# 5 Snowflake Features Every Data Engineer Should Know

- Canonical: https://33rdsquare.com/5-features-of-snowflake-that-data-engineers-must-know/
- Published: 2024-09-03
- Author: Jordan Brown
- Categories: [Artificial Intelligence & Machine Learning & ChatGPT](https://33rdsquare.com/category/tech/ai/)

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Snowflake has seen explosive growth in recent years to become the most valuable software company in the world, with a market capitalization over $90 billion as of March 2023 [^1^](https://www.fool.com/investing/2023/03/05/is-snowflake-stock-a-buy-now/). Snowflake‘s cloud data platform has been rapidly adopted by enterprises of all sizes across industries looking to modernize their data architecture.

But Snowflake goes far beyond a traditional data warehouse. It offers a set of unique features enabled by its modern multi-cluster, shared data architecture that sets it apart from other databases and data platforms. For data engineers evaluating or working with Snowflake, here are five key capabilities to understand:

## 1. Farewell Integer IDs, Hello Metadata-Rich ROWID

In traditional databases, rows are typically identified by simple integer values that monotonically increase as rows are inserted. In contrast, Snowflake generates a unique 64-bit ROWID value for each row stored as 16 hexadecimal characters [^2^](https://docs.snowflake.com/en/sql-reference/functions/rowid.html).

This ROWID encodes metadata that enables various Snowflake features and optimizations:

- The first 2 hex characters identify the partition range
- The next 4 characters identify the partition within that range
- The remaining 10 characters provide uniqueness within the partition

Altogether, a ROWID provides a globally unique identifier for each row. Some key benefits and use cases include:

- Pinpointing specific rows across tables for troubleshooting or tracing
- Optimizing queries by clustering on ROWID ranges to eliminate scanning partitions
- Syncing and replicating data between Snowflake and other systems

The storage overhead of the wider 64-bit ROWID is minimal compared to the benefits it provides. For a 1 billion row table, using the ROWID as a clustering key only adds about 8 GB of storage [^3^](https://www.analytics.today/blog/clustering-in-snowflake-part-1). This is a small price for the metadata richness and global uniqueness the ROWID enables.

## 2. Micro-Partitioning Delivers Massive Performance

One of the key innovations in Snowflake‘s architecture is how it automatically micro-partitions all table data. Rows are grouped into micro-partitions, each containing between 50 MB to 500 MB of uncompressed data [^4^](https://www.snowflake.com/blog/how-snowflakes-micro-partition-architecture-powers-modern-data-workloads/).

When queries are issued, Snowflake‘s query optimizer uses clustering metadata about columns to determine which micro-partitions actually need to be scanned. By intelligently pruning out partitions that are not needed, the amount of data scanned can be dramatically reduced.

In benchmark tests, this partition pruning has been shown to improve query latency by up to 100x compared to full table scans [^5^](https://www.snowflake.com/blog/automatic-table-optimization-snowflake/). And Snowflake can effectively optimize queries even without secondary indexes, which add overhead and complexity in traditional databases.

Micro-partitions also enable useful features like:

- Time Travel: Snapshots of micro-partitions are kept according to the specified retention period, allowing queries against historical data
- Zero Copy Cloning: "Cloned" tables and databases are created by referencing the same underlying micro-partitions

For data engineers, this automatic micro-partitioning delivers fast, scalable query performance out-of-the-box without the tuning and optimization often required in other databases. The small partition sizes also enable granular snapshot isolation for Time Travel and efficient cloning.

## 3. Data Sharing Powers Live Analytics

In the past, sharing data across an organization, with partners, or with customers often required complex ETL pipelines, FTP batch file transfers, or creating copies in data marts or extermal systems. This led to stale data, governance challenges, and security risks.

Snowflake upends this old model with its Data Sharing functionality. Snowflake allows sharing live, governed, secure access to databases across regions, clouds, and organizations without copying or moving data [^6^](https://www.snowflake.com/workloads/data-sharing/). Key benefits include:

- Recipients access shared data in real-time through Snowflake SQL as if it was in their own account
- Data is always live and up-to-date; no stale copies
- Providers retain full control and governance over shared data
- No data movement or external pipeline required; just SQL

Data Sharing enables a wide variety of impactful use cases, such as providing real-time personalized offers to customers, collaborating with supply chain partners, or monetizing data assets as data products to external consumers.

Many companies are using Snowflake Data Sharing to power their internal "Data Mesh" architectures [^7^](https://www.snowflake.com/blog/how-to-build-a-data-mesh-on-snowflake-part-1/). With a Data Mesh, domain teams maintain their own data products which are shared live to other teams as needed. This federated approach improves agility compared to centralized monolithic data architectures.

## 4. Flexibility for Any Data with VARIANT

While Snowflake is a relational database at its core, it also offers native support for semi-structured data like JSON, Avro, or XML. This is enabled by the VARIANT data type, which allows loading and storing semi-structured data without defining a schema [^8^](https://docs.snowflake.com/en/user-guide/semistructured-concepts.html).

Some key features of Snowflake‘s semi-structured data support include:

- Load semi-structured data into VARIANT columns with a single COPY command
- Query fields within VARIANT data using SQL dot notation or flatten into rows using LATERAL joins
- Automatic pruning of VARIANT columns and type inference to optimize query performance

Snowflake also provides a schema detection feature which can be used to extract structured columns from VARIANT data if desired. But the flexibility of VARIANT enables use cases like:

- Storing machine-generated JSON data like weblogs or IoT sensor data
- Schemaless ingest of structured data to handle changing business requirements
- Sparse wide tables with thousands of columns where each row only uses a subset

According to Snowflake, some customers have ingested over 200 TB per day of semi-structured data, demonstrating Snowflake‘s scalability for unstructured data workloads [^9^](https://www.snowflake.com/blog/modern-approach-processing-semi-structured-data-in-snowflake/). The VARIANT type allows data engineers to easily work with semi-structured data when needed while preserving full SQL support.

## 5. Powerful Scripting for End-to-End Pipelines

While known for its data warehousing and SQL query capabilities, Snowflake also provides a procedural scripting language that supports tasks typically handled by external data integration and orchestration tools. This allows defining end-to-end ELT pipelines in Snowflake using SQL.

Key Snowflake scripting features for data pipeline development include:

- Support for stored procedures, user-defined functions (UDFs), and user-defined table functions (UDTFs)
- Flow control with conditional statements, loops, cursors, and exception handling
- Ability to perform DML operations to load and transform data
- Transactional semantics and error handling

By building pipelines in Snowflake scripting, data engineers can unify batch and streaming semantics and combine ELT and ETL-style transformations. Pushing data-intensive workloads into Snowflake improves performance by minimizing data movement and takes advantage of its scalable compute and storage.

According to Snowflake, scripting pipelines can scale to process billions of rows and terabytes of data in minutes [^10^](https://www.snowflake.com/blog/ingesting-processing-and-transforming-semi-structured-data-at-scale-with-snowflake-pipelines/). Pre-built functions and templates also make it easy to get started with only SQL skills.

With Snowflake‘s scripting and pipeline capabilities, data engineers can develop complete, automated data solutions within Snowflake. This can improve end-to-end development agility and efficiency.

## The Future is the Data Cloud

Snowflake‘s unique architecture and features like its ROWID, micro-partitioning, Data Sharing, semi-structured data, and scripting set it apart as a cloud data platform. These innovations allow data engineers to build modern data applications and pipelines not possible with legacy technologies.

By providing a unified platform to load, integrate, analyze, and share data, Snowflake is helping to enable the move toward what it calls the "Data Cloud" – a seamless network to connect enterprises, teams, and people with live data [^11^](https://www.snowflake.com/trending/what-is-the-data-cloud). This platformization of the data architecture is a key trend as organizations look to maximize the value of their data assets.

Snowflake is also investing heavily to support machine learning and data science workloads. Snowflake‘s support for Python stored procedures, large scale data processing, and online feature stores make it well-suited as a platform to build and deploy ML models and applications [^12^](https://www.snowflake.com/workloads/data-science/).

As more organizations look to become data-driven through scalable self-service analytics, modern data sharing and monetization, and applied AI/ML, Snowflake is well positioned to continue its meteoric rise. Expect Snowflake to continue to innovate on its cloud data platform with new features and integrations.

Understanding Snowflake‘s differentiated capabilities allows data engineers to design modern, agile data architectures and pipelines that deliver more value from data. With the ability to easily access and integrate live, governed data at scale, the possibilities are endless.

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Source: [5 Snowflake Features Every Data Engineer Should Know](https://33rdsquare.com/5-features-of-snowflake-that-data-engineers-must-know/)