The reason behind the rising importance of data lakes is their ability to provide an analytical environment that supports multiple tools, languages, and workloads. Data lakes provide raw informational materials that can be extracted for numerous purposes including business intelligence (BI), machine learning (ML), and artificial intelligence (AI) processing.
Constructing a Data Lake
Data lakes can be built using on-premises hardware or cloud resources. There are several characteristics of cloud data lakes that make them a more flexible and effective way to handle big data resources.
Data growth is one of the major challenges of managing data lakes. As new data streams are made available, capacity requirements often change. In an on-premises data lake, this entails continually monitoring capacity and purchasing new hardware when necessary.
Cloud lakes remove any worries about exceeding storage capacity. Cloud storage resources are essentially infinite and can easily be added to address evolving capacity requirements.
Compute power and flexibility
The compute and software resources of the cloud provider are available to cloud data lakes. This means the analytic engines and compute power can be used on-demand for a variety of purposes. Multiple teams can access the same data using the cutting-edge software solutions made available by the provider.
Replicating the infrastructure elasticity of a cloud data lake in an on-premises data center requires a substantial effort in planning and capital expenditures to procure the necessary hardware. Inaccurate planning can result in a lot of expensive hardware sitting around waiting to be deployed.
Costs for cloud data lakes are minimized by the “pay for what you need” nature of cloud computing. Using on-demand software tools is often less expensive than obtaining dedicated licenses. Unused hardware for erroneously anticipated compute or storage needs is a budgetary nightmare. Cloud data lakes eliminate the problems associated with purchasing unnecessary processors or storage devices.
Choosing a Cloud Data Lake Provider
All major cloud providers have the resources to furnish their customers with the resources to create a data lake. Following some simple guidelines can help ensure that you select the right provider to address the needs of your business.
- Make sure the data lake solution you choose can easily be integrated with your current computing environment. You don’t want to use incompatible systems that lead to data silos and inefficient use of enterprise information.
- Enterprise-grade security is a must in any cloud data lake implementation.
- Select an offering that your budget can afford.
- Ensure the data lake solution chosen has the capabilities of working with the type of data you plan to store in it.
Some additional management complexity may accompany housing a data lake in the cloud versus on-premises, but the benefits promise to make these issues negligible. You can provide your analysts with a horizonless data lake from which to pull unimagined insights from big data resources. Sounds like a good place to be.