June 24, 2022
With the rise of Big Data over the last decade, more and more businesses started moving their applications to the cloud. It is estimated that over 90% of the entire data in today’s world has been generated in the last two years only. With the increase in such data volume, the velocity at which data is generated has also changed. As a result, new tools and applications need to be developed that are specially designed to cope with this ever-increasing volume and velocity of data.
Data warehousing has been a popular concept when it comes to generating insights and reporting on the application data. A typical data warehouse is a database application that is specially designed to handle analytical queries without affecting the performance of an application database. Such a data warehouse can be installed locally or on a public cloud vendor. However, a data warehouse application is quite resource-intensive and needs maintenance in order to be able to perform substantially. A few cloud data warehouse providers also encapsulate the responsibility of managing the data warehouse application and allow users to focus on the application only.
Snowflake is one such cloud provider that allows users to create data-driven insights using a multitude of application platforms such as a Data Warehouse, Data Lake, Data Engineering, etc. In order to move data into a Snowflake Data Warehouse, there are several Data Integration tools (also known as ETL tools) available. In this article, we will focus on some of the popular Snowflake ETL tools available and also discuss some of the key concepts while considering ETL with Snowflake. Some of the key benefits of using Snowflake are listed below.
Before diving deeper into Snowflake ETL concepts and Snowflake ETL best practices, let us first understand some key concepts. This will be helpful as we move through the article.
In addition to these, there are certain Snowflake ETL best practices that you might consider while implementing your data pipelines.
Now that we have an idea about the differences between ETL and ELT concepts, let us understand how can we achieve those with Snowflake. By default, Snowflake offers tools to extract data from source systems, and there are also some third-party tools that allow users to build custom integrations with Snowflake. I have listed below some of the best ETL tools for Snowflake, however, the list is not exhaustive.
Skyvia is a cloud-based platform for data integration with different cloud integration services and tools to perform different data-related tasks. It is mostly oriented for ETL and reverse ETL tasks, but also provides tools for API-based integration. Skyvia supports over 80 different cloud applications and a number of most widely used databases.
integrate.io is a no-code web interface that allows users to connect with multiple database systems on the cloud. You can choose a source and a target data system based on the list of available connections. Once you have access to both the source and target data systems, you can create a mapping between the two and allow data transformations within the pipeline.
With Integrate.io users can easily start building their data pipelines. A pre-requisite to this would be to already have access to the source and target data systems. Once the access has been defined, it is just a matter of a few minutes to get the data moving between the systems.
Apache Airflow is an open-source scheduling platform that allows users to schedule their data pipelines. It allows users to programmatically author data pipelines and manage them in a distributed fashion. Airflow has a concept of implementing data pipelines in Direct Acyclic Graphs, also known as DAGs. A DAG comprises of multiple individual tasks that can be arranged in a way such that a task dependency can be established and each task depends on the complement of its upstream tasks.
Matillion is a cloud-native data integration tool that provides a rich user interface to develop data pipelines. There are two products that Matition provides, Matilion Data Loader which allows users to move data from any service to the cloud, and Matilion ETL which allows users to define data transformations and build data pipelines on the cloud.
Stitch offers its users an extensible Data Integration platform that can be used to connect to a plethora of databases and other SaaS applications. Stitch provides an easy-to-use orchestration tool, with which you can monitor your data pipelines on the go. Some advanced and enterprise features include sending alerts to Slack, notifications to DataDog, etc. Stitch makes it extensible with the support of open-source project Singer taps and targets. With the help of the Singer project, you can start building your custom connectors as well.
HevoData is a SaaS offering that allows users to create data pipelines without writing a single line of code. It allows users to perform data extraction, transformation, and loading from multiple data sources. HevoData offers integrations with various platforms like Google Drive, Salesforce, Snowflake, AWS, etc. HevoData provides a useful feature to anonymize data before moving it to final destinations or data warehouses.
In this article, we have reviewed some of the popular ETL tools available for Snowflake. As we now understand the concepts of ETL and ELT with Snowflake, it is easy for us as a user to choose how to integrate with Snowflake. Along with the native data connectors, there are various other third-party data integration tools that allow users to extract, load and transform data into Snowflake.
If you are looking to implement your own data integration solution with Snowflake, I would recommend you to check out Skyvia’s integration with Snowflake. It is one of the best Snowflake ETL tools, and it allows users to visually load data into Snowflake. To learn more about Skyvia, please visit the official documentation.