/Overview of Snowflake Data Lake – Benefits and Best Practices
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Overview of Snowflake Data Lake – Benefits and Best Practices

Before going into the intricacies of Snowflake Data Lake, it is necessary to first understand what is a data lake per se.

In its primary and basic form, data lakes are the data architecture structures that make sure that massive data volumes which can be processed later for analytics can be stored in one place. In the past, these data storage capabilities had several components like data warehouses, data marts, and more. But now, with rapid technological advancements in this field and databases being operated in the cloud, so many components are not required.

The main advantage of data lakes is that it is possible to store unstructured, semi-structured, and structured data in one place instead of storing them in different silos. Hence, this is a great boon for organizations as all data is now available on one platform on a cloud-based data warehousing solution like Snowflake. 

Snowflake Data Lake

Snowflake Data Lake is a cloud-based data warehousing solution providing unlimited storage and computing facilities. Users have the option to scale up or down in data usage for computing or storing by paying only for the resources used. This scaling option is important for businesses that face a sudden spike in demand for data storage and can meet this requirement without investing additionallyin hardware or software.

Snowflake Data Lake is a high-performing solution. Multiple users can simultaneously execute multiple intricate queries without facing any lag or slowdown in speeds and performance. This efficiency of Snowflake is very important in the modern data-driven and powered business environment.

Additionally, Snowflake Data Lake has an extendable architecture. This ensures that there is seamless loading of databases within the same cloud environment. Hence, businesses do not have to choose a specific data warehouse or a data lake to operate on. For example, data generated via Kafka can be transferred to a cloud bucket from where the data is converted to a columnar format with Apache Spark. This is directlyloaded to the conformed data zone and the work of businesses to choose between a data lake or a data warehouse iseliminated.

The efficiency of Snowflake Data Lake is also increased manifold by the ability of the platform to load native data and help cutting-edge analysis in mixed data formats. And since Snowflake is scalable, it reacts immediately to any decrease or increase in data volumes. 

Features of Snowflake Data Lake

There are several features of Snowflake Data Lake that makes it ideal for today’s data-powered organizational ecosystem.

·        Scalable features: Snowflake has very dynamic and scalable computing resources that vary based on the current volume of data requirements and the number of users. Whenever there is a rise and fall in computing needs, the quantum of resources provided changes automatically without affecting running queries. Further, when there is a huge rise in demand due to heavy usage, the compute engine auto-adjusts to the increased flows without a drop in speeds or performance.

·        One-point data storage: Massive volumes of structured and semi-structured data such as JSON, CSV, tables, Parquet, ORC, and moreare easily and directly ingested into Snowflake Data Lake without using separate silos for data storage.

·        Affordable data storage: Highly flexible and affordable data storage is offered on the Snowflake platform. Users have to pay only the base cost for using Microsoft Azure, Amazon S3, and Google Cloud – all Snowflake cloud providers.

·        Guaranteed data consistency: Data consistency is assured on Snowflake Data Lake. This ensures that data can be easily manipulated to carry out cross-database links and multi-statements transactions.

In a nutshell, then, Snowflake Data Lake users have the advantages of affordable computing and storage facilities along with maximized scaling capabilities. However, DBAs often face a challenge in correlating the features of a data lake and Snowflake. This is because the concept of data lake was introduced almost a decade ago across business systems, ecosystems, countries, regions, and levels of data control. On the other hand, in comparison, Snowflake Data Lake is a recently-introduced, technologically advanced cloud-based platform.

Benefits of Snowflake Data Lake

There are several benefits of Snowflake Data Lake which is why more and more organizations are switching to this cloud-based platform.

Here are a few of them.

·        Optimizing data lake strategy: Whatever be the location, a Snowflake data warehouse can optimize any data lake strategy through its new feature the Database Replication. All databases can be replicated and synchronized across different regions, cloud providers, and across the network of an organization, regardless of the region and cloud provider. The advantage here is that in the case of an outage in the primary database, a secondary database in another region is automatically triggered and work goes on as usual without any downtime. When the outage is resolved, this feature works in the reverse direction and updates the primary database with the changes that occurred during the period of outage.

·        Single operating system: Snowflake Data Lake ensures better data control. This is because there is only a single cloud ecosystem that makes sure that the data lake can be expanded across the globe if required. It helps organizations to maximize their data management requirements on a single data lake platform that spans regions and countries.

It is thus natural that organizations worldwide prefer to operate on the Snowflake Data Lakeplatform.