Foster Data-Driven Decisions Through Snowflake Data Analytics

Data analytics helps to derive valuable insights from your raw data. It helps you align your business processes for better outcomes by identifying trends and patterns in the data that would otherwise be lost. 

As your business accumulates large amounts of data, the challenge lies in implementing an efficient data analytics process that can help you scale your data. Snowflake data analytics is a robust way to derive business insights and process better outcomes. 

By leveraging Snowflake’s functionalities, you can analyze your business data in-depth. The architecture allows independent storage and computing resources for faster data processing and querying. It helps you make data-driven decisions and enhance overall business performance.

In this article, you will explore data analytics Snowflake, how to implement it, and its benefits.

What is Snowflake Data Analytics?

Using Snowflake for data analytics tasks allows you to manage, process, and analyze your business data within a unified environment. It is suitable for all business sizes and helps you scale your data based on your needs. 

Snowflake natively supports various programming languages and data structures, such as Python, Java, SQL, JSON, Avro, and Parquet, allowing you to work with diverse data types. It provides scalability, flexibility, integration, and secure data-sharing features that help you enhance your data analytics process and increase overall performance efficiency.

What is the Role of Snowflake Data Cloud in Analytics?

Cloud data analytics in Snowflake is a robust way to manage your data. It helps you create an ecosystem to share data and services for better performance. Here is how you can use Snowflake Data Cloud to optimize the process of data analytics.

• Ease of Access: Snowflake provides a centralized repository where all the relevant data is stored and available. You can quickly retrieve data without navigating through multiple sources, enabling them to collect data easily for analysis. 
• Advanced SQL support: The data cloud in Snowflake supports advanced SQL capabilities such as joining, having a function within another function, group queries, and more. These built-in functions allow you to perform complex queries on your dataset, making it a powerful tool for implementing data analytics.
• Data Sharing: Snowflake’s data-sharing capabilities allow you to access live data across your organization. It enables data synchronization, which is useful when multiple teams access the data simultaneously for collaborative analysis. 
• Columnar Storage: Snowflake’s columnar storage organizes data in columns, minimizing the data read from the disk when performing a query. It improves query performance when performing analytical operations such as retrieving relevant rows when calculating sales for a particular region.

Snowflake Data Analytics Tutorial 

Snowflake’s data architecture makes handling heavy workloads easy. In this tutorial, you will learn how to set up Snowflake for data analytics. 

Step 1: Create a Snowflake Account

You need to create a Snowflake account by signing up on their website, with a free trial of 30 days. It will also ask you to choose between the different editions it provides and the region. Once you have registered, Snowflake will email you a URL. Go to the link provided to log in to your  Snowflake account. Images are from projectpro.io.

Step 2: Create a New Worksheet

Once logged in, you will see icons in the top left corner, such as Database, Data Marketplace, Warehouse, Worksheet, and more. You can create a new worksheet for computing data or select from the existing ones.

Step 3: Create a Database 

Create a database by executing the following command in your Snowflake worksheet.

Create database database_name;

Step 4: Create a Data Warehouse

Create a data warehouse to perform different operations, such as data transformation, loading, and analytics. 

• Go to the side menu and click on warehouses. There, you will find the option to create a new warehouse or choose from the ones listed. 
• Once the data warehouse is chosen/created, you must set its size and the policies you want to apply.

Step 5: Create Schema and Table in Snowflake

You need to create a schema and tables to load the data in Snowflake.

• Creating Schema

 To create a schema, you can use the following command: 

create or replace schema mySchema;

• Create table

You need to create a table to hold and query the incoming data. You can use the following command to create a table: 

create or replace table table_name (column1_name(datatype), column2_name(datatype));

Snowflake Data Analytics: Create Table

Step 6: Loading Data in Snowflake

To load data into Snowflake, you can use the traditional UI interface. 

• Go to the Database option and click on the database you created. 
• From the list of tables, select the table from which you want to load the data. 
• Then click on the Load option. It will require you to specify the following options: 

• The data warehouse you want to use.
• Location from where you load the data. 
• File format of the data. 
• An Action plan in case an error occurs.
• Once you specify all the above options, click on the load button. 

Using data analytics in Snowflake, you can perform various queries to process and transform the ingested data. You can utilize the Snowflake analytics data to generate reports using different functionalities, including fail-safe, staging, and query profile. It can also be used to optimize data analytics operations such as point-in-time recovery for data continuity and accuracy.

