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.

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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?

Data analytics refers to the process of analyzing, cleaning, transforming, and modeling data with the view of extracting useful information and drawing meaningful conclusions that could help you make decisions. It may be defined as a process using statistical, computational, and machine-learning techniques to extract knowledge and insights from data to help you make an informed decision for your business.

Key Aspects of Data Analytics

Let us deep dive into some of the key aspects of Data Analytics.

• Data Collection: It is a process of collecting the raw data from different sources such as databases, surveys, or online behavior.
• Data Cleaning: The process of removing or correcting inaccurate, incomplete, or irrelevant data to ensure quality and consistency.
• Data Transformation: The process of transforming the data into a format or structure which would be suitable for analysis; for example, aggregation, normalization, or encoding of data.
• Data Modeling: Having a proper statistical or machine learning model fitted to understand the trends and relationships in data.
• Data Visualization: Charts, graphs, and dashboards are used to represent data insights, making the findings more comprehensible and actionable.

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.

Key Features

• Business Reporting: You can create customized and interactive reports to monitor key metrics and track business performance across departments.
• Data Exploration: You can explore and analyze data without requiring any technical expertise, thus allowing for ad-hoc querying and discovery.
• Financial Analysis: It can keep track of your financial metrics, budgets and then forecast financial performance with reports. 

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. 

Pros

• It allows you to share information and collaborate with other team members.
• It can be used to handle large volumes of data.

Cons

• Looker can be relatively expensive, making it less accessible for small-sized businesses.
• You need to have a solid understanding of advanced SQL features, making it complex for non-technical users.

2. Tableau

Researchers describe Tableau as a “highly interactive and intuitive visual-based exploration experience for business users to easily access, prepare, and analyze their data without the need for coding.” Tableau is a data visualization and Business Intelligence tool that is used for business applications like data modeling, creating live dashboards, and assembling data reports to empower business teams to make data-driven decisions. 

Key Features

• Interactive Dashboard: You can design interactive and dynamic dashboards with a range of data visualization options.
• Data Connectivity: It can connect to numerous data sources.
• Drag and Drop Interface: It provides a user-friendly interface for building visualizations without coding.

Pricing

Tableau’s pricing plans are mentioned below:

• Tableau Public
• Tableau Creator
• Tableau Explorer
• Tableau Viewer
• Tableau Server/Online

Pros

• It has an intuitive drag-and-drop interface that makes it accessible to users of all skill levels.
• It supports real-time data analysis and updates for timely insights.

Cons

• It may experience performance issues with very large datasets.
• While basic use is straightforward, advanced features and customizations can be complex and require additional training.

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. It has now been acquired by Qlik.

Key Features

• ETL and ELT capabilities: It facilitates both ETL and ELT methods of processing; that is, extract-transform-load and extract-load-transform. This gives flexibility to the ways in which data might be processed and integrated.
• Data Governance: It contains end-to-end data governance tools that provide the functionality of data lineage, metadata management, and data stewardship to ensure compliance and integrity of data.
• Cloud and On-Premises Deployments: Supports cloud-based and on-premise deployments, providing flexibility to different IT environments and strategies.

Pricing

It offers 4 pricing plans that are

• Starter 
• Standard
• Premium 
• Enterprise

To read more about the price, you can visit Talend’s(Qlik) pricing page.

Pros

• It has flexibility in its wide coverage of sources and targets of data.
• It provides a user-friendly interface with easy setup and configuration tools.
• It includes features of monitoring and management that track the performance of data replication.

Cons

• It requires training and expertise in its more advanced features to optimize performance.
• It may involve frequent updating and maintenance to be at par with the best performance and compatibility.

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.

Key Features

• Spreadsheet-like Interface: Sigma has an intuitive, user-friendly interface that simplifies data exploration and analysis, making it easily accessible for uses.
• Live Data Connection: It allows you to work with refreshed data by giving you direct, live access to cloud data warehouses like Snowflake, BigQuery, and Redshift without the need for extraction or replication.
• Custom Visualizations: You can develop your own custom visualizations and dashboards relevant to the business and tailor them according to business needs and individual preferences.

Pricing

Sigma Computing offers customized pricing based on the specific needs and scale of an organization.

Pros

• No coding is required to create complex data analyses and visualizations.
• Allows multiple users to generate reports and dashboards simultaneously, thereby improving productivity and communication when facilitating a teamwork environment.

Cons

• Since this is a cloud-based tool, internet access is needed for it to work to its full potential; offline functionality is therefore limited.
• Some of its advanced analytics features are less robust as compared to other BI tools.

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. 

Key Features

• Master Data Management (MDM): It provides centralized management for master data, providing a ‘single version of truth’ for each key business entity.
• Governance of Data: It has in place policies and mechanisms of control over access management, security, and compliance requirements for data.
• Management of MetaData: It provides data asset cataloging and data lineage tracking to know the origin and relations among the data.

Pricing

To know about its pricing plans, you can directly visit the IBM Cognos pricing.

Pros

• It supports a very wide range of functionalities, thus being a robust, all-in-one solution to a wide range of different BI needs.
• It provides rich analytics capabilities in predictive modeling, statistical analyses, and advanced data visualizations, etc.

Cons

• Configuration of IBM Cognos can be complex and time-consuming.
• It can be difficult to integrate with non-IBM systems or legacy data sources.

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.

Key Features

• Data Preparation and Blending: It provides a strong toolkit to clean, transform, and combine data from sources like databases, cloud services, and spreadsheets.
• Geospatial Analytics: It has inbuilt geospatial data analysis tools, where one can work with location-based data to have geographic visualizations.
• Scalability: It is designed to work with large data sets and complex workflows, it is thus appropriate for both small projects and large enterprise applications.

Pricing

You can get a free trial. For more details about the pricing, you can visit their official website.

Pros

• It streamlines and automates all data preparation and processing tasks, saving hours of precious time and reducing the high risk of manual errors.
• It provides easy integrations with all kinds of data sources and business intelligence tools.

Cons

• It can become relatively expensive due to subscription fees.
• It can face performance issues with larger datasets.

Why Snowflake Is Great for Analytics?

• Scalable Performance: Snowflake uses cloud storage from AWS, Azure, and Google to give high scalability and reliability. It offers high-speed data loading and handling of huge datasets efficiently.
• Flexible Data Loading: It provides various data loading options, including web applications, cloud storage, and local files. It features Snowpipe for real-time data ingestion, thus allowing third-party tools to integrate for the same.
• Advanced Data Modeling and Sharing: It supports multiple data models, such as star or snowflake schemas, and easily allows the sharing of data with Snowflake Data Marketplace and secure data sharing with partners.
• Cost-Effective Management: Eliminate administrative tasks like upgrades and backups; it has an auto-suspend feature for cost control and zero-copy cloning, and time-travel features for efficient and effortless data management and recovery.

Conclusion

In this blog, we have talked about some of the best Snowflake Analytics Tools, including their description, use cases, and pricing. These tools are specifically conceptualized to provide advanced features for enhancing your Snowflake analytics experience, including advanced data visualization, real-time analytics, seamless integration, and flexible data management. 

Knowing the strengths, more so the functionality of each tool, will help in making the right choice that aligns with your organization’s analytics needs and goals. Smoothening data workflows, gaining deeper insights, or driving strategic decisions, the right Snowflake analytics tool can help lift your data capabilities—hence, overall business performance.

FAQ about Snowflake Data Analytics