Top ETL Tools

Top 10 ETL Tools for Data Integration

Best 4 Snowflake ETL Tools

January 31, 2024

In the ever-evolving world of data management, the aim for seamless data integration has become the priority of organizations striving for proficiency and making informed business decisions. At the forefront of this transformative journey lies the Snowflake architecture, known for its versatility and scalability. To unleash the complete capabilities of Snowflake, organizations leverage a diverse range of ETL tools crafted to enhance data handling. From streamlining data extraction to orchestrating intricate transformations and loading processes, these tools help drive efficiency by improving analysis capabilities.

As you read along, you will gain insights into the ETL process, factors for choosing ETL tools, and leading ETL tools for Snowflake available in the market.

What is ETL?

ETL stands for Extract, Transform, and Load, which represents the execution of data integration and manipulation. Here’s what each component of ETL means:

  • Extract: In this phase, data is extracted from one or multiple sources such as databases, applications, or external systems. The goal is to gather relevant data for analysis or storage.
  • Transform: The extracted data is then modified to meet specific requirements. This step may involve cleaning, restructuring, aggregating, or converting data into a suitable format compatible with the target system for further processing.
  • Load: The transformed data is then loaded into a target destination, often a warehouse or a database. This step completes the process, making the information available for downstream applications.

The ETL process is crucial for maintaining data quality, consistency, and accessibility. Utilizing ETL tools for automating several data integration processes is key. These tools are the software solutions that play an essential role in implementing the data integration process, helping you manage and transform large volumes of data efficiently.

Snowflake Overview

Image Source: Snowflake

Snowflake is a cloud-based data warehousing platform that provides data storage, processing, and analytics. It employs a collaborative architecture in its storage system, enabling the seamless storage and real-time management of extensive databases. 

The snowflake architecture is a hybrid combination of shared-disk and shared-nothing database architecture. In shared-disk, processing occurs on multiple nodes connected to a single memory disk, enabling you to access all data simultaneously. Conversely, shared-nothing involves independent nodes that process data in parallel, resulting in improved data warehouse performance and enhanced SQL query processing.

Some of the key features of Snowflake are:

  • Cloning: Snowflake’s cloning capability helps you to duplicate databases, schemas, and tables by editing metadata rather than replicating storage contents. This facilitates the quick creation of clones for testing the whole database.
  • Time-travel: The time-travel feature enables you to access past data within a set time, even if altered or deleted. It aids you in restoring deleted data, creating backups, and examining changes over time.
  • Snowsight: It is Snowflake’s interface that replaces SQL worksheets. It allows you to create and share charts and dashboards, supporting data validation and ad-hoc analysis.

Criteria to Choose the Right Snowflake ETL Solution

For efficient data extraction from diverse sources, avoid employing distinct data migration tools for each source. Instead, adopt a clear integration strategy and specific criteria to choose your Snowflake ETL tool.

Here are some of the factors to consider when choosing the right Snowflake ETL tool:

  • Ease of Use: Emphasize tools that offer a user-friendly interface and intuitive functionalities with an aim to enhance the capability of the ETL process and facilitate a smoother learning experience for your team.
  • Extending Connectors: Check if you can modify connectors to add new endpoints or address any issue related to connectors. Ensure the tool has a feature to create custom connectors, ensuring adaptability to new technologies, seamless integration, and smooth functioning.
  • Flexibility: Look for tools that offer flexibility in handling different data formats, sources, and transformation requirements to accommodate diverse business needs.
  • Integration Capabilities: Check whether the ETL tool can integrate seamlessly with other tools and systems within your data ecosystem. This promotes a cohesive and interoperable infrastructure.
  • Cost-effective: Evaluate the cost of maintenance, licensing, and potential scaling to ensure the tool aligns with your budget constraints.

Top 4 Snowflake ETL Tools

Here are the top Snowflake ETL tools determined by their popularity.

Airbyte

Image Source: Airbyte

Airbyte is a data integration platform that helps you merge data from APIs and databases to destinations such as data warehouses and lakes. With an extensive set of around 350+ pre-built connectors, Airbyte is crafted to simplify seamless data transfer and synchronization. A distinctive factor of Airbyte that makes it unique is that it supports both structured and unstructured data from different sources, allowing you to work smoothly with different data types and formats.

Some of the key features of Airbyte are:

  • You can build custom connectors quickly using Airbyte’s no-code connector builder, low-code Connector Development Kit (CDK), or other language-specific CDKs.
  • Airbyte allows you to define how to handle changes in the source schema in each transfer. This flexibility significantly ensures a robust data migration process when the source schema changes.
  • You have two options to deploy Airbyte: Airbyte Cloud, a cloud-hosted solution managed by Airbyte, or Self-Managed deployment in your infrastructure. This gives you the flexibility to choose the deployment method that best fits and aligns with your preferences and requirements for a tailored data integration experience.

StreamSets

Image Source: StreamSets

StreamSets is a cloud-native data integration platform that helps you build, run, and monitor data pipelines. It allows you to streamline your pipeline by connecting with various external systems, such as cloud data lakes, warehouses and on-premises storage systems like relational databases. While a pipeline executes, you can actively observe real-time statistics and error information as data moves from source to destination systems, ensuring efficient and transparent data flow.

Some of the key features of StreamSets are:

  • With the StreamSets Transformer component, you can perform complex transformations in Snowflake with a no-code approach, surpassing SQL limitations. 
  • You can use the StreamSets Python SDK to quickly template and scale data pipelines with just a few lines of code. It also allows you to smoothly integrate with the UI-based tool for programmatic creation and handling data flows and jobs. 

Azure Data Factory

Image Source: Azure Data Factory

Azure Data Factory (ADF) is a fully managed, serverless data integration platform. It helps you streamline the process by connecting data sources to various destinations through 90+ built-in connectors, including Snowflake. With Azure Data Factory’s infographics and visual designer, simplify the creation and management of data workflows through an intuitive drag-and-drop interface. This user-friendly approach allows you to design complex data pipelines smoothly.

Some of the amazing features of Azure Data Factory are:

  • With ADF, you can track the data lineage, gaining insights into the origin and flow of data throughout the integration pipeline. This facilitates you to evaluate the potential effects of changes on downstream processes.
  • Azure Data Factory enables you to automate the scheduling and triggering of data pipeline processes based on specific time intervals or events. This guarantees optimal execution without the need for manual intervention.

AWS Glue

Image Source: AWS Glue

AWS Glue is a serverless data integration platform that simplifies and expedites data preparation. It allows you to explore and connect over 70 varied data sources, monitor ETL pipelines, and manage a centralized data catalog for your data. The data catalog will enable you to swiftly explore and search AWS datasets without relocating the data. It becomes accessible for querying through Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum. 

Some of the amazing features of AWS Glue are:

  • AWS Glue’s auto-scaling helps you dynamically adjust resources in response to workload fluctuations, assigning jobs as needed. You can add or remove resources based on task distribution or idle resource costs as your job advances.
  • Integrating AWS Glue with AWS DataBrew, a user-friendly, point-and-click visual interface, lets you clean and normalize data effortlessly without requiring you to write code. 

How to Import Data into Snowflake in Minutes

Unlocking the potential of Snowflake for various business objectives, including analytics, compliance, and performance optimization, involves implementing the ETL process. 

To fully harness the potential of Snowflake, it is necessary to extract data from your desired sources and load it into Snowflake. For this purpose, we recommend leveraging Airbyte as it facilitates data replication with ease. This process can be achieved with just a few clicks by following the three steps mentioned below.

Step 1: Configure a Source Connector

Login to your Airbyte account and, using the user-friendly interface, set up a source connector from which you want to extract data.

Step 2: Configure Snowflake as a Destination Connector

  • Navigate to the dashboard and click on the Destinations option.
  • Type Snowflake in the Search box of the destination page and click on the connector.
  • On the Snowflake destination page, fill in the details such as Host, Role, Warehouse, Database, Default Schema, Username, and Optional fields like JDBC URL Params and Raw Table Schema Name. Then click on Set up destination.

Step 3: Configure the Snowflake Data Pipeline in Airbyte

After you set both the source and destination, proceed to configure the connection. This step includes choosing the source data (step 1), defining the sync frequency, and specifying the destination as your Snowflake table.

Completing these three steps will help you finalize the data integration process in Airbyte, enabling you to migrate data from your chosen sources to Snowflake. Additionally, with Airbyte, you can seamlessly configure Snowflake as your preferred source.

Conclusion

This article presents the top four ETL tools that offer unparalleled data integration capabilities for your business. With user-friendly interfaces and robust features, these tools empower you to streamline the data workflows and replicate data seamlessly within Snowflake. So, choose the Snowflake ETL tool based on your requirements to enhance analytics and derive actionable insights, ultimately driving efficiency and informed decision-making.