How Can We Use Snowflake for Customer-Facing Analytics  

Customer-facing analytics is a process of collecting and analyzing data from various sources, such as transactional systems, customer service, online activity, websites, and more. Understanding their behavior and purchase patterns allows you to interact better with customers. By analyzing this information, you can increase user engagement and satisfaction. 

Snowflake offers a robust platform for implementing customer-facing analytics by providing essential tools for processing and analyzing customer data. Here is how you can use Snowflake for customer-facing analytics:

• Real-Time Data Processing: Snowflake allows real-time data ingestion, querying, integration, and processing. It can improve the user experience by analyzing social media feeds for current trends and responding promptly to feedback.
• Data Integration: This cloud platform seamlessly integrates data from multiple sources and helps you consolidate customer data into a centralized warehouse. It can be used to analyze the time customers spend on various channels and generate a personalized campaign for them. 
• Scalability: To conduct customer-facing analytics, you need a platform that can handle growing volumes of data without compromising the data model’s performance. With Snowflake, you can scale your data and perform efficient data analysis even at peak workloads.

How Hevo Helps to Streamline Data Analytics in Snowflake Through Seamless Integration

Hevo is a real-time ELT platform that helps you streamline the data analytics process through its no-code, flexible, and automated data pipelines. With near real-time data syncing, you can ensure that analytics data to Snowflake is updated and available promptly for performing analysis. 

Here are some of the critical features of Hevo that help you streamline data analytics in Snowflake:

• Data Transformation: Hevo offers two types of data transformation features that help you clean, enrich, and transform your source data before ingesting it into Snowflake. It eliminates the need to prepare data for analysis, saving time manually. 
• Automated Schema Mapping: You have to collect diverse data from various sources, making it complex and difficult to query. Hevo’s schema mapping feature automatically maps the source’s data schema to Snowflake, ensuring its compatibility with the destination. This helps in faster querying while performing data analysis. 
• Incremental Data Loading: This feature of Hevo assists you in loading the modified or updated data in the destination. It captures the changed data and helps you maintain up-to-date databases, eliminating any chance of data duplication and overwriting.

How Does Snowflake Data Analytics Benefit Different Individuals?

Snowflake data analytics provide various benefits to different individuals within the organization. Some professionals use Snowflake analytics data in their work. Here is how different data science professionals can use the versatile functions of Snowflake to derive value from data: 

• Data Analyst: Data analysts can use Snowflake to perform sophisticated data analytics processes, run complex queries to get quick results, and generate actionable insights.
• Data Engineer: Snowflake’s scalable structure simplifies ELT processes. Data engineers can automate workflows and handle real-time data streams to obtain the latest data and conduct accurate analysis.
• Business Decision Makers: Your organization’s key stakeholders can utilize Snowflake’s dashboards and reports to study results. This helps them get a clear and concise view of business performance.
• Data Scientist: Snowflake’s Snowpark API offers different functions and libraries that allow data scientists to query and process data at any scale. They can use different languages, including Python, R, SQL, and Scala, to perform advanced analytics. 

Conclusion 

Snowflake is an indispensable tool that helps organizations perform advanced analytics through integration, its flexible architecture and storage, and real-time data processing. The platform also facilitates columnar storage and data compression, allowing you to perform faster queries on larger datasets.

To simplify data integration within Snowflake, you can use Hevo. It is a real-time data integration tool that automates data transformation, ensuring high data quality, which is beneficial when performing data analysis. Streamlining data analytics through Snowflake allows you to make better decisions and derive business growth.

FAQs 

Q1. Does Snowflake use indexing for performance tuning? 

Snowflake does not use indexes for performance tuning. It calculates the stats of columns and records in the files you load to determine which part to load to execute a query.

Q2. Why does a data analyst prefer Snowflake to perform data analytics over other Saas platforms? 

Here are some of the reasons why a data analyst prefers Snowflake to perform data analytics over other Saas platforms: 

• Snowflakes can scale the data up and down in seconds. 
• It supports extensions such as JDBC, ODBC, dbAP, and ELT tools for data integration. 
• The documentation of Snowflake is laid down better, making it easy to understand its functions and workings. 
• The platform assists you in handling concurrent queries with ease. 
• Snowflake offers you real-time data synchronization that enhances your data analytics process.