premise while moving the rest to the cloud. If you’re using Azure, you’ll be able to track big data in multiple repositories and network locations.
Azure is also useful for setting up a big data solution for Internet of Things (IoT) devices. You can use the Azure Resource Manager and Azure IoT Edge modules to configure and manage your big data solution. In Azure, backup is built-in and you’ll have a variety of features to ensure your big data is safely stored and secured. The on-demand pricing mode of Azure services is sophisticated, providing users with payment optimization capabilities that promote growth at scale.
Best Practices for Setting Up a Big Data Solution on Azure
A big data solution is a complex operation that requires decisive thinking and strategic planning. You’re dealing with huge volumes of data, and the architecture and costs could be overwhelming without a specific course of action. Determine your needs in advance and then choose the services accordingly.
Here are a few best practices to get you started:
- Choose Your Data Sources Wisely—It Starts With a Question
While data storage services have become much more affordable, storing huge amounts of data can quickly turn into a huge expense. For some organizations, it makes sense to hoard every byte of data for future analysis and use. However, many organizations can’t and shouldn’t shoulder the overhead. In these cases, it’s best to be specific, and choose a specific source to analyze for a specific purpose. Ask a question first, and then go in search of the data.
- Be Picky—Choose the Big Data Solution That Is Right For You
Now that you know what information you’re looking for, and what data sources you’re going to analyze, it’s time to choose a storage solution for your data. If you’re streaming real-time data from your user’s devices, you might want to opt for Azure Stream Analytics, which is a service that provides “serverless real-time analytics, from the cloud to the edge” for mission-critical workloads. If you’ve decided to hoard your data, you can store it in Azure’s Data Lake Storage.
- Maintain Data Consistency With Azure Cosmos DB
Once you’ve chosen your data source and your data storage solution, it’s important to maintain data consistency. The term data consistency refers to the usability of the data. The goal is to prevent the loss of data as it passes from one environment to another. To do that, you need to configure a set of rules all your data must follow. You can use Azure Cosmos DB—which is a fully managed globally distributed, multi-model database service—to set up levels of data consistency.
- Design Your Own Data Processing Model
Azure offers a range of services for many purposes. There’s hardly anyone who needs everything, but you can make use of the tools that suit your needs. For example, you can process your data through Azure HDInsight, which is “a processing framework that runs large-scale data analytics applications”. It was designed with open source in mind, to provide organizations with simple and easy open source integration. You can use it with Apache Hadoop, Spark, and Kafka for stream processing, along with a variety of options for batch, SQL, and NoSQL processing.
- Look to the Future—Machine Learning and Artificial Intelligence
Artificial intelligence and machine learning capabilities can supercharge your big data solution. You can use AI to automate many of your tasks, especially repetitive jobs, send you alerts if a real-time analysis turned up something important, and provide you with insightful reports. You can use Azure Machine Learning services to build, train, and deploy your own models. If you want to start quickly, you can use Azure Databricks to set up a ready-made Apache Spark–based analytics workflow.
It’s a Wrap!
A big data solution could be a valuable asset. Once you have everything set up, you’ll be able to glean insights that promote data-driven decision making in your organization. However, if not planned right, a big data solution can turn into a costly operation that drains your resources. Take your time and plan your big data solution wisely.
Make sure that you start with a specific goal that keeps you focused and on budget. Pick the Azure services you need, and grow at scale. With big data driving your organization forward, you’ll be able to turn raw data into meaningful information. You can turn your information into strategic steps that get you as much wins as possible on the business chess board.