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Begin by ensuring your Oracle database is ready for data extraction. Identify the tables and data you wish to move. Ensure you have the necessary permissions to access and extract data from the Oracle database. Verify that the data types and formats in the database are compatible with AWS services.
Use Oracle SQLPlus, a command-line utility, to connect to your Oracle database and extract the data. You can write SQL queries to export data into a flat file format, such as CSV. For example, you can use the `SPOOL` command in SQLPlus to write query results to a local file.
Save the extracted data files (e.g., CSV files) to a secure location on your local machine or on-premises server. Ensure that the data files are organized and named clearly for easy identification and access.
Install the AWS Command Line Interface (CLI) on your local machine. Configure the AWS CLI with the necessary credentials (Access Key ID and Secret Access Key) and default region settings using the `aws configure` command. Ensure your IAM user has the necessary permissions to access and upload data to Amazon S3.
Use the AWS CLI to upload the extracted data files from your local storage to an Amazon S3 bucket. Create a new S3 bucket if necessary and organize the data files within the bucket using a logical folder structure. Use the `aws s3 cp` or `aws s3 sync` command to perform the upload.
Set up AWS Glue to catalog your data stored in Amazon S3. Create a new AWS Glue Crawler, which will automatically scan the data in your S3 bucket and populate the AWS Glue Data Catalog with metadata definitions. This step is crucial for making your data easily queryable and analyzable.
Use Amazon Athena to query the data directly from the S3 bucket. Athena allows you to run SQL queries on your data in S3 and is fully integrated with the AWS Glue Data Catalog. You can easily analyze your data and perform various operations without moving the data to another database or service.
By following these steps, you can successfully move data from an Oracle database to an AWS Data Lake 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.
Oracle DB is a fully scalable integrated cloud application and platform service; it is also referred to as a relational database architecture. It provides management and processing of data for both local and wide and networks. Offering software-as-a-service (SaaS), platform-as-a-service (PaaS), and infrastructure-as-a-service (IaaS), it sells a large variety of enterprise IT solutions that help companies streamline the business process, lower costs, and increase productivity.
Oracle DB provides access to a wide range of data types, including:
• Relational data: This includes tables, views, and indexes that are used to store and organize data in a structured manner.
• Spatial data: This includes data that is related to geographic locations, such as maps, satellite imagery, and GPS coordinates.
• Time-series data: This includes data that is related to time, such as stock prices, weather data, and sensor readings.
• Multimedia data: This includes data that is related to images, videos, and audio files.
• XML data: This includes data that is stored in XML format, such as web pages, documents, and other structured data.
• JSON data: This includes data that is stored in JSON format, such as web APIs, mobile apps, and other data sources.
• Graph data: This includes data that is related to relationships between entities, such as social networks, supply chains, and other complex systems.
Overall, Oracle DB's API provides access to a wide range of data types that can be used for a variety of applications, from business intelligence and analytics to machine learning and artificial intelligence.
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:
