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Snowflake Data Warehouse: A Powerhouse for the Data Economy

POSTED ON: July 30, 2021 TAG: Data Warehouse

Managing an influx of data from multiple sources can be daunting, especially if you are dependent on a traditional data warehouse. The growing quantum and variety of data requires extensive data storage and processing power to derive meaningful and actionable business insights.

Businesses are thus rapidly gravitating towards modern cloud data warehouse solutions. A modern enterprise data warehouse enables organizations to meet their scalability, elasticity, affordability, concurrency, and improved return-on-investment (ROI) needs.

Among the many robust and popular data warehouse solutions available in the market today, Snowflake stands out as the most preferred data management platform. Snowflake has emerged as an enabler for data-driven organizations and has been recognized by Gartner1 as a leader in Gartner Magic Quadrant for data management solutions for analytics.

While the recognition from Gartner emphasizes the fact that Snowflake is the data management platform of the future, this blog outlines the performance indicators that have made Snowflake – the top choice of Chief Technology Officers (CTOs).

Data Warehousing: Stores, Manages, Processes Efficiently

An enterprise data warehouse is a relational database designed for analytical work. The data warehouse gathers and combines data from one or multiple sources to analyze it and generate business insights. Data warehousing serves as a historical repository to integrate the data and information needed by the business. It also acts as a query execution and processing engine for that integrated data. Users can thus interact with the data stored in the database. As you decide on the right data management approach for your business, learn considerations for data leaders on Data Warehouse and Master Data Management (MDM).

The Snowflake cloud data platform involves a pure cloud, SQL cloud data warehouse from the ground up. Snowflake’s unique data warehouse architecture physically separates but logically integrates compute, storage, and services, including user management and metadata. Each of these components being isolated can be expanded and contracted independently. Snowflake can thus grow responsive and adaptable. Snowflake processes queries through massively parallel processing (MPP) clusters. Each node in the cluster stores a part of the entire data set locally.

Snowflake has a centralized data repository. In this repository, data is persisted and is accessible by all the compute nodes. Besides, the storage layer is infinitely sized and inexpensive. It can be scaled independently too. Snowflake data warehouse solution stores data in an internal, compressed, optimized, and columnar format, better known as micro-partitions. There is faster data retrieval due to the storage of metadata of these micro partitions. This solution offers built-in redundancy too for disaster recovery.

Snowflake Data Warehouse Architecture

Data Sharing: Fosters Collaboration

Data sharing is the ability to share the same data resource with multiple users or applications. The data are stored in one or more servers in the network. Due to the software locking mechanism, two people at the same time cannot change the data. According to Gartner2, by 2023, organizations promoting data sharing will surpass their competitors on most business value metrics.

The enterprise data warehouse by Snowflake enables organizations to share their data across their business ecosystem in a secure way, thus ensuring data security and governance. Using Snowflake data sharing, you can easily share data with customers without a Snowflake account. You can create a reader account with read-only access to your live data. Though data sharing between Snowflake accounts has the same ease of use and data governance, users can join their data for data enrichment. Moreover, you can get live, ready-to-query data from third-party data providers without any delays through Snowflake’s Data Marketplace. With access to live data sets, you don’t need to copy, transform, or move data. Live data also accelerates time-to-insight for advanced analytics and business intelligence. Data sharing with Snowflake data warehouse solution also enables data discovery. You can easily publish standard and personalized data sets for discovery and control access.

Snowflake Data Sharing

Data Integration: Consolidates Data for a Unified View

Data integration can be defined as the process of integrating data from multiple sources into a unified view to offer users valuable and actionable information. The rapid growth of data sources and their volume has made integration critical for businesses, especially when they seek more and improved ways to sense and share their enterprise data.

