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Begin by manually exporting the data from Smartsheets. Log into your Smartsheet account, navigate to the desired sheet, and click on "File" > "Export". Choose a suitable format like CSV or Excel that can be easily handled later. Save the exported file to your local machine.
Review and prepare your exported data on your local system. Open the CSV or Excel file and ensure that the data is clean and formatted properly. Check for any inconsistencies or errors such as missing values, incorrect data types, or special characters that might cause issues during upload.
Install and configure the AWS Command Line Interface (CLI) on your local machine if it is not already set up. This will allow you to interact with AWS services directly from your command line. You can download the AWS CLI from the AWS website. After installation, configure it by running `aws configure` and entering your AWS access key, secret key, region, and desired output format when prompted.
Log into your AWS Management Console and navigate to the S3 service. Create a new S3 bucket where you'll initially upload the Smartsheet data. Click on "Create bucket", specify a unique bucket name, select your preferred region, and configure the necessary settings such as access permissions and versioning according to your requirements.
Use the AWS CLI to upload your prepared data to the newly created S3 bucket. Open your command line terminal and run a command like `aws s3 cp /path/to/your/file.csv s3://your-bucket-name/` to transfer the file from your local machine to the S3 bucket.
If necessary, transform the uploaded data to fit the schema or format of your AWS Datalake. This may involve using AWS Glue or custom scripts to process and convert the data to formats such as Parquet or ORC, which are optimized for analytics and can be used efficiently in your Datalake environment.
Finally, load the processed data into your AWS Datalake. If you’re using AWS Lake Formation, register the S3 location containing your data or create a new data catalog table if needed. Ensure that the data is accessible for analytics and that access permissions are properly configured to allow users to query and analyze the data using tools like Amazon Athena or AWS Glue.
By following these steps, you will successfully move data from Smartsheets to AWS Datalake 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.
A cloud-based management platform, Smartsheet empowers businesses to accomplish all things business. Smartsheet drives collaboration, supports better decision making, and accelerates innovation, enabling businesses to advance from ideation to impact in record time. Chosen by more than 70,000 brands in 190 different countries, Smartsheet simply makes business smarter—and simpler, since it integrates seamlessly with applications businesses already use from Google, Atlassian, Salesforce, Microsoft, and more.
Smartsheet's API provides access to a wide range of data types, including:
1. Sheets: Access to all sheets within a Smartsheet account, including their metadata and contents.
2. Rows: Access to individual rows within a sheet, including their metadata and contents.
3. Columns: Access to individual columns within a sheet, including their metadata and contents.
4. Cells: Access to individual cells within a sheet, including their metadata and contents.
5. Attachments: Access to all attachments associated with a sheet, row, or cell.
6. Comments: Access to all comments associated with a sheet, row, or cell.
7. Users: Access to information about users within a Smartsheet account, including their metadata and permissions.
8. Groups: Access to information about groups within a Smartsheet account, including their metadata and membership.
9. Reports: Access to all reports within a Smartsheet account, including their metadata and contents.
10. Templates: Access to all templates within a Smartsheet account, including their metadata and contents.
Overall, Smartsheet's API provides a comprehensive set of tools for accessing and manipulating data within a Smartsheet account, making it a powerful tool for developers and businesses looking to integrate Smartsheet into their workflows.
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:
