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Begin by exporting the data from Azure Table Storage. You can achieve this by using Azure Storage Explorer or writing a custom script in a language like Python or C#. The goal is to extract the data into a CSV, JSON, or Parquet file format. These formats are commonly used for data processing and analysis.
Once you have exported the data, upload it to Azure Blob Storage. This can be done using Azure Storage Explorer or programmatically using Azure SDKs. Storing the data in Blob Storage ensures it is easily accessible for further processing and can be accessed by Starburst Galaxy.
Install and set up a local instance of Starburst Presto on your machine. Starburst Presto is an open-source distributed SQL query engine that can be used for querying data from various sources. Ensure that you have Java installed on your system, as it is required to run Presto.
Configure your Presto instance to connect to Azure Blob Storage. This involves editing the catalog configuration to include the Azure Blob Storage details. Create a new catalog file (e.g., `azure.properties`) in the `etc/catalog` directory of your Presto installation, specifying the connector name (`hive`), and the Azure Blob Storage details, including the account name and key.
Use Presto to load the data from Azure Blob Storage. You can write SQL queries using Presto's Hive connector to read the data stored in CSV, JSON, or Parquet format. Verify that the data is correctly loaded and can be queried using Presto.
If the data requires transformation before being loaded into Starburst Galaxy, perform the necessary transformations using SQL queries in Presto. This might include cleaning, filtering, or aggregating data to match the desired schema in Starburst Galaxy.
Finally, import the transformed data into Starburst Galaxy. This can be done by uploading the transformed data files directly into Starburst Galaxy's supported storage location or by using Starburst Galaxy's built-in connectors to access the data from its current location. Ensure that the data is correctly mapped to the target tables in Starburst Galaxy.
By following these steps, you can move data from Azure Table Storage to Starburst Galaxy without relying on third-party connectors or integrations.
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 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.
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.
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.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
