In the world of big data, companies need efficient and scalable solutions to store, process, and analyze their data. One platform that is synonymous with modern big data solutions is Snowflake. Snowflake, a cloud-based data warehousing solution, has gained massive popularity due to its ease of use, scalability, and performance. ELT, which stands for Extract, Load, Transform, is an alternative approach to data integration that has become popular among Snowflake users who’d normally use ETL. In this blog post, we will provide an overview of Snowflake, explain what ELT is, discuss the benefits of Snowflake ELT tools, and review some of the best Snowflake ELT tools available, including Arcion.
What is Snowflake? ( H2 )
Our first stop is digging a little deeper in Snowflake. As mentioned in the introduction, Snowflake is a super popular cloud-based data warehousing solution. Although Snowflake has many uses and use cases, much of its popularity is driven by its ability for organizations to store and process large amounts of data with ease. Snowflake offers a variety of features that make it easy to use and scalable, including automatic scaling, zero-copy cloning, and seamless integration with other data sources.Â
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Built on a SQL database engine, Snowflake supports both structured and unstructured data. This ability to support multiple types of data is one of the key benefits of Snowflake and organizations to easily store and process data from various sources. Snowflake also provides a range of security features, including encryption, multi-factor authentication, and data masking. These key features add to Snowflake’s popularity for organizations that need to store complex and sensitive data.
Aside from its features, Snowflake enables many organizations in quite a few different ways. Some well-known use cases and scenarios where Snowflake is used include:
Data Analytics: Snowflake is commonly used for data analytics, allowing users to easily query and analyze large datasets. Its cloud-based architecture allows for fast and efficient data processing, and its flexible storage options enable users to store structured and semi-structured data in a single location.
Business Intelligence: Snowflake can be used for business intelligence, providing users with real-time insights and reporting capabilities. Its SQL-based interface makes it easy to create dashboards and visualizations, and its built-in security features ensure that sensitive data is protected.
Data Sharing: Snowflake allows users to securely share data with external partners and customers. This feature is particularly useful for organizations that need to collaborate with other companies, such as in the healthcare or financial industries.
Data Science: Snowflake can be used for data science applications, allowing users to run machine learning algorithms and build predictive models. Its integration with popular data science tools like Python and R makes it a popular choice for data scientists.
Overall, Snowflake is a powerful and versatile data warehousing platform with massive flexibility and capabilities. Its rich feature set allows it to be used by a wide range of organizations for a variety of use cases. Its cloud-based architecture, scalability, and ease of use make it a popular choice for many big data use cases.
What is ELT?
A common use for Snowflake is in the realm of data integration. Of course, most people working with data in any capacity are familiar with the traditional ETL pattern for data integration. ELT is a modern approach to data integration that stands for Extract, Load, Transform. Unlike traditional ETL (Extract, Transform, Load) processes, ELT changes the ordering of the stages of data integration from the more well-known ETL processes. ELT processes first load the raw data into a data warehouse before transforming it. With the data being transformed after it has landed on the target, ELT processes take advantage of the powerful transformation capabilities of modern data warehouses and can offer potential performance gains over more traditional approaches.
When it comes to ELT processes, they have become popular among Snowflake users since they offer more efficient, scalable, and cost-effective data integration versus traditional ETL processes. By loading the raw data into the data warehouse first, organizations can take advantage of the powerful transformation capabilities of the warehouse and reduce the overall data processing time.
To illustrate the difference between ELT and ETL using Snowflake as an example, let's consider a hypothetical scenario where we want to integrate data from two different sources: a customer relationship management (CRM) system and an e-commerce platform.
In an ETL process, we would first extract data from the CRM system and the e-commerce platform, then transform the data into a format suitable for loading into Snowflake, and finally load the transformed data into Snowflake. The transformation process could involve tasks such as data cleansing, data mapping, and data aggregation. Once the data is in Snowflake, it can be queried and analyzed.
In an ELT process, on the other hand, we would first extract data from the CRM system and the e-commerce platform and load the raw data into Snowflake. We could then perform transformations directly within Snowflake using SQL or other programming languages it supports. This approach would leverage Snowflake's capabilities and allow us to take advantage of its scalability, performance, and parallel processing capabilities.
