5 benefits of Moving your on-premise data warehouse to the cloud
Now-a-days, a lot is being written about emerging technology and invariably, ‘Cloud’ always gets a mention along with Big Data and Mobile. We agree that moving to the cloud isn’t as easy as turning on a fan, and when it comes to data management, business intelligence or reporting, it definitely isn’t a cakewalk given that there are perceived problems such as performance, security, lack of control etc. However, modernizing your data platform by moving your data warehouse or business intelligence (BI) solution to the cloud is worth trying out. Here’s why-
- Security– Let’s start with the most hated but high important aspect of solutioning – security & privacy. For ages, having your data on the cloud as against on-premises has raised questions about security, data breach & privacy issues. However, Microsoft Azure, the world’s most robust cloud platform, places a high tag on security. Its data platform tools are tightly coupled with Azure Active Directory (AAD) to provide authorization and data-level security, encryption of data in motion and at rest, enable IP restrictions, auditing, and threat detection. Azure presents the most comprehensive compliance coverage amongst cloud providers. It has more certifications than any other cloud provider, and is an industry leader for customer advocacy and privacy protection with its unique data residency guarantees.
- Economy- The cloud model lowers the barriers to entry—especially cost, complexity, and lengthy time-to-value. Cloud pricing differs greatly compared to on-premises infrastructure. You have to take into consideration licensing, man-power, hardware, real estate, electricity, support cost, security, deployment cost and depreciation. All this comes with fixed capacity. But with the cloud, you get to pay for what you use and can even vary the desired configuration and performance levels. And it isn’t just the time and money; Cloud deployment can also free up your resources that otherwise would have been dedicated to managing the new environment
- Transformation– Traditional data warehouses consist of data models, extract, transform, and load processes, and data governance, with BI tools sitting on top. Instead of doing things the old way, which includes structuring, ingesting and analyzing, enterprise data warehouses need to flip the paradigm and ingest, analyze, and structure by utilizing the cloud, data lakes, and polyglot warehousing. You need to think of your data warehouse not as a single technology but as a collection of technologies.
- Agility– Many business functions, hitherto not associated with BI, have taken to data analytics for justifying spends, analyzing performance etc. It will be unproductive for these lines of business to wait for central IT to provision a data warehouse for them so they can start analyzing their data. The cloud offers a relatively quick as well as robust solution to cater to these warehousing needs. On the contrary, for on premise infra, procurement as well as deployment cycles are very long. Add to that the pain of going through upgrades every 2-3 years.
- Intersection with Big Data– Big data has empowered the world to tap any kind of unstructured data sources to gain insights. Cloud data warehousing can be a bridge for bringing the world of structured data from legacy on-premises data warehouses together with these newer big data sources.
To conclude, on-premises workloads will continue to shift to the cloud. In the days to come, the cloud data warehouse will replace the on-premises warehouse as the main source of decision support and business analytics. Azure SQL Data Warehouse, a cloud based data warehouse hosted on Microsoft Azure is capable of processing massive volumes of data and can provide your business the speed & scale that it needs to manage enterprise data.
At CloudMoyo, we help you migrate your data platform to the Azure cloud, as well as help build customized solutions in Azure to make the most out of your data. To know more, book a 5-day Azure Assessment to jointly build the strategy and roadmap to move to a cloud-based data deployment.
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