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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.
Azure Blob Storage is a cloud-based storage solution provided by Microsoft Azure. It is designed to store large amounts of unstructured data such as text, images, videos, and audio files. Blob Storage is highly scalable and can store data of any size, from a few bytes to terabytes. It provides a cost-effective way to store and access data from anywhere in the world. Blob Storage also offers features such as data encryption, access control, and data redundancy to ensure data security and availability. It can be used for a variety of applications such as backup and disaster recovery, media storage, and data archiving.
Azure Blob Storage's API provides access to various types of data, including:
1. Unstructured data: This includes any type of data that does not have a predefined data model or structure, such as text, images, videos, and audio files.
2. Structured data: This includes data that has a predefined data model or structure, such as tables, columns, and rows.
3. Semi-structured data: This includes data that has some structure, but not enough to fit into a traditional relational database, such as JSON, XML, and CSV files.
4. Metadata: This includes information about the data stored in Azure Blob Storage, such as file size, creation date, and last modified date.
5. Access control data: This includes information about who has access to the data stored in Azure Blob Storage and what level of access they have.
6. Logging data: This includes information about the activities performed on the data stored in Azure Blob Storage, such as read and write operations, and access attempts.Overall, Azure Blob Storage's API provides access to a wide range of data types, making it a versatile and flexible storage solution for various types of applications and use cases.
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.
Azure Blob Storage is a cloud-based storage solution provided by Microsoft Azure. It is designed to store large amounts of unstructured data such as text, images, videos, and audio files. Blob Storage is highly scalable and can store data of any size, from a few bytes to terabytes. It provides a cost-effective way to store and access data from anywhere in the world. Blob Storage also offers features such as data encryption, access control, and data redundancy to ensure data security and availability. It can be used for a variety of applications such as backup and disaster recovery, media storage, and data archiving.
BigQuery is an enterprise data warehouse that draws on the processing power of Google Cloud Storage to enable fast processing of SQL queries through massive datasets. BigQuery helps businesses select the most appropriate software provider to assemble their data, based on the platforms the business uses. Once a business’ data is acculumated, it is moved into BigQuery. The company controls access to the data, but BigQuery stores and processes it for greater speed and convenience.
1. First, navigate to the Airbyte website and create an account.
2. Once you have logged in, click on the "Sources" tab on the left-hand side of the screen.
3. Scroll down until you find the "Azure Blob Storage" connector and click on it.
4. Click on the "Create Connection" button.
5. Enter a name for your connection and fill in the required fields, such as your Azure Blob Storage account name and access key.
6. Test the connection to ensure that it is working properly.
7. Once the connection has been successfully tested, click on the "Save & Sync" button to save your connection and begin syncing data.
8. You can then configure your sync settings, such as the frequency of syncing and which data to include.
9. Once you have configured your sync settings, click on the "Save & Sync" button again to start syncing your data from Azure Blob Storage to Airbyte.
1. First, navigate to the Airbyte dashboard and select the "Destinations" tab on the left-hand side of the screen.
2. Scroll down until you find the "BigQuery" destination connector and click on it.
3. Click the "Create Destination" button to begin setting up your BigQuery destination.
4. Enter your Google Cloud Platform project ID and service account credentials in the appropriate fields.
5. Next, select the dataset you want to use for your destination and enter the table prefix you want to use.
6. Choose the schema mapping for your data, which will determine how your data is organized in BigQuery.
7. Finally, review your settings and click the "Create Destination" button to complete the setup process.
8. Once your destination is created, you can begin configuring your source connectors to start syncing data to BigQuery.
9. To do this, navigate to the "Sources" tab on the left-hand side of the screen and select the source connector you want to use.
10. Follow the prompts to enter your source credentials and configure your sync settings.
11. When you reach the "Destination" step, select your BigQuery destination from the dropdown menu and choose the dataset and table prefix you want to use.
12. Review your settings and click the "Create Connection" button to start syncing data from your source to your BigQuery destination.
With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.
We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
You may be able to improve your analytics capabilities by moving data from Azure SQL Database, into a centralized and unified data warehouse such as BigQuery. As discussed in Airbyte’s article on data integration, moving data from across the enterprise into a centralized data analytics platform provides the following benefits:
- A unified view of data from across the enterprise, and a single source of truth.
- A platform that is purpose built for running large analytics jobs.
- A single location to transform and join data from across the enterprise.
- Improved security – limit the number of people that require access to operational systems such as Azure SQL Database, as they will be able to access and analyze the required data in BigQuery.
In this tutorial, you will learn how to build a data replication pipeline from Azure SQL Database to Google’s BigQuery, without writing any code. Let’s get started!
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Prerequisites
- A Microsoft Azure account to create a SQL Server using Azure. Because Azure SQL Database is built on SQL Server database engine, the instructions given in this tutorial should also be applicable to Microsoft SQL Server.
- A Google cloud account to create a BigQuery data warehouse.
- This tutorial was created using Airbyte cloud. Alternatively, you can choose to run Airbyte locally. You can follow the instructions to set up Airbyte on your system using docker-compose.
Step 1: Set up SQL Server as an Airbyte Source
Create an SQL Server host on Azure. Once you log in to the Azure Portal, choose to create an SQL database from the dashboard
Choose Create option from the SQL databases page.
