Snowflake Data Lake

The Snowflake platform offers the advantages of various streams such as data lakes, warehousing, and cloud storage. Your organisation will benefit from relational querying, best-in-class performance, governance, security, and governance when Snowflake serves as your core data store. In this article, we will discuss more about Snowflake data lake, the snowflake platform, and its features. We will also talk about the data lake, its features, and the snowflake for the data lake.

What is Snowflake?

Snowflake, a fully managed SaaS that was created in 2012, offers a one-place platform for the process of data warehousing, data engineering, data lakes, development of data apps, data science, as well as safely sharing and consuming real-time and shared data. To meet the demanding needs of expanding businesses, includes out-of-the-box capabilities including storage and compute separation, on-the-fly scalable computation, data cloning, data sharing, as well as support for third-party tools.

All of your data records may be stored and analyzed in Snowflake, a cloud data warehouse. For loading, integrating as well as analyzing data, it can scale up and down its computing resources automatically.
The foundation of Snowflake is made of mainly 3 components :

Cloud services :

Snowflake employs ANSI SQL to use the cloud services that helps the users to enable and manage their infrastructure as well as optimize their data. Data encryption and security are handled by Snowflake. They continue to hold dependable data warehousing certifications including PCI DSS and HIPAA. Services include query processing, access control, infrastructure management, and optimization, query authentication, and metadata management. The Snowflake Platform is basically a database of snowflakes

Query Processing :

The virtual cloud-based data warehouses that make up Snowflake's computing layer allow you to request data analysis. Workload parallelism is never an issue because each Snowflake virtualized warehouse is a separate cluster that does not compete with or negatively impact the performance of the others.

Database storage :

Here, the uploaded semi structured as well as structured data sets from an organization are stored for analysis and processing. All aspects of the data storage process, such as file size, metadata, the process of compression, as well as analytics, are automatically managed by Snowflake.

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Key Features of Snowflake

Scalability :

The computing and storage resources are separated by the multi-cluster data architecture of Snowflake. This approach gives users the option to expand resources during the need to load big volumes of data quickly and then scale back down after the completion of the process without causing any service interruptions. Customers can begin with a small virtual storehouse and increase or decrease it as necessary.

Concurrency :

Separating apart workloads to be conducted against the own computing clusters, known as a virtual warehouse, is one of this architecture's primary advantages. Virtual warehouse inquiries will never have an impact on those from another virtual warehouse. Users and apps may conduct data analysis activities, ETL/ELT processing, as well as reporting without vying for resources when they have dedicated virtual warehouses.

Near Zero Administration :

Snowflake is offered in the form of a service-based data warehouse (DWaas). It enables businesses to set up as well as administer a device without the need for intensive It or a DBA team engagement. Software installation or hardware commissioning is not necessary. The days of managing server and cluster size are long gone thanks to contemporary features like auto-scaling, which may increase clusters and virtual warehouse sizes automatically.

Semi-Structured Data :

The need of managing the semi-structured data, mostly done in JSON format, has given a huge rise to NoSQL database solutions. A number of data pipelines had to be created in order to get the attributes out of JSON and then mix them up with the structured data. Because VARIANT is a model on read data type, Snowflake's design enables structured as well as semi-structured data within the same location. Both organized and semi-structured data can be stored using the VARIANT data type. Snowflake automatically parses data as it is loaded, extracts the properties, and saves the data in a parallel format. Consequently, data extraction pipelines are no longer required.

Security :

Snowflake includes a wide range of security protections, from user access to the way data is kept. You can control security policy as well as limit the access of the account simply by adding the IP addresses to a whitelist. Snowflake supports a variety of authentication methods, including federalised verification and two-factor authentication for single sign-on (SSO). To control access to the account's objects, a hybrid model of role-based network access and voluntary access control is used. Each account object has a holder controlling the access to the object. This hybrid strategy provides a high level of flexibility as well as control.

What is a Data Lake?

Large amounts of semistructured, organized, and unstructured data can be stored, processed, and secured using a data lake, a centralized repository. It can process any type of data, regardless of its variety or magnitude, and save it in its original format. Learn more about updating your Google Cloud data lake.
The major difference between a database and a data lake is that the most recent data needed to fuel an application is kept in a database. For the purpose of data analysis, a data lake holds raw historical and current data from more than one system.

Data Lake

A data lake is unique because it holds both relational as well as non-relational social media data along with IoT devices, and line-of-business apps. When data is captured, its structure or schema is not specified. As a result, you can keep all the data without meticulous planning or the requirement to anticipate the queries you might have in the future. To find insights, you can analyse your data using a variety of methods, including full-text search, big data analytics, machine learning, and real-time analytics.

Features of a Data Lake

Dataflow :

It provides uniform streaming and batching of data in a fast, cost-effective, and serverless manner.

CloudData Fusion :

it enables fully loaded cloud-native integration of data that users use for the efficient building as well as managing of data for ETL pipelines.

Dataproc :

This makes the open source data and the processing time for analysis a lot faster as well as secure in the cloud.

Modernization :

Data Lake helps securely and cost-effectively ingest as well as store very large quantities of data with enormous diversity.

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What is a Snowflake Data Lake?

The Snowflake platform has the advantages of data warehousing, data lakes, as well as cloud storage. Your organisation will benefit from the greatest result, security, relational querying, as well as governance. When Snowflake serves as your core data store as an alternative, you can utilise Snowflake to speed up analysis and data transformations while storing the information in the cloud from Amazon S3 or Azure Data Lake.

Data lakes have the following qualities that set them apart from other big data storage methods:

  • Open for all types of data, irrespective of its format or source
  • Data is kept in its unaltered, unprocessed original form.
  • Only when data is supplied for analysis based on meeting query conditions is it changed.

Why snowflake for Data lake 

All data, one platform :

Easily combine organised, unstructured as well as semi-structured data using the storage strategies that are most appropriate for your requirements.

Fast Query Processing :

Using elastic engines for powering numerous workloads will simplify your architecture and essentially eliminate concurrency problems and resource conflicts.

Secure collaboration :

Protect your data lake, understand what is present in it, and have control over how it's utilised. Without using ETL, easily incorporate outside data.

How Snowflake Enables Your Data Lake

The snowflake platform helps in enabling the data lake as :

Unifying data in one place :

  • Support a variety of workloads on all types of data using your preferred language on a single platform, doing away with the requirement to connect services and systems.
  • Using effective compression, intelligent micro-partitioning, and both at-rest and in-transit store data and encryption in Snowflake-managed storage.
  • You can access data stored in the cloud without having to relocate it.

Process and Query Data with high reliability and speed :

  • Use Snowflake's elastic processing engine to run pipelines for dependable cost savings, performance, and almost no maintenance.
  • The speed and flexibility of relational queries as well as of Schema-on-Read, query semi-structured data.
  • With nearly unlimited, dedicated computing resources, support an almost infinite number of concurrent queries and users.
  • Using Snowpark, you can streamline pipeline construction using SQL or your preferred language without having to manage additional services, clusters, or copies of the data.

Secure Collaborations :

  • Role-based access controls can be used to enforce security across clouds, doing away with the need to manage numerous copies of the same data.
  • With built-in Access History, you can see who is accessing which data.
  • Use categorization and object tagging to identify and monitor sensitive data, and dynamic masking of data as well as exterior tokenization to protect it while maintaining its analytical usefulness.
  • Facilitate external and internal stakeholder collaboration, or even enrich the data lake with real-time, safe data exchange.

Snowflake as a Data Lake Solution

The snowflake platform is a data lake solution:

  • Flexibility due to the ease and speed with which data scientists can configure queries
  • All users have access to all data thanks to accessibility.
  • due to the open-source nature of many data lake technologies, affordability
  • Adaptability to the majority of data analytics techniques
  • comprehensive, incorporating information from all enterprise data sources, such as IoT.

Challenges faced in the Data Lake

Updation of data :

Hadoop does not support edits or deletes because those operations were not part of its original architecture.

