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.
Table of Contents
- What is Snowflake?
- What is a Data Lake?
- What are the Characteristics of a Data Lake?
- What is a Snowflake Data Lake?
- Data Lake Architecture and Snowflake
- What are the Benefits of a Snowflake Data Lake?
- What is the Snowflake Data Lake Pricing?
- Conclusion
What is Snowflake?
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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.
Snowflake requires no hardware or software to Select, Install, Configure, or Administer, making it suitable for enterprises that do not want to devote resources to in-house server Setup, Maintenance, or Support.
Since Snowflake decouples the storage and computes functions, which means you can have an unlimited number of concurrent workloads against the same single copy of data and not interfere with the performance of other users, it is highly scalable, flexible with Big Data, and the sharing functionality of Snowflake makes it easy for organizations to quickly share and secure data in real-time.
Key Features of Snowflake
- Cloud Agnostic: Snowflake is a cloud-independent solution, offering solutions for all three major cloud providers: AWS, Azure, and GCP. Customers can easily integrate Snowflake into their existing cloud architecture and deploy it in areas that make sense for their business.
- Scalability: The computing and storage resources are separated in the snowflake multi-cluster shared data architecture. This method gives consumers the opportunity to scale up resources when huge amounts of data are needed to be quickly loaded, and scale back down after the process is completed without disrupting service.
- Concurrency and Workload Separation: One of the primary advantages of this design is the ability to separate workloads. Queries from one virtual warehouse will never have an impact on queries from another. Having dedicated virtual warehouses for users and apps allows ETL/ELT processing, data analysis activities, and reporting to run without competing for resources.
- Near-Zero Administration: Snowflake is available as a Data Warehouse as a Service (DWaas) that helps businesses set up and administer without requiring extensive engagement from DBA or IT teams. With contemporary capabilities like auto-scaling, which increases both the virtual warehouse size and the number of clusters, the days of server size and cluster administration are over.
- Semi-Structured Data: The necessity to manage semi-structured data, often in JSON format, drove the creation of NoSQL database solutions. To parse JSON, data pipelines were needed to extract attributes and merge them with structured data. Snowflake’s architecture enables the storage of structured and semi-structured data in the same location by leveraging the VARIANT schema on reading data type.
- Security: Snowflake provides a wide range of security protections, from how users use Snowflake to how data is stored. To restrict access to your account, you can set network policies by whitelisting IP addresses. Snowflake supports a variety of authentication techniques, including two-factor authentication and SSO via federated authentication.
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.
Hevo Data is a No-code Data Pipeline that helps you transfer data from 100+ sources (including 40+ Free Data Sources) to Snowflake in real-time in an effortless manner.
Hevo with its minimal learning curve can be set up in just a few minutes allowing the users to load data without having to compromise performance. Its strong integration with umpteenth sources allows users to bring in data of different kinds in a smooth fashion without having to code a single line.
Get Started with Hevo for Free
Key Features of Hevo Data:
- Fully Managed: Hevo Data is a fully managed service and is straightforward to set up.
- Transformations: Hevo provides preload transformations through Python code. It also allows you to run transformation code for each event in the Data Pipelines you set up. You need to edit the event object’s properties received in the transform method as a parameter to carry out the transformation. Hevo also offers drag and drop transformations like Date and Control Functions, JSON, and Event Manipulation to name a few. These can be configured and tested before putting them to use.
- Connectors: Hevo supports 100+ integrations to SaaS platforms, files, databases, analytics, and BI tools. It supports various destinations including Amazon Redshift, Snowflake Data Warehouses; Amazon S3 Data Lakes; and MySQL, SQL Server, TokuDB, DynamoDB, PostgreSQL databases to name a few.
- Ensure Unique Records: Hevo Data helps you ensure that only unique records are present in the tables if Primary Keys are defined.
- Multiple Sources: Hevo Data has various connectors incorporated with it, which can connect to multiple sources with ease.
- Automatic Mapping: Hevo Data automatically maps the source schema to perform analysis without worrying about the changes in the schema.
- Real-time Data Transfer: Hevo Data works on both batch as well as real-time data transfer.
- Resume from Point of Failure: Hevo Data can resume the ingestion from the point of failure if it occurs.
- Advanced Monitoring: Advanced monitoring gives you a one-stop view to watch all the activity that occurs within pipelines.
- 24/7 Support: With 24/7 Support, Hevo provides customer-centric solutions to the business use case.
Steps to load Snowflake data using Hevo Data:
- Sign up on the Hevo Data, and select Snowflake as the destination.
- Provide the user credentials and connect to the server.
- Select the database, and schema to load the data.
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What are the Characteristics of a Data Lake?
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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 |
Cost for AWS (US East Northern Virginia)| 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 |
Cost for Microsoft Azure (East US 2 Virginia)| 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 |
Cost for Google Cloud Platform ( us-central1 Iowa)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. Hevo Data is a one-stop solution where all your data needs and analysis are handled by a simple unified interface.
Visit our Website to Explore Hevo
Hevo Data provides its users with a simpler platform for integrating data from 100+ sources (Including 40+ Free Sources) for Analysis. It is a No-code Data Pipeline that can help you combine data from multiple sources. You can use it to transfer data from multiple data sources into your Data Warehouses such as Snowflake, Database, or a destination of your choice. It provides you with a consistent and reliable solution to managing data in real-time, ensuring that you always have Analysis-ready data in your desired destination.
Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You can also have a look at our unbeatable pricing that will help you choose the right plan for your business needs!
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Ofem is a freelance writer specializing in data-related topics, who has expertise in translating complex concepts. With a focus on data science, analytics, and emerging technologies.