Consider leveraging the convenience of Airbyte, a user-friendly tool equipped with a diverse range of connectors and robust security features. Simplify your workflows effortlessly by giving Airbyte a try today!

What should you do next?

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TL;DR

The most prominent ETL and ELT tools to transfer data from include:

  • Airbyte
  • Fivetran
  • Stitch
  • Matillion
  • These ETL and ELT tools help in extracting data from and other sources (APIs, databases, and more), transforming it efficiently, and loading it into a database, data warehouse or data lake, enhancing data management capabilities. Airbyte distinguishes itself by offering both a self-hosted open-source platform and a Cloud one..

    What is ETL?

    ETL (Extract, Transform, Load) is a process used to extract data from one or more data sources, transform the data to fit a desired format or structure, and then load the transformed data into a target database or data warehouse. ETL is typically used for batch processing and is most commonly associated with traditional data warehouses.

    What is ELT?

    More recently, ETL has been replaced by ELT (Extract, Load, Transform). ELT Tool is a variation of ETL one that automatically pulls data from even more heterogeneous data sources, loads that data into the target data repository - databases, data warehouses or data lakes - and then performs data transformations at the destination level. ELT provides significant benefits over ETL, such as:

    • Faster processing times and loading speed
    • Better scalability at a lower cost
    • Support of more data sources (including Cloud apps), and of unstructured data
    • Ability to have no-code data pipelines
    • More flexibility and autonomy for data analysts with lower maintenance
    • Better data integrity and reliability, easier identification of data inconsistencies
    • Support of many more automations, including automatic schema change migration

    For simplicity, we will only use ETL as a reference to all data integration tools, ETL and ELT included, to integrate data from .

    How data integration from to a data warehouse can help

    Companies might do ETL for several reasons:

    1. Business intelligence: data may need to be loaded into a data warehouse for analysis, reporting, and business intelligence purposes.
    2. Data Consolidation: Companies may need to consolidate data with other systems or applications to gain a more comprehensive view of their business operations
    3. Compliance: Certain industries may have specific data retention or compliance requirements, which may necessitate extracting data for archiving purposes.

    Overall, ETL from allows companies to leverage the data for a wide range of business purposes, from integration and analytics to compliance and performance optimization.

    Criterias to select the right ETL solution for you

    As a company, you don't want to use one separate data integration tool for every data source you want to pull data from. So you need to have a clear integration strategy and some well-defined evaluation criteria to choose your ETL solution.

    Here is our recommendation for the criteria to consider:

    • Connector need coverage: does the ETL tool extract data from all the multiple systems you need, should it be any cloud app or Rest API, relational databases or noSQL databases, csv files, etc.? Does it support the destinations you need to export data to - data warehouses, databases, or data lakes?
    • Connector extensibility: for all those connectors, are you able to edit them easily in order to add a potentially missing endpoint, or to fix an issue on it if needed?
    • Ability to build new connectors: all data integration solutions support a limited number of data sources.
    • Support of change data capture: this is especially important for your databases.
    • Data integration features and automations: including schema change migration, re-syncing of historical data when needed, scheduling feature
    • Efficiency: how easy is the user interface (including graphical interface, API, and CLI if you need them)?
    • Integration with the stack: do they integrate well with the other tools you might need - dbt, Airflow, Dagster, Prefect, etc. - ? 
    • Data transformation: Do they enable to easily transform data, and even support complex data transformations? Possibly through an integration with dbt
    • Level of support and high availability: how responsive and helpful the support is, what are the average % successful syncs for the connectors you need. The whole point of using ETL solutions is to give back time to your data team.
    • Data reliability and scalability: do they have recognizable brands using them? It also shows how scalable and reliable they might be for high-volume data replication.
    • Security and trust: there is nothing worse than a data leak for your company, the fine can be astronomical, but the trust broken with your customers can even have more impact. So checking the level of certification (SOC2, ISO) of the tools is paramount. You might want to expand to Europe, so you would need them to be GDPR-compliant too.

    Top ETL tools

    Here are the top ETL tools based on their popularity and the criteria listed above:

    1. Airbyte

    Airbyte is the leading open-source ELT platform, created in July 2020. Airbyte offers the largest catalog of data connectors—350 and growing—and has 40,000 data engineers using it to transfer data, syncing several PBs per month, as of June 2023. Major users include brands such as Siemens, Calendly, Angellist, and more. Airbyte integrates with dbt for its data transformation, and Airflow/Prefect/Dagster for orchestration. It is also known for its easy-to-use user interface, and has an API and Terraform Provider available.

