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Cleaning Data From Snowflake Database Storage Layers

October 24, 2019

What is a Snowflake database? Snowflake is a cloud data warehouse that provides various layers for cloud services, query processing, and database storage. Snowflake provides many advantages over traditional data warehouses given its infrastructure as a service, allowing for agile and scalable storage and processing of data in the cloud. Additionally, Snowflake is capable of handling semi-structured data like JSON, giving Snowflake more flexibility over traditional data warehouses. Combined, these advantages allow raw, pre-processed data to be stored as databases in snowflake, which require preparation before being consumable by downstream analysis tools, and machine learning and AI models. Below are some best practices when developing data preparation processes with Trifacta on Snowflake. 

Empower all Data Professionals

Your data preparation platform should empower the right users to clean up Snowflake databases.

  • Data analysts, who need to explore, structure, clean, blend, Snowflake databases, and validate data quality with data closer to the source to improve time to value and open up new areas for insights
  • Data scientists, who perform data exploration, analytics, modeling, and algorithm development on a wide variety of data sources and structures compatible with snowflake databases, and collaborate with business leadership to determine the analytical insights that drive innovation and achieve business objectives
  • Data engineers, who design, build, and manage database processes to support analysts and data scientists who do a majority of the preparation, aggregation, and modeling of snowflake databases.

Don’t Break the Wheel

A lot of databases will come from well-defined, well-structured data routing to analytics processes. Instead, focus on new use cases and new insights first. Snowflake’s ability to take in a broader range of data structures allows organizations to store more diverse and insight-rich data as snowflake databases. These diverse databases are ripe with insights, and have often been left out of analytics processes without the right tooling in place. These use cases are a great candidate for maximizing the value of your data preparation investments as they can lead to immediate value on data not previously utilized. 

Self-Service with Centralized Governance

Self-service data preparation is necessary for well functioning data operations within organizations. Non-technical users need solutions for exploring, profiling, structuring, cleaning, enriching, and automating manual data preparation work without having to rely on limited IT resources. 

How does your organization find the right balance between empowering users to derive value from Snowflake databases while protecting data assets as part of good data governance and security practices?  Here are three ways:

  1. Keep data silos from proliferating as users collect data extracts and run their own preparation routines, often on spreadsheets and instead store data as snowflake databases.
  2. Use shared central catalogs or glossaries to manage data definitions and metadata, and changing database schemas.
  3. Track and document data lineage during preparation and transformation.

Data Quality at Scale with Continuous Validation

Snowflake databases can store huge volumes of data for and a diverse variety of data types for cheap—everything from raw, semi-structured data to structured, transactional data from multiple systems. As such, they have a wide array of data to discover value from, which opens up many opportunities for insights when the right data preparation tool is in place, allowing for flexible, real-time data quality discovery and continuous data quality monitoring.

Your organization can improve the accuracy, consistency, and completeness of data by using data preparation solutions that combine a visual approach with machine learning to automate data cleaning procedures and provide insights into anomalies and data quality issues, with the flexibility to monitor and adapt to changing data as it comes in.

Automated Data Preparation for Downstream Analytics and Machine Learning

In your snowflake databases, a vast and growing volume of data is collected from a huge number of sources, including Internet of Things (IoT) sensors, mobile devices, cameras, customer behavior, applications, and more. As the data generated by the digital revolution explodes, so too does the opportunity for outcompeting on differentiated, value-rich data. 

Data preparation routines should scheduled, published, and operationalized and shared to reduce redundancies and ensure broad access to a value-rich set of snowflake databases. Your organization should consider running data preparation natively and automatically within Snowflake’s data warehouse to:

  • Accelerate time to value
  • Reduce operational costs
  • Improve monitoring and governance

Centralizing the scheduling, publishing, operationalizing of data preparation routines results in less redundancy and inconsistency, more portability, and better management and governance. When coupled with integration with data catalogs, centralization increases the potential for reuse across different data consumers who can share knowledge of how data needs to be massaged for front-end tools, machine learning development frameworks, visualizations, and reports.

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