Unlocking the Power of Data Analytics in Snowflake
Businesses have been handling massive amounts of data in recent years. These large data sets – collectively called ‘Big Data’ – consist of both unstructured and structured data. The need to analyze Big Data has given rise to a whole new industry of applications that cater to businesses of all sizes, helping them to analyze complex data sets from varied sources to make sense of them.
Software applications have also evolved considerably from just being off-the-shelf solutions designed for local use to sophisticated web-based platforms. Snowflake is one such cloud-based data warehousing software platform that has been gaining global momentum. Analyzing data in Snowflake is useful if you want a scalable solution for your business during a growth phase.
In this post, we’ll outline Snowflake’s primary advantages and offer some advice on how to maximize its potential.
An Overview of Snowflake Data Cloud
Snowflake Data Cloud is a cloud-based data platform that provides a data warehouse as a service. It allows users to store, manage, and analyze data from a variety of sources, including structured and semi-structured data, such as JSON and XML.
Snowflake provides a range of features to help users manage their data, such as data sharing, which allows them to securely share data with other Snowflake accounts, and automated data management tools that can optimize query performance and minimize storage costs. Overall, Snowflake is designed to make it easier for organizations to manage and analyze large volumes of data in the cloud.
The Snowflake platform is rapidly expanding, having more than 1,300 partners and 30 offices around the globe.
Features of Snowflake
1. Cross-Cloud Compatibility
Effective data analytics requires a reliable cloud-based platform to store data efficiently. The most commonly available solutions include Microsoft’s Azure, Amazon’s AWS, or Google’s GCP. With Snowflake, you can deploy and connect various tools using all of these cloud-based services. This multi-cloud functionality gives you flexibility when working with multiple vendors.
2. Exceptional Performance on JSON
Snowflake is known for its efficient JSON capabilities. With Snowflake, your organization can easily process and analyze a variety of inputs from custom documents and forms. JSON and other semi-structured data are supported natively by Snowflake alongside relational data. In fact, you can directly load JSON data into relational tables using Snowflake.
3. Vast Query Processing
Snowflake supports almost an unlimited amount of queries for data analytics and reporting. You can use it for rapidly expanding data warehousing needs and high-workload environments where multiple queries are pulling data.
4. High Performance on Structured Data Types
When dealing with highly-structured data, nothing can beat Snowflake. It is compatible with several standardized data structures commonly used across industries. This feature makes software deployments and integrations seamless.
5. Scalable and Elastic Computing Resources
Snowflake can dedicate extra computing resources during periods of high activity. Depending on your requirements, you can schedule workloads for a selection of VMs for different use cases. If demand falls, Snowflake can scale down compute resources accordingly. This elasticity makes Snowflake ideal for datasets that draw data from multiple sources for real-time analytics.
6. Cost-Effective Pricing
If you are planning to host your data on the cloud, it makes sense to consider cost-effectiveness. Snowflake excels here too, with some of the most cost-effective pricing plans you can find in the market. A big plus about Snowflake’s pricing is that it is consumption-based with separate costs for compute resources and storage. In a sense, the platform can provide a pay-as-you-go pricing plan.
7. Data Security
Snowflake invests heavily in cybersecurity while offering high-level data privacy and protection. The company allows 90-day data recovery sufficient for withstanding most malicious attacks. Snowflake servers are also covered by disaster management plans, providing a safe fallback against any potential physical damage to your hardware.
7 Reasons Why Snowflake is Great for Data Analytics
1. A Big Data Platform from the Start
Snowflake was designed with large data volumes in mind. That means if you have a complex requirement for data analysis, Snowflake is built to provide with you the required data warehousing capacity. Furthermore, you can set up a tenant server that easily connects to your Big Data sources on AWS, Azure, or GCP. Using Snowflake in this way can speed up your loading processes.
2. Flexible Data Loading
Snowflake is more than just an SQL database storage facility. It works equally well with ODBC and JDBC databases. It can be used to host a range of web application servers and general files. The application also works for On-Prem services.
Snowflake has a standout feature called Snowpipe which allows you to ingest data into Snowflake tables in real time. With Snowpipe, you can set up a pipeline that automatically detects new files added to your cloud storage location. Snowpipe immediately loads them into a Snowflake table, without any manual intervention. This can be useful for organizations that need to process and analyze large volumes of data in real time, such as those in the financial, healthcare, or retail industries.