    What's unique about Airbyte?

    Their ambition is to commoditize data integration by addressing the long tail of connectors through their growing contributor community. All Airbyte connectors are open-source which makes them very easy to edit. Airbyte also provides a Connector Development Kit to build new connectors from scratch in less than 30 minutes, and a no-code connector builder UI that lets you build one in less than 10 minutes without help from any technical person or any local development environment required.. 

    Airbyte also provides stream-level control and visibility. If a sync fails because of a stream, you can relaunch that stream only. This gives you great visibility and control over your data. 

    Data professionals can either deploy and self-host Airbyte Open Source, or leverage the cloud-hosted solution Airbyte Cloud where the new pricing model distinguishes databases from APIs and files. Airbyte offers a 99% SLA on Generally Available data pipelines tools, and a 99.9% SLA on the platform.

    2. Fivetran

    Fivetran is a closed-source, managed ELT service that was created in 2012. Fivetran has about 300 data connectors and over 5,000 customers.

    Fivetran offers some ability to edit current connectors and create new ones with Fivetran Functions, but doesn't offer as much flexibility as an open-source tool would.

    What's unique about Fivetran? 

    Being the first ELT solution in the market, they are considered a proven and reliable choice. However, Fivetran charges on monthly active rows (in other words, the number of rows that have been edited or added in a given month), and are often considered very expensive.

    Here are more critical insights on the key differentiations between Airbyte and Fivetran

    3. Stitch Data

    Stitch is a cloud-based platform for ETL that was initially built on top of the open-source ETL tool Singer.io. More than 3,000 companies use it.

    Stitch was acquired by Talend, which was acquired by the private equity firm Thoma Bravo, and then by Qlik. These successive acquisitions decreased market interest in the Singer.io open-source community, making most of their open-source data connectors obsolete. Only their top 30 connectors continue to be  maintained by the open-source community.

    What's unique about Stitch? 

    Given the lack of quality and reliability in their connectors, and poor support, Stitch has adopted a low-cost approach.

    Here are more insights on the differentiations between Airbyte and Stitch, and between Fivetran and Stitch.

    Other potential services

    Matillion

    Matillion is a self-hosted ELT solution, created in 2011. It supports about 100 connectors and provides all extract, load and transform features. Matillion is used by 500+ companies across 40 countries.

    What's unique about Matillion? 

    Being self-hosted means that Matillion ensures your data doesn’t leave your infrastructure and stays on premise. However, you might have to pay for several Matillion instances if you’re multi-cloud. Also, Matillion has verticalized its offer from offering all ELT and more. So Matillion doesn't integrate with other tools such as dbt, Airflow, and more.

    Here are more insights on the differentiations between Airbyte and Matillion.

    Airflow

    Apache Airflow is an open-source workflow management tool. Airflow is not an ETL solution but you can use Airflow operators for data integration jobs. Airflow started in 2014 at Airbnb as a solution to manage the company's workflows. Airflow allows you to author, schedule and monitor workflows as DAG (directed acyclic graphs) written in Python.

    What's unique about Airflow? 

    Airflow requires you to build data pipelines on top of its orchestration tool. You can leverage Airbyte for the data pipelines and orchestrate them with Airflow, significantly lowering the burden on your data engineering team.

    Here are more insights on the differentiations between Airbyte and Airflow.

    Talend

    Talend is a data integration platform that offers a comprehensive solution for data integration, data management, data quality, and data governance.

    What’s unique with Talend?

    What sets Talend apart is its open-source architecture with Talend Open Studio, which allows for easy customization and integration with other systems and platforms. However, Talend is not an easy solution to implement and requires a lot of hand-holding, as it is an Enterprise product. Talend doesn't offer any self-serve option.

    Pentaho

    Pentaho is an ETL and business analytics software that offers a comprehensive platform for data integration, data mining, and business intelligence. It offers ETL, and not ELT and its benefits.

    What is unique about Pentaho? 

    What sets Pentaho data integration apart is its original open-source architecture, which allows for easy customization and integration with other systems and platforms. Additionally, Pentaho provides advanced data analytics and reporting tools, including machine learning and predictive analytics capabilities, to help businesses gain insights and make data-driven decisions. 

    However, Pentaho is also an Enterprise product, so hard to implement without any self-serve option.

