6 Best Snowflake Analytics Tools: A Complete Guide

Carrying out an insightful data analysis for your business requires having the ability to not only store or access data, but also to transform it into a form, that can be used to draw powerful and holistic insights. This article focuses on Snowflake Analytics and provides you with a comprehensive list of some of the best Snowflake Analytics Tools. It also introduces you to the various factors that you must consider before selecting a tool for your business.

What is Snowflake?

Snowflake is a fully-managed data warehouse available as a Software-as-a-Service offering. Snowflake follows a unique pricing model with a pricing structure that separates the execution cost from the storage cost, allowing customers to manage their budget and expenditure effectively with a high level of granularity.

Snowflake is highly scalable and allows users to manage their growing business data needs with ease. It further provides users with robust and fast querying abilities, allowing them to process diverse and complex data in a matter of minutes. Snowflake also houses reporting abilities and allows users to create compelling visualizations.

For further information on Snowflake, you check the official website here.

Key Features of Snowflake

The following features of Snowflake are responsible for its growing popularity in the global market:

• Scalability: Snowflake provides storage and computation facilities separately. A Database is used to store the data in Snowflake and the calculation can be done from a virtual Data Warehouse. Thus it can provide high scalability at low costs.
• Low Maintainance: Snowflake is designed to ensure that minimal user interaction and effort are required for any performance or maintenance-related activity.
• Query Optimization: Snowflake which has automatic query optimization saves you the trouble of manually improving your queries.
• Load Balancing: Snowflake can be useful if you want to separate your routine workloads into different virtual Data Warehouses so that analytical loads are not affected by a busy cluster during peak routine loads.

How to choose the best analytics tool for Snowflake?

Selecting an analytics tool for your data warehouse is a critical decision that can make or break the effectiveness of your data pipeline. Broadly this is done based on the following factors:

• Integration support is provided by the analytics tool for various data-warehouses such as Snowflake.
• The ability to create complex reports and visualizations in real-time.
• The ease of use or the learning curve of the platform. 
• Features provided by the platform that supports easy collaboration between the team.
• The total cost of ownership includes the cost of training, documentation, license, and usage cost of the platform. 
• The quality of customer support provided by the software provider.

What is Data Analytics?

The definition of data analytics captures the field’s broad scope as the process of analyzing raw data to find trends and answer questions. It does, however, include a variety of techniques with a variety of objectives.

Some elements of the data analytics process can aid a variety of initiatives. A successful data analytics initiative will provide a clear picture of where you are, where you have been, and where you should go by combining these components.

In most cases, descriptive analytics is the first step. This is the method of describing historical data trends. The goal of descriptive analytics is to figure out “what happened.” This frequently entails calculating traditional indicators like return on investment (ROI). Each industry will have its own set of indicators. Descriptive analytics does not make predictions or make decisions for you. It emphasizes the importance of summarising data in a meaningful and descriptive manner.

Advanced analytics is the next critical component of data analytics. To extract data, make predictions, and discover trends, this branch of data science employs advanced tools. Classical statistics and machine learning are among the tools available. Advanced analytics are enabled by machine learning technologies such as neural networks, natural language processing, sentiment analysis, and others. This information provides new data insights. “What if?” questions are addressed by advanced analytics.

Machine learning techniques, large data sets, and inexpensive computing power have enabled the use of these techniques in a variety of industries. These techniques rely heavily on the collection of large data sets. Because of advances in parallel processing and low computational power, big data analytics allows businesses to draw meaningful conclusions from complex and varied data sources.

What is Data Analytics Tools?

Data Analytics can be defined as the process in which data is queried to obtain useful Insights and general information from them. It can also be referred to as scientific methods used to get deductions from raw data gathered from research using Data Collection and Analytic tools. These methods of retrieving relevant information from data have now been automated into processes and algorithms and it encompasses many diverse types of Data Analysis.

Data Analysis has been in existence for a long time but, it is only recently that it has become prominent in the industry as a result of the explosion of data usage and consumption. 

Data Collection and Analytic tools, on the other hand, can be defined as the processes in which Charts, Maps, Diagrams, etc are used for the gathering and interpretation of data to gain results which will then be presented for a wide range of use in various applications. There are now several programs and methodologies developed for use in almost every industry to cater to their specific needs, be it in manufacturing, quality assurance and control, reliability, and lots more.

Data Analytics is made up of a multitude of practices ranging from SQL Analysis to Data Science and Business Intelligence and using these techniques for Analytics reveals trends, metrics that will allow the growth of your company by harnessing useful information for the optimization of processes to increase the overall efficiency of your business or system. These important deductions would have otherwise been lost in the bulk of data if not analyzed.

What are the Top 6 Snowflake Analytics Tools?

