Snowflake Data Integration Guide (ETL Strategy & Tools)

CEO, Portable

Intro to Integrating Data with Snowflake Data Cloud

Snowflake is a cloud data warehouse service usually available on a pay-as-you-go SaaS model.

  • Snowflake's architecture separates the application's storage and computing resources.

  • It can be used with all major cloud platforms, such as Microsoft Azure, AWS, and GCP.

  • Many data science teams use Snowflake for data integration from multiple sources.

  • You can use its 'storage' and 'compute' parts independently.

Snowflake can be readily used alongside your existing ETL solutions and help with efficient real-time data operations via its quick sharing features. It is a perfect solution for organizations requiring a flexible data integration solution.

Data Integration Use Cases

Many different types of data integration tools and services are available in the market. Each of these solutions applies well to particular use cases. So, before you pick up Snowflake, know whether it's the best choice for your needs.

A Snowflake data integration solution would benefit most data integration use cases, like:

Analytics pipeline enhancement

Moving from batch loads to real-time data streams can significantly enhance the performance of your analytics applications. And this can be made possible with the help of Snowflake data integration.

Snowflake data integration helps provide a uniform, secure, concurrent data warehouse across your organization. This, in turn, enhances your analytical applications as you can always access consistent and latest data.

Persistent query results

Snowflake makes use of caches to deliver faster results. When nothing has changed with your data source, it shouldn't take more time to get the same type of reports repeatedly.

With the Snowflake data service, you can fetch the latest query results from the cache and get your reports faster.

Breaking down data silos

You run into several issues when your data systems are isolated into silos across your departments. You could face redundancy, inconsistent data, lowered performance and decreased data quality.

Snowflake can avoid these problems and help you avoid silos. It can thus lower your operational costs, increase your performance and drive better data-based decision-making.

Improving business intelligence

With Snowflake, you can easily integrate customer data from multiple channels and use it to understand user behavior and yield actionable insights.

You can use the information thus gained to improve your product further and enhance customer satisfaction.

Implement effective data exchange

Snowflake is the best tool for establishing a data exchange, where you can communicate in real time with collected data.

It helps you create data exchange and share data across your departments, clients, and partners as and when required.

Snowflake ETL Examples

ETL stands for Extract, Transform, and Load. It's the basic three-step data integration process used by most organizations worldwide.

You would extract data from multiple data sources, transform it into a more efficient structure, and load it into the final target system. The target system could be any data warehouse, cloud storage, or application. Historically, engineers had to write Python scripts for automation and validation.

The ETL process provides the framework for collecting and integrating data from multiple sources into a unified data warehouse.

Snowflake supports ETL and can be used with various data integration tools. It also supports a similar process called ELT, where the loading step is carried out before the data transformation step.

While Snowflake simplifies the cloud data integration process, it might involve extra transformation steps and data ingestion.

Some of the unique features of Snowflake integrations are:

  • COPY command: To perform bulk loading operations
  • Supports numerous data types: Numeric, string, date-time, structured and semi-structured, arrays, geospatial data, blobs, and more
  • Stored procedures: These combine SQL and Javascript and can be used to implement logical procedures for data operations.
  • Streams: Help you keep track of any data updates or changes
  • Tasks: These can be set up and scheduled to carry out data operations
  • Snowpipes: Allow for continuous loading of data in the form of micro-batches

Snowflake Data Integration Best Practices

The Snowflake integration process involves multiple stages: Data extraction, data transformation, loading data, and data governance.

Data extraction

The data extraction stage is where data is gathered from one or more data sources. It can also be called data ingestion. The sources can be anything from a website, database, web data, documents, spreadsheets, app data, and more.

  • An excellent way to optimize your data ingestion with Snowflake is to use native methods such as Snowpipe.

  • Snowpipe is a data ingestion tool that allows you to load data as soon as it reaches the staging stage. It helps make optimal usage of your available resources.

  • You can choose a real-time streaming or batch-loading method to extract data with Snowflake.

  • Real-time streaming is best suited when you want immediate insights and quick decision-making.

Data transformation

Data can come in multiple formats when ingested from various data sources. Data might also demand differing computation and storage requirements.

Here are tips that you can use to optimize the transformation process:

  • Retain the raw data history to build automatic schemas. This could also help with ML algorithms and data analytics in the future.

  • Use multiple data models as it gives you better results when reloading and reprocessing data.

  • Use the right tools for suitable file formats. Snowflake provides native modules to handle a wide range of file formats. Do read up and use them appropriately.

  • Develop a plan and transform data in a step-by-step manner.

Data loading

The data loading stage involves data migration, loading the data into the snowflake data warehouse, and using the best data platform. The best-recommended way to load data into Snowflake is to use the COPY command.

Get a good understanding of this command to be able to make optimized loading of data.

  • Before loading, stage data in possible locations such as an AWS S3 bucket, Azure container, or an external location.

  • A batch load is better for running repetitive procedures on data such as daily reports. You can also alternate between these methods or use them as and when necessary.

Data governance

The data governance stage refers to managing data workloads and the compliance that must be handled while working with the data.

Here are some tips for effective data governance:

  • Identify the data domains you use and define your data control points. Make sure your automation and workflow processes are well-defined.

  • Establish and document the scope of data, such as personally identifiable data (PII), often found with CRM software like Salesforce.

  • Identify repetitive processes, automate them, and review them periodically to further improve your data governance structure.

Snowflake Data Integration Tools

1) Portable

Portable is one of the best data integration tools with the Snowflake data platform. It has many connectors, and the team can help you with custom connectors with a quick turnaround time.

Best for

  • People looking for no code, ETL/ELT functionality, and working with large datasets

Key features

  • Hundreds of ready-to-use SaaS and data warehousing integrations

  • Fully compatible with diverse data lake environments (BigQuery, Amazon Redshift, PostgreSQL, and Microsoft SQL Server)


Portable is the best choice to bridge the gap of your cloud data platform, and it comes in both free and paid subscription models. The paid versions start from $200 per month, and as for tailored solutions, you can contact the team for a quote.

2) Hevo

Hevo is an ETL application that also provides accessible data replication features. It can work with both SaaS-based data sources and on-premise databases.

Best for

It is best suited for companies looking to automate their data pipelines and create automatic data schemas for their data sets.

Key features

  • Support for Snowflake, AWS, Redshift, and Google BigQuery

  • Automatic data schemas

  • 100+ ready-to-use connectors


Hevo comes under a free version, with around 50+ connectors, free initial load, and support for unlimited users and models. Its paid versions start from $239 per month and provide over 150+ connectors, support for on-demand events, free setup assistance, and advanced customer support. They also offer custom solutions where the pricing could vary depending on your requirements.

3) SAP

SAP is a well-known data suite with several tools, such as the SAP data integrator, cloud platform, and more. Data engineers can use it alongside the flagship ERP platform from SAP.

Best for

It is best suited for organizations already familiar with SAP products.

Key features

  • Support for both cloud-based and on-premise data management

  • A complete data management platform

  • Support for efficient batch processing

4) Informatica

Informatica is a high-performance data virtualization tool. It includes features for data governance, integration services, application integration, analytics, and more.

Best for

Informatica PowerCenter is best for enterprise businesses looking for a robust, comprehensive solution for all data needs.

Key features

  • A comprehensive suite of integration tools

  • Cloud-based and on-premises deployments are available.

  • Advanced data transformation functionality.


  • Professional Edition - This edition requires a license, with an annual cost of $8000 per user.

  • Personal Edition - You can use it for free and as needed.

5) Matillion

Matillion is an ELT tool hosted in the cloud with support for on-premises data connectors. It integrates with Snowflake and several more data warehouses. Its user interface is in the form of a no-code design canvas.

