Efficiently moving and transforming data from Oracle databases is crucial for modern analytics and business intelligence. This guide will help database engineers and architects choose the right Oracle ETL tool for their database requirements.
About Oracle Database
Oracle Database is a multi-model database management system that uses a storage architecture known as System Global Area (SGA) and Process Global Area (PGA). The architecture splits data storage into tablespaces containing segments (tables, indexes) which are further divided into extents and blocks.
What is ETL?
ETL (Extract, Transform, Load) is a process used to extract data from one or more data sources, transform the data to fit a desired format or structure, and then load the transformed data into a target database or data warehouse. ETL is typically used for batch processing and is most commonly associated with traditional data warehouses.
What is ELT?
More recently, ETL has been replaced by ELT (Extract, Load, Transform). ELT Tool is a variation of ETL one that automatically pulls data from even more heterogeneous data sources, loads that data into the target data repository - databases, data warehouses or data lakes - and then performs data transformations at the destination level. ELT provides significant benefits over ETL, such as:
- Faster processing times and loading speed
- Better scalability at a lower cost
- Support of more data sources (including Cloud apps), and of unstructured data
- Ability to have no-code data pipelines
- More flexibility and autonomy for data analysts with lower maintenance
- Better data integrity and reliability, easier identification of data inconsistencies
- Support of many more automations, including automatic schema change migration
For simplicity, we will only use Oracle DB ETL as a reference to all data integration tools, ETL and ELT included, to integrate data from .
How data integration from Oracle DB to a data warehouse can help
Companies might do Oracle DB ETL for several reasons:
- Business intelligence: Oracle DB data may need to be loaded into a data warehouse for analysis, reporting, and business intelligence purposes.
- Data Consolidation: Companies may need to consolidate data with other systems or applications to gain a more comprehensive view of their business operations
- Compliance: Certain industries may have specific data retention or compliance requirements, which may necessitate extracting data for archiving purposes.
Overall, ETL from Oracle DB allows companies to leverage the data for a wide range of business purposes, from integration and analytics to compliance and performance optimization.
Overview of Native Oracle ETL Tools
Oracle Data Integrator
A comprehensive data integration platform built on ELT architecture instead of traditional ETL. ODI leverages the database engine to perform transformations, eliminating the need for a separate transformation server. It supports Oracle changed data capture (CDC) and offers Knowledge Modules for different integration patterns.
Oracle GoldenGate
A real-time data replication and CDC tool that captures changes from Oracle Database transaction logs. It uses a microservices architecture with separate processes for extract (capture), route (distribution), and replicate (delivery) operations, enabling minimal impact on source systems.
OCI Streaming
A managed, Kafka-compatible streaming service that provides partitioned, append-only logs for real-time data movement. It offers built-in encryption, fault tolerance across availability domains, and integration with OCI Functions for stream processing.
Top 10 Oracle DB ETL tools
Here are the top Oracle DB ETL tools based on their popularity and the criteria listed above:
1. Airbyte
Airbyte stands out as the leading open-source Oracle ETL tool, offering both cloud-hosted and self-hosted deployment options. Key features include:
- Extensive Connector Library: 400+ pre-built connectors, with the catalog growing rapidly through community contributions
- Customization Capabilities: Users can build custom connectors in minutes using the no-code Connector Builder or Connector Development Kit (CDK)
- Advanced CDC Support: Both log-based and timestamp-based Change Data Capture options for efficient data replication
- Enterprise Features: Includes automated schema change handling, data normalization, and integration with tools like dbt, Airflow, Dagster, and Prefect
- Security Compliance: SOC2, ISO, GDPR, and HIPAA Conduit certifications for cloud deployment
- Support Options: Enterprise-level support with 10-minute average response times and 96/100 customer satisfaction score
2. Fivetran
A fully-managed, cloud-native Oracle ELT platform known for its reliability and zero-maintenance approach. Key features include:
- Automated Management: Zero-maintenance pipelines with automatic schema migration and error handling
- Enterprise-Ready: Built-in SOC 2, HIPAA, GDPR, and ISO compliance
- Standardized Data Models: Pre-built data models for common business applications
- Oracle Support: Optimized Oracle connector with log-based CDC capabilities
Here are more critical insights on the key differentiations between Airbyte and Fivetran
3. Stitch Data
Stitch is a cloud-based platform for ETL that was initially built on top of the open-source ETL tool Singer.io. More than 3,000 companies use it.
Stitch was acquired by Talend, which was acquired by the private equity firm Thoma Bravo, and then by Qlik. These successive acquisitions decreased market interest in the Singer.io open-source community, making most of their open-source data connectors obsolete. Only their top 30 connectors continue to be maintained by the open-source community.
What's unique about Stitch?
Given the lack of quality and reliability in their connectors, and poor support, Stitch has adopted a low-cost approach.
Here are more insights on the differentiations between Airbyte and Stitch, and between Fivetran and Stitch.
4. Matillion
Matillion is a self-hosted ELT solution, created in 2011. It supports about 100 connectors and provides all extract, load and transform features. Matillion is used by 500+ companies across 40 countries.
What's unique about Matillion?
Being self-hosted means that Matillion ensures your data doesn’t leave your infrastructure and stays on premise. However, you might have to pay for several Matillion instances if you’re multi-cloud. Also, Matillion has verticalized its offer from offering all ELT and more. So Matillion doesn't integrate with other tools such as dbt, Airflow, and more.
Here are more insights on the differentiations between Airbyte and Matillion.
5. Airflow
Apache Airflow is an open-source workflow management tool. Airflow is not an ETL solution but you can use Airflow operators for data integration jobs. Airflow started in 2014 at Airbnb as a solution to manage the company's workflows. Airflow allows you to author, schedule and monitor workflows as DAG (directed acyclic graphs) written in Python.
What's unique about Airflow?
Airflow requires you to build data pipelines on top of its orchestration tool. You can leverage Airbyte for the data pipelines and orchestrate them with Airflow, significantly lowering the burden on your data engineering team.
Here are more insights on the differentiations between Airbyte and Airflow.
6. Informatica PowerCenter
Informatica PowerCenter is an ETL tool that supported data profiling, in addition to data cleansing and data transformation processes. It was also implemented in their customers' infrastructure, and is also an Enterprise product, so hard to implement without any self-serve option.
7. AWS Glue
A serverless data integration service that seamlessly integrates with the AWS ecosystem. Key features include:
- Native AWS Integration: Deep integration with AWS services like RDS, Redshift, and S3
- Serverless Architecture: Pay-only-for-use pricing with automatic resource scaling
- Visual ETL Editor: Drag-and-drop interface for building ETL workflows
8. Singer
Singer is also worth mentioning as the first open-source JSON-based ETL framework. It was introduced in 2017 by Stitch (which was acquired by Talend in 2018) as a way to offer extendibility to the connectors they had pre-built. Talend has unfortunately stopped investing in Singer’s community and providing maintenance for the Singer’s taps and targets, which are increasingly outdated, as mentioned above.
9. Rivery
Rivery is another cloud-based ELT solution. Founded in 2018, it presents a verticalized solution by providing built-in data transformation, orchestration and activation capabilities. Rivery offers 150+ connectors, so a lot less than Airbyte. Its pricing approach is usage-based with Rivery pricing unit that are a proxy for platform usage. The pricing unit depends on the connectors you sync from, which makes it hard to estimate.
10. Meltano
Meltano is an open-source orchestrator dedicated to data integration, spined off from Gitlab on top of Singer’s taps and targets. Since 2019, they have been iterating on several approaches. Meltano distinguishes itself with its focus on DataOps and the CLI interface. They offer a SDK to build connectors, but it requires engineering skills and more time to build than Airbyte’s CDK. Meltano doesn’t invest in maintaining the connectors and leave it to the Singer community, and thus doesn’t provide support package with any SLA.
All those ETL tools are not specific to Oracle DB, you might also find some other specific data loader for Oracle DB data. But you will most likely not want to be loading data from only Oracle DB in your data stores.
How to Select the Right Oracle ETL Tool?
When choosing an Oracle ETL tool, focus on these key decision factors:
Data Volume and Performance Requirements
- For small data volumes (<100GB): Any modern tool will suffice
- For medium volumes (100GB-1TB): Consider tools with efficient CDC support
- For large volumes (>1TB): Look for tools with native Oracle support
Technical Expertise Available
- Limited technical team: Choose managed solutions
- Strong engineering team: Consider open-source options like Airbyte
- Oracle expertise: Oracle Data Integrator might be suitable
Budget Considerations
- Startup/Small Business: Open-source tools or usage-based pricing
- Enterprise: Tools with predictable pricing and SLA guarantees
Integration Requirements
- Simple data sync: Basic tools suffice
- Complex transformations: Tools with strong dbt integration
- Real-time needs: Solutions with CDC capabilities
Quick Decision Guide
- Want flexibility? → Airbyte
- Enterprise Oracle ecosystem? → Oracle Data Integrator
- Budget conscious? → Open-source options
Which data can you extract from Oracle DB?
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.
How to start pulling data in minutes from Oracle DB
If you decide to test Airbyte, you can start analyzing your Oracle DB data within minutes in three easy steps:
Step 1: Set up Oracle DB as a source connector
1. Open the Airbyte platform and navigate to the "Sources" tab on the left-hand side of the screen.
2. Click on the "Oracle DB" source connector and select "Create new connection".
3. Enter a name for your connection and click "Next".
4. In the "Connection Configuration" section, enter the following information:
- Host: the hostname or IP address of your Oracle DB server
- Port: the port number used to connect to your Oracle DB server
- Database: the name of the database you want to connect to
- Username: your Oracle DB username
- Password: your Oracle DB password
5. Click "Test connection" to ensure that the connection is successful.
6. If the connection is successful, click "Next" to proceed to the "Schema Selection" section.
7. In the "Schema Selection" section, select the schema(s) you want to replicate data from.
8. Click "Create connection" to save your connection settings.
9. You can now create a new Oracle DB source in Airbyte and start replicating data from your Oracle DB database.
Step 2: Set up a destination for your extracted Oracle DB data
Choose from one of 50+ destinations where you want to import data from your Oracle DB source. This can be a cloud data warehouse, data lake, database, cloud storage, or any other supported Airbyte destination.
Step 3: Configure the Oracle DB data pipeline in Airbyte
Once you've set up both the source and destination, you need to configure the connection. This includes selecting the data you want to extract - streams and columns, all are selected by default -, the sync frequency, where in the destination you want that data to be loaded, among other options.
And that's it! It is the same process between Airbyte Open Source that you can deploy within 5 minutes, or Airbyte Cloud which you can try here, free for 14-days.
Conclusion
Selecting the right Oracle ETL tool requires careful consideration of various factors including:
- Technical requirements
- Performance needs
- Security compliance
- Cost constraints
- Implementation complexity
For most organizations, modern ELT tools like Airbyte or Fivetran provide the best balance of features and ease of use.
Does Oracle have an ETL tool?
Yes, Oracle offers several ETL tools including Oracle Data Integrator (ODI) as its primary ETL platform and Oracle GoldenGate for real-time data replication and integration.
Is Oracle Golden Gate an ETL tool?
While Oracle GoldenGate can be used in ETL processes, it's primarily a real-time data replication and Change Data Capture (CDC) tool rather than a traditional ETL tool.
Is Oracle Data Integrator an ETL tool?
Yes, Oracle Data Integrator (ODI) is Oracle's enterprise-grade ETL tool that uses an ELT approach (Extract, Load, Transform) to leverage the processing power of target databases.
Why use ETL tools like Airbyte & Fivetran instead of Oracle Native ETL tools?
Third-party ETL tools often offer better cloud integration, simpler pricing, modern features like no-code interfaces, and broader connectivity to non-Oracle systems without the vendor lock-in associated with Oracle's native tools.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
Frequently Asked Questions
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.
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: set it up as a source, choose a destination among 50 available off the shelf, and define which data you want to transfer and how frequently.
The most prominent ETL tools to extract data include: Airbyte, Fivetran, StitchData, Matillion, and Talend Data Integration. These ETL and ELT tools help in extracting data from various sources (APIs, databases, and more), transforming it efficiently, and loading it into a database, data warehouse or data lake, enhancing data management capabilities.
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.