This blog series, the Hidden Costs of Cloud Data Lakes, presents the various tasks and challenges you may be faced with if you decide to build a cloud data lake for analytics and machine learning yourself. It’s important to note that modern cloud data lakes must be more than just a storage repository. Today, a cloud data lake must be built for accessibility and accelerating “Time to Analytics.”
Time to Analytics refers to the amount of time between when an enterprise gets data to when the right stakeholder has access to that data for analysis.
Time to Analytics is the first challenge in building your own data lake. If you do it yourself, how long will it take until you can begin to generate business value from your data?
A complete cloud data lake solution will allow you to:
- Harvest and securely store unlimited data, both historical and real-time
- Access unlimited computing power
- Use services and applications that will allow you to run analytics, data science workloads and machine learning models
Time spent building a data lake yourself will delay the time until your company can make business decisions based on data analytics and data science. This delay can be anywhere from several months to well over a year — assuming you manage to successfully complete the project. The DIY effort is formidable, spanning optimal infrastructure provisioning, defining security of both the network and the data, managing user identity and authorization, defining and implementing a DevOps and SecOps strategy for the cloud data lake, all while keeping costs in check.
Cazena’s Instant Cloud Data Lake is available instantly, cutting your Time to Analytics from months to minutes. It is fully secure, with single sign-on access to the analytic engines and services within. Data is encrypted in motion and at rest. Cazena’s Cloud Data Lake is a single-tenant deployment, customized for your workloads and budget.
Future blog posts will cover these capabilities as well as many more – be sure to subscribe so you don’t miss a post!
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