Snowflake is one of the most opted data warehouse service providers for building self-managing cloud data warehouses, data lakes, and other cloud storage. It runs on popular cloud providers such as Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform. The platform can handle all aspects of authentication, configurations, resource management, data protection, availability, and optimization. It is known for its range of unique features, which includes data sharing, time travel, database replication and failover, and inbuilt zero-copy cloning. It enables its users with greater agility by decoupling storage and computing.
Altogether, the Snowflake ecosystem consists of –
Snowflake for Data Science
What is Snowflake? 3 Major Snowflake Use Cases
Snowflake is a single platform that can be used to build an entire data architecture by dividing it into logical data zones – data lake, data warehouse, and modelled view for data science.
Data Storage: Data Lakes
A data lake is a highly scalable repository of raw, unprocessed data that remains in its native format until required. It holds data from disparate sources with a mix of different data formats – structured, semi-structured, and unstructured.
• Unlimited inexpensive data storage (different structures under one platform)
Snowflake allows you to store different structures of data like CSV, JSON, AVRO, XML, Parquet, ORC, etc., at a low cost without any storage limit. It stores data in Snowflake-managed smart storage for efficient compression, automatic micro-partitioning, and encryption at rest and in transit.
• Easy SQL queries for any structured data
The platform has the flexibility of running data query on different formats/ structures using simple SQL queries. It also works on external cloud storage such as S3 and ADLS Gen2 without loading data to snowflake. It streamlines pipeline development using SQL or in your language of choice with Snowpark with no additional clusters, services, or copies of your data to manage.
• Fast, Reliable Processing and Querying
Simplify your architecture with an elastic engine to power many workloads. With virtual warehouse capabilities, you’ll face no concurrency issues or resource contention. It offers ease of loading data from different cloud provider services in real time and batch. You can also secure your data lake, know what’s in it, and control how it’s used. The platform enables you to easily integrate external data without ETL and collaborate among internal and external stakeholders while enriching your data lake with live, secure data sharing.
Data Processing: Data Warehouses (DWH)
A data warehouse is a relational database, designed for analytical work and producing business insights. It focuses on collecting data from multiple sources to facilitate broader access.
• Massive Parallel Processing (MPP) Architecture
An MPP database is a storage structure designed to handle multiple operations simultaneously by several processing units. This allows MPP databases to manage massive amounts of data and provide faster analytics on large datasets.
Snowflake works on a central storage, accessible from all the compute nodes. In addition, like an MPP architecture, Snowflake processes queries using MPP compute clusters, also known as virtual warehouses. Thereby, Snowflake combines the simplicity of data management and scalability with a shared-nothing architecture.
• Data Integration
Snowflake supports both transformations during (ETL) or after loading (ELT). Snowflake supports various data integration tools, such as Informatic, Talend, Fivetran, Matillion, and others.
In data engineering, new tools and self-service pipelines are putting an end to traditional tasks like manual ETL coding and data cleaning activities. Additionally, Snowflake’s Snowpark is designed to build complex data pipelines and to allow developers to interact with Snowflake directly without moving data.
• Columnar Storage
Snowflake is fundamentally built to be a complete SQL database – columnar-stored relational database. It enables a high compression ratio, reducing physical storage size and storage cost. Columnar storage also helps in a faster data aggregation.
• Micro Partitions
All data in Snowflake tables is automatically divided into micro-partitions, which are contiguous units of storage containing uncompressed data of size between 50 MB and 500 MB. Groups of rows in tables are mapped into individual micro-partitions, organized in a columnar fashion. This size and structure allow extreme granular pruning of huge data tables, which can be comprised of millions, or even hundreds of millions of micro-partitions.
Analytics: Data Science
Data science makes one of the major pillars of analytics. It deals with vast volumes of data using complex Machine Learning algorithms to identify data patterns and derive insightful information.
• Data sharing feature to use cleansed data from data warehouse
By leveraging the data sharing feature for ML models in the Snowflake, you can use the transformed/cleansed data from data warehouse to perform Exploratory Data Analysis (EDA) or develop the model without any extra storage cost.
• Exploratory Data Analysis using Snow sight
Snow sight accelerates a user’s query scripting and data visualization activities. It helps you identify outliers and quality issues with the initial data load. You get data exploration and model distribution capabilities. It also facilitates data preparation and data visualization while offering a large-scale computing infrastructure.
• Snowflake integration with different Data Science tools/ Partners
Snowflake is partnered with a broad category of vendors, tools, and technologies that provide advanced capabilities for statistical and predictive modeling. It serves as a one-stop-shop for data modeling as data science platforms contain APIs for model production and testing with minimal outside engineering
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