Create a server to host our database in it. From the Database details section, choose Create new server.
We will use SQL Authentication for authentication methods, creating a new username and password as shown below.
Once the server setup is complete, let’s add other details. For Subscription, choose Free Trial if you don’t already have a paid plan. Next, a new Resource group needs to be created to store all our resources related to our database.
Choose Next: Networking to move forward. Make sure to pick Public Endpoint for the Connectivity method.
Next, under Additional Settings, you can load sample tables by choosing Sample under Use existing data. We will use data from this table to sync to BigQuery later in the tutorial.
Once you are done with configurations, Choose Create to create the database, this step may take a few minutes. Once Azure creates the database for us, you can view more details.
Let’s run a sample SQL query by going to the Query Editor and logging in with the username/password you just created. Then run an SQL query as shown below:.
Azure doesn't allow connections to the database from IPs unless they are listed in the firewall settings. Therefore we need to add Airbyte’s public IP addresses to the firewall settings.
To whitelist the IPs, click on Add a firewall rule and add the same IP address for both start and end address.
Step 2: Configure Azure SQL Database as a source
You will now add the newly created Azure SQL Database as an Airbyte Source. Login to your Airbyte Cloud Dashboard and, select Sources on the left bar, then click on +New source. After selecting Microsoft SQL Server as your source , you should complete the UI as shown in the image below:
ℹ️ Since Azure SQL Database uses the SQL Server engine, using the MSSQL source connector works.
Once you have entered all the necessary details, choose Set up source.
Step 3: Set up BigQuery
To set up a big query Airbyte destination, we need first to create a BigQuery dataset to load our books dataset.
Login in to your Google cloud dashboard. From the Welcome page, choose the Run a query in BigQuery button.
From the 3-dot menu of your cloud project, choose the only option to create a dataset
For Dataset ID, choose a descriptive name like mongodb_dataset. Only alphanumeric names are allowed as dataset ID names.
Choose the dataset location from the menu and click Create Dataset. You should now be able to see a newly created dataset. Take note of the Dataset ID you just entered since we will need it later to set up BigQuery as an Airbyte Destination.
Getting the appropriate account keys to access our BigQuery project is the only thing left. Choose API & Services from the Quick Access menu in your cloud dashboard.
From the credential menu, choose to create a new Service account.
Pick a valid name for your Service account and click Create and continue.
Next, we need to specify what resources can be accessed from this service account. From the available roles, BigQuery Data Editor and BigQuery Job User should be sufficient. Alternatively BigQuery Admin should work (and is shown in the image below), but for security should not be used in production systems..
Click Done, and you should see a new service account.
Choose this newly created service account email and add a new key by clicking the Add Key button.
From the Create private key pop-up, choose JSON.
Once you select Create, a new file must have been downloaded to your system. The final step involves creating a new Airbyte destination.
Step 4: Configure BigQuery as a destination
From your Airbyte dashboard, choose Destinations and, select New Destination, pick BigQuery from the available options.
Next, under Service Account Key JSON, copy the contents of the whole credentials file. Finally, click Set up destination once you are done with the changes.
Step 5: Set up Airbyte Connection from Azure to BigQuery
The final step for this tutorial involves building a connection between our newly setup SQL Server and our BigQuery warehouse. To achieve this, go to Connections and choose to set up a New connection.
Select the source and destination that we just created, and Airbyte will show you the tables (called streams in Airbyte) that can be synced.
Once you have selected the namespaces to sync, Choose Set up connection, and Airbyte should automatically start syncing data. Once the sync is complete, Airbyte will tell you the status of the rows synced.
With the sample data in Azure SQL Database, around 6083 rows were synced to BigQuery. Once normalization has completed, you can go to the BigQuery console to verify and run a sample SQL query such as the following:
Wrapping Up
To summarize, this tutorial has shown you how to:
- Set up Azure SQL Server.
- Configure Azure SQL Server as an Airbyte source.
- Set up a Google BigQuery as a data warehouse.
- Configure BigQuery as an Airbyte destination.
- Create an Airbyte connection that replicates data from a Azure SQL Database to BigQuery.
With Airbyte the data integration possibilities are endless, and we look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, to participate in discussions on Airbyte’s discourse, or to sign up for our newsletter. You may also be interested in Airbyte tutorials and Airbyte’s blog!
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
Ready to get started?
Frequently Asked Questions
Azure Blob Storage's API provides access to various types of data, including:
1. Unstructured data: This includes any type of data that does not have a predefined data model or structure, such as text, images, videos, and audio files.
2. Structured data: This includes data that has a predefined data model or structure, such as tables, columns, and rows.
3. Semi-structured data: This includes data that has some structure, but not enough to fit into a traditional relational database, such as JSON, XML, and CSV files.
4. Metadata: This includes information about the data stored in Azure Blob Storage, such as file size, creation date, and last modified date.
5. Access control data: This includes information about who has access to the data stored in Azure Blob Storage and what level of access they have.
6. Logging data: This includes information about the activities performed on the data stored in Azure Blob Storage, such as read and write operations, and access attempts.Overall, Azure Blob Storage's API provides access to a wide range of data types, making it a versatile and flexible storage solution for various types of applications and use cases.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: