Best ETL Tools for Azure Table Storage in 2025
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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.
What should you do next?
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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.
Azure Table storage, which is a service that stores non-relational structured data in the cloud and it is well known as structured NoSQL data. Azure Table storage is a service that stores structured NoSQL data in the cloud, providing a key/attribute store with a schema less design. Azure Table storage is a very popular service used to store structured NoSQL data in the cloud, providing a Key/attribute store. One can use it to store large amounts of structured, non-relational data.
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.
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.
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.