Reasons and Best Practices for Choosing Snowflake Data Lake

Reasons and Best Practices for Choosing Snowflake Data Lake

The concept of data lake was introduced a decade back and immediately made a mark by increasing data management efficiencies in organizations around the world. It was a new notion where a single storage function could have unstructured, semi-structured, and structured data simultaneously. At that time, even though it gave a new twist to optimized data management capabilities, getting insights from raw data and solutions to this effect was not easy and businesses did not get the desired results. It was only after the advent of cloud-based Snowflake Data Lake that data structures and architecture came of age.

Before going into the many intricacies of the new Snowflake offering, it will be relevant to know more about data lakes first.

Data lakes are repositories of high volumes of data in any form, processed and formatted or not. These can be analyzed at a later date to know about the functioning of an organization. In the past, data lakes came in various forms – data marts, data warehouses, and more. But now that Snowflake Data Lakeoperates in the cloud, such multiple silos are no longer required.Hence businesses get all data at one point instead of in multiple ones, doing away with the need to maintain several data silos. It can holistically manage tables and JSON as well as structured and unstructured data.

Data Lake Architecture of a Cloud-based Platform

A cloud-based Snowflake Data Lakehas several unique characteristics, the most critical being maintaining workload isolation. Here, the data isolates workloads into sections and allocates the most resources to the crucial ones, thereby preventing work-flows from slowing down. This is particularly useful when organizations are suddenly faced with an increase in demand for computing or storage resources.

In such cases, Snowflake Data Lakefelicitates the following functionalities.

  • Data queries and loading of data can be carried out simultaneously without any lag or drop in performance.
  • Multiple users can work at a time without facing reduced speed of operations or lag.
  • Provides a highly optimized multi-cluster and shared-data architecture
  • Has a robust metadata service that fulfills all specific needs of the object storage environment.
  • Flexible scaling of storage and computing resources that work in isolation and independent of each other.

These are some of the features of cloud-based Snowflake Data Lakethat make it a preferred option for businesses. 

Benefits of Snowflake Data Lake

There are several benefits of this data lake primarily because it offers all the advantages of a cloud-based platform such as the speed of operations and a high level of data security.

Here are a few of the main benefits.

  • Optimizes data lake strategy – Snowflake Data Lake provides a highly maximized data lake strategy regardless of the location where the data is located. The recently-introduced Snowflake Database Replication feature has added value to the data lake. Businesses can replicate databases in various cloud providers and regions and keep the multiple accounts always in sync. In case of an outage in a primary location, secondary databases are automatically triggered and the work goes on without interruptions. The process is reversed after the issue is resolved and the primary databases are updated. This is also very useful when the primary database has to be shut down for maintenance or replication.
  • Fast data portability – Being a cloud-based platform, the Data Lake ensures quick and easy data portability across regions and locations.
  • Singleoperating environment – Since Snowflake Data Lakeoperates in one specific environment – the cloud – a higher degree of data control is assured and the data lake can be expanded to include operations globally. This is critical for organizations that are based in multiple locations across the world. Hence, businesses can maximize their data management strategies on one cloud-based platform.

These are some of the reasons for working on the Snowflake Data Lake.

Apart from these benefits, the Data Lake is also very user-friendly on the following counts.

  • Scalable computing resources – Snowflake scales up and down as per the demand of the users for computing resources. Multiple users can execute intricate queries simultaneously, each scaling as per compute resources needed. At times of heavy usage, the data lake adjusts to the increased demand automatically without any performance degradation and users have to pay only for the quantum of resources used.
  • Scalable storage resources – Snowflake Data Lakeoffers highly scalable and affordable storage resources. Here too, users pay only for the storage resources used at very affordable rates. The storage fees are the base cost for using Snowflake cloud providers – Amazon S3, Google Cloud, Microsoft Azure.
  • One-point data storage – Snowflake Data Lakeallows single point huge volumes of data ingestion of both structured and unstructured data. It includes JSON, CSV, ORC, tables, Parquet, and more. The data lake does away with the need for maintaining different data silos.
  • Guaranteed data consistency – This aspect makes sure that data can not only be manipulated as per specific needs of organizations but cross-database links with multi-statement transactions can also be carried out.

It is therefore seen that moving to Snowflake Data Lakecloud-based platform has multiple benefits including unlimited computing and storage facilities, affordable pricing options, and high performance, security, and control.

Read More: Setting Up Snowflake Secure Views


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