A cloud data lake is a cloud-hosted storage solution for an organization’s data, structured and unstructured and from various sources. A cloud data lake serves as a unified source of truth for an entire organization’s data needs, including analysis and developing insights.
Cloud data lakes provide near-unlimited capacity and scalability for the storage and computing power you need, combining the power of analytics with the flexibility of big data models and the agility and limitless resources of the cloud. A cloud data lake dramatically simplifies the effort to derive insights and value from all that data and ultimately produces faster business results.
Benefits of Cloud Data Lakes
The ability to store unlimited amounts of data of all types and from diverse sources makes the cloud well-suited for data lakes. Benefits of cloud data lakes include:
- Minimized capital expenses for hardware and software
- Ability to get new analytic solutions to market quickly
- Elimination of data silos by consolidating multiple data types into a single, unified, infinitely scalable platform
- The capture of batch and streaming data in a common repository with strong governance, security, and control
- Simultaneous execution of multiple workloads — data loading, analytics, reporting, and data science
- Establishment of a robust, fully managed, extensible environment
Early data lake systems helped businesses seeking scalable, low-cost data repositories. These on-premise data lakes allowed for analysis that led to smarter business decisions. However, as organizations saw the volume and importance of their big data systems grow, they found that on-premise data lake solutions were unsustainable.
Traditional on-premise data lakes typically fail because of their inherent complexity, poor performance, and lack of governance, among other issues.
Advantages of Cloud-Based Data Lakes Over On-Premise Data Lakes
- No silos: Easily ingest petabytes of structured, semi-structured, and unstructured data into a single repository.
- Instant elasticity: Supply any amount of computing resources to any user or workload. Dynamically change the size of a compute cluster without affecting running queries, or scale the service to include additional compute clusters to complete intense workloads faster.
- Concurrent operation: Deploy to a near-unlimited number of users and workloads to access a single copy of your data, all without affecting performance.
- Embedded Governance Present new and accurate data to users, focusing on collaboration, data quality, access control, and metadata (data about data) management.
- Transactional consistency: Confidently combine data to enable multi-statement transactions and cross-database joins.
- Fully managed: With a software-as-a-service (SaaS) solution, the data platform manages and handles provisioning, data protection, security, backups, and performance tuning so you can focus on analytic endeavours rather than on managing hardware and software.
Native Cloud Data Lake
With most data now in the cloud, the natural place to integrate this data is also in the cloud. Astute organizations are now using cloud-built data lakes that can weave these various threads of information into a cohesive fabric. Modern cloud data lakes allow them to capture, store, and facilitate analysis to discover trends and patterns.
But most on-premises data warehouse and data lake offerings are copied to the cloud, or what the industry calls “cloud-washed.” A solution should be built for the cloud from the ground up to take full advantage of what the cloud offers.
Multi-cloud is a cloud practice that entails the use of two or more cloud computing services. A multi-cloud approach allows organizations to leverage the specific benefits of different clouds, such as vendor variances in architecture and application features, and mitigate risks inherent to single-cloud storage.
A multi-cloud data lake is a data lake in which multiple cloud storage offerings are combined. Maintaining numerous data lakes means you benefit from the advantages of each platform, but it also requires expertise to get the platforms to work together.
Snowflake as Cloud Data Lake
Snowflake introduced significant enhancements, further blending the benefits of data lakes with the efficiency of data warehousing and the scalability of cloud storage.
Snowflake now supports Apache Iceberg tables, enhancing its ability to manage data lakehouse workloads. This integration enables users to treat Iceberg tables as standard Snowflake tables, thereby simplifying the management of diverse data formats and enhancing query performance.
Key to Snowflake's data lake strategy is its commitment to security, scalability, and cloud independence. The platform's architecture allows for independent scaling of storage and computing, ensuring optimal performance and cost efficiency. Snowflake's data lake also offers advanced security features like auditing, granular access control, and encryption, crucial for modern data management and compliance.
Explore Snowflake's enhanced data lake capabilities with a free trial, and discover its full potential for unified data management and advanced analytics.