There has been a dramatic increase in the variety and complexity of the data companies can use. In many companies, machine learning and sensor data provide the most intriguing business insights. However, to put this data to use, it must first be analyzed effectively as a large data set.
The Snowflake data cloud is ideally suited to the agility, sharing, and big data volumes required by contemporary Business Intelligence. It was designed from the ground up to serve as an analytics data cloud rather than a transactional database.
This article will discuss snowflake cloud analytics by covering the following topics:
- What is Snowflake Analytics?
- The Snowflake Cloud Computing Architecture
- Examples of Snowflake Analytics
- 3 Snowflake Analytics Best Practices
- Conclusion
This is part of Satori’s Snowflake Guide
What is Snowflake Analytics?
Data software varies greatly in quality. IT professionals built the Snowflake cloud data platform from the ground up to take advantage of the potential of big data analytics. The Snowflake data analysis architecture provides relational database support for structured and semi-structured data types. It is distinguished by the fact that its store and computation components are kept physically apart while being logically integrated. Organizations can do the following using a cloud-based Snowflake data warehouse:
- Integrate all of their data into a unified platform to facilitate data-driven decision-making.
- Create a Snowflake data store instead of using multiple incompatible data formats to avoid the hassle of data silos, integration, and transformation in your data pipeline.
- Provide consistent performance at any scale without human tuning or optimization as the cloud design of the Snowflake Data Platform provides great performance at any data, workload, and concurrency scale.
The Snowflake Cloud Computing Architecture
The Cloud Computing Snowflake architecture enables flexibility when working with large amounts of data.
Organizations with a high storage requirement but a lower need for CPU cycles, or vice versa, can save money by using the Snowflake cloud technology, which separates these two operations. With this, users can dynamically adjust their resource usage and pay only for what they consume. Billing for Snowflake cloud storage is done on a per-terabyte basis per month, whereas computing is billed on a per-second basis.
Notably, the Snowflake cloud services architecture has three scalable layers:
- Database Storage: All information, whether structured, semi-structured, or unstructured, that is imported into Snowflake is stored in its database storage layer. Organization, file size, structure, compression, metadata, and statistics are some of the data storage considerations that Snowflake takes care of automatically.
- Compute Layer: The computing layer utilizes virtual data centers. All data in the storage layer is available to any virtual warehouse, but the warehouses do not need to communicate or compete for processing power. In the end, avoiding the need to redistribute or rebalance data in the storage layer allows for automatic, seamless scaling even as queries are processed.
- Cloud Services: The system is coordinated by the cloud services layer, which employs ANSI SQL. Manual data administration and fine-tuning, thus rendered unnecessary.
Examples of Snowflake Analytics
You can use Snowflake for a wide variety of data analysis purposes. The most common forms of Snowflake data analysis include the following:
- Descriptive Analytics: Address the question of “what happened” with descriptive analytics. With this information, a company can make high-level decisions over time regarding where to invest more money.
- Diagnostic Analytics: By analyzing data to pinpoint the source of a problem, diagnostic analytics offer unique perspectives. Diagnostic data analytics is typically considered post-facto – using data to solve challenges after they have transpired.
- Predictive Analytics: Naturally aiming to explain “what will happen,” predictive analytics draws on information gleaned from both diagnostic and descriptive analytics to spot outliers and recurring patterns that, when modeled, can assist in determining future trends.
- Prescriptive Analytics: The “what action to take” in a given situation is determined using prescriptive analytics. It offers a method of finding opportunities and avoiding potential issues. A use case of prescriptive analytics is detecting chances for repeat business using descriptive analytics data.
The Snowflake cloud data platform makes it simple to load, integrate, analyze, and securely exchange data for anybody in any industry, from IT specialists to business executives.
Satori integrates with Snowflake to ensure that regardless of the type of analytics you are using, any sensitive data is protected and secured. This enables analysts to easily request and gain access to data through Satori’s frictionless access control and make use of the data without having to worry about unsecured private data.
To learn more:
- Book a demo with one of our experts
- Review our Satori for Snowflake capabilities
- Read our Snowflake Guide
3 Snowflake Analytics Best Practices
Here are three of the best practices when using Snowflake for data analytics:
1. Adjust for Optimum Size
You can improve data loading times by optimizing the size of individual files. Snowflake is the premier data warehouse for efficient data loading for massive files and splitting the data into multiple smaller files.
In the end, the amount of data stored depends on the number and size of the servers.
2. Implement Data Segmentation
Even if Snowflake stores data in the virtual data warehouse, it is still necessary to divide the data into categories. Take into account the following recommendations for improving the performance of data queries:
- To improve data retrieval and use, it is beneficial to collocate users who have similar queries within the same virtual data warehouse.
- The Snowflake Query Profile provides support for query analysis, which can assist in identifying and addressing performance issues.
Snowflake, drawing from the same virtual data warehouse, supports various data science operations. This includes business intelligence queries, ELT data integration, and sophisticated data processes.
3. Enhance Database Design
Database design and development characteristics have the potential to become a nightmare in the absence of adequate oversight and planning. The following is a list of the best practices for designing databases:
- Make sure potential shifts are accounted for in advance, and schedule a meeting with the team to plot your data model.
- Configure and test in the development environment to prevent deployment that has not been tested.
- Maintain open lines of communication with the team to ensure that everyone is operating from the same playbook.
Snowflake should not suffer any design challenges if the company plans and communicates well.
Conclusion
Snowflake is an easy-to-use, fully-managed solution that can support almost unlimited, simultaneous workloads. In addition to providing a safe space for data sharing and consumption, Snowflake’s platform also facilitates data warehousing, data lakes, data engineering, data science, and the creation of data-driven applications.
Satori’s Snowflake capabilities ensure that your organization can leverage, share and analyze the data stored within the Snowflake environment to optimize business decisions and value. Satori provides fine-grained access control and dynamic masking capabilities to ensure that even when data analysts use and share sensitive data it remains protected and secure.
To learn more:
- Book a demo with one of our experts
- Review our Satori for Snowflake capabilities
- Read our Snowflake Guide