Large Datasets :

For large datasets, a data lake is appropriate, but it is not the best option for tiny datasets. It is a parallel programming model that stores everything in 260 megabyte-sized blocks. Hadoop doesn't function at its best if you have less data. Hadoop would require a minute to answer a query if a table contains up to one million records, but Oracle would just require a second. However, Hadoop will answer your query in 2–5 minutes when you possess a million records, whereas Oracle would not or might take hours.

Decoupling of metadata :

People struggle to comprehend this shift because they're used to a paradigm wherein both metadata and data are tightly intertwined.

Too many moving parts :

A Hadoop developer has to deal with HBase, Zookeeper, Hive, Nifi, Flume, Scoop, Druid, Impala, etc. while an Oracle programmer is only worried about one main product, Oracle.

Open source platform :

Everyone is able to contribute their unique feature, which very few people are aware of. However, it introduces a bug into the whole system. For instance, Ambari provides functionality that restarts a service automatically if it goes down. Why? The cause is that no one was able to determine why services were initially failing.

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What are the Benefits of a Snowflake Data Lake?

  • Flexibility due to the ease and speed with which data scientists can configure queries
  • All users have access to all data thanks to accessibility.
  • Due to the open-source nature of many data lake technologies, affordability
  • Adaptability to the majority of data analytics techniques
  • Comprehensive, incorporating information from all enterprise data sources, such as IoT.

Snowflake: Data lake or data warehouse?

A data lake is a massively scalable storage repository that stores vast volumes of unprocessed raw data in its original format until they are needed. Data from data lakes are frequently gathered from several sources and may be presented in a variety of semi-structured, organised, and unstructured formats of data.
However, a Data Ware Warehouse process as well as converts the data within a more traditional database environment for enhanced query and analytics. Data lakes are typically viewed as alternatives to data warehouses.

With the help of Snowflake's platform, your company may access a more comprehensive and governed data lake than was before feasible. You have two options: either deploy Snowflake as your primary data repository and use the Snowflake Data Cloud to boost performance, querying security, and governance, or store the data in Azure Data Lake, AWS S3, or Google Storage and then uses Snowflake to quicken data transformation and analytics.

What is the Snowflake Data Lake Pricing

Standard Pricing

  •      Complete SQL data warehouse
  •      Secure Data Sharing across regions/clouds
  •      Premier Support 24 x 365
  •      1 day of time travel
  •       Always-on enterprise-grade encryption in transit and at rest
  •      Customer-dedicated virtual warehouses
  •       Federated authentication
  •       Database replication 
  •       External Functions
  •       Snowsight
  •      Create your own Data Exchange
  •      Data Marketplace access

Enterprise Pricing

     All the features of standard pricing plus:

  • Multi-cluster warehouse
  • Up to 90 days of time travel
  • Annual rekeying of all encrypted data
  • Materialised views
  • Search Optimization Service
  • Dynamic Data Masking
  • External Data Tokenization

Business Critical Pricing

     All the features of Enterprise pricing plus :

  • HIPAA support
  • PCI compliance 
  • Tri-Secret Secure using customer-managed keys
  • AWS PrivateLink support
  • Azure Private Link support
  • Google Cloud Private Service Connect support
  • Database failover and failback for business continuity
  • External Functions - AWS API Gateway Private Endpoints support

Companies Currently Using Snowflake Data Lake

Various companies using snowflake data lake are mentioned below:

  • Amazon
  • Google
  • Microsoft
  • Capital One
  • Warner Music Group
  • JetBlue
  • DoorDash
  • Allianz
  • Frontify
  • Autodesk
  • Disney Ad Sales
  • Pizza Hut

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Conclusion :
In this article, we have discussed Snowflake data lake. The Snowflake platform offers the advantages of various streams such as data lakes, warehousing, and cloud storage. Your organisation will benefit from relational querying, best-in-class performance, governance, security, and governance when Snowflake serves as your core data store. We have also discussed the benefits and key features of snowflake and data lake for your better understanding of the topic. 

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As a content writer at HKR trainings, I deliver content on various technologies. I hold my graduation degree in Information technology. I am passionate about helping people understand technology-related content through my easily digestible content. My writings include Data Science, Machine Learning, Artificial Intelligence, Python, Salesforce, Servicenow and etc.

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