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Begin by accessing your Azure portal and navigating to the Azure Table Storage account that contains the data you want to move. Ensure you have the necessary permissions to read and export the data. You'll also need the access keys for authentication purposes.
Use Azure Storage Explorer or Azure SDKs for a programmatic approach to export data. You can write a script using Azure SDK for Python or PowerShell to fetch the data from the table and save it in a CSV or JSON file format locally. This script will iterate over the rows in the table and write them to a file.
Log into your AWS Management Console and navigate to Amazon S3, the storage service used by AWS Data Lake. Create a new S3 bucket where the data will be uploaded. Note down the bucket name and region, as you will need this information for uploading data.
Install the AWS Command Line Interface (CLI) on your local machine if it's not already installed. You can download it from the [AWS CLI official page](https://aws.amazon.com/cli/). Configure it with your AWS credentials using the `aws configure` command. You will need your AWS Access Key ID, Secret Access Key, and default region.
Use the AWS CLI to upload the exported data file to your newly created S3 bucket. The command will look something like this:
```bash
aws s3 cp /path/to/your/localfile.json s3://your-s3-bucket-name/
```
Ensure that you replace `/path/to/your/localfile.json` with the actual path to your file and `your-s3-bucket-name` with your actual S3 bucket name.
Access AWS Lake Formation via the AWS Management Console. Register the S3 bucket in Lake Formation. This involves defining what data is contained in the bucket and setting up permissions. You can configure Lake Formation to crawl your S3 bucket and catalog the data automatically.
Once the data is in AWS Data Lake, perform checks to ensure the data integrity. You can use AWS Athena to query the data in the data lake to ensure it matches the source data from Azure Table Storage. Make sure that access permissions are correctly configured so that authorized users and systems can access the data.
By following these steps, you should be able to successfully move data from Azure Table Storage to AWS Data Lake without using 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:
