Snowflake Data Lake: A Comprehensive Guide 101

Over the years, there has been a great desire to quickly and cost-effectively gather insights from a variety of Data Sources in a single location, and as such, there have been different technologies trying to meet this peculiar need. And, Snowflake Data Lake is one such technology.

Businesses today deal with a wide range of large, fast-moving Data Sources that need to be extracted, transformed, and loaded to the right warehouses to derive meaningful insights from them but, they may require a single location where these data are stored in their raw format before being changed into suitable structures and this is where a Data Lake comes in. 

A Data Lake that holds all kinds of data in its native format acts as a repository for such data and is a comprehensive way to explore, refine, and analyze petabytes of information constantly generated from multiple sources. Having a single repository for all your raw data is a compelling proposition.

There are numerous Data Lakes available and this article aims at explaining what a Data Lake is by dwelling on Snowflake Data Lake showcasing its advantages, implementation methodologies, and details on how it operates.

What is Snowflake?

Snowflake is a Cloud-based Software-as-a-Service (SaaS) platform that offers Cloud-based Storage and Analytics service. Its Cloud Data Warehouse is built on Amazon Web Service,  Microsoft Azure, and Google infrastructure providing a platform for storing and retrieving Data.

Snowflake’s Design is unique in that it separates its storage unit from its computation unit, allowing users to utilize and pay for each separately.

What is a Data Lake?

A Data Lake is defined as a repository of data stored in its original or natural format.

A Data Lake stores large volumes of structured data such as On-Premise or Cloud Databases, semi-structured data such as JSON, AVRO, Parquet, XML, and other raw files, and unstructured data such as audio, video, and binary files in their native format ingested from several batches or in a continuous Datastream. 

Businesses produce lots of data every second sometimes making it difficult to secure and store the data in real-time leading to the potential risk of losing valuable insights that would have been obtained from such data, Data Lakes become useful as they can hold these data in whatever format they come in.

The data stored in a Data Lake do not have defined data requirements and structures until when the data is needed for consumption and as such, the data will not be discarded before harnessing benefits from them.

What are the Characteristics of a Data Lake?

A modern Cloud Data Lake has the following characteristics:

• A Data Lake can store all kinds of data in a raw form in which their format, schema, and content cannot be modified.
• A Data Lake has the flexibility of allowing you to design a data schema in any phase when required to meet your business needs, that is you can keep your data as it is in its raw state and only process them when needed.
• A Data Lake also gives you the ability to manage your data efficiently as it provides centralized storage for the data of an organization.
• A Data Lake has a Multi-Cluster, Shared-Data Architecture to enable users to access data easily.
• A Data Lake has independent Compute and Storage Resources to meet your business desires.
• A Data Lake makes it easy to Trace Data as data stored in a lake is managed in the lake throughout its lifecycle from data definition, access, storage, processing, and analytics.
• In a Data Lake, the addition of more users should not affect its performance as it can handle lots of users at the same time.
• Data Lakes have tools to load and query data concurrently without harming performance.
• An effective Data Lake has a Metadata Service that will be fundamental for storage and provides a built-in multi-modal storage engine to enable data access by different applications.

What is a Snowflake Data Lake?

Snowflake’s Cloud-built architecture gives you a flexible solution to support your Data Lake strategy to meet your business requirements. Snowflake has an in-built Data Access Control and Role-Based Access Control (RBAC) that enables rapid data access, query performance, and complex transformations of your data through native SQL support therefore governing and monitoring your access security.

Due to Snowflake’s Massively Parallel Processing (MPP), allows you to securely and cost-effectively store data of any volume by providing flexibility and robust architecture to handle your data workloads of diverse formats in a single SQL query in your Snowflake Data Lake. With this in place, moving, transforming structured, semi-structured, and unstructured data from storage on a single architecture enables you to access raw Snowflake Data Lake sets where Analysis can be carried out.

Snowflake also allows Data Engineers and other data experts to build custom Data Applications on the Snowflake platform for Data Management and overall consumption and with Snowflake as your central data repository, you gain insights for your business through best-in-class performance, relational querying, security, and governance.

Data Lake Architecture & Snowflake

The cloud has incredibly eased data architecture planning and maintenance costs, but the lack of analytics (as well as the ability to construct data applications on top of a data lake environment) has caused hitches in the data management and data engineering flow today.

By eliminating the need to create and maintain separate data storage and Enterprise Data Warehouse (EDW) systems, Snowflake has made a distinction between Data Lakes and Data Warehouses.

Business users can now quickly access raw data in data lakes for analysis by seamlessly transporting and processing both structured and semi-structured data. Furthermore, Snowflake enables data engineers and other data experts to easily construct custom data applications in the Snowflake platform, resulting in a comprehensive data cloud for elastic data management and consumption.

What are the Benefits of a Snowflake Data Lake?

By using the Snowflake Data Lake to mix and match design patterns, you can get the following benefits:

• It helps you to have a Unified Data Infrastructure landscape on a single platform to handle your most important data workloads.
• Build and run an integrated data pipeline to process all your data from any location and easily unload the data back to your Snowflake Data Lake.
• You may allow data consumers to run a near-infinite number of Concurrent Queries without compromising the performance of Snowflake Data Lake.
• Snowflake Data Lake ensures Data Governance and Security.
• Snowflake Data Lake offers low-cost storage and has multiple mechanisms of consumption.
• It offers Batch Mode Analytics and automatically registers new files from your Data Lake with partition auto-refresh.
• Handling Semi-structured Data Types like (JSON, AVRO, XML, Parquet, and ORC) is done with ease on Snowflake Data Lake. 

What is the Snowflake Data Lake Pricing?

Snowflake’s Data Cloud service is available in a variety of editions. The pricing models are agile and available as usage-based and per-second pricing with no long-term commitment. The pricing is also dependent upon the region and platform you prefer.

There are three platform options available: AWS, Microsoft Azure, and Google Cloud Platform. Their prices in the US region are as follows:

STANDARD	ENTERPRISE	BUSINESS- CRITICAL	ON-DEMAND STORAGE	CAPACITY STORAGE	VIRTUAL PRIVATE SNOWFLAKE (VPS)
$2.00 cost per credit	$3.00 cost per credit	$4.00 cost per credit	$40 per TB / per month	$23 per TB / per month	Contact Snowflake

STANDARD	ENTERPRISE	BUSINESS-CRITICAL	ON-DEMAND STORAGE	CAPACITY STORAGE
$2.00 cost per credit	$3.00 cost per credit	$4.00 cost per credit	$40 per TB / per month	$23 per TB / per month

STANDARD	ENTERPRISE	BUSINESS-CRITICAL	ON-DEMAND STORAGE	CAPACITY STORAGE
$2.00 cost per credit	$3.00 cost per credit	$4.00 cost per credit	$35 per TB / per month	$20 per TB / per month

Conclusion

• This article has discussed Data Lakes and shown why it has become important today to have a storage location where your produced data of any format can be kept in order not to lose valuable information that can be gotten from them.
• It particularly looked at Snowflake Data Lake as a case study to show this importance defined what a Data Lake is, explained its characteristics as well as its advantages.
• Having gotten a Snowflake Data Lake where all kinds of data are kept, you will need to transform them into an acceptable format before loading them into a data warehouse, this is where Hevo Data comes in.