Snowpipe uses an event-driven architecture that scales automatically to handle the amount of incoming data, so you can be confident that your data will always be available and up-to-date. Snowpipe also supports different file formats, including CSV, JSON, and Avro, and allows you to define transformations on your data using SQL or JavaScript code.
3. Broad Support for Data and BI Tools
We’ve already covered the platform’s support for different cloud services. But Snowflake goes beyond that. It supports a wide variety of data analytics software. There are two categories of tools that you need to consider.
Data Management and Automation Tools: These software tools are primarily used for automating data science workflows at scale. They include:
- Talend
- Matillion
- Alteryx
- Segment
- Informatica
- Stitch
- Fivetran
Business Intelligence (BI) Tools: BI software is popularly used for creating detailed visualizations for strategic decision-making. You can use Snowflake Data cloud to quickly integrate your data with many BI packages, including:
- Power BI
- Tableau
- Looker
- Domo
- QuickSight
- Qlick
- Sisense
- Tibco
- Thoughtspot
- MicroStrategy
- Chartio
- Cognos
- OBIEE
- Lumira
4. Standard Data Analysis Model Support
For anyone who wants to get quick results, Snowflake provides users with standard templates that are ready for data analysis – also called Data Analysis Ready templates. DAR templates work for common business use cases (for example in marketing and finance). DAR models are ideal for businesses that want to implement best practices in data analysis and modeling.
These models are designed to be highly optimized for querying and analysis and can help organizations accelerate their time-to-insight by providing a framework for analyzing their data without the need for extensive data modeling or schema design.
The DAR models are built using Snowflake’s virtual data warehousing technology, which allows users to spin up a dedicated, on-demand compute cluster for their analytical workloads. With DAR models, users can quickly run data queries without spending too much on bandwidth.
The DAR models also include a range of pre-built dashboards and reports that can be customized to meet the specific needs of your organization. This can help you get up and running quickly with your analysis, thus, providing a starting point for building more complex reports and visualizations.
5. Database Cloning
If you want to replicate a database for in-house needs or a client, Snowflake offers a cloning feature that allows database admins to copy a database with just a few clicks. Cloning in Snowflake creates a new, independent copy of the selected database or object, with its own set of metadata and storage resources. This enables you to create multiple copies of your data environment for a variety of use cases, such as testing, development, and training.
It uses Snowflake’s unique “zero-copy” technology, which copies your data environment without actually copying any data, and only copies metadata and pointers to the original data. This means that you can create multiple clones of your environment quickly and with minimal additional storage costs.
Cloning can be done at any level of granularity, from a single table or view to an entire database. You can also choose whether to clone data, metadata, or both.
6. Time Travel
Snowflake has a Time-travel feature that allows you to quickly browse through historical data. With Time Travel, you can recover from data corruption or user errors by simply querying the data as it existed at a previous time. This can help you to quickly identify and fix data issues, without the need for complex data restoration processes that can be time-consuming and costly.
In addition, Time Travel can also be used for auditing and compliance purposes, as it allows you to track changes to your data. The feature can also help to reduce storage costs, as Snowflake automatically retains all changes to your data for a set period, allowing you to query historical data without needing additional storage.
7. Secure and Easy Data Sharing
Snowflake allows quick and secure data distribution with colleagues and partners over the cloud. Additionally, you can also quickly search and integrate datasets published by public or government agencies, giving you access to rich and trusted datasets.
Snowflake also features a Data Marketplace where you can sell your data to clients and industry vendors. This feature opens an additional revenue stream for businesses that invest significant resources into data management and analytics.
Final thoughts
Snowflake is a cloud-based data platform that offers powerful features and tools for data analytics. Its scalable architecture, elastic compute resources, and advanced security features make it an ideal option for organizations looking for a dependable analytics solution.
Snowflake’s Data Analysis Ready models and Time Travel features enable businesses to gain deep insights faster and more efficiently, while the cloning feature provides a cost-effective way to create multiple copies of your data environment. In short, analyzing data in Snowflake offers greater flexibility, scalability, and cost-effectiveness than traditional data platforms, enabling organizations to make the most of their data and drive business success.
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