Before studying the many aspects of Snowflake Data Lake, it will be relevant to study first what it is and its many intricacies.
Data lakes in its simplest form are the data architecture structures that ensure that massive volumes of data can be stored to be processed and analyzed later. Previously, data lakes had consisted of several components like data warehouses, data marts, and more but with technological advancements and databases being run in the cloud, these differentiations are no longer required.
Data lakes have the advantage of being able to store structured, semi-structured, and unstructured data. This helps businesses to have direct access to raw unfiltered data in one place instead of having to access various data silos. Data today in any organization does not exist in separate systems and thanks to data lakes, are all available on one platform. Hence, it is easy to manage both structured and unstructured data on a cloud platform like Snowflake.
Snowflake Data Lake
A cloud-based platform, Snowflake data warehousing solution brings a host of benefits to the table. One of the critical ones is that it offers unlimited storage and computing facilities. Users can utilize the quantum required and pay only for the resources used. This scaling up or down is important as businesses have ready additional storage facilities when new projects are launched and do not have to invest in additional hardware or software.
Further, Snowflake Data Lake is a high-performing system. Multiple users at a time can execute various intricate queries without experiencing any lag in speed or performance. This is a massive advantage in the modern data-driven business environment.
An extendable architecture ensures that within the same cloud ecosystem there is a seamless movement of databases. This does away with the necessity of choosing a data lake or a data warehouse to operate on. The functionality of Snowflake Data Lake is also optimized with the capability of the platform to load native data and help precise analysis in a mix of data formats. And being scalable, Snowflake reacts instantly to increase (or decrease) in data volumes.
Why Snowflake is considered the best for a cloud-based Data Lake?
Several features of Snowflake make it perfect for a cloud-based data lake.
- Easy scalability – Computing resources within Snowflake can be adjusted depending on the amount of the workload and the number of users. This automatically scales up and down and when there is large concurrency, the computing engine regulates as per current requirements without hampering running queries.
- Storage in one place – Structured and semi-structured data like JSON, CSV, tables, Parquet, ORC, and more can be effortlessly ingested in Snowflake as there are no separate silos as per data types.
- Flexible data storage – Storage of data is flexible and only the basic cost of using Snowflake needs to be paid for Google Cloud, Microsoft Azure, and Amazon S3, all cloud storage providers of Snowflake.
- Quick data manipulation – Data consistency is fully assured on Snowflake for multi-statement transactions with cross-database links. Hence, data can be quickly manipulated and moved as per needs.
A look at these features of Snowflake proves that this cloud platform is ideal as a data lake and affordable computing and storage options. Initially, though, there might be some matching problems. Data Lake is a concept that is more than a decade old, spanning a wide network of business units, countries, regions, and organizational ecosystems, all with different control levels. Snowflake on the other hand is created on the latest technological advancements. What then are the benefits and how can all these be managed on a single Snowflake Data Lake environment?
Benefits of Snowflake Data Lake
Here are some of the benefits of the Snowflake Data Lake environment.
- Snowflake Data Lake environment maximizes any data lake strategy irrespective of the location of the data. Snowflake has recently introduced the Database Replication feature where it is possible to replicate databases and keep them in sync in various regions and within multiple cloud providers. When an outage occurs in one region, another region is automatically triggered and business continuity is maintained. After the issue is resolved, the feature works in the reverse direction and the original database is updated.
- Data portability is ensured in the cloud and helps users to move to another cloud provider or region if required. It also helps in securing data across locations and regions safely and securely.
- The combination of a single operating environment in the cloud makes for better data control and the data lake can be expanded to include operations globally. The future is therefore bright as organizations look to maximize their data management on a Snowflake Data Lake strategy and address their critical data management needs on a single platform that spans across regions and countries.
It is hence natural that organizations worldwide are switching to Snowflake Data Lake.