The key advantage of ELT over ETL is its flexibility and scalability. By loading raw data into the data warehouse first and performing transformations later, within the platform itself, data integration becomes more efficient and less complex.
Benefits of Snowflake ELT Tools
To implement a robust ELT process, you will likely require one or a few tools depending on your use case. An ELT tool can help facilitate the loading and transformation of data into a data warehouse like Snowflake. Unlike traditional ETL (Extract, Transform, Load) tools, which involve transforming data before it is loaded into a data warehouse, ELT tools load raw data into the data warehouse first. Once the data is loaded, the process then leverages the platform that the data has been loaded into to transform it within the warehouse itself.
ELT tools can be quite simple, just helping to load the data in its current state or be more feature rich. Most modern ELT tools provide features that can be used for data extraction, data loading, data transformation, and data integration. More advanced tools may additionally offer features for data profiling, data cleansing, and data validation. These additional features can help ensure data accuracy and consistency.
When selecting an ELT tool to use, you’ll want to be aware of whether it can support your use case. When looking at a cloud-native product like Snowflake, you must be particularly mindful that it can support the technologies in your stack. Fortunately, ELT tools are particularly well-suited to cloud-based data warehousing solutions, such as Snowflake. By leveraging the power of cloud-based data warehousing and ELT tools, organizations can handle large volumes of data more efficiently and with less complexity. The result is that utilizing these tools allows them to make better-informed business decisions with less technical complexity to get there.
Let’s briefly look at a few key points where ELT shines when used with Snowflake. There are several benefits to using Snowflake ELT tools which are listed below.
ScalabilityÂ
Snowflake ELT tools are designed to handle large volumes of data and can easily scale up or down depending on the organization's needs. Since Snowflake is easy to scale and the transformation happens after the data has been loaded into the platform, there is almost infinite scalability.
Efficiency
Snowflake ELT tools are designed to be efficient, which means that they can process data quickly and reduce the overall time required to perform data transformations. Since Snowflake is heavily optimized and can easily scale up compute for transformations, transformations can run more quickly than they would on most ETL-based tools.
Cost-EffectiveÂ
Snowflake ELT tools are cost-effective because they take advantage of the powerful transformation capabilities of Snowflake, which eliminates the need for expensive ETL tools. The ability for Snowflake to scale up and down based on need is also a major cost reduction feature when it comes to compute costs.
Ease of UseÂ
Snowflake ELT tools are designed to be easy to use, which means that organizations can quickly set up and run data integration processes without the need for extensive technical expertise. Snowflake ELT tools can also handle structured and unstructured data which makes them more flexible than traditional ETL tools.
Best Snowflake ELT Tools
Now that you are aware of ELT and its benefits, it’s time to take a look at some of the most popular tools that are in use. Each tool shown below has different capabilities, some are overlapping and some are not. Selecting a tool that meets your current needs and potential needs in the future is the best way to future-proof your ELT stack. Let's take a look at some of the most popular tools below.
Arcion
Arcion is a tool that can assist with Snowflake ELT. Action covers the Extract and Load components of the ETL pipeline through scalable and efficient Change Data Capture (CDC) capabilities. With no-code required, Arcion is a flexible tool with a host of source connectors that can assist with moving data into Snowflake following ELT patterns. Arcion is available as a full-managed service through Arcion Cloud and as an on-prem, self-managed tool through Arcion Self-Hosted. Arcion's key benefits include its no-code setup, sleek UI, high-performance, and real-time monitoring and alerting features.
If you’re looking for an real-time data replication that supports schema evolution (DDL changes) without any manual supervision and pipeline downtime, and can scale large data operations that contain billions of rows without an army of engineers, Arcion can be a great choice.Â
Arcion also has an intuitive UI for both Cloud and the Self-hosted version to allow you set up production pipelines in minutes.
Interested to see Arcion in action? Read our other blog that walks you through Implementing Change Data Capture with MySQL, Snowflake, and Arcion and Connect Oracle to Snowflake. Here is also a 5-min video that shows you the step-by-step process.
BlendoÂ
Blendo is a cloud-based ELT tool that allows users to integrate data from various sources into Snowflake. Blendo supports a variety of data sources including popular sales, marketing, and financial platforms. Key benefits of Blendo include its ease of use, scalability, and flexible pricing options. Blendo also provides real-time data integration and transformation capabilities, as well as data profiling and data quality checks. One limitation of Blendo is its limited support for custom data transformations.
Matillion
Matillion is a cloud-based ELT tool that provides a visual interface for building data integration workflows. Matillion supports a variety of data sources, including databases, SaaS applications, and file formats, and provides advanced transformation capabilities through its visual interface. Matillion also offers real-time monitoring and alerting features, as well as data profiling and data quality checks. Limitations of Matillion include its relatively high pricing compared to other ELT tools and the confusing credit system used for usage-based charges.
LuigiÂ
Luigi is an open-source ELT tool developed by Spotify. The tool allows users to easily build and schedule data integration workflows. Luigi supports a variety of data sources and provides advanced transformation capabilities through its Python-based interface. Luigi also provides real-time monitoring and alerting features, as well as data profiling and data quality checks. One limitation of Luigi is its lack of a user-friendly interface compared to other ELT tools, most of which contain some sort of no-code option. A fair amount of code must be written to create pipelines with Luigi.
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Apache Airflow
Apache Airflow is another popular open-source ELT tool. Like Luigi, Airflow allows users to build and schedule data integration workflows using Python. Airflow supports a variety of data sources and provides advanced transformation capabilities through its Python-based interface. Airflow also provides real-time monitoring and alerting features, as well as a web interface for managing the pipelines. One limitation of Apache Airflow is its relatively steep learning curve for users unfamiliar with Python.
StreamSetsÂ
StreamSets is an ELT tool built by Software AG. The tool provides real-time data integration and transformation capabilities. StreamSets supports a variety of data sources, including databases, SaaS applications, and file formats, and provides advanced transformation capabilities through its visual interface. StreamSets also offers real-time monitoring and alerting features, as well as data profiling and data quality checks. For multi-cloud and hybrid deployments, StreamSets also provides a central control plane to manage everything in one spot. Despite the features of StreamSets, one major limitation is its limited support for custom data transformations.
EtleapÂ
Etleap is a cloud-based ELT tool that provides an easy-to-use interface for building and scheduling data integration workflows. Etleap supports a variety of data sources that can be configured through an easy-to-use UI. Etleap’s UI provides a single place to configure and manage pipelines to reduce the learning curve for adoption. Etleap also offers real-time monitoring and alerting features, as well as data profiling and data quality checks. As with many other ELT tools mentioned in the list, Etleap has limited support for custom data transformations.
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TalendÂ
Talend is a cloud-based ELT tool that provides a no-code visual interface for building data integration workflows. Talend offers a visual pipeline designer, as well as Stitch, which can assist with aggregating data for analytics. Talend ELT pipelines can leverage real-time monitoring and alerting features. Even though Talend offers a well-featured solution that is battle-tested, a limitation of Talend is its relatively high pricing compared to other ELT tools that are available with similar features.
Conclusion
As Snowflake has gained significant popularity in recent years, so has the need for ELT capabilities. ELT, which stands for extract, load, and transform, is a modern approach to data integration that allows for more flexible and scalable data integration workflows. Although not always necessary, ELT can offer a massive boost in performance when utilized correctly.
When it comes to implementing ELT, most users will opt for a tool to assist them. ELT tools provide a range of benefits, including real-time data integration, advanced transformation capabilities, and data profiling and quality checks. However, it is important to carefully evaluate the features and pricing of each tool to determine which one best suits your organization's needs. Ensuring a tool supports current and future needs is a must for anyone implementing ELT.
In this article, we have looked at some of the top ELT tools available for use with Snowflake, including Arcion, Luigi, Apache Airflow, StreamSets, Talend, and many more. While each tool has its strengths and weaknesses, assessing the available tools allows organizations to see what is possible and which tools can empower their data integration needs.
When selecting an ELT tool to use with Snowflake, Arcion stands out as one of the most flexible and easy-to-use tools on our list. With its no-code configuration, high performance, and scalable architecture, Arcion can cover your organization’s current ELT needs as well as those well into the future. To try out Arcion for yourself, connect with our team of experts to get started.