    Informatica PowerCenter

    Informatica PowerCenter is an ETL tool that supported data profiling, in addition to data cleansing and data transformation processes. It was also implemented in their customers' infrastructure, and is also an Enterprise product, so hard to implement without any self-serve option.

    Microsoft SQL Server Integration Services (SSIS)

    MS SQL Server Integration Services is the Microsoft alternative from within their Microsoft infrastructure. It offers ETL, and not ELT and its benefits.

    Singer

    Singer is also worth mentioning as the first open-source JSON-based ETL framework.  It was introduced in 2017 by Stitch (which was acquired by Talend in 2018) as a way to offer extendibility to the connectors they had pre-built. Talend has unfortunately stopped investing in Singer’s community and providing maintenance for the Singer’s taps and targets, which are increasingly outdated, as mentioned above.

    Rivery

    Rivery is another cloud-based ELT solution. Founded in 2018, it presents a verticalized solution by providing built-in data transformation, orchestration and activation capabilities. Rivery offers 150+ connectors, so a lot less than Airbyte. Its pricing approach is usage-based with Rivery pricing unit that are a proxy for platform usage. The pricing unit depends on the connectors you sync from, which makes it hard to estimate. 

    HevoData

    HevoData is another cloud-based ELT solution. Even if it was founded in 2017, it only supports 150 integrations, so a lot less than Airbyte. HevoData provides built-in data transformation capabilities, allowing users to apply transformations, mappings, and enrichments to the data before it reaches the destination. Hevo also provides data activation capabilities by syncing data back to the APIs. 

    Meltano

    Meltano is an open-source orchestrator dedicated to data integration, spined off from Gitlab on top of Singer’s taps and targets. Since 2019, they have been iterating on several approaches. Meltano distinguishes itself with its focus on DataOps and the CLI interface. They offer a SDK to build connectors, but it requires engineering skills and more time to build than Airbyte’s CDK. Meltano doesn’t invest in maintaining the connectors and leave it to the Singer community, and thus doesn’t provide support package with any SLA. 

    All those ETL tools are not specific to , you might also find some other specific data loader for data. But you will most likely not want to be loading data from only in your data stores.

    Which data can you extract from ?

    How to start pulling data in minutes from

    If you decide to test Airbyte, you can start analyzing your data within minutes in three easy steps:

    Step 1: Set up as a source connector

    Step 2: Set up a destination for your extracted data

    Choose from one of 50+ destinations where you want to import data from your source. This can be a cloud data warehouse, data lake, database, cloud storage, or any other supported Airbyte destination.

    Step 3: Configure the data pipeline in Airbyte

    Once you've set up both the source and destination, you need to configure the connection. This includes selecting the data you want to extract - streams and columns, all are selected by default -, the sync frequency, where in the destination you want that data to be loaded, among other options.

    And that's it! It is the same process between Airbyte Open Source that you can deploy within 5 minutes, or Airbyte Cloud which you can try here, free for 14-days.

    Conclusion

    This article outlined the criteria that you should consider when choosing a data integration solution for ETL/ELT. Based on your requirements, you can select from any of the top 10 ETL/ELT tools listed above. We hope this article helped you understand why you should consider doing ETL and how to best do it.

    What should you do next?

    Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:

    flag icon
    Easily address your data movement needs with Airbyte Cloud
    Take the first step towards extensible data movement infrastructure that will give a ton of time back to your data team. 
    Get started with Airbyte for free
    high five icon
    Talk to a data infrastructure expert
    Get a free consultation with an Airbyte expert to significantly improve your data movement infrastructure. 
    Talk to sales
    stars sparkling
    Improve your data infrastructure knowledge
    Subscribe to our monthly newsletter and get the community’s new enlightening content along with Airbyte’s progress in their mission to solve data integration once and for all.
    Subscribe to newsletter

    Frequently Asked Questions

    What is ETL?

    ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.

    What is ?

    What data can you extract from ?

    How do I transfer data from ?

    This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: set it up as a source, choose a destination among 50 available off the shelf, and define which data you want to transfer and how frequently.

    What are top ETL tools to extract data from ?

    The most prominent ETL tools to extract data include: Airbyte, Fivetran, StitchData, Matillion, and Talend Data Integration. These ETL and ELT tools help in extracting data from various sources (APIs, databases, and more), transforming it efficiently, and loading it into a database, data warehouse or data lake, enhancing data management capabilities.

    What is ELT?

    ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.

    Difference between ETL and ELT?

    ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.