How Data Lake as a Service is Creating Intelligence for Businesses
With the rapid advancement in the technological world, numerous business owners are now in search of a better way to ensure that organizational data is kept well organized and safe.
One way through which businesses are doing this is through the use of Data Lakes, which is helping organizations to create a centralized infrastructure to manage, store, analyze and classify their data, effectively.
In the current scenario, “Data Lake as a Service” provides prebuilt cloud services that stow away the complexity of the underlying platform and infrastructure layers.
The platform allows anyone in the organization/business to create their data lake without the requirement of maintaining or installing the technology themselves and can leverage the benefits of data analytics. Data Lake as a Service provides organization’s big data processing in the cloud for quick and efficient business outcomes in a cost-effective way.
Why do organizations need it?
In today’s hyper-competitive data-driven world, every organization wants to leverage the benefits of analytics for business growth and better decision making. They want their data in a separate data lake or data warehouse where it can be analyzed as per the requirements to make business-related decisions.
In the current scenario, organizations also want to generate the billing corresponding to a particular request from the department according to the usage of the environment provisioned.
How does it work?
“Data Lake-as-a-Service” leverages cloud resources, which are managed and maintained by a vendor “as a service.” It’s often advantageous to deploy data lakes in the cloud because of easy scalability for huge data volumes and inexpensive storage.
The raw big data is progressively generated in the cloud from sources such as- mobile apps, sensors, or social media. Although, it can be challenging to learn, install and maintain the complex software used for data lakes in a cloud environment.
A “Data Lake-as-a-Service” provides a prebuilt cloud service that abstracts the complexity of the underlying platform and infrastructure layers, so a company can use a data lake without having to install or maintain the technology themselves.
This category is emerging, so the specific software and services provided by vendors vary greatly. Common capabilities generally include scalable data storage, automated provisioning, simplified interface for management and varying levels of analytic functions.
Beyond those, Data Lake-as-a-Service providers can differ significantly, with different features catered to different use cases.
Some interesting use cases of Data Lake as a service
# Many companies use a Data Lake-as-a-Service to collate and process incoming raw data from the mobile, cloud, or external sources. For instance, manufacturers can gather sensor data in a Data Lake-as-a-Service, so that research and development teams can accumulate information about product usage or operational problems and common error patterns. Some organizations create “data pipelines” where they gather raw data in a Data Lake-as-a-Service, then filter, cleanse, or query the data to fabricate a valuable subset, which they move into an analytic environment like a data warehouse on-premises or a data mart in the cloud.
# Organizations use Data Lake-as-a-Service to merge huge volumes of data for analytics activities or data science. It is often beneficial to have the entire data at one place, where it can be integrated, analyzed and queried to discover new insights or patterns. Some refer to this as a “sandbox” environment, where analysts can discover data with no impact to other production processes or systems.
Final thoughts
So, ‘Data lake as a service’ is becoming more central to enterprise data strategies. Data lakes best address today’s data realities: much greater data volumes and varieties, higher expectations from users, and the rapid globalization of economies.
