Snowflake Data Analytics Guide: From Insight to Action

In this article, we will define data analytics, Snowflake’s data analytics capabilities and share our top 7 recommended tools for data analysis within Snowflake.

Although data analysis has been practiced for many years, the recent boom in data has hastened its ascent to popularity.

Snowflake is a perfectly designed platform for data scientists and data analysts to inter-operate, analyze, and identify patterns in the data.

 (Fret not…in layman’s terms, what I just said translates to “Snowflake enables data analytics”).

As a SELECT Snowflake partner, we know all too well the benefits of Snowflake to our customer base.

What does Snowflake have to do with my analytics?

 

“The data economy is the economy now. Any business that
is not participating in it will become legacy before too long.”

— CINDI HOWSON, Chief Data Strategy Officer, Thoughtspot

The data economy represents the global supply and demand for live data, applications, and services. 

Organizations use technologies to share and access data locally and globally, whether on cloud data platforms, data exchanges spaces, marketplaces, etc.

Snowflake, the winner of the 2019 edition of Forbes Cloud 100, the world’s best cloud companies, has made a stamp in today’s data economy.

Snowflake is a data cloud platform delivered as a Saas solution.

It doesn’t require installation, configuration, or management of hardware or software of in-house servers and acts as a database and data warehouse, making it the first choice of any ideal organization.

This cloud computing platform is distinguished from all the other available tools due to its all-in-one solutions.

Before starting the data analytics guide, we first need to understand the meaning of data analysis and the tools required for interacting with data.

What do you mean by Data Analytics?

In a general sense, it is a process of exploring and analyzing large datasets to uncover unknown patterns and trends present in the data by correlating the strings of different hypotheses.

The field of data analytics groups a wide variety of data-related professions. Still, the main objective is always characterized by the eagerness to find solutions and identify KPIs in response to a business-specific set of questions.

Next up on the ladder is data science. Data Science which is now mainstream is closely related to data analysis.

In a 2019 TDWI Best Practices Report titled “Driving Digital Transformation Using AI and Machine Learning,” 92 percent of survey respondents reported using ML technology, and 85 percent said they are building predictive models using ML tools.

Data science includes tools and techniques for analyzing large amounts of data and making predictions using machine learning technology.

Data Analytics on Snowflake Platform:

Here are some important aspects of Data Analytics in Snowflake:

1. Data discovery in Snowflake: 

Before proceeding to activities such as BI and predictive analysis, data discovery is a mandatory prerequisite.

During this phase, the data analyst must liaise with the business users to understand the relevant metrics. Subsequently, the data analyst implements some ETL processes to consolidate the data from disparate sources.

This ETL approach is a time-consuming process and usually affects the time to market.

Snowflake’s ELT approach performs the transformations in Snowflake with simultaneous loading, transformation, speed & efficiency.

Data analysts can quickly go from data acquisition to exploratory data analysis and insights within minutes.

2. Data Storage and Monetization In Snowflake:

If your company has a large amount of data available for analysis, then Snowflake is the king cloud platform your company has always wanted. Snowflake offers a data warehouse for storing large datasets and has its Data Marketplace in case you need more data for data analysis processing.

3. Deployment of Data in Snowflake:

Let’s revisit the data science topic.

As data scientists or analysts, we know that developing robust ML models are compute-intensive and require high-performance systems.

With Snowflake’s scalable architecture and its tight integration with ML tools like Spark, R, Qubole, and Python, Snowflake is an essential Data Science technology.

4. Monitoring, Automation, and Versioning in Snowflake:

Snowflake runs on a “pay-only-for-what-you-use” type of policy.

With resource monitors, you can now track your compute. This can help you plan your spending and budget appropriately.

Snowflake has objects like streams and tasks for running procedures and automating recurring tasks. By scheduling tasks that use UDFs with Snowflake, you can keep an eye out for common ML problems like data drift.

Snowflake also comes with a “Time travel” feature that can store data for a limited period, avoiding headaches for early prototyping projects.

Irrespective of your data persona, whether you’re a BI developer, data engineer, or SQL analyst, you can utilize Snowflake platform to improve your processes.

Recommended Tools required for Data Analysis on Snowflake:

• Alteryx
• Talend
• Informatica
• Datameer
• Matillion
• Tableau
• Metabase

For an in-detail understanding of the use and importance of these tools, try to check out the article, Top 5 Snowflake tools for Analysts and Top 5 Snowflake reporting tools in 2022.

Datameer + Snowflake, The Perfect Combo

Success is contagious. The results from one analytic initiative may spawn an interest in several more. 

That’s why by leveraging Snowflake technology, we decided to come up with Datameer.

Datameer is a saas solution that provides a modern analytics interface and semantic layer for your Snowflake data.

With Datameer, you can efficiently perform ad-hoc analysis and modeling of your Snowflake datasets in minutes. 

The good thing is, your data never leaves Snowflake 😊.

So if you’re a business ( regardless of your size) looking to optimize your data analytics process and stay competitive, you should check out Datameer.