StreamSets Transformer for Snowflake: Eliminate the Mundane, Simplify the Complex

In today’s digital landscape, data has emerged as a game-changing factor. It plays a pivotal role in shaping an organization’s strategic goals, from understanding customer behavior and personalizing their experiences to streamlining operations and building innovative products. To leverage this wealth of data, businesses require a robust platform that can manage and analyze this vast resource, which is where the Snowflake Data Cloud comes into play. 

Snowflake Data Cloud, known for its scalability, performance, and near-infinite computing power, offers businesses the capacity to harness their data from diverse sources and in multiple formats. However, raw data, in its untouched form, often harbors inconsistencies, making it unsuitable for meaningful analysis. These inconsistencies can range from data type mismatches, spelling errors, missing entries to more complex issues like semantic inconsistencies. 

This is where StreamSets Transformer for Snowflake enters the picture. This tool not only cleans and standardizes data, but also assists with restructuring and enrichment, while enabling business teams to sift through their data and convert it into actionable information. Whether aligning data into a uniform format, filling in missing values, or correlating separate data sets, data transformation is the cornerstone of any successful data-driven strategy.  

Product Overview

Figure A. StreamSets Transformer for Snowflake transforms, joins, and cleanses data in Snowflake from raw staging tables to a final conformed state so it can be used for reporting, analytics and data science initiatives. 

StreamSets Transformer for Snowflake allows data practitioners and line-of-business teams to transform their raw data into a usable format, directly within the Snowflake ecosystem. By utilizing the transformative capabilities of StreamSets, businesses can not only extract accurate and insightful data but also scale data access across a wider audience. This can help unlock the full potential of their data, empowering organizations to make informed decisions. 

Here’s an example of how it can help, from the perspective of a user on a digital marketing team: 

As a digital marketing manager in my organization, StreamSets Transformer for Snowflake has significantly streamlined my workflows. We use Snowflake to house our data, and it’s not just my department relying on it; Sales, Finance, HR, and Marketing (to name a few) all lean heavily on the insights we glean from our data. In this particular case, I need to analyze campaign performance by comparing landing page conversion rates with quarterly sales metrics. However, both data points come from separate tables and require some level of transformation before I can make use of them. Given my role as a digital marketer, I don’t have deep SQL knowledge, and with the pace of my work, I often can’t afford to wait for extended periods of time before gaining access to the necessary insights. Transformer for Snowflake has become my go-to tool in these scenarios. It allows me to independently pull data from different tables, and even perform functions like unions and joins on my own without writing a single line of code. I no longer have to wait on the data engineering team for everything. Instead, I can quickly access and utilize the data I need, empowering me to drive our business initiatives with confidence and speed. 

How It Works

StreamSets Transformer for Snowflake operates as a vital component for data transformation, making even complex SQL functions manageable for users without extensive technical knowledge. It can be purchased as a standalone data transformation tool and its intuitive interface enables users to access and manipulate data housed within Snowflake, allowing for complex transformation functions that include unions, pivots, and slowly changing dimensions (SCDs) to be performed directly within the platform.  

Pivot functions, often required when converting row-based data into a column-based format, are simplified with this tool. Same with unions- combining rows from two or more tables based on related columns, are also easy to perform, and both functions can be executed without having to write any code. The support of slowly changing dimensions allows users to easily manage and track changes in data over time, which is crucial for maintaining historical data accuracy. The tool’s visual interface and drag-and-drop capability handles what are traditionally complex SQL tasks in a user-friendly way, enabling users to harness their data to its full potential without the need for prior technical experience.

Figure B. Drag-and-drop design canvas for transformations 

Benefits and Use Cases

StreamSets Transformer for Snowflake offers various benefits that foster a new era of data empowerment for businesses. It democratizes data access by providing self-service capabilities to all business users, regardless of their technical background or SQL knowledge. This means even those without a data science or engineering background can independently extract, transform, and analyze their required data. It also promotes collaboration across the organization thanks to the prebuilt transformation components that can be easily customized and shared with teams. This not only accelerates the data transformation process, but also ensures consistency of data across different departments.  

Furthermore, organizations leveraging this tool experience significant time and cost savings due to its native integration with Snowflake. This eliminates the need to pull data into an external environment for transformations. StreamSets Transformer for Snowflake automatically adapts to changes, effectively reducing pipeline breakages. There’s no need for additional tools either, which reduces the complexity, risk of error, and latency associated with multi-tool data processing. By consolidating data transformation within Snowflake itself, the entire data workflow is streamlined.   

Data transformations are an integral part of data management and analytics, and apply to various use cases across diverse industries that have their data in Snowflake. Here are a few examples: 

Healthcare and Life Sciences: StreamSets Transformer for Snowflake can help expedite drug delivery by providing timely and accurate data on drug manufacturing and distribution processes. It can also help improve outcomes using real-world evidence by transforming raw patient data into usable formats for analysis, in addition to assisting in modernizing manufacturing and supply chain processes by enabling real-time insights into production lines.  
Financial Services: The tool can also help with accelerating various use cases for financial services organizations that range from better understanding ESG (Environmental, Social, and Governance) impact, customer 360 views, as well as insurance underwriting. All of these use cases require complex data transformations and teams that require insights for each of these areas typically don’t have deep knowledge of SQL.  
Manufacturing: StreamSets Transformer for Snowflake can also enhance various use cases that require data transformations for the manufacturing vertical. For example, data comes from various sources and formats, including IoT devices, sensors, as well as internal systems. This data would need to be transformed and cleansed before it can be leveraged for various use cases including predictive maintenance and supply chain optimization, to name a few. 

How To Get Started

As we wrap up this blog on StreamSets Transformer for Snowflake, let’s revisit the significant value it can bring to your organization. This powerful tool democratizes data transformation, enabling business teams to self-serve their data needs without the necessity of extensive technical knowledge.  

In addition, organizations who leverage this tool can expect to reap significant time and cost savings. This comes from increased team productivity, as users can now access and manipulate data independently, and from streamlined data management processes, given the ability to transform data directly within Snowflake. 

It’s essential to note that for existing StreamSets customers, using StreamSets Transformer for Snowflake requires you to be on StreamSets platform version 4.x. Users on earlier versions must first upgrade their platform. If you’re not already on 4.x, please reach out to StreamSets to learn about the upgrade process.  

For those who are ready to start taking advantage of StreamSets Transformer for Snowflake, getting started is as simple as visiting Snowflake Partner Connect for a free trial. Step into the future of data transformation and unlock the full potential of your data with StreamSets Transformer for Snowflake today.  

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