How to load data from Oracle DB to Postgres destination

Learn how to use Airbyte to synchronize your Oracle DB data into Postgres destination within minutes.

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Set up a Oracle DB connector in Airbyte

Connect to Oracle DB or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Postgres destination for your extracted Oracle DB data

Select Postgres destination where you want to import data from your Oracle DB source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Oracle DB to Postgres destination in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

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How to Sync Oracle DB to Postgres destination Manually

1. Install Oracle Database Utilities: Ensure that you have Oracle Database utilities like SQL*Plus and Data Pump (expdp and impdp) installed on your Oracle server.

2. Install PostgreSQL: Make sure PostgreSQL is installed on the target server.

3. Access Credentials: Have the necessary credentials (username, password, database names, hostnames, port numbers) for both Oracle and PostgreSQL databases at hand.

1. Identify Data to Migrate: Determine which schemas, tables, or data need to be migrated.

2. Data Type Mapping: Analyze the data types used in Oracle and determine the equivalent PostgreSQL data types.

3. Character Set Considerations: Ensure that the character sets are compatible between Oracle and PostgreSQL or plan for conversion if they are not.

1. Prepare for Export: Disable any foreign keys, triggers, or other dependencies that might interfere with the export process.

2. Export with SQL*Plus:

   - Connect to Oracle using SQL*Plus:

     ```shell

     sqlplus username/password@//hostname:port/SID

     ```

   - Spool the data to a flat file:

     ```sql

     SET ECHO OFF

     SET FEEDBACK OFF

     SET HEADING OFF

     SPOOL /path/to/exported_data.txt

     SELECT * FROM schema_name.table_name;

     SPOOL OFF

     EXIT

     ```

   - Repeat the above step for each table you wish to export.

1. Create Database and Schema: If not already present, create the database and schema in PostgreSQL.

2. Create Tables: Based on the data type mapping, create the corresponding tables in PostgreSQL with appropriate data types.

3. Adjust PostgreSQL Settings: Modify `postgresql.conf` if necessary to increase settings like `max_allowed_packet` to accommodate large data imports.

1. Prepare for Import: Disable triggers, foreign keys, and indexes in PostgreSQL to speed up the import process.

2. Import Using psql:

   - Connect to PostgreSQL using psql:

     ```shell

     psql -U username -d database_name -h hostname -p port

     ```

   - Use the COPY command to import data:

     ```sql

     \COPY schema_name.table_name FROM '/path/to/exported_data.txt' WITH (FORMAT csv, DELIMITER '|', NULL 'NULL');

     ```

   - Repeat the above step for each exported file corresponding to a table.

1. Check Row Counts: Compare the row counts in both Oracle and PostgreSQL to ensure they match.

2. Check Data Consistency: Run sample queries on both databases to verify that the data is consistent.

3. Re-enable Constraints: Re-enable foreign keys, triggers, and indexes in PostgreSQL and validate them.

1. Performance Tuning: Analyze the imported tables and run `VACUUM ANALYZE` to update statistics for the PostgreSQL query planner.

2. Test Applications: Update your application connection strings and thoroughly test to ensure that the applications work as expected with the new PostgreSQL database.

3. Backup: Take a backup of the PostgreSQL database after the migration is confirmed to be successful.

Additional Tips

- Always perform the migration first on a test environment before applying it to production.

- For large datasets, consider using PostgreSQL's `pg_dump` and `pg_restore` utilities with custom scripts to handle data type conversion.

- Thoroughly document the migration process, including any data type transformations and issues encountered.

Remember, this is a high-level guide, and actual migration may involve additional complexities depending on the specific use case and data involved. Always ensure you have a backup and recovery strategy in place before beginning any migration.

How to Sync Oracle DB to Postgres destination Manually - Method 2:

FAQs

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.

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: 
1. Set up Oracle DB to PostgreSQL as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from Oracle DB to PostgreSQL and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud. 

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.

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.

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