Data warehouses and business intelligence derived from them are a very critical aspect of the modern business world. ETL is the process of populating data warehouses by extracting data from various sources, transforming them into usable forms, and loading them to destinations.
Having the data from all these sources at one single point helps businesses to combine, aggregate, and analyze them to form insights that can lead to cost savings or higher revenue. Some of the sources needs specially designed tools like Oracle ETL tools for managing the flow of data in the Oracle Database.
This article introduces you to the best tools for Oracle ETL.
Table of Contents
What is Oracle?
Oracle is one of the most popular Relational Databases that can be used to run Transactional Loads, Data Warehouse Loads, and Mixed Workloads. This flexibility means that even in the current age of Cloud-based Databases, Oracle still maintains a sizable portion of the Database Systems in use. Lately, Oracle’s Cloud-based services are also becoming popular because of their flexibility, enterprise support, and its already existing legacy customer base.
The objective of this post is to familiarise you with the various Oracle ETL tools for executing extract-transform-load workflows. This post gives in-depth information with regard to the key features, prices, and suitable use cases that fit the bill. By the end of this post, you will be able to decide which ETL tools for Oracle is perfect for your use case.
Oracle’s flexibility in acting as a Transactional Database as well as a Data Warehouse means it can act as a source or target in ETL use cases. ETL operations with Oracle as a source or target can be executed using the following tools.
Setting up data pipelines with Hevo is a simple 3-step process by just selecting the data source, providing valid credentials, and choosing the destination.
With Hevo, fuel your analytics by not just loading data into Warehouse but also enriching it with in-built no-code transformations.
Key Features of Hevo
Let us discuss some key features of Hevo that might help you make the better choice among the other ETL Tools.
- Pre-Load Transformations: It facilitates pre-load data transformations using Python or an easy-to-use drag-and-drop interface.
- Fault-Tolerant Architecture: Your data is safe even when there is a pipeline failure. Hevo keeps your data in the staging area and notifies you about it.
- Auto Mapping: This is one of the most important features that Hevo provides. Hevo’s schema detection engine automatically detects the schema of the incoming data and by itself creates a compatible schema in the destination.
- Monitoring and Observability: Monitor pipeline health with intuitive dashboards that reveal every state of the pipeline and data flow. Bring real-time visibility into your ETL with Alerts and Activity Logs.
Hevo delivers near real-time replication with prioritization for databases and adaptable timing for SaaS sources. Its in-built transformations offer seamless data formatting through drag-and-drop or Python.
While advanced monitoring ensures pipeline health with real-time alerts and intuitive dashboards. Use Hevo as an ETL Tool to ease your work.
Try Hevo as an ETL Tool
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Hevo provides Transparent Pricing to bring complete visibility to your ETL spend.
Hevo’s dashboards consist of the pipeline list view, as shown below.
Key Features of Oracle Data Integrator
- Oracle Data Integrator is Oracle’s own product for everything related to Oracle ETL as a source or destination. It is available as an on-premise solution or cloud-based solution.
- ODI is enterprise-grade with well-endowed security and compliance structures.
- Oracle Data Integrator supports most of the on-premise databases like SQL Server, Postgres, etc. as source and destination. Since it supports JDBC drivers, even the cloud databases that support JDBC are supported by ODI.
- Transformations are executed mainly using Oracle queries and exploit the Oracle execution engine.
- Recent developments have also brought in real-time data capability to ODI.
- It natively supports change data capture and real-time replication based on Oracle’s own tools like Golden Gate.
- Support for SaaS offerings is limited in ODI.
Use Case of Oracle Data Integrator
ODI makes a strong case if your primary data source or destination is Oracle. If your organization uses a wide variety of databases and Oracle is just one of them, then it is better to look elsewhere since the support for SaaS offerings and other upcoming databases is limited in ODI.
That said, it provides enterprise-grade security and compliance structures. So if your organization’s architecture is primarily an on-premise Oracle-based one, ODI should feature in your list of most preferred tools.
Pricing
Oracle Data Integrator on-premise pricing is not publicly disclosed and is done under negotiated contracts. ODI Cloud starts at 1.2 $ per GB per hour.
Key Features of Apache Airflow
- Airflow is an open-source ETL tool that is primarily meant for designing workflows and ETL job sequences.
- Airflow is meant as a batch processing platform, although there is limited support for real-time processing by using triggers.
- Airflow supports a wide variety of sources and destinations including cloud-based databases like Redshift.
- Airflow connectors are based on plugins. Since it is open-source, it is possible to develop custom plugins and deploy them on-premise. Airflow has a strong community that keeps on adding connector plugins and thus supports the popular SaaS offerings too.
Use Case
Airflow is primarily meant for companies with teams that can maintain their infrastructure and code. Airflow is typically deployed on-premise and is not available as a completely managed solution.
