You must be operating with limited infrastructure when you have to deal with limited datasets. But once in a while, you suddenly experience a surge in demand and you need to manage several petabytes of data originating from different sources - processes, applications, and devices. Now you have to run a query to search an object out of millions of images, you have to perform ETL, or you have to analyze the patterns. How would you go about that? You must need to have adequate servers, processors, virtual machines, clusters, etc. like infrastructure and parallel processing capability.
But, with Microsoft Azure's Data Lake Analytics, you needn't worry about it. It offers a pay-as-you-go model where you can write the query in U-SQL, R, Python, and .NET, and you get the desired result to be it analytics, query, ETL, or others. It manages infrastructure on its own, besides speed, scale, security, and compliance. Data Lake Analytics helps you quickly perform big data jobs without worrying about scalability, cost, and time to perform big data jobs.
Quickly start operations by easily writing programs, integration, and with an already set environment; you just need an active account.
Without worrying about server, clusters, processors, or environment quickly scale from some GBs to several Petabytes data analytics jobs.
With a pay-as-you-go model, it doesn't require initial investment, you only need to pay for how much and when you use it.
You can run several parallel processes simultaneously and you can perform a variety of analytics, ETL, query, etc. like jobs.
You can debug codes as easily and efficiently as you do in your local environment and save time and effort.
Perform virtualization by integrating multiple, distant and different data sources without the physical movement of data sets.
We at flint help you build your Data Lake Analytics based solutions, right from setting up a pay-as-you-go account setup and then writing programs for parallel processes for data analytics jobs. Moreover, we also help you in ensuring the integration of multiple data sources to be used in virtualization and performing the Big Data analytics jobs.