Best for

Matillion is ideal for those that want to perform ELT (Extract, Load, Transform) operations in the cloud — unlike ETL (Extract, Transform, and Load)

Key features

  • Native GUI-based data transformation feature to help teams organize queries and relationships

  • Both managed SaaS and on-premises deployment solutions are available

  • Supports major destinations, including Snowflake, Google BigQuery, Amazon Redshift, Microsoft Azure Synapse Analytics, and Databricks


  • Free: Limited to 1M rows per month

  • Basic: $2.00/credit

  • Advanced: $2.50/credit

  • Enterprise: $2.70/credit

Snowflake Data Connector Challenges

Snowflake has plenty of features to ease your data integration process. But it also has limitations, which must be addressed in your data strategy.

Here are some Snowflake data challenges that you have to tackle:

Challenge: Data Ingestion

The data ingestion process with snowflakes can be the most time-consuming and complex, especially when you have vast amounts of data. It can be particularly resource intensive.

One way to optimize resource utilization would be to use automation. You can also follow a staged process to migrate data in phases over time.

Challenge: Developing Snowflake ETL tools

Developing snowflake ETL tools can be another challenge involving several considerations, such as learning unfamiliar APIs, ensuring it has a valid schema, extensive testing, and compatibility issues.

To overcome these challenges, test your staging environment before the migration process. You can also get additional support from the Snowflake team or expert consultants.

Challenge: Data Warehouse Pricing

Snowflake allows for an efficient payment model where you can scale computation and storage resources independently. But the more data storage you need, your pricing will increase considerably.

To reduce cloud storage costs, you must plan your data integration efficiently and optimize resource allocation.

Benefits of Integrating Snowflake Data

Snowflake integrations between your data warehouse and other SaaS data sources can be beneficial in multiple ways. Snowflake is built for the cloud and is designed to overcome the many limitations of hardware-based data integration solutions.

Here are some key benefits of adopting Snowflake:

Achieve Scalability

Snowflake, as mentioned already, is a cloud-based integration tool that can help you achieve high scalability. You can scale up and down your computational requirements and storage.

Thus, it helps reduce costs and optimize your resource usage, letting you make efficient data-driven decisions from your enterprise data.

Leverage Automation

Snowflake architecture allows for a high degree of flexibility, allowing you to use other cloud technologies, such as Big Data and machine learning, for advanced analytics.

You can easily automate your workflows and save a lot of time and costs that otherwise go into data management.

Real-time Business Intelligence

Snowflake helps with real-time data processing and can be used by anyone without extensive coding knowledge.

It supports features that can quickly run your SQL queries, set up efficient data pipelines, and provide you with quick insights.

Stronger Data Science Teams

Snowflake is one of the best data integration tools to help empower your data engineering teams.

With its ability to process massive data volumes from multiple sources, your data team can derive answers to queries at a much faster pace.

Try Our Free ETL Data Integration Platform

Portable is a free ETL data platform with unlimited data volume and 300+ ready-to-use connectors. It integrates with Snowflake Data Cloud and other popular data warehouses. It's optimal for teams with long-tail data connector needs.

Ready to automate data synchronization? Automate data syncing to any data warehouse in an affordable way. Portable ticks all the right boxes for $200/mo.

Enterprise Data Integration

We can build new integrations on-demand to support data science and data engineering teams. If you have a data source that isn't in our connector library, you can request development of it.

Stay Current With ELT & ETL Best Practices

ELT/ETL is an ever-evolving space. Keeping yourself updated with the best ETL practices is important. These tutorials help you sharpen your knowledge of data transformation and automation.

Q&A About Snowflake Data Integrations

What is a data integration tool?

A data integration tool is a software application that helps extract data from multiple sources, consolidate them, transform them as required to be compatible, and store them into a single unified destination platform.

How do we sync data to the Snowflake Data Cloud?

Data synchronizations in the Snowflake Data Cloud can be carried out with the help of secure, enterprise-grade APIs. You have several scheduling options to perform the synchronization. You can set up various configurations for the sync operation on the connection detail page of your Snowflake connection.

Which ETL tool should I use with Snowflake?

Snowflake supports a wide range of ETL and ELT tools for data integration. Some popular tools you can choose include Portable, Informatica, Talend, Fivetran, and Hevo.

How to transform data with Snowflake?

Snowflake data transformations can be done using the COPY INTO <table> command. This command contains several options that, when used appropriately, can help you perform the data transformation operations while loading the data.