The Ultimate Guide on Snowflake Data Transformation: 3 Easy Steps

on Data Transformation, Data Warehouses, Snowflake • June 3rd, 2022 • Write for Hevo

Snowflake Data Transformation FI

As new technologies are released, the field of Data Modeling continues to evolve. Data Warehousing came first, followed by MPP Data Warehouses, Hadoop, and Data Lakes. You are now living in the era of Cloud Data Warehouses.

The rise in popularity of Cloud Data Warehouses like Snowflake has changed our perceptions about Data Transformation and Modeling. The new ELT process extracts and loads raw source data into Snowflake, which is subsequently transformed into the final form for your analytics.

In this article, you will learn about the Snowflake Data Transformation process and how to optimize the Data Transformation well.

Table Of Contents

What is Snowflake?

Snowflake Data Transformation: snowflake logo
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Snowflake is a cloud-based Data Warehousing platform that allows enterprises to store data in a scalable and flexible manner. It’s ideal for storing information that can be searched and retrieved later by a business intelligence system. Despite being wholly built and hosted in the cloud, it integrates seamlessly with both cloud and on-premise BI solutions.

A subscription-based technique can be used to purchase storage and computational resources separately. It also has elastic storage, which uses both hot and cold storage strategies to save money, as well as scalable computing, which avoids the concurrent constraints that other Data Warehousing systems have.

Snowflake’s design is unusual in that it combines computing and storage natively. Your users and data workloads can virtually access a single copy of your data while retaining performance with this architecture. Snowflake will enable you to deploy your data solution across several locations and clouds while maintaining a consistent user experience. By abstracting the underlying difficulties of Cloud infrastructures, Snowflake makes it possible.

Key Features of Snowflake

Snowflake Data Transformation: Architecture
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The following are the main aspects of Snowflake:

  • Better Decision Making: Snowflake enables you to reduce data silos and provide access to important insights across your organization, allowing you to make better decisions. This is a vital first step toward bettering partner relationships, pricing optimization, lowering operational costs, and increasing sales effectiveness, among other things.
  • Improved User Experience: You can have a deeper understanding of user behavior and product usage with Snowflake. Data can also be used to improve customer satisfaction, expand product options, and encourage data science innovation.
  • Secure Data Lake: A secure data lake can serve as a central repository for all compliance and cybersecurity information. Snowflake Data Lakes allow for quick response to incidents. By combining vast volumes of log data in a single area and evaluating years of log data in seconds, you can get a complete picture of an occurrence. In a single data lake, semi-structured logs and structured corporate data can now be merged. Snowflake lets you get your foot in the door without indexing and then edit and alter data once it’s there.
  • Better Analytics: Snowflake allows you to improve your analytics pipeline by moving from nightly batch loads to real-time data streams. Allowing secure, concurrent, and managed access to your Data Warehouse across the enterprise can increase the quality of analytics in your company. This allows businesses to optimize resource allocation in order to maximize revenue while lowering costs and decreasing human labor.

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What is Data Transformation?

Snowflake Data Transformation: data transformation
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The process of changing data from one format to another, such as a database file, XML document, or Excel spreadsheet, is known as Data Transformation.

The conversion of a raw data source into a cleansed, validated, and ready-to-use format is the most common transformation. Data Integration, Data Migration, Data Warehousing, and Data Preparation are all Data Management procedures that need Data Transformation.

Extract/transform/load is another name for the Data Transformation process (ETL). The extraction step entails locating and extracting data from numerous data-generating systems, as well as transporting the data to a single repository. The raw data is then cleaned if necessary. It’s then converted into a target format for use in business intelligence and analytics applications, such as a Data Warehouse, a Data Lake, or another repository. Converting data types, deleting duplicate data, and improving the source data are all possible transformations.

Processes such as Data Integration, Data Management, Data Transfer, Data Warehousing, and Data Wrangling all require Snowflake Data Transformation.

It’s also a must-have for any company looking to use its data to develop actionable business insights. Organizations must have an efficient means to harness data in order to efficiently put it to business use as the number of data has grown. One aspect of leveraging this data is Data Transformation, which, when done correctly, guarantees that data is easy to access, consistent, safe, and ultimately trusted by the intended business users.

Snowflake Data Transformation Process

The rise in popularity of Cloud Data Warehouses like Snowflake has changed our perceptions about data transformation and modeling. The new ELT process extracts and loads raw source data into Snowflake, which is subsequently transformed into the final form for your analytics.

This allows businesses to take advantage of Snowflake’s low-cost, scalable compute and storage capabilities while also increasing agility by separating the Data Loading and Snowflake Data Transformation processes and workloads, with Data Engineers handling the former and Data Analysts handling the latter. Organizations can employ modern organizing approaches like Snowflake Virtual Data Warehouses to construct a variety of subject-specific analytical data models that are optimized for their unique needs.

Snowflake Data Transformation Process: Getting Data into CDW

The first step in Snowflake Data Transformation is getting the data into CDW (Cloud Data Warehouse). Data originates from a wide range of sources in today’s data world. SaaS apps and cloud services are the fastest-growing sources of data for analytics. The data structures and APIs for these sources are highly complicated. 

As a result, the first Data Model your team will employ is a set of tables in the Cloud Data Warehouse that look like objects from your Data Sources, are categorized similarly, and have the same fields. However, because the data is still in a format similar to that of a SaaS application or cloud service object, it may be difficult to decipher for a Data Analyst.

There is one extremely crucial Snowflake Data Transformation step that must be completed. Any data that is personal or sensitive should be anonymized or concealed. This is necessary to protect data privacy and ensure regulatory compliance.

After the raw data has been loaded, the data engineering team can begin the Data Purification process. Data Engineers could use this first phase to (a) discover and fix missing or inaccurate values, (b) modify poorly structured fields, and (c) extract particular fields from complicated, multi-faceted columns using generic, standardized cleanses.

Snowflake Data Transformation Process: Canonical Data Modeling

The next step in Snowflake Data Transformation is Canonical Data modeling. The Data Engineering team can transform raw data into Canonical Data Models that represent certain subjects once the data is in the CDW and has gone through the first pass of Data Transformation. Data Models representing consumers, contacts, leads, prospects, activities, and more are examples of this.

The basic goal of Canonical Data Models is to establish shared, reusable components that may be used in a variety of scenarios. Additional advantages occur as a result of this:

  • Creating a single version of the truth for each subject and field within that subject, as well as providing shared and consistent definitions and documentation for each subject’s data.
  • To develop trust in the analytics community, transparency into Data Models and how they are built is required.
  • To construct the Canonical Data Models, the Data Engineering team will collect needs from multiple business and analytics teams. Typically, these Data Models will be supersets of the criteria for maximum reuse and consumption. As new requirements or Data Sources become available, the Data Models will continue to evolve.

The Canonical Data Models will often blend (JOIN, UNION, etc.) data from several objects to build a rich and full set of fields to represent the subject because the raw data from the data sources is often normalized (in some cases lightly normalized and others strongly normalized). Furthermore, the Canonical Data Models may include some Data Enrichment in order to calculate new fields for standardized use in various use cases.

Snowflake Data Transformation Process: Use Case Data Modeling

The final phase in the Snowflake Data Transformation is to develop datasets tailored to the analytics use case. This is often done by the Data Analyst in Snowflake for current data modeling because it all comes down to duties and abilities:

  • Data Engineers are more concerned with the data itself. Where it is stored, how it is structured and formatted, and how to obtain it rather than how the business uses the data. As a result, they’re well-suited to importing data into Snowflake and performing first-pass data modeling.
  • Data Analysts are less familiar with raw data but are well-versed in how the business will use it and how it will be incorporated into analytics. As a result, use case Data Modeling and transformation is their optimal job.

Data Analysts may have a wide range of technical abilities, but typically prefer to focus on what they do best – analysis – rather than coding data transformation. This is where a low-code or no-code data transformation user interface comes in handy, as it eliminates the need for analysts to write sophisticated SQL code or Python-like programs.

Modeling and Snowflake Data Transformation of Use Case Data will often entail:

  • Data cleansing that is particular to the use case, such as detecting and correcting outliers or deduping records
  • Data shaping and reduction, such as sorting and structuring the data, removing unnecessary fields, or limiting the scope of the data to specified time periods or dimensions, are all examples of data shaping and reduction.
  • Adding new computed fields related to the analysis or uploading local files specific to the use case, such as external or department-specific data, are examples of data enrichment.

In the Data Model, the final form will be a single flattened data structure – a very large, wide table. This, together with materialization, reduces the requirement for time-consuming JOINs every time a query is run for the analysis.

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Optimizing Snowflake Data Transformation 

Three factors come into play when it comes to optimizing Snowflake Data Transformation:

  • Using virtual data warehouses to their full potential
  • What method do you use to run your data transformation queries?
  • Using a variety of data transformation techniques

Virtual Data Warehouses

Snowflake Data Transformation: virtual data warehouse
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In Snowflake Data Transformation, virtual data warehouses are a common way to scale out your data and analytics. Virtual Data Warehouse instances can also assist each analytics project’s workload to be optimized. Each use case should have its own Virtual Data Warehouse, according to Snowflake.

Users should also split “loading” and “execution” into different virtual Data Warehouses, with loading being an area where external data is imported and execution being areas for user query and reporting, according to Snowflake. Because of the differing demands for data loading and user queries, this separation is advised.

Snowflake Data Transformation queries have a different workload than other queries. Furthermore, the way you deploy your Snowflake Data Transformation queries has a significant impact on the workload. As a general guideline, segregate your Snowflake Data Transformation views/queries from your raw data in a different database. This gives you the freedom to employ several virtual data warehouses – one for your EL and another for your T – and allows you to tailor those virtual data warehouses to the computational requirements of each while lowering expenses.

Data Transformation Execution

Snowflake Data Transformation is greatly influenced by how your data transformation queries are run. In general, the choice is whether to materialize the data transformation views or run them “at query-time.”

Materialized views are another technique to boost query speed in workloads with a lot of the same queries. And Snowflake Data Transformation models are often created with this goal in mind – to build a dataset that can support a common, repeatable query pattern.

A typical recommended practice shared by Snowflake is to employ materialized views to increase query performance for frequent and repetitive queries. Storage is always less expensive than computing. Materialized views will increase your storage expenses slightly, but storage costs are so low that you’ll save far more in computing costs and query response time. As a result, materialize your models if you want them to enable repetitive query patterns.

The Snowflake Data Transformation views could be stored in the use case database, for example. When (a) the views are unique to the use case and (b) the views have unique workload or computation requirements, this solution works effectively. Another alternative is to keep all of your Snowflake Data Transformation views in a single database. When you have Snowflake Data Transformation views that are shared across multiple use cases and have similar workload characteristics, this solution works effectively.

Data Transformation Techniques

For improving your Snowflake Data Transformation views/queries, you can use a variety of strategies. These will have an effect on the performance. They can be summed up in four points:

  • Wherever possible, keep things simple. Combine numerous requests into one if possible. It’s usually preferable to keep things simple.
  • JOINs should be materialized. In Cloud Data Warehouses, JOINS are relatively expensive procedures. Materialize a Data Transformation view that JOINS numerous tables.
  • Remove any data that you don’t require. Use projection in your Snowflake Data Transformation models to get rid of fields you don’t need, which saves you money on query time.
  • Materialized pre-aggregation should be used. Create materialized aggregated views if there are frequently used aggregation values and dimensions.

Conclusion

The utilization of Cloud Data Warehouses and ELT procedures in current data stacks has necessitated the necessity for modernized Data Modeling inside the Data Stack. A highly modular approach to Data Modeling and Transformation, as well as a highly collaborative process between Data Engineering and Analytics teams where each may best leverage their abilities and knowledge, are both necessary.

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