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Ensure that your AWS S3 bucket is properly configured with the necessary permissions. Make sure the data you want to move is stored in a structured format such as CSV, Parquet, or JSON for seamless integration with Starburst Galaxy. Verify that you have the necessary access credentials to read the data from S3.
Log into your Starburst Galaxy account and ensure your environment is correctly set up. This includes having the necessary compute resources and configurations in place to handle the data import process from S3.
In Starburst Galaxy, create a new catalog specifically for accessing the data stored in your S3 bucket. This involves defining a catalog file with the necessary properties, such as the AWS Access Key, Secret Key, and the S3 bucket name. You can use the Hive connector for this purpose as it natively supports S3.
Within Starburst Galaxy, securely configure the AWS credentials that allow access to your S3 bucket. This typically involves entering your AWS Access Key ID and Secret Access Key into the catalog properties, ensuring these are encrypted and stored securely.
Once your catalog is set up, create a schema that maps to the structure of the data in your S3 bucket. This step involves defining the format and structure of the tables you wish to query in Starburst Galaxy, aligning them with the data files in S3.
Use SQL queries in Starburst Galaxy to access and manipulate the data directly from the S3 bucket. Starburst Galaxy supports querying data in-place, allowing you to perform transformations, aggregations, or any data operations needed without moving the data physically.
After querying and verifying the data, optimize the performance by adjusting configurations such as parallelism, caching, and partitioning strategies. Additionally, ensure that security best practices are followed, such as using IAM roles for fine-grained access control and auditing access logs for compliance.
By following these steps, you can effectively move and utilize data from AWS S3 in Starburst Galaxy without the need for 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.
Amazon S3 (Simple Storage Service) is a cloud-based object storage service that provides developers and IT teams with secure, durable, and scalable storage for their data. It allows users to store and retrieve any amount of data from anywhere on the web, making it easy to build and scale applications, backup and archive data, and analyze data. S3 is designed to provide high availability and durability, with data automatically replicated across multiple availability zones within a region. It also offers a range of features such as versioning, lifecycle policies, and access control to help users manage their data effectively.
Amazon S3's API provides access to a wide range of data types, including:
1. Object data: This includes the actual files stored in S3 buckets, such as images, videos, documents, and other types of files.
2. Metadata: S3 stores metadata about each object, including information such as the object's size, creation date, and last modified date.
3. Access control data: S3 provides access control mechanisms to restrict access to objects in a bucket. The API provides access to information about access control policies and permissions.
4. Bucket data: S3 buckets are containers for objects. The API provides access to information about buckets, such as their names, creation dates, and region.
5. Logging data: S3 can log access requests to objects in a bucket. The API provides access to these logs, which can be used for auditing and compliance purposes.
6. Inventory data: S3 can generate inventory reports that provide information about the objects stored in a bucket. The API provides access to these reports.
7. Metrics data: S3 can generate metrics about the usage of a bucket, such as the number of requests and the amount of data transferred. The API provides access to these metrics.
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:
