

Building your pipeline or Using Airbyte
Airbyte is the only open source solution empowering data teams to meet all their growing custom business demands in the new AI era.
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes



Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
What sets Airbyte Apart
Modern GenAI Workflows
Move Large Volumes, Fast
An Extensible Open-Source Standard
Full Control & Security
Fully Featured & Integrated
Enterprise Support with SLAs
What our users say


"The intake layer of Datadog’s self-serve analytics platform is largely built on Airbyte.Airbyte’s ease of use and extensibility allowed any team in the company to push their data into the platform - without assistance from the data team!"


“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”


“We chose Airbyte for its ease of use, its pricing scalability and its absence of vendor lock-in. Having a lean team makes them our top criteria. The value of being able to scale and execute at a high level by maximizing resources is immense”
- Identify the tables and columns in your Oracle database that you want to migrate.
- Determine how you will map your Oracle data types to Elasticsearch data types.
- Decide on the structure of your Elasticsearch indices and type mappings.
1. Connect to your Oracle database using a database client or a programming language with database connectivity support (e.g., JDBC for Java, cx_Oracle for Python).
2. Write and execute SQL queries to extract the desired data from Oracle. You may want to export the results to a CSV or JSON file for easier processing.
3. Ensure you handle any data conversion that might be necessary during the extraction, such as date formats or character encoding.
- If the data extracted from Oracle is not in a JSON format, you will need to convert it. Elasticsearch expects data in JSON format for indexing.
- Write a script or use a tool to convert your data into JSON. Each row from your Oracle database should be converted into a JSON object.
1. Set up the index and mappings in Elasticsearch:
- Use the `PUT /index_name` API to create an index in Elasticsearch.
- Define mappings that correspond to your Oracle data structure using the `PUT /index_name/_mapping` API.
2. Index the data:
- Use the `POST /index_name/_doc` or `POST /index_name/_bulk` API to add documents to your Elasticsearch index.
- If you have a lot of data, use the `_bulk` API to index multiple documents in a single request to improve performance.
3. Write a script to read the transformed JSON data and use Elasticsearch's REST API to index it:
- Loop through each JSON object and send it to the Elasticsearch cluster.
- Handle any errors or retries that might be necessary if the indexing fails.
- Once the data has been indexed, perform some test queries against your Elasticsearch index to ensure that the data has been correctly migrated and is searchable.
- Check the count of documents in Elasticsearch and compare it with the number of rows you exported from Oracle to ensure completeness.
- Monitor the performance of your Elasticsearch cluster and optimize the index settings if necessary.
- Set up monitoring and alerting to track the health and performance of your Elasticsearch cluster over time.
Step-by-Step Example in Python:
Here's a simplified example of how you might write a Python script to move data from an Oracle database to Elasticsearch:
```python
import cx_Oracle
from elasticsearch import Elasticsearch, helpers
# Connect to Oracle
dsn = cx_Oracle.makedsn('host', port, sid='sid')
connection = cx_Oracle.connect('user', 'password', dsn)
# Connect to Elasticsearch
es = Elasticsearch(['http://localhost:9200'])
# Query Oracle
cursor = connection.cursor()
cursor.execute("SELECT * FROM your_table")
# Transform data to JSON and index in Elasticsearch
actions = []
for row in cursor:
action = {
"_index": "your_index",
"_type": "your_type",
"_source": {
"column1": row[0],
"column2": row[1],
# Add all necessary columns
}
}
actions.append(action)
# Bulk index data
helpers.bulk(es, actions)
# Close the Oracle cursor and connection
cursor.close()
connection.close()
```
Remember that this is a simplified example and does not include error handling, data type conversion, or performance optimizations. You'll need to adapt the script to suit your specific use case and data requirements.
Important Considerations:
- Security: Ensure that your data transfer is secure, especially if your Elasticsearch cluster is exposed to the internet.
- Data Integrity: Make sure that the data is consistent and valid after the migration.
- Downtime: Depending on the size of the data, you might need to plan for downtime or migrate the data in batches.
- Compliance: Be aware of any legal or compliance requirements regarding data transfer and storage.
By following these steps and adapting them to your specific needs, you can successfully move data from an Oracle database to Elasticsearch 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.
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: