Azure: The Role it Plays in Building Big Data Solutions
Big data has gathered quite the momentum in the corporate world and understandably so. After all, look at the value it brings to the table. Companies are now starting to realize the scope to achieve better value from big data albeit on the cloud. Now, the market has plenty of options to offer in this regard but none quite as potent as Azure. It has made a point of prioritizing AI and analytics services while making it an appealing option for many looking to combine big data analysis and the benefits of cloud computing. Further Azure, you can seamlessly process massive amounts of data — both structured and unstructured. It is with real-time analytics that offers faster performance that you are likely to get with on-premise resources.
So, whether you have a team of data scientists or just want to begin taking advantage of the insights big data can offer, read on to learn about what setting up big data analysis on Azure entails and a bit about some of the services available to you.
So, if you too are looking to build a big data solution on the cloud, managing data collection, storage, analytics, and visualization constitute a key part of the process. Here is how Azure can help with that.
1. Analysis: The first step in the process of building a big data solution on Azure is perhaps also the trickiest of them all. You see, before you can decide precisely which services you will need, it is imperative to first analyze the company’s goals in the context of big data. The services your operations will vary based on if you are collating data from across IoT sensors or web scraping. One must also remember that the amount and type of data used also to be a deciding factor when it comes to aspects such as ingestion methods, storage type, etc.
2. Architecture: If you are looking to build your solution, a good place to start is identifying at least a basic architecture depending on your company’s requirements. It must be noted that the specifics of the architecture ideal for your business requirements will be determined based on the company’s legacy systems, your development and operational teams’ skill sets, and the workload among other things.
3. Data ingestion: On account of Azure being an all-encompassing managed service, companies also gain access to ingestion and processing services such as Azure Analysis Services, Stream Analytics, HDInsight, etc. However, if you want, you can also opt for individual options.
4. Storage: Azure offers plenty of options when it comes to storage as well; so be it storage through combined databases or individual databases, you will have access to data warehouses, data lake, etc. You can start with a basic setup, i.e. host an SQL server on a VM, but if you need more agility, we recommend using Cosmos DB which is a fully-managed database service including transparent multi-master replication, global distribution, etc.
5. Analytics: Based on the team’s skill level and the sources of data, Azure offers a variety of analytics services such as IoT stream analytics, log analytics, etc.
6. Configuration: Once the requisite services have been identified, the next step is configuration and bracing for the production environment. It must be noted that the specifics of the configuration will be based on the selected services, data sources, and if you want a cloud-only or hybrid environment.
The key to gleaning the best out of big data analytics is embracing cloud computing. Now, of course, the market has many, many options to offer in this context, but there is a reason why Azure consistently fares as the top choice for companies that are looking to optimize the potential of their big-data-driven processes. Azure application development not only eases the switch to the cloud but also brings forth exceptional levels of agility that empower companies to transition several existing processes straight to the cloud, quickly embrace one-stop shop-like service, or perhaps even build something that brings together the best of both worlds.