We performed a comparison between Azure Data Factory and Oracle Data Integrator (ODI) based on real PeerSpot user reviews.Find out in this report how the two Data Integration Tools solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
"In StreamSets, everything is in one place."
"I have used Data Collector, Transformer, and Control Hub products from StreamSets. What I really like about these products is that they're very user-friendly. People who are not from a technological or core development background find it easy to get started and build data pipelines and connect to the databases. They would be comfortable like any technical person within a couple of weeks."
"It is a very powerful, modern data analytics solution, in which you can integrate a large volume of data from different sources. It integrates all of the data and you can design, create, and monitor pipelines according to your requirements. It is an all-in-one day data ops solution."
"StreamSets’ data drