Open-source data movement to

Open-source ETL from

Azure Table Storage

to any destination

Azure Table Storage offers scalable, NoSQL cloud storage for structured data. It supports key-value pairs, ensuring fast access and cost-effective storage for large datasets. Airbyte enables you to sync from any data source to Azure Table Storage, in minutes.

Azure Table Storage offers scalable, NoSQL cloud storage for structured data. It supports key-value pairs, ensuring fast access and cost-effective storage for large datasets. Airbyte allows you to extract and sync data from Azure Table Storage to any data warehouse, lake, database, or other destination within minutes.

20,000+
community members
6,000+
daily active companies
2PB+
synced/month
900+
contributors

Start syncing data from any source to Azure Table Storage in three easy steps

Start leveraging your Azure Table Storage data in three easy steps

Set up a source connector to extract data from Airbyte

Setup a Azure Table Storage connector in Airbyte

This can be any API tool, cloud data warehouse, database, data lake, file, or many other source types.

Set up Azure Table Storage as the destination connector

Set up a destination for your extracted Azure Table Storage data

Connect to Azure Table Storage or one of 50+ Airbyte data destinations through simple account authentication.

Configure the connection in Airbyte

Select the data you want to extract, the sync frequency, and where in Azure Table Storage you want that data to be loaded.

Airbyte's
Open Data Movement Platform

Modernize your data infrastructure with Airbyte's high speed data replication. Move large volumes of data with best-in-class CDC methods and replicate large databases within minutes.

Before
After

Why Airbyte?

Airbyte is the only unified data movement platform built on the open standard. It is uniquely positioned in terms of data sovereignty, connector extensibility, and support for AI workflows.

Syncing data from Azure Table Storage is only one of your 1,000 future data pipeline needs. Leverage the largest Marketplace of 400+ pre-built and 10,000+ custom  structured and unstructured connectors. Join 2,000 + data engineers who built 7,000+ custom connectors in minutes with low-code/no-code Connector Builder or AI Assistant.

Create context for AI agents by leveraging Airbyte's 600+ connectors. Airbyte's pipelines transfer structured and unstructured data together for metadata preservation. With support for flexible destinations such as Iceberg, Airbyte is the ideal data movement solution for agentic application.

Any specific way you would like to sync data from Azure Table Storage? Airbyte has you covered.
UI: Create connections and custom connectors in minutes.

API: Programmatic interactions, data syncing, and embedded connectors.

Terraform: Integration with CI/CD tools and rapid deployment with Infrastructure as Code.

PyAirbyte: Build LLM applications with Python libraries, SQL tools, and AI frameworks.

Flexible deployment options: self-hosted, cloud, and hybrid. Secure and compliant: ISO 27001, SOC 2, GDPR, HIPAA, data encryption, audit/monitoring, SSO, RBAC, and more. Centralized multi-tenant management with self-serve capabilities.

Trusted by the world's leading companies

Immediate ROI and productivity gains for your data teams.

"With our legacy framework, if one of the pipelines fails for one client, it will stop everything for the rest of our clients. But with Airbyte, things are run in parallel because of the platform’s distributed nature, which means that we can process multiple clients at the same time without impacting performance."

Raman Singh, Tech Lead at Symend

75%
reduction in sync times
$ 900K
in annual savings

"The real ROI is in our ability to iterate quickly, especially at our increasing scale. At the end of the day, you want a tool like that to just work. We can forget about it and know that it's configured and it's connecting and it's working. That hands-free capability is a big appeal for the platform.”

Sean Carver, Director of Data at Petvisor

20+
data sources integrated and growing
+1
FTE engineer in productivity efficiency
85%+
reduction in  data source integration time

"For TUI Musement, Airbyte cut development time in half and enabled dynamic customer experiences."

Andre Exner, Director of Customer Hub and Common Analytics

50%
faster development time

"What's different from Stitch Data or Informatica is the way that we can configure Airbyte connections and Airbyte entities through code. That's a huge plus to us as data engineers, because we are used to checking code and being able to manage changes from Github."

Amy Zhao, Senior Manager of Data Engineering at Peloton

3 to 1
reduction in data integrations solutions for reduced TCO
1
week Shopify and Stripe integration with Airbyte

"Airbyte allows us to stay flexible while scaling from hundred-million to billion-dollar enterprise clients."

Franziska Ibscher, Product Manager at Drivepoint

75%
of customers increased profitability
6.7%
EBITDA increase for customers

FAQs

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 Azure Table Storage?

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.

What data can you extract from Azure Table Storage?

Azure Table Storage provides access to a wide range of data types, including:

1

Structured data: This includes data that is organized into tables with defined columns and data types, such as CSV, JSON, and Avro files.

2

Semi-structured data: This includes data that has some structure, but not necessarily a fixed schema, such as XML and JSON files.

3

Unstructured data: This includes data that has no predefined structure, such as text, images, and videos.

4

Time-series data: This includes data that is organized by time, such as stock prices, weather data, and sensor readings.

5

Geospatial data: This includes data that is related to geographic locations, such as maps, GPS coordinates, and spatial databases.

6

Machine learning data: This includes data that is used to train machine learning models, such as labeled datasets and feature vectors.

7

Streaming data: This includes data that is generated in real-time, such as social media feeds, IoT sensor data, and log files.

Overall, Azure Table Storage's API provides access to a wide range of data types, making it a powerful tool for data analysis and machine learning.

How do I transfer data from Azure Table Storage?

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:

1

Set up Azure Table Storage as a source connector (using Auth, or usually an API key)

2

Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector

3

Define which data you want to transfer from Azure Table Storage and how frequently

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:

What are top ETL tools to transfer data from Azure Table Storage?

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

Airbyte

Fivetran

StitchData

Matillion

Talend Data Integration

These tools help in extracting data from various sources (APIs, databases, and more), transforming it efficiently, and loading it into Azure Table Storage and other databases, data warehouses and data lakes, 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.

Sync your data from any source to Azure Table Storage

Get your Azure Table Storage data in whatever tools you need

Airbyte supports a growing list of sources, including API tools,  cloud data warehouses, lakes, databases, and files, or even custom sources you can build.

Airbyte supports a growing list of destinations, including cloud data warehouses, lakes, and databases.

Github

Unstructured

Gitlab

Unstructured

Google Drive

Unstructured

Microsoft OneDrive

Unstructured

Microsoft Sharepoint

Unstructured

Notion

Unstructured

S3

Unstructured

Slack

Unstructured

Apify Dataset

Unstructured

Azure Blog Storage

Unstructured