Best ETL Tools for Azure Table Storage to follow in 2024

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

These ETL and ELT tools help in extracting data from Azure Table Storage 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More recently, ETL has been replaced by ELT (Extract, Load, Transform).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 Azure Table Storage ETL as a reference to all data integration tools, ETL and ELT included, to integrate data from .

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.

Criterias 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.

Top Azure Table Storage ETL tools

Here are the top Azure Table Storage 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.

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.

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.

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

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.

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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.