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StreamSets Transformer for Snowflake
Run powerful data transformations natively on Snowflake.
Data has become a critical success factor for virtually every aspect of an organization’s strategic goals, and the Snowflake Data Cloud offers a powerful platform for managing and analyzing data. However, raw data in its original form is often inconsistent or incomplete, making it unsuitable for analysis. This is where data transformation tools become essential for businesses to make sense of their data.
With StreamSets Transformer for Snowflake, data practitioners can transform their data into a usable format natively on Snowflake, empowering businesses to make informed decisions based on accurate and meaningful insights. By leveraging StreamSets transformation capabilities, organizations can scale data access to a broader audience and unlock the full potential of their data.
Problems addressed by Transformer for Snowflake
Mundane nature of SQL
Data engineers often deal with large datasets and complex transformations, which require efficient methods for processing and analyzing data. SQL is a powerful tool for manipulating data, but it can also be tedious and time-consuming, especially when working with complex transformations.
One common challenge that data engineers face when using SQL is the mundane nature of certain operations, such as writing unions. Unions can be useful for combining multiple tables or queries into a single dataset, but they can also be time-consuming to write. If you need to combine data from multiple sources with different schemas, you may need to write a series of unions that can quickly become complex and difficult to manage.
How Transformer for Snowflake helps:
Transformer for Snowflake helps address this challenge by simplifying common SQL tasks. The visual interface and drag-and-drop functionality for building complex data transformations can save time and reduce errors. This means data engineers can create complex transformation pipelines without writing a single line of SQL code. The tool also offers pre-built transformation components that can be easily customized to meet specific data processing requirements.
Complexity of SQL & data transformations
Data analysts experience challenges with complexity in SQL when running data transformations such as window functions or slowly changing dimensions (SCDs) due to the intricate nature of these tasks. Window functions require extensive knowledge of SQL and syntax, as well as understanding how to properly order and partition data. SCDs require a deep understanding of how data changes over time and how to properly track and manage those changes. These complexities can lead to errors and inaccuracies in data analysis if not properly managed, making it a difficult task for data analysts to handle.
How Transformer for Snowflake helps:
Transformer for Snowflake solves these challenges by including pre-built transformations for commonly used SQL functions such as window functions and SCDs, which reduces the amount of manual coding required. Its drag-and-drop capability also makes it easy to manipulate data and create data pipelines, while its error handling and data validation ensure the accuracy of data transformations.
Operationalizing SQL queries
Snowflake is a cloud-based data warehousing platform that utilizes a proprietary SQL dialect, which semi-technical users such as Product Managers or other LoB owners may be unfamiliar with. When it comes to operationalizing SQL queries from Snowflake, it requires a difficult and lengthy process that often forces users to deviate from their main priorities and spend hours on end trying to execute tasks that require additional technical knowledge and permissions. For example, uploading their SQL file to GitHub and going through code review requires additional permissions, while writing Python to schedule an airflow DAG requires technical knowledge that they might not have. Furthermore, they may even need to make a request with IT to accommodate these needs, which can yield a lengthy turnaround time.
How Transformer for Snowflake helps:
Transformer for Snowflake eliminates the lengthy process of operationalizing SQL queries by enabling users with self-service capabilities and making it easier to collaborate with others. The no-code user interface provides a drag-and-drop transformation canvas with pre-defined processors to create transformation jobs easily. Users can share and reuse transformation jobs, schedule workflows, and enable collaboration across teams.
Users can also rely on their transformations to continue running smoothly even if changes occur. The tool's self-documenting nature simplifies the process of debugging transforms in case of failure, making it easy for users, even non-engineers, to self-serve their data needs. This feature empowers organizations to streamline their data pipelines and enables data practitioners to focus on generating valuable insights rather than troubleshooting technical issues.
Push-down processing allows for complex data transformations natively within Snowflake, eliminating the need to move and transform data within a different environment. This results in reduced costs and security risks.
Supported transformations
Transforming raw data into a usable format for business intelligence and analytics can be a daunting task for data practitioners. Factors such as inconsistent or incomplete data, managing data integration from multiple sources, and handling large datasets can pose significant challenges.
To overcome these obstacles, Transformer for Snowflake enables data practitioners to streamline the process and generate accurate and meaningful insights. In this section, we will explore the key features of our data transformation tool and how it can help you unlock the full potential of your data.
- Slowly changing dimensions (SCDs) – SCDs allow users to track changes to data over time, enabling them to perform trend analysis. By supporting SCDs, Transformer for Snowflake can help organizations maintain accurate and up-to-date data, leading to more informed decision-making and improved business outcomes.
- Pivots – By supporting pivots, Transformer for Snowflake allows for easy conversion of datasets between wide and long formats. This capability enables users to visualize data in a more structured and accessible format, enabling them to make informed decisions based on accurate data.
- Window functions – This feature enables users to perform complex calculations on subsets of data, such as running totals or moving averages. By supporting window functions, Transformer for Snowflake allows you to perform advanced analytics to identify trends and patterns that may not be obvious through traditional data analysis.
- Denormalization – Transformer for Snowflake supports denormalization, which enables users to join multiple tables in order to simplify data analysis. This process makes it easier to perform advanced analytics for insights from large datasets.
- No code ETL and beyond – Transformer for Snowflake allows data practitioners to go beyond SQL to express powerful data transformation logic using an intuitive design canvas. Users can choose a no-code approach or drop in code whenever they want by applying user-defined functions (UDFs) and third-party integrations directly to data transformations. Transformer for Snowflake can directly invoke any Snowflake UDF that has already been created, while new Java UDFs can also be created on the fly.
