No-code pipeline · Azure Table Storage → BigQuery

Send data from Azure Table Storage to BigQuery

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From Azure Table Storage to BigQuery: managed, scheduled, no code.

Kondado establishes an automated pipeline between Azure Table Storage and BigQuery, replicating your NoSQL data on a configurable schedule without requiring any coding or manual extraction.

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Replicate Azure Table Storage Data to BigQuery

Sending data from Azure Table Storage to BigQuery requires configuring a data source in Kondado and selecting your Google Cloud project as the destination. Once connected, Kondado automatically discovers all available tables and views in your Azure storage account, allowing you to choose which datasets to replicate. The platform handles the schema mapping and data type conversion between the NoSQL structure and BigQuery’s columnar format, ensuring your information arrives ready for analysis.

You can set updates to run every 5 minutes, 15 minutes, hourly, or daily depending on your business needs. This automated workflow eliminates the need for custom scripts or manual CSV exports, letting your team focus on building analytics rather than data engineering. Once in BigQuery, your Azure data joins the rest of your cloud information for comprehensive reporting.

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The Tabelas e Views pipeline automatically maps every table and view from your Azure Table Storage account into BigQuery datasets. This means your entity collections, partition keys, and row data become available as structured tables within Google’s analytics warehouse. Analysts can then combine this NoSQL information with transactional data from PostgreSQL or marketing metrics from other sources to build unified business intelligence.

With your Azure data available in BigQuery, teams can create custom dashboards that track application performance, user behavior patterns, or IoT device telemetry alongside financial records. The replicated data supports complex SQL queries and machine learning models, enabling deeper insights into your operational metrics. Whether you are generating reports for stakeholders or feeding data into Looker Studio, having Azure Table Storage information in BigQuery centralizes your analytics workflow.

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Replicated to BigQuery

Dynamic data

Kondado automatically reads the schema of your Azure Table Storage. All tables, views, and fields available in your account are extracted without manual configuration.

available pipeline

BigQuery

Destination

What Kondado extracts

Tabelas e Views

Kondado automatically maps all tables and views available in your database

See all available data → Send Azure Table Storage to BigQuery →

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How to send Azure Table Storage data to BigQuery

Sync data automatically — no code, no manual exports.

Connect Azure Table Storage

Log in to Kondado and add Azure Table Storage as a new data source by providing your storage account credentials and connection string. The platform will automatically detect all available tables and views in your Azure account, displaying them as selectable pipelines.

Configure BigQuery destination

Select BigQuery as your destination and specify the Google Cloud project and dataset where your Azure data should reside. You will need to authorize Kondado to write to your BigQuery project, establishing the target location for the replicated data.

Select data and schedule

Choose which tables and views to replicate from the available pipelines, then set your preferred update frequency ranging from 5 minutes to daily intervals. Once activated, Kondado will automatically begin replicating your selected Azure Table Storage data to BigQuery according to your configured timeline.

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Send data from Azure Table Storage to other destinations

Azure Table Storage > Looker Studio

Azure Table Storage > Power BI

Other visualizations

Azure Table Storage > Amazon S3

Azure Table Storage > BigQuery

Azure Table Storage > Excel

Azure Table Storage > Google Sheets

Azure Table Storage > MySQL

Azure Table Storage > PostgreSQL

Azure Table Storage > Redshift

Azure Table Storage > SQL Server

Choose a tool to visualize your Azure Table Storage data

If the software you need is not listed, drop us a messagem. You can use almost every tool

Azure Table Storage > Airtable

Azure Table Storage > Alteryx

Azure Table Storage > Amazon Quicksight

Azure Table Storage > BIMachine

Azure Table Storage > Cortex

Azure Table Storage > Data Studio

Azure Table Storage > Geckoboard

Azure Table Storage > Grafana

Azure Table Storage > IBM Cognos Analytics

Azure Table Storage > Klipfolio

Azure Table Storage > Looker Studio

Azure Table Storage > Looker

Azure Table Storage > Looqbox

Azure Table Storage > Metabase

Azure Table Storage > Microstrategy

Azure Table Storage > Pentaho

Azure Table Storage > Power BI

Azure Table Storage > Qlik Cloud Analytics

Azure Table Storage > Qlik Sense

Azure Table Storage > Qlik

Azure Table Storage > QlikView

Azure Table Storage > Redash

Azure Table Storage > Superset

Azure Table Storage > Tableau

Azure Table Storage > BI TOTVS

Azure Table Storage > Zoho Analytics

Frequently Asked Questions (FAQ)

Answers about sending Azure Table Storage data to BigQuery automatically

How does Kondado handle schema mapping between Azure Table Storage and BigQuery? ▼

Kondado automatically converts Azure Table Storage's NoSQL schema into BigQuery's structured format, mapping entity properties to appropriate column types. The platform handles nested JSON objects and dynamic fields, flattening complex structures where necessary to ensure compatibility with BigQuery's columnar storage.

What specific data entities can I replicate from Azure Table Storage to BigQuery? ▼

The Tabelas e Views pipeline replicates all tables and views available in your Azure storage account, including entity collections and their associated metadata. You can select specific tables to replicate or choose to bring over your entire dataset depending on your analytics requirements.

How often can I schedule updates from Azure Table Storage to BigQuery? ▼

Kondado supports configurable schedules ranging from every 5 minutes to daily updates, allowing you to balance data freshness with processing costs. You can set different frequencies for different tables based on how rapidly your Azure data changes and how current your reports need to be.

Can I combine Azure Table Storage data with other sources in BigQuery? ▼

Yes, once your Azure data resides in BigQuery, you can join it with information from Azure Table Storage and other platforms like PostgreSQL or marketing data sources. This enables comprehensive analysis across your entire data ecosystem using standard SQL queries.

What data types are supported when replicating from Azure Table Storage to BigQuery? ▼

Kondado supports standard Azure Table Storage data types including String, Boolean, Int32, Int64, Double, DateTime, and Binary, converting them to corresponding BigQuery types. Complex properties and nested objects are handled through JSON serialization or flattening depending on the structure.

Do I need to manually create tables in BigQuery before replicating from Azure Table Storage? ▼

No, Kondado automatically creates the necessary datasets and tables in your BigQuery project during the initial replication process. The platform manages table creation and schema evolution, adding new columns as your Azure Table Storage schema changes over time.

How does Kondado handle partition keys from Azure Table Storage in BigQuery? ▼

Partition keys and row keys from Azure Table Storage are preserved as dedicated columns in BigQuery, maintaining the original data relationships and query performance characteristics. You can use these keys to optimize query costs in BigQuery by partitioning or clustering your tables accordingly.

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