As organizations embrace Big Data, they need solutions that help to accommodate such massive data volumes. One such solution is a data warehouse based in the cloud that offers optimal flexibility and scalability. Snowflake is an example of such a product.
Initially released in 2014, Snowflake is today one of the leading cloud data platforms on the market that boasts $1.4 billion of VC funding. The unique thing about Snowflake is that it provides a data warehouse-as-a-service that is designed for the cloud. The platform can enable a wide range of data workloads and help teams to develop modern data applications.
Snowflake is a perfect match of data warehousing power and the flexibility you find in Big Data platforms – only at a fraction of the cost of traditional solutions.
Are you wondering if your team could benefit from Snowflake? Read this article to learn more about its architecture and key concepts:
- What is Snowflake database?
- Advantages of Snowflake data warehouse
- Snowflake Database – the Big Data service of the future
- Connecting your data to Snowflake DB
What is Snowflake database?
Snowflake is a cloud data platform that provides a fully managed service to organizations looking to reap the benefits of Big Data.
Teams can use Snowflake for many tasks like:
- data warehousing,
- data lakes,
- data engineering,
- data analytics,
- data science projects,
- data application development,
- secure sharing and consumption of the shared data.
Snowflake supports an almost unlimited number of concurrent workloads – this allows users to have full freedom regarding what they do and when they do it.
Snowflake charges its users by credits. A credit is a unit of measurement by which you pay for the consumption of resources on Snowflake – for example when your virtual warehouse is running,or you use services like the cloud services layer or serverless features.
Snowflake platform architecture – basics
Snowflake comes with its architecture that includes storage, query processing, and cloud services layers. These layers scale independently of one another.
- Database storage – this layer is Snowflake’s scalable cloud blob storage you can use for storing structured and semi-structured data (including JSON, AVRO, and Parquet). It includes tables, schemas, databases, and diverse data. Note that data is automatically divided into micro-partitions (contiguous units of storage containing 50-500 MB of uncompressed data) in Snowflake.
- Cloud services – this layer offers services such as authentication, infrastructure management, access control, and metadata management.
- Query processing – this layer is responsible for handling query execution using “virtual warehouses.” A virtual warehouse is an MPP compute cluster that consists of multiple compute nodes. It’s an independent compute cluster that operates independently and has zero impact on how other warehouses perform.
These layers are physically separated, but they’re logically integrated. What does this mean? That you can enable all of the users and data workloads to access a single copy of data without impacting the performance. And when everyone accesses the same version of data, you no longer deal with data silos.
A deeper dive into Snowflake architecture
Snowflake’s architecture combines the shared-disk and shared-nothing architectures:
- Shared-nothing architecture – this is a distributed architecture where every node is independent and self-sufficient.
- Shared-disk architecture – in this architecture, all data is accessible from all cluster nodes.
Snowflake combines them using a central data repository for persisted data that you can access for all compute nodes. When Snowflake processes queries, it uses massively parallel processing (MPP) compute clusters.
Every node in the cluster stores some of the data set locally. Thanks to this hybrid model, Snowflake offers the outstanding data management simplicity of a shared-disk architecture together with the performance of a shared-nothing architecture.
Snowflake is cloud-agnostic
Unlike other cloud data warehouses, Snowflake doesn’t run on its own cloud platform – it’s available on Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure.
Since it comes with a common and interchangeable code base, you can easily move your data to any cloud in any region without having to rewrite the application code. Note that Snowflake can’t run on a private cloud infrastructure (be it on-premises or hosted).
Advantages of Snowflake data warehouse
A data warehouse is basically a system that allows integrating large data sets from a variety of sources, processing them, and – finally – delivering analytics reports on request. Business analysts and decision-makers can use such a system to submit queries and get immediate responses.
Traditionally, companies developed their own Big Data storages in-house, with developers leveraging open-source tools like Apache Hadoop. But to build and maintain such a system, you'd need a team of data engineers who are in high demand and in short supply today.
That’s how Snowflake helps. It’s a data warehouse that is ready to use in a SaaS model. You don't have to worry about virtual or actual hardware. There is no device to install, and the Snowflake team looks after the system's upkeep. You can also get app upgrades for the most recent versions easily.
Traditional data centers are slower, more difficult to use, and less versatile than Snowflake. Note that Snowflake's data warehouse isn’t based on a pre-existing database or Big Data computing framework like Hadoop. Instead, it employs a modern SQL database engine with a cloud-specific architecture. Snowflake can be easily understood and used by any software developer who has ever worked with SQL.
Since Snowflake is an independent solution, it can be used with all of the big cloud storage services out of the box. Integrating this data warehouse with third-party applications is easy.
Snowflake Database – the Big Data service of the future
Why is Snowflake so valuable today? Dragoneer Investment Group and Salesforce Ventures are the two venture capital firms that have invested in this startup. The latter investment is particularly significant because it comes after Snowflake and Salesforce formed a strategic alliance. Salesforce is no longer a purely sales and marketing automation platform.
Snowflake embodies a business approach that simplifies the collection, sorting, and use of Big Data. Platforms like Google BigQuery, Amazon Redshift, and Azure SQL Warehouse compete with it.
Snowflake adds value to businesses by offering a complete, 360-degree data analytics stack. Salesforce has also acquired companies like Einstein Analytics and Tableau, which specialize in data visualization. They’re going to make a fantastic product with Snowflake.
Connecting your data to Snowflake DB
If a SaaS company wishes to succeed in international markets, it must prioritize accessibility.
Snowflake invests a large portion of the funds raised in it and allows combining its services with other platforms in a variety of ways thanks to:
- A web-based user interface (UI)
- Command-line clients (like SnowSQL)
- ODBC and JDBC drivers
- Native connectors (like Python)
- Applications (for example, ETL and BI tools)
Snowflake is on its way to setting the new standards for data warehousing. Any organization looking to drive innovation with Big Data needs such a solution.
Are you planning to use Snowflake? Or maybe you’ve already implemented it at your organization? Please share your thoughts and experiences in the comments sections, we look forward to learning how teams are leveraging Snowflake in their Big Data projects. And if you need data science experts – don’t hesitate to reach out to us!