Snowflake works with a wide range of tools and technologies, enabling you to access it through an all-encompassing network of drivers, connectors, utilities, and programming languages. Snowflake offers a comprehensive list of native connectors for Spark, Python, Kafka, and drivers for Go, Node.js, ODBC, and JDBC. It also offers a SnowSQL command-line client. Besides, certified partners, such as Dbt, Fivetran, Azure Data Factory, HVR, Google Cloud Data Fusion, and Google Cloud Dataflow, could be used for data integration with Snowflake. This modern data warehouse solution brings the benefits of using streaming data through Kafka and Snowpipe streaming.

Continuous Data Integration with Snowpipe

Snowpipe enables you to load constantly generated data into the Snowflake cloud data warehouse seamlessly. This automated service uses a REST API to asynchronously listen for new data arriving in the staging environment, loading it into Snowflake as and when it arrives. Snowpipe is a serverless process utilizing servers isolated from the customer environment, thus bringing about workload isolation. You can keep your ingestion costs affordable and predictable this way. You are also assured of near-real-time data reporting.

Data Security and Governance: Prevent Unauthorized Data Access

Data governance is how an organization manages its data availability, usability, consistency, integrity, and security. Data governance consists of the processes, policies, roles, standards, and metrics to guarantee effective data management throughout the data lifecycle and how an organization can use that data. In the absence of a data governance strategy, organizations may face data quality issues and miss regulatory compliances, which is why you should have a checklist of these best data governance practices.

Modern Snowflake cloud data warehouse supports multi-level, role-based access control (RBAC). The role-based control helps to exercise granular control with flexible user management. Only users thus have access to data they can view and access. With Snowflake enterprise data warehouse, you can create secure views with cell-level security. You can even mask your Personally Identifiable Information (PII) data. Then, there is column-level and row-level security. Besides, for every action a user performs, there are audit trails. Snowflake supports federated authentication and SSO while integrating with third-party services, like Active Directory and Oakta. Snowflake also offers you a host of security features that include multi-factor authentication, AES 256 end-to-end solid encryption, and IP whitelisting.

Benefits of Snowflake Data Warehouse

Ease of Use

With instant and almost infinite elasticity and concurrency, Snowflake enables you to automatically spin up and down compute clusters, regardless of their size, for any user or workload without affecting other jobs.

Automated, Zero Administration

Snowflake enterprise data warehouse supports modern features, such as auto-scaling warehouse size, big data workloads, auto suspends, and data sharing. Your IT team no longer needs to worry about software updates, configuration, failures, or scaling infrastructure.

High Performance and Speed

Due to the elastic nature of the cloud data warehouse, you can scale up your virtual warehouse to benefit from extra compute resources. Due to this elasticity, it becomes possible to load data faster and even run a high volume of queries. If required, you can scale down the virtual warehouse and pay for the time you use it.

Unparalleled Flexibility

Along with a data lake, Snowflake gives businesses exceptional value and flexibility. Besides, you have the flexibility to use this data warehouse architecture while being flexible to leverage Redshift Spectrum and Amazon Athena on the same data lake. This way, you can use Snowflake when you want to. As you use an optimized data lake, besides the Snowflake enterprise data warehouse, you can apply varied patterns to varied use cases.

More Durability

When combined with a data lake, Snowflake cloud data warehouse, like Microsoft Azure or Amazon S3, assures you that your data is compressed, encrypted, distributed, and geo-redundant, making it quite durable and available for extensible access. Your data and information are thus redundantly stored across multiple devices and facilities, including structured data lakes.

Lower Cost

In the Snowflake data warehouse, pricing depends on the amount of data stored and the compute hours used. This on-demand, cost-optimized data warehouse solution ensures your data is ready for analysis and reporting without incurring any “idle” system costs.

Make better, quicker business decisions with an agile data warehousing solution.

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Are You Ready for Success with Snowflake?

Many big tech companies are Snowflake customers, making up its 18% global market share. The data warehousing giant accelerates these companies’ data science and enterprise application development projects. Besides, we aid businesses in controlling their data in powerful ways through our Enterprise Data and Analytics practice, comprising cloud data warehouse implementation, data warehouse-as-a-service, cloud migration, data management and integration, and cloud data analytics.

POSTED ON: July 30, 2021 TAG: Data Warehouse

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