As more businesses embark on a digital transformation journey for reasons such as achieving further growth amidst changing consumer behavior, many will at one point make use of cloud technology.
This is partly because cloud-native apps offer a wide range of functionality in the realm of scaling and efficient data management, which are necessary for enterprises that want to gain a strategic advantage from the use of trusted data.
Cloud-native apps deliver this value by relying on automation and other techniques made possible by the host platform.
This however creates a need to gain a better understanding of the data services used since design approaches like the twelve factor criteria common amongst these apps do not extend to the data resources used.
Cloud data warehouses, data lakes and lake houses are some of the most vital elements when it comes to data management in a cloud setting.
However, while a technique like hand coding may have been effective in developing prototypes for other innovations, it may not be as cost-effective for data management in this case.
Furthermore, with hand coding, it is harder to swiftly respond to upgrades and other adjustments in the underlying cloud infrastructure and other tools that are part of the larger ecosystem.
Not only will most developer teams get stretched thin as growing organization’s data needs escalate, the solutions they offer will be handicapped since they can’t fully utilize automation.
This inevitably heightens the risk of scenarios such as data not making it into data lakes or being available where needed, let alone being at the desired level of integrity. So, to achieve proper management and have data that is reliable, organizations need to put emphasis on these three pillars:
Metadata management is all about examining, recording, and understanding how data moves within your organization. The goal here is to map out what data is needed, plus where and when it is needed, and subsequently, how best to get it there.
An ideal metadata management setup should offer data discovery, asset tagging, end-to-end lineage and data curation.
Primarily, data integration offers wholesome support for the absorption of all relevant files, databases, and Internet of Things (IoT) streaming data into your data lakes.
Additionally, it enables you to extract, transform and load (ETL) data, while optimizing data processing by pushing transformation logic to the source database. Not forgetting that all this can happen with seamless connectivity for hybrid or multi-cloud settings, with elastic scaling runtime.
With data quality, you can carry out profiling, generate quality-geared rules, create a data dictionary, and make use of many other capabilities in order to deliver trusted data.
On top of this functionality, processes like parsing, cleansing, verification, standardization, and elimination of duplicates also go a long way in producing high quality data. It also goes without saying that data quality is not complete without analytics tools to assess the results of these processes.
For these pillars to stand strong, it is important to apply artificial intelligence and machine learning techniques. These help organizations to realize self-integrating systems also capable of recommending the next best transformations, auto-tuning, operational alerts, and data pipeline similarity.
A business that has managed to find all this in a particular cloud solution or a set of cloud solutions can also benefit from supplementary attributes like automatic upgrades, usage-based pricing plans, serverless architecture, lean installation procedures and advanced security.
Are you looking to get a robust cloud solution for your business’ growing data needs?
Let the experts at ASB Resources help you draw up a plan for how to continuously manage your data in the cloud and keep it reliable for critical business decisions. Schedule a call with one of our experts today!