The primary objective of data lake architecture is to store large volumes of structured, semi-structured, and unstructured data, all in their native formats. Data lake architecture has evolved in recent years to better meet the demands of increasingly data-driven enterprises as data volumes continue to rise.
And, the modern data lake environment can be operated with well-known SQL tools. Since all storage objects and required compute resources are internal to the modern data lake platform, data access is rapid, and analytics can be run efficiently and quickly. This differs significantly from legacy architectures, where data was stored in an external data bucket and had to be copied to another storage-compute layer for analytics, affecting both speed to insights and overall performance.
Traditional Data Lake Architecture
Traditional data lakes were naturally on-premise deployments but even the first wave of cloud data lakes, such as Hadoop, were architected for on-premises environments. These traditional architectures were created long before the cloud emerged as a viable stand-alone option and failed to realize the full value of the cloud. These first-generation data lakes required administrators to constantly adjust capacity planning, resource allocation, performance optimization, and other tasks.
In response, some businesses began creating cobbled-together data lakes in cloud-based object stores, accessible via SQL abstraction layers that required custom integration and constant management. Although a cloud object store eliminates security and hardware management overhead, its ad hoc architecture is often slow and require lots of manual performance tuning. The result is inadequate analytics performance. Today’s more versatile lakes are often a cloud-based analytics layer that maximized query performance against data stored in a data warehouse or an external object store. This enables more efficient analytics that can dig deeper and faster into an organization’s wide array of data sets and data formats.
With specialized technology in the cloud analytics layer, such as materialized views, organizations can use a cloud data warehouse to store all of its data and enjoy a level of external table performance that is comparable to data ingested directly into a data lake. With this versatile architecture, organizations can have seamless, high-performance analytics and governance, even if the data arrives from multiple locations. By eliminating the need to transform data into a set of predefined tables, users can instantly analyze raw data types via schema-on-read. Unlike a structured data warehouse, data transformation happens automatically inside the data lake once the data is ingested.
Modern cloud data lake architecture also helps organizations maintain workload isolation. User concurrency can consume large amounts of resources. To prevent ad hoc data-exploration activities from slowing down important analyses, the data lake must isolate workloads and allocate resources to the most important jobs. Since many organizations have periodic compute resource bursts (such as end of quarter accounting jobs) it is important to have a data lake architecture that enables workload isolation.
A cloud-optimized architecture will simplify the data lake. For optimal performance, flexibility and control, a modern cloud data lake should possess the following characteristics:
- Multi-cluster, shared-data architecture
- The ability to add users without performance degradation
- Independent compute and storage resource scaling
- The right tools to load and query data simultaneously without impacting performance
- A robust metadata service that is fundamental to the object storage environment
Snowflake and Data Lake Architecture
The Snowflake Data Cloud provides the most flexible solution to support your data lake strategy, with a cloud-built architecture that can meet a wide range of unique business requirements. By utilizing innovative design patterns, Snowflake unlocks the vast potential of your data, enabling:
- Integration of Apache Iceberg tables, which significantly elevates Snowflake's ability to handle data lakehouse workloads. This ensures efficient management of varied data formats and boosts query performance.
- The use of Snowflake as a central data lake, harmonizing your data infrastructure on a singular platform adept in managing key data workloads.
- Creation and execution of integrated, scalable, and efficient data pipelines. These pipelines can process a wide array of data, with the flexibility to easily transfer the processed data back into your data lake.
- Advanced data governance and security features, ensuring protection and compliance, especially crucial when data is stored in existing cloud data lakes.
- New developer-focused capabilities like the Snowflake Python API, enriching integration and simplifying operations across various data workloads.