Snowflake ELT: Process for Building Data Pipelines

Building a business based on data can be challenging yet highly rewarding. But as you make the efforts to make the best use of your business data, it is necessary to educate yourself on the optimal methods to do it.

In this article, you will learn how to build Snowflake ELT data pipelines. We will begin by covering the fundamentals.

What Is a Data Pipeline?

A data pipeline is a collective term used to denote the various stages data moves through from a source to its destination.
Be it any workflow or a process, data goes through certain transformations as necessary to prepare it for its destination.
Data is either stored or used to generate more data at the end of the pipeline.
Successful data pipeline execution involves a multitude of steps. The tasks such as data ingestion, extraction, transformation, enrichment, aggregation, and more could be executed in a data pipeline sequence.
Raw data enters the data pipeline, goes through these various stages of data operations, and reaches the target destination at the end of the process.
Data engineering aims to optimize these data pipelines to be reliable and easy to execute with minimal effort.
When the output of the data pipeline remains unchanged and can be repeated without any changes in the stages, it's the perfect candidate for automation through ELT and ETL tools.
How your data pipelines are designed determines your data systems' overall performance and data integration strategies.

What is ELT?

ELT stands for Extract, Load, and Transform. ELT is heavily used in data integration systems where data from multiple sources are moved to an integrated target system such as a data warehouse or cloud-based data lake.

Unlike the ETL process, where you extract, transform, and load data, in ELT, data is first Extracted from its sources, Loaded into the larger data integration platform such as a data warehouse, and then Transformed to be fit for use.

While very similar to the ETL pipelines, ELT differs in the sequence of the stages where loading happens first, and then the data is transformed.

Here are the three stages of ELT:

Extract

Data extraction is the first step in the ELT process, where the datasets are copied from the source system through APIs, connectors, and tools like Fivetran and Airflow. This marks the start of the data pipeline.

Load

Loading data is the second step. The data collected from multiple sources is copied to the final target system, such as a data lake or the data warehouse.

Transform

Data transformation is the last step of the modern ELT process, where operations convert data into a compatible format after being loaded onto the target system. This is generally achieved through dbt, a data build tool. The nature of the transformed data depends on the actual need, requirements, and the other business processes and tools that will be used.

ELT vs. ETL

ELT is often considered an upgrade over the previously used ETL data pipelines as it reduces the complexity of running transformations before the loading process. And in some cases, ELT is used along with ETL to improve the overall data integration results.

ELT also helps streamline the data pipelines as the transformation process is moved to a later point. While ELT may not be suitable for cases where the target system expects a specific data format, it is well suited for cloud-based warehouses such as the Snowflake.

Some other instances in which ELT might not be the best choice include:

Security requirements that restrict storing raw data as is
Target systems that enforce strict data types and formats
Quick scaling up of the loading process in a destination

If not for these concerns, ELT can be a good choice for building data pipelines, especially for cloud-native data solutions like Snowflake, Google BigQuery, Amazon Redshift, AWS Lambda, Azure SQL, etc.

Benefits of ELT for Cloud-Based Data Warehouses

Fast turnaround time — ELT speeds up the data processes. It moves the time-consuming transformation process to the last stage after data integration, thus allowing business intelligence (BI) tools to access data much faster.
Scalability — ELT works great with cloud-based solutions with auto-scale features, as it can take advantage of a cloud environment's vast resources.
Flexibility — ELT data pipelines allow more flexibility for the BI tools to run transformations on demand. So after data ingestion, data can be transformed depending on the requirement as and when needed, unlike ETL, where data is already changed and offers less flexible options after the loading stage.

What Is a Snowflake Data Warehouse?

Snowflake is a cloud-based data warehouse SaaS-based solution that can work with all major cloud platforms, such as Amazon Web Services, Azure, and Google Cloud.
It is quite a unique data warehouse with a considerable performance advantage compared to many other data warehouse solutions.
The Snowflake data warehousing service consists of three layers: service, compute, and storage.
- The service layer handles the database connectivity, concurrency, and data management of metadata and transactions.
- The compute layer handles the virtual warehouses.
- The storage layer deals with the huge data pool.
Snowflake allows for unlimited virtual warehouses, each capable of quick resizing, thus allowing you to adjust your configurations per your workload.
It also provides impressive scalability with its multi-cluster support.
Users need only pay for the resources they use, and based on the load, the multi-cluster features automatically scale up and down as required. This is made possible by the EPP database architecture that Snowflake pioneered.
Snowflake also uses the Massively Parallel Processing (MPP) architecture, where a cluster of independent machines is deployed, and the data is distributed across the system.
The version of MPP used by Snowflake is called EPP or Elastic Parallel Processing, which gives the advantage of quickly starting new clusters. It allows for an elastic way to scale up and down the resources as and when required.
Working with Snowflake also requires a low learning curve if you are familiar with similar platforms and SQL standards.
Besides supporting the standard SQL statements, it also supports many extensions for quick SQL migration.
Snowpark is a new offering from Snowflake that offers a better experience for developers. They can write code in their preferred language and run it directly on Snowflake. Developers can code in Python, Scala, or Java and supplement Snowflake's original SQL interface.

