What Snowflake ETL Tool is Best?

  • Jeffrey Agadumo
  • November 24, 2022
What Snowflake ETL Tool Is Best?

Is Snowflake an ETL Tool?

Short Answer: No.

Long Answer: It’s not that simple.

This article will provide a thorough guide on E-T-L processing, how to do your “ETLing” with Snowflake, and our top 5 recommendations for Snowflake ETL tools.

Let’s dig in!

First off, What’s Snowflake?

Simply put, Snowflake is an SQL database built for the cloud. It is primarily a cloud-based data warehouse, although it can be used as a data lake if required. 

The Snowflake data management architecture is that of a Data Lakehouse, which combines a data lake’s flexibility with a data warehouse’s data management capabilities.

Snowflake provides a scalable cloud architecture using cloud services such as GCP, AWS, or Azure as its platforms.

What is ETL?

ETL stands for extract, transform and load. It is a procedure that refers to extracting data from several sources, converting it in a staging server, and loading it into a single repository such as a data warehouse, data lake, or cloud data platform. 

Click to learn more about ETL.

What is ELT?

ELT stands for Extract, Load, and Transform and is just a swapped-around version of ETL. In ELT scenarios, data is extracted from the source, loaded into a destination, and then transformed.

How does the ETL Process work?

Snowflake ETL: Which ETL Tool Is Best For Snowflake?

There are three significant steps in the ETL process:

1. Extraction:

This is simply obtaining raw data from one or more sources. This data may come through transactional applications like CRM data from Salesforce, ERP data from SAP, or IoT (Internet of Things) sensors that collect readings, for instance, a manufacturing line or factory floor operations. 

Data from these sources are often combined into a single data set for extraction to establish a data warehouse. 

The data is then validated and set up for marking or eliminating invalid data. Various formats for extracted data include relational database exports in CSV, XML, or JSON file formats.

2. Transformation:

The raw source data undergoes data processing. This means data is converted and consolidated for its intended analytical use case. This step may see your data:

    • Filtered, cleansed, deduplicated, validated, and authenticated.
    • Used to do computations, translations, or summarizations. Changing row and column headings for uniformity, converting currencies or other units of measurement, modifying text strings, and more are all examples of this.
    • Audited to verify data quality and compliance.
    • Removed, encrypted, or protected by industry or government regulations.
    • Formatted into tables or connected tables to meet the schema of the destination data warehouse.

3. Loading:

In this final stage, the transformed data is transported from the staging area into the target data warehouse. This often comprises an initial load of all data, followed by recurrent loads of incremental data updates and, less frequently, complete refreshes to wipe and replace data in the warehouse.

ETL is often performed during off-hours when the source systems and data warehouse traffic is at their lowest. Most ETL processes are automated, well-defined, continuous, and batch-driven in most businesses.

Why ETL your data with Snowflake?

As a data warehouse, Snowflake supports transformations during (ETL) or after loading (ELT).

Here are three key ways Snowflake supports your ETL process.

  • Using Snowflake as the target in the ETL process:

With Snowflake as your target data warehouse, you have access to Snowflake’s decoupled layers of storage, compute and cloud services that allow independent scaling. 

Additionally, Snowflake offers custom features like Query optimization, scalable cloud architecture, cloning, and time travel for data duplication, backup, Restoration, and analyzing data usage from key points in the past.

Snowflake ETL: Which ETL Tool Is Best For Snowflake?

  • Using Snowflake’s compute capabilities for your T (transformation) in the ELT process: 

Snowflake provides scalable multi-cluster engines and leverages processing techniques such as MPP (Massive Parallel processing) to aid transformations and aggregations within the data warehouse. 

In some scenarios, employing the Snowflake ETLmeans the ETL processing can be effectively avoided if you use the Snowflake as your data lakehouse. This is because no pre-transformations or pre-schemas are required, as Snowflake handles everything for you.

Snowflake ETL: Which ETL Tool Is Best For Snowflake?

  • Using Pre-built ETL tools for data integration, loading, automation, and transformation:

Data engineers can spend more time working on essential data strategies and pipeline improvement projects thanks to Snowflake’s simple architecture for accommodating 3rd-party ETL or ELT solutions.

Which Pre-built ETL tool is best for Snowflake?

Many competitors in the Snowflake ETL market provide dozens of features; it’s important to remember that it’s not about what looks good but what works for you.

Picture yourself as an auto mechanic for a second, a swiss army knife may contain lots of tools, but I doubt you’d purchase it over a complete toolbox filled with wrenches, screwdrivers, and pliers.

Get it? 

The same applies to choosing the right Snowflake ETL tool for your organization. It should be an all-in-one solution befitting your needs if you’re deploying one. You’d first want to analyze your company’s needs and select the best based on your requirements.

To make this easy, we have compiled a list of the top ETL tools for Snowflake available in the market. 

Take your ETL processing to the next level with the following tools:

You can also check our detailed guide on choosing the best ETL tool for Snowflake for your business.

Tackling the ‘T’ In Snowflake ETL the Datameer Way!

Datameer’s SaaS data transformation platform is a cloud analytics stack that focuses on the T–transformation in your ELT flow.  

Datameer is the industry’s first collaborative, multi-persona data transformation platform integrated into Snowflake. 

With Datameer, any user in your team, regardless of their SQL coding skills, can collaborate with your core data team and perform self-service ad-hoc analysis on your Snowflake datasets.

Datameer is powered by Snowflake and utilizes Snowflake’s scalable computation and storage, thereby maintaining data security and reducing expenses.

Snowflake ETL: Which ETL Tool Is Best For Snowflake?

Are you interested in learning about Datameer and how it can deliver agility and collaboration for the “T++” in your modern ELT data stack?  

Please visit our website or Sign up for your free trial today!