Simplifying Feature Engineering With Data Vault On Snowflake
Data has replaced the traditional way of decision-making across all industries and sectors. Data is at the center of the transformation of modern businesses.
The total amount of data created, captured, copied, and consumed globally is forecasted to reach more than 180 zettabytes by 2025. (source: Global Data Creation: Statista)
A data-first approach is required to drive business performance across all sectors. Data platforms like Snowflake, Azure, Amazon Redshift, and Teradata are changing the game of data management and governance with a wide range of services and products.
In this blog, we are focusing on Data Vault on Snowflake with some highlights on feature engineering and business vault. Snowflake is a popular cloud-based platform offering cloud-based data storage and analytics services.
Before we start this blog on Data Vault on Snowflake, let’s first understand what Data Vault is.
What Is Data Vault?
In simple terms, Data Vault is a database modeling method.
A data vault is a data modelling method or architecture designed to facilitate data analysis, business intelligence, and data science requirements at the enterprise scale.
In a nutshell, it is an architecture or an approach to handle large-scale data integration and offers a historical view of data. It is an effective method to architect an enterprise data warehouse that is flexible, scalable, and agile.
Does Snowflake Support Data Vault?
The answer is yes. One of the key objectives of Snowflake is to make it easier to store, manage, and analyze data effortlessly without worrying about maintenance, upgrades, and tuning.
A data vault is a specific way to prepare your data for analytics. It can be customized to meet your enterprise data warehousing needs. You can easily implement Data vault architecture in Snowflake.
Additionally, Snowflake’s advanced features such as optimized columnar storage format, MPP clusters, and Adaptive Data Warehouse technology make it easier to implement Data Vault in Snowflake. It supports tables and views like all the other relational database platforms.
In Snowflake, there is a set of standards to automate data extraction, cleaning, and modeling into raw vault tables. Data Vault does not impose any rules on how you transform your data. All you require is to follow some standards to populate the business vault link and satellite tables like you populate the raw vault link and satellite tables.
Raw Data Vault is a data vault model with no soft business rules or transformation applied. Business Data Vault is a data vault object with soft business rules applied. These “soft business rules” allow you to perform auditable transformations with raw data artifacts.
Snowflake Data Vault: Feature Engineering For ML
It is quite apparent that data scientists spend most of their time in data preparation such as collecting, organizing, and cleaning data for further use. In machine learning, data engineering is different compared to dimensional data model transformation.
ML models require a specific ‘Feature Engineering’ process to transform raw data into features that machine learning algorithms can understand. A feature is an individual measurable input that can be used for analysis and as the input for ML models.
Feature Engineering is the important pre-process of machine learning that extracts important features from raw data.
A Feature Store, yet another important concept in machine learning, is an ML-specific data system to store features for ML pipelines. Teams can share, discover, and use curated features from this Feature Store to support ML experiments.
Now, let’s see how Data Vault on Snowflake supports ML projects.
First and foremost, we need to perform data ingestion using batch, micro-batch, or streaming ingestion process whatever fits the needs. This can be done through the following options:
- File-based batch with COPY command
- Snowpipe micro-batch
- External table registration and use
Now, we have data modelled into the raw vault, let’s see what are the Feature Engineering options available in Snowflake.
Feature Engineering Transformations for ML
In this transformation process, the business vault offers additional intelligence to existing business processes to derive features and complete this process..
This process prepares data for ML model training. The business vault consists of reusable and shareable feature values that convert raw data into meaningful features.
To make Feature Engineering effective and easy, developers can try their hands on Snowpark. Snowpark is a secure and comprehensive framework for running transformations and ML algorithms. It supports native SQL, Java, and Scala languages for faster and more secure data processing at scale. Snowpark simplifies the overall architecture and feature engineering process.
Snowflake also offers a comprehensive set of observability views to monitor the information. Feature stores in Snowflake act as a framework for managing and storing features for ML pipelines
Feature Store supports data governance and data privacy through features such as dynamic data masking, row access policies, auto-classification, tokenization, and anonymization.
Snowflake’s industry-leading standards and powerful features help users scale data science deployments effortlessly and quickly.