Building an ELT Data Pipeline for Snowflake

Data pipelines are essential to provide a streamlined data integration process. They give you a better idea of where the data comes from, the processes and transformations run on the data, and the applications and use cases where it is utilized.

Data pipelines help better utilize your data analytical apps, as gathering data from multiple spreadsheets, invoices, and reporting documents will be a colossal challenge otherwise.

When you opt for a cloud-based solution such as Snowflake, using an ELT data pipeline is best.

Snowflake data cloud, a cloud-based solution, can provide highly scalable access to resources. Thus, it is best suited for running transformations after loading so that you get more flexibility with the types of modifications to run on the data as well as scalability to run the otherwise resource-expensive data transformations. Thus, data analytical apps and BI tools can also take in the raw data and transform it as required to gather better insights.

As transformation is moved to a later stage, ELT provides a streamlined process that ensures standard outputs as the data is loaded into the snowflake data lake.

Choose Your Data Integration Tool

The first step to building a data pipeline is identifying the data sources. Once your new data sources are ready, you need a proper data integration tool to extract data from multiple sources.

Here are several factors to consider when choosing a data integration tool:

Data platform compatibility with Snowflake data warehouse — Confirm that the data integration tool works with your selected data warehouse. The good news here is that Snowflake is widely supported.
Extensive data connector library — A data connector extracts data from a particular source. Ensure your data integration tool has the necessary support for the data connectors you need. If not, look into any support for custom data connectors. You can also validate data connectors to support normalization and standardization based on their features.
Easy and lightweight setup — Some Snowflake ETL tools can be set up with minimal configuration as a ready-made solution, and some with many options. The tools with many configurable options often require additional data engineering effort and expertise.
Supports automation — Use tools that help you minimize any manual effort in the data pipeline process. Scheduled data syncs are superior to manual sync.
ELT support — Check if the tool supports ELT data pipelines to run the transformations in the Snowflake data warehouse.
Data recovery functionality — Choosing robust tools that can help you quickly recover data in case of a failure is always a good idea.
Security and compliance — Ensure the data integration tool meets data standards and regulations. You should also look into the security features to avoid leaking sensitive data and prevent unauthorized access.

Choosing the Right Business Intelligence Tools

To complete the purpose of your ELT pipelines, you must also pay attention to the open-source and paid BI tools and analytical apps (like Talend and Tableau) you use.

The raw data loaded into the Snowflake can be transformed by these applications to provide better insights and predictions on the collected data.

Some qualities you should be looking for in your BI tools include:

Easy integration with Snowflake data warehouse
Intuitive UI with easy-to-use options like drag-and-drop interfaces
Support for real-time data
Automation support for reporting and alerts
Support for ad hoc reports based on exported data files
Good performance and responsiveness
Flexibility for data modeling and development teams
Visualizations library for a better understanding of the data reports

Best ELT Pipeline Tools

Here are the best ELT pipeline tools to build a data pipeline:

1. Portable

Portable is a data integration solution with ETL and ELT data connectors. It has 500+ no-code data connectors that can be readily employed in Snowflake and other data warehouses. Portable also develops custom connectors on demand. The company is known for its excellent support as well.

Portable's free plan offers manual data syncs from unlimited data sources. Its paid plans start at $200 per month. More advanced features, such as advanced automation capabilities, begin at $1000/month.

2. Hevo

Hevo is a no-code ELT data pipeline tool with 100+ data connectors. It is compatible with Snowflake and uses a Models-and-Workflows approach to move data into the data warehouse.

Hevo offers a tiered subscription plan under three tiers, free, starter, and business. The free plan supports unlimited data sources, which is quite impressive. The starter plan is priced starting from $249 per month. As you scale up your resources, the pricing also goes up.

3. Blendo

With over 40+ connectors, Blendo is a well-known data integration tool offering ELT data pipeline support. It is best suited for businesses moving data from social media ad accounts like Facebook, LinkedIn Ads, MailChimp, and similar channels to a cloud-based cloud data warehouse.

Blendo's plans start from $150 per month. Their advanced plan is priced at $500 monthly and includes more than 150 pipelines.

4. Matillion

Matillion is a data integration tool specifically built to support cloud-based data warehouses and ELT pipelines. It is a performance-optimized tool that helps offload resource-intensive tasks from your on-premises servers.

Matillion follows a pay-as-you-go model where you pay per hour without long-term financial commitments. Starting price is $1.37 per hour, which can go up to $5.48 per hour for larger teams and high workloads.