Choosing the ideal analytics tool that perfectly meets your business requirements can be a challenging task, especially when there’s a large variety of tools available in the market. To simplify your search, here is a comprehensive list of the 6 best Snowflake Analytics Tools, that you can choose from and start analyzing your data:

• Looker
• Tableau
• Talend
• Sigma
• IBM Cognos
• Alteryx

1. Looker

Looker is one of the most popular cloud-based data intelligence tools that support seamless integration to Snowflake. Looker houses robust reporting & data transformation abilities, allowing users to create informative and compelling dashboards based on predefined logic in real-time.

Looker has a minimal learning curve and hence provides users with an easy-to-use platform. It supports robust integration not only with Snowflake data sources but also with numerous cloud-based and on-premise databases and sources.

Use Case

If you’re looking for a tool that has robust visualization capabilities and houses moderate data transformation abilities, then Looker can be the ideal choice. Looker provides users with a fully-managed easy-to-use SaaS platform, making it one of the best Snowflake Analytics Tools available in the market today. It will ensure that your team remains productive and doesn’t have to worry about maintaining any physical hardware.

Pricing

Looker doesn’t have transparent pricing. It follows a quote-based pricing model that charges users based on various factors such as the deployment scale, number of users, etc. To get the exact tailored pricing for your business needs, you will have to get in touch with the Looker team. Looker further provides a free trial to the users.

For further information on Looker and how you can use it for Snowflake Analytics, you can check the official website here.

2. Tableau

Tableau is a popular business intelligence tool that supports robust integration with a diverse set of cloud-based and on-premise databases. It also houses various connectors that let users integrate with services such as Salesforce and also bring in data using CSV, JSON, etc. files. It has a strong focus on the interactivity of visualizations and allows users to create informative visualizations that match their business use cases.

Tableau is not only available as a desktop application but also as an online service in the form of Tableau Server, which allows users to create, share and view critical business reports and dashboards on the go, by logging into Tableau using their mobile device. It further provides a hosted analytics offering called Tableau Online that provides users with similar functionalities along with unlimited storage support.

Use Case

If you’re looking for a tool that houses robust integration support for most databases and supports intuitive reporting & visualization capabilities, then Tableau can be the ideal choice for you. Its easy-to-use platform, strong integration support, flexible deployment modes, etc. make it one of the best Snowflake Analytics Tools available today.

Pricing

Tableau provides users with three different use-case-based subscription offerings, namely Individuals, Team & Organizations, and Embedded Analytics, with the “Individuals plan” starting at $70/month per user.

For further information on Tableau and how you can use it for Snowflake Analytics, you can check the official website here.

3. Talend

Talend is a popular enterprise-grade solution that houses robust data transformation and ETL-related functionalities. It uses a code-generation approach and hence involves a build process before any deployment. It further supports analytics and allows users to create comprehensive dashboards & business reports.

Talend is available to users as both a desktop-based application and a web-based offering that users can access on the move. Talend’s robust ETL & data transformation support and reporting abilities, make it one of the best Snowflake Analytics Tools available in the market, that’s suitable for both analysts and developers.

Use Case

Talend is an ideal solution for users that have enterprise-grade requirements with a high focus on data governance and associated compliance. Talend can further be the right choice for users that are looking for Snowflake analytics tools that house robust ETL-based functionalities.

Pricing

Talend follows a quote-based pricing model and provides users with five different subscription offerings, namely Open Source, Stitch Data Loader, Pipeline Designers, Cloud Data Integration, and Data Fabric, with the Open Source subscription available free of cost. It also provides users with free trials.

For further information on Talend and how you can use it for Snowflake Analytics, you can check the official website here.

4. Sigma

Sigma is one of the most easy-to-use business intelligence platforms available in the market with an almost flat learning curve. It provides users with decent integration support and lets them connect with on-premises and some prominent cloud-based data-warehouses such as Google BigQuery, Snowflake, etc.

Sigma follows a codeless approach that generates SQL codes for your analysis workloads and lets you analyze large amounts of data by exploiting the power of your cloud data warehouse with the help of its spreadsheet-like interface.

Use Case

If you’re looking for a tool that has an easy-to-use interface and allows you to create comprehensive dashboards and business reports, then Sigma can be the ideal choice for you. It is a suitable tool for users that have a high focus on analytics and don’t mind having moderate ETL & integration support.

Pricing

Sigma doesn’t have a transparent pricing model, and hence you will have to get in touch with their team to get an exact price for your business needs depending on the data warehouse you have in place. Sigma, however, does provide users with a 14-day free trial.

For further information on what makes Sigma, one of the best Snowflake Analytics Tools and how you can use it, you can check the official website here.

5. IBM Cognos

IBM Cognos is one of the most popular web-based data intelligence tools that houses both data transformation and reporting functionalities. IBM Cognos is available to users not only as a desktop application but also as a Software-as-a-Service application that users can access on the fly. It supports robust integration with numerous cloud-based and on-premises databases, along with moderate support for few SaaS offerings such as Salesforce. It further provides enterprise-grade data security to users, ensuring that their data remains safe at all times.

