At Cazena, we have just released the latest version of our Instant Cloud Data Lake. Unlike DIY (do-it-yourself) cloud data lakes, the Instant Data Lake aims to shrink time to analytics from months to minutes with zero operational resources required. How does it do that?
The Instant Cloud Data Lake architecture is based on a new, fully automated SaaS platform with three key capabilities:
1. SaaS Orchestration: The Instant Data Lake is turnkey “SaaS-as-code”, meaning the entire stack including cloud resources, security, IDM, data processing engines, and analytical tools are integrated, instantiated, configured, secured, and hybrid-connected to the enterprise. Production-ready with certified compliance in minutes.
2. Self-Service Analytics Console: Data scientists and data engineers can immediately start using their tools or access popular third-party tools with a simple Self-Service console. All the tools are automatically and securely wired into the data lake. No platform skills are needed.
3. Continuous Ops: The Instant Data Lake is continuously optimized and monitored to ensure the best performance, cost, and security. All DevOps, SecOps and PlatformOps are built-in, so zero operational resources are required by the enterprise.
Figure 1 below shows the Instant Data Lake architecture.
Figure 1 Instant Cloud Data Lake Architecture – A New SaaS Platform to accelerate time to analytics
The novel part about this cloud data lake architecture is that it can flexibly and portably embed best of breed or cloud-native or open-source PaaS stack with a variety of data processing engines (SQL, Spark, etc.). This provides enterprises with flexibility and ease of use, and future-proofs their cloud data lake.
What’s a typical Instant Data lake experience?
The outcomes speak for themselves. At Cazena, we have had 100% renewal rates on our production cloud data lakes, with over 60% average growth. Most enterprises have deployed immediately and experienced production outcomes within weeks. Zero operational or platform engineering resources are required, so enterprise data and analytics teams can focus entirely on their strategic mission and outcomes.