Connectors

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Azure Table Storage

Data replication

Sync Azure Table Storage data anywhere.

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.

  • Standard
  • Alpha
Sync Azure Table Storage dataRead the docs
Azure Table Storage

One connector

Everything Azure Table Storage can do in Airbyte

  • Sync to your warehouse

    Land Azure Table Storage data in 50+ destinations on a schedule you control.

  • Incremental syncs

    Pull only the records that changed since the last run instead of reloading everything.

  • One authorization

    Authenticate Azure Table Storage once and Airbyte keeps every scheduled sync running on it.

  • Cloud or self-hosted

    Run the Azure Table Storage connector on Cloud, Self-Managed Enterprise.

Specifications

What to know before you sync Azure Table Storage

  • Support levelStandard
  • Available onCloud, Self-Managed Enterprise
  • Connector version0.1.57
  • Release stageAlpha

Sync capabilities

  • Full Refresh SyncSupported
  • Incremental SyncSupported
  • NamespacesNot supported
  • Destinations50+ Airbyte connectors

Set up in 7 steps

  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
  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.

Authentication

Authenticate Azure Table Storage once

  • Access Key

    Azure Table Storage Access Key. See the docs for more information on how to obtain this key.

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Common questions

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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'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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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.

The most prominent ETL tools to transfer data to 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 and other databases, data warehouses and data lakes, enhancing data management capabilities.

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.

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.

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