If your organization has interests in a variety of sources and destinations and wants to maintain close control over the code and infrastructure using its own employees, Airflow is a good choice.
Pricing
Airflow is free to use and customize. Infrastructure and development costs are the only costs involved in using Airflow.
Key Features of Informatica Power Center
- Informatica Power Center is primarily an on-premise ETL tool that can integrate with a variety of databases. Oracle is well supported.
- Like Oracle Data Integrator, Informatica Power Center is enterprise-grade with support for data masking, master data management, etc.
- Support for cloud-based databases is higher in Informatica Power Center when compared to Oracle Data Integrator. Databases like Redshift and DynamoDB are supported.
- It is meant for batch-based operation.
- It is also available as a cloud platform as a pay-as-you-go model where tasks will be executed using cloud infrastructure.
Use Case
Power center makes a strong case for organizations that prefer on-premise architectures where Oracle is just one of the sources and with strict enterprise-grade security requirements.
Pricing
Pricing is based on negotiations and established contracts. The cloud version starts at $2000 per month for its most basic plan. Cloud providers like AWS and Azure provide this as a completely managed service on a pay-as-you-go model.
Key Features of AWS Glue
- AWS Glue is a completely managed ETL solution from Amazon that supports a variety of databases through custom drivers and JDBC drivers. Since Oracle supports JDBC drivers, Glue can connect to Oracle as well.
- It provides an execution engine based on Spark.
- Glue can execute batch-based workloads and near real-time workloads. Glue works based on crawlers which can trigger jobs based on data availability. It can automatically discover data and catalog them.
- Using Glue and AWS Lambda functions, a completely serverless ETL pipeline can be implemented.
- The major limitation here is the lack of support for non-AWS-based databases. Support for SaaS offerings is also not great.
Use Case
Glue fits well in scenarios where the organization is primarily AWS-based and Oracle is just one of the data sources. Organizations with strict on-premise data requirements can not use Glue since it is a completely managed service.
Pricing
Pricing for Glue starts from $ 0.44 per data processing unit per hour. Each data processing unit is a 4 Core CPU with 16 GB of memory.
Key Features of StreamSets
- StreamSets is a real-time data processing tool with excellent monitoring capabilities. It markets itself as a data ops tool.
- StreamSets’ data collector can connect to a variety of sources including Oracle.
- StreamSets’ data protector can help an organization stay within major compliance requirements like HIPAA and GDPR.
- StreamSets does not do a good job of supporting major SaaS offerings.
- Like AWS Glue, it is also based on a spark execution engine and hence your source and destination database’s execution power is not much used.
Use Case
StreamSets can be a good choice where the use case is primarily real-time based and the organization is open to a completely managed cloud service. Since the support for SaaS offerings is limited, it is better in scenarios where sources and destinations are primarily traditional databases.
Pricing
StreamSets’ pricing strategy is not transparent and is agreed upon via negotiated contracts.
Key Features of Talend Open Studio
- Talend provides an open-source ETL tool called Talend Open Studio. Talend bundles a set of tools for data integration, preparation, etc. with Talend Open Studio and markets it as Talend Data Fabric. It is free to use in case of self-deployments. It also provides a completely managed service and enterprise-grade customer support both of which are paid features.
- It supports a large number of sources and destinations through support for SaaS offerings is limited.
- It provides a simple UI to design Oracle ETL jobs and transformation logic.
- Talend targets customers with strict data protection requirements and architecture involving multi-cloud and hybrid cloud.
Use Case
Organizations with strict data protection requirements and multi-cloud strategies with a liking for open-source tools can prefer Talend.
Pricing
Talend Open Studio is free to use. The most basic plan with enterprise-grade support starts at $12000 per year. Pricing for the complete suite is based on negotiated contracts.
8) Hadoop
Key Features of Hadoop
- Scalability: Hadoop’s distributed architecture enables horizontal scaling of processing and storage for large volumes of data across clusters of machines.
- Fault Tolerance: HDFS guarantees the integrity of data; this is so because, even though individual nodes may go down, it builds in redundancy and fault tolerance.
- MapReduce Framework: Provides a programming model to process large data sets in parallel across the distributed cluster.
- Cost Effective: It runs on commodity hardware and open-source software. This brings down the cost drastically when compared with traditional data processing solutions.
Use Cases
- It is ideal for handling and processing large datasets that won’t fit in traditional RDBMS systems, such as log files, Web analytics, and sensor data.
- It provides scalable training and evaluation of models in large datasets—next-level support for advanced analytics and machine learning models.
- It facilitates the ETL process by extracting, transforming, and loading data from multiple sources; process using MapReduce or other processing frameworks as needed; and storing it finally in data warehouses or other storage systems.
Pricing
Hadoop itself is open-source software, so there is no direct cost for using Hadoop. Many cloud providers offer managed Hadoop services with pricing based on the resources used, such as Microsoft Azure HDInsight: you can check the pricing plan from the official website.
9) IBM Infosphere DataStage
Key Features of IBM Infosphere Datastage
- Comprehensive ETL Capabilities: It enables robust ETL, which aids in integrating data from different sources and turning that data into the proper structure for analysis, then loading it into target systems.
- Data Integration: It can connect to a huge variety of data sources—databases, cloud services, and flat files. Supports structured and unstructured data.
- Metadata Management: It offers advanced metadata management for data lineages, impact analyses in case of a change, and quality monitoring of the data.
- High Availability and Fault Tolerance: It demonstrates robust mechanisms that ensure high availability and fault tolerance in the data integration workflow.
Use Cases
- It facilitates big data integration and processing, such as Hadoop and Spark, for complex analytics and data management tasks.
- It enhances business intelligence initiatives by ensuring the correct and on-time availability of data for reporting and analytics.
- It enables real-time data processing and integration to support applications that require immediate insights and operational intelligence.
Pricing
Their pricing plans are customizable. You can contact their team for details on the price.
Factors that Drive the Decision of Oracle ETL Tools
The question of how to evaluate the above Oracle ETL tools, to find the one that is the best fit for your use case does not have a straightforward answer. The decision of choosing the right Oracle ETL Tools depends on a lot of criteria as listed below:
- Ease of Setup and Implementation: This factor plays an important role in your time to production. Cloud-based Oracle ETL tools offer the best value in this regard since there is no setup and implementation time involved.
- Integration Support for Various Data Sources and Destinations: The flexibility of an Oracle ETL is all about aggregating data from a wide variety of sources. Once an ETL tool is chosen, changing that requires a lot of time and effort. Hence it is imperative that the Oracle ETL tool that you choose has support for all your future data source requirements.
- Monitoring and Job Management: ETL frameworks work day and night executing jobs and are always in a race against time to finish the jobs within the service level agreements. Timely identification of problems and status reporting is very important in maintaining the stability of the pipeline.
- Real-time Data Processing: Modern Oracle ETL pipelines are fastly moving towards real-time data processing. Support for real-time data streams must be considered at least for future-proofing the ETL pipeline.
- Support for Data Transformations: Data from various sources needs to be transformed into an easily analyzable and searchable form before pushing to the data warehouse.
As time progresses, business analysts will dig up more and more metrics that denote the success or failure of their business. Generating these metrics will involve complex transformations and these will also become part of the Oracle ETL Data Pipeline jobs.
- Support for Various Replication Measures: It is a common practice to replicate various staging views and destination views to logically separate the different jobs. Your ETL framework should have the ability to support such replication out of the box.
- Data Load Reliability: The last part of ETL, loading data into the destination warehouse is very closely coupled with the choice of the Oracle ETL platform. Your chosen platform must be able to reliably load data to your target database and recover from unexpected failures.
- Ease of Use: Configuring new ETL jobs and testing them is a big part of the lifecycle of ETL frameworks. Frameworks with support for simple UI-based ETL job design can prove to be very easy to use and enable the lowest time to production.
Conclusion
The article introduced you to Oracle ETL tools and the list of some of the best tools you can find for ETL in Oracle. It also provided you with the key factors that drive the decision of choosing the right Oracle ETL Tools for your business. One of the best on that list is Hevo Data.
With the manual ETL data pipeline consuming lots of resources and proving to be very time-consuming, businesses are leaning more towards automated ETL or Automated Data Pipeline Solutions.
Hevo Data is the right choice! It helps you load data from Oracle and 150+ data sources to a Data Warehouse of your choice and visualizes it in a BI tool like Power BI, Tableau, etc.
Learn more about Hevo’s integration with Oracle
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Did we miss out on any great tools for Oracle ETL? What is your preferred Oracle ETL tool? Let us know in the comments below!
FAQs about ETL Tools
1. Does Oracle have an ETL tool?
Yes, Oracle offers an ETL tool known as Oracle Data Integrator (ODI).
2. What is ELT in Oracle?
In Oracle, ELT (Extract, Load, Transform) involves extracting data from sources, loading it directly into the target system, and performing transformations within the target database.
3. Is Oracle Warehouse Builder an ETL tool?
Yes, Oracle Warehouse Builder (OWB) is an ETL (Extract, Transform, Load) tool.
4. Does Oracle have a data warehouse?
Yes, Oracle offers data warehousing solutions including Oracle Autonomous Data Warehouse, a fully-managed cloud service, and Oracle Exadata, a high-performance on-premises or cloud-based system.
Suraj has over a decade of experience in the tech industry, with a significant focus on architecting and developing scalable front-end solutions. As a Principal Frontend Engineer at Hevo, he has played a key role in building core frontend modules, driving innovation, and contributing to the open-source community. Suraj's expertise includes creating reusable UI libraries, collaborating across teams, and enhancing user experience and interface design.
