Best ETL Tools for Azure Table Storage in 2025

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

In today’s data-driven world, organizations increasingly rely on cloud-native services like Azure Table Storage to store vast amounts of structured, scalable NoSQL data. However, unlocking the true value of that data often means going beyond simple storage. Businesses need robust ways to move their Azure Table Storage data into analytics platforms—whether for business intelligence, data science, compliance, or cross-system unification. This is where ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) tools come in.

ETL/ELT tools enable you to seamlessly extract data from Azure Table Storage, transform it into meaningful formats, and load it into destinations such as cloud data warehouses, data lakes, or analytics platforms. These pipelines help convert raw transactional data into actionable insights, operational reports, and predictive analytics—fueling smarter decision-making across teams.

Whether you're a startup looking to centralize your customer and application data, or an enterprise migrating to a modern cloud data stack, choosing the right Azure Table Storage ETL/ELT tool is crucial. With the rapid evolution of the data integration landscape, today’s top solutions offer not just connectivity and scalability, but also automation, AI-enhanced features, low-code interfaces, and integration with your broader data ecosystem.

Top Azure Table Storage ETL tools

1. Airbyte

Airbyte is a data integration and replication tool that facilitates swift data migration through its pre-built and customizable connectors. With over 600+ connectors, Airbyte enables seamless data transfer to a wide range of destinations, including popular databases and warehouses. Its uniqueness lies in its ability to manage structured and unstructured data from diverse sources. This feature facilitates smooth operations across analytics and machine learning workflows, distinguishing Airbyte as a highly adaptable platform.

To enhance ETL workflows with Airbyte, you can use PyAirbyte, a Python-based library. PyAirbyte enables you to utilize Airbyte connectors directly within your developer environment. This setup allows you to extract data from various sources and load them in SQL caches, which can then be converted into Pandas DataFrame objects for transformation using Python’s robust capabilities.

Once transformed into an analysis-ready format, you can load it into your preferred destination using Python’s extensive libraries. For example, to load data into Google BigQuery, you can use pip install google-cloud-bigquery, establish a connection, and eventually load data. This method offers flexibility in terms of the transformation you want to perform before loading the data into a destination.

Some of the key features of Airbyte are:

• Streamline GenAI Workflows: You can use Airbyte to simplify AI workflows by directly loading semi-structured or unstructured data in prominent vector databases like Pinecone. The automatic chunking, embedding, and indexing features enable you to work with LLMs to build robust applications.‍
• AI-powered Connector Development: If you do not find a particular connector for synchronization, leverage Airbyte’s intuitive Connector Builder or Connector Developer Kit (CDK) to craft customized connectors. The Connector Builder’s AI-assist functionality scans through your preferred connector’s API documentation and pre-fills the fields, allowing you to fine-tune the configuration process.‍
• Custom Transformation: You can integrate dbt with Airbyte to execute advanced transformations. This enables you to tailor data processing workflow with dbt models.‍
• Robust Data Security: Airbyte guarantees the security of data movement by implementing measures, including strong encryption, audit logs, role-based access control, and ensuring the secure transmission of data. By adhering to popular industry-specific regulations, including GDPR, ISO 27001, HIPAA, and SOC 2, Airbyte secures your data from cyber-attacks.‍

Active Community: Airbyte has a open-source community. With over 20,000 members on Airbyte Community Slack and active discussions on Airbyte Forum, the community serves as a cornerstone of Airbyte’s development.

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.

4. 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.

5. 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. 

6. 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.

7. 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.

8. Microsoft SQL Server Integration Services (SSIS)

Microsoft SQL Server Integration Services (SSIS) is Microsoft’s native data integration and ETL (Extract, Transform, Load) platform, designed primarily for organizations operating within the Microsoft ecosystem. It is tightly integrated with SQL Server and the broader Microsoft infrastructure, making it a natural choice for teams already invested in tools like Azure Data Factory, Power BI, and other Microsoft data services.

Unlike modern ELT-focused platforms, SSIS follows the traditional ETL model—extracting data from multiple sources, applying transformations before loading it into the target system. This approach is ideal when heavy transformations are required before the data reaches the data warehouse or database, especially when the transformation logic is complex and must be executed outside of the target system

9. 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.