Use Case

IBM Cognos is a potential candidate in case you are looking for a completely managed cloud service with enterprise-grade data security and governance features. Relatively cheaper pricing is another point in favor of Cognos Cloud, making it one of the most secure and low-cost Snowflake Analytics Tools available in the market today.

Pricing

IBM Cognos provides users with three different subscription offerings, namely Trial, On-demand, and Enterprise, with the Trail available free of cost for 30-days. The On-demand plan further starts at $15/month per user.

For further information on IBM Cognos and how you can use it for Snowflake Analytics, you can check the official website here.

6. Alteryx

Alteryx is a popular cloud-based analytics tool that supports a unique do-it-all interface that combines analytics, data science, and process automation functionalities. It allows users to collaborate with ease and lets them create impeccably informative dashboards. It supports robust integration with most on-premise databases but has moderate support for SaaS-based offerings such as Marketo, etc.

Use Case

If you’re looking for an enterprise-grade solution with an easy-to-use platform, then Alteyrx can be the ideal Snowflake Analytics Tool for you. Since it is a relatively new entrant, community support is not as great as other solutions like Tableau, Talend, etc.

Pricing

Alteryx provides users with three different subscription offerings, namely Designer, Server, and Analytics Hub, with the Designer plan available at $5195/annum per user. It also provides users with a month-long free trial.

For further information on Alteryx and how you can use it for Snowflake Analytics, you can check the official website here.

Why Snowflake Is Great for Analytics?

• Large data volumes: Snowflake is highly scalable and reliable because it uses AWS, Azure, and Google for storage. Data from Amazon S3, Azure Blobs, or Google Cloud Storage can be directly plugged into your Snowflake instance. Furthermore, large datasets are loaded extremely quickly.
• Data loading flexibility: Depending on your data source, Snowflake offers a variety of options for loading data into the system:
  • Applications for the web
  • Storage in the cloud
  • Databases and files stored locally
  • Snowpipe and third-party tool support are two features that stand out in terms of data loading flexibility.
  • Snowpipe is Snowflake’s data ingestion service that runs in the background. Snowpipe is triggered when new data is generated. It imports the new data into Snowflake, giving you the most up-to-date information possible.
• Supports multiple analysis-ready data models: Despite the platform’s name, Snowflake supports a variety of data modeling approaches. (The founders of the company enjoy skiing, and snowflakes are created in the cloud.) Tableau, Power BI, and Cognos are examples of modern BI tools that work well with star and snowflake data models. On top of Snowflake, you can create analysis-ready views modeled after stars and snowflakes, which you can then secure with users and roles.
• Minimized administration: Snowflake makes data loading, sharing, and querying a breeze. Unlike traditional databases, there is no need to manage upgrades, partitioning, indexes, backups, or a variety of other tasks with the Snowflake service.
• Performance scalability: In Snowflake, you can have a variety of warehouses of various sizes. Cost is linked to compute performance and usage. You can start and stop the warehouses as needed to keep costs under control. A built-in auto-suspend feature helps to keep costs down right away.
• Semi-structured data: Snowflake, like many other modern data lakes, allows semi-structured data to be imported into database tables. Data with constantly changing or inconsistent schemas are good examples of where this capability can be put to use. Snowflake allows you to pull this data into a VARIANT column and then use SQL level extensions to query that data into structured views in a clean and efficient manner. Support for unstructured data is on the way as well.
• Cloning: Snowflake’s “zero-copy” data storage technology enables the company to quickly copy large databases. Snowflake creates a pointer back to the source data when it makes a copy, capturing only the changes in the newly copied database rather than the entire database. This cloning method is quick and inexpensive, and it has a lot of advantages in DevOps situations.
• Time travel: You can go back in time to troubleshoot data loading and recover accidentally dropped tables or data. It’s as if your database had a back button! Up to 90 days of historical data changes can be saved. Because the length of time you store data affects your storage costs, you may not want to keep data for as long if you have tables that change frequently.
• Data sharing and the Snowflake Data Marketplace: Snowflake’s data sharing does not require a lot of extra storage space or money. It also opens up two interesting applications:
  • The ability to import curated data sets from the Snowflake Data Marketplace is the first. Many organizations, including non-profits and governments, publish extensive databases of data on the Data Marketplace, which you can connect to your Snowflake environment. You can quickly combine your data with datasets like weather, census, and similar with a quick search and a few clicks. You’ll see those updates replicated in your linked copy of the data as those databases of information are updated.
  • The ability to securely share data with your partners is the second feature. You can share a database with a partner or pull a partner’s shared database into your environment, just like you can with databases published on the Snowflake Data Marketplace. Your linked copy of the data stays up to date as those databases are updated. A good example of this capability is in the supply chain, where buyers and sellers can see inventory levels across the entire supply chain.

Conclusion

This article introduced you to some of the best Snowflake Analytics Tools available in the market that you can use to perform and refine your Snowflake Analytics. It also provides in-depth knowledge about their features, use cases, and pricing.

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Share your experience of working with Snowflake Analytics Tools! Let us know in the comments section below.