10. 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.

How data integration from Azure Table Storage to a data warehouse can help

Companies might do Azure Table Storage ETL for several reasons:

1. Business intelligence: Azure Table Storage 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 Azure Table Storage, coupled with Azure ETL tools, allows companies to leverage the data for a wide range of business purposes, from integration and analytics to compliance and performance optimization.

Criteria to select the right Azure Table Storage 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 Azure Table Storage 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.

Which data can you extract from Azure Table Storage?

Azure Table Storage's API gives access to structured data in the form of tables. The tables are composed of rows and columns, and each row represents an entity. The API provides access to the following types of data:  

1. Partition Key: A partition key is a property that is used to partition the data in a table. It is used to group related entities together. 
2. Row Key: A row key is a unique identifier for an entity within a partition. It is used to retrieve a specific entity from the table. 
3. Properties: Properties are the columns in a table. They represent the attributes of an entity and can be of different data types such as string, integer, boolean, etc. 
4. Timestamp: The timestamp is a system-generated property that represents the time when an entity was last modified. 
5. ETag: The ETag is a system-generated property that represents the version of an entity. It is used to implement optimistic concurrency control. 
6. Query results: The API allows querying of the data in a table based on specific criteria. The query results can be filtered, sorted, and projected to retrieve only the required data.  

Overall, Azure Table Storage's API provides access to structured data that can be used for various purposes such as storing configuration data, logging, and session state management.

How to start pulling data in minutes from Azure Table Storage

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

Step 1: Set up Azure Table Storage as a source connector

1. First, you need to create an Azure Table Storage account and obtain the account name and account key. You can find these details in the Azure portal under the "Access keys" section of your storage account. 
2. In Airbyte, navigate to the "Sources" tab and click on "Add Source". Select "Azure Table Storage" from the list of available sources. 
3. In the "Configure Azure Table Storage" page, enter the account name and account key that you obtained in step 1. 
4. Next, enter the name of the table that you want to connect to. You can find the name of the table in the Azure portal under the "Tables" section of your storage account. 
5. If you want to filter the data that you retrieve from the table, you can enter a filter expression in the "Filter" field. This expression should be in the OData syntax. 
6. Finally, click on "Test Connection" to ensure that Airbyte can connect to your Azure Table Storage account. If the connection is successful, click on "Create Source" to save your configuration. 
7. You can now use this source to create a new Airbyte pipeline and start replicating data from your Azure Table Storage account.

Step 2: Set up a destination for your extracted Azure Table Storage data

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

Step 3: Configure the Azure Table Storage 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 Azure Table Storage 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 Azure Table Storage ETL and how to best do it.

FAQs

1. What is the difference between ETL and ELT when working with Azure Table Storage?
ETL (Extract, Transform, Load) transforms the data before loading it into the destination, while ELT (Extract, Load, Transform) loads raw data first and then transforms it within the destination (like a data warehouse). ELT is typically preferred for modern cloud data stacks due to scalability and performance.

2. Why should I use a dedicated ETL/ELT tool instead of writing custom scripts for Azure Table Storage?
While custom scripts offer flexibility, they are time-consuming to maintain, error-prone, and lack scalability. ETL/ELT tools provide pre-built connectors, automation, error handling, change data capture (CDC), and seamless integration with analytics platforms—saving engineering time and improving reliability.

3. Does Airbyte support real-time data replication from Azure Table Storage?
Yes, Airbyte supports incremental syncs and real-time replication for many sources. While Azure Table Storage supports querying and filtering, performance will depend on your partitioning strategy and sync frequency. Airbyte allows you to schedule or trigger syncs with flexibility.

4. Can I transform Azure Table Storage data before loading it into my destination?
Yes. Many tools like Airbyte (with dbt), Matillion, and HevoData allow you to perform transformations either before or after loading. Tools with dbt or Python integration let you customize transformation logic to meet specific analysis or compliance needs.

5. What are the key factors to consider when choosing an ETL/ELT tool for Azure Table Storage?
Consider connector availability, real-time support, ease of transformation, integration with your stack (e.g., dbt, Airflow), pricing model, scalability, and compliance (e.g., GDPR, SOC2). Choose a platform that can support both your current and future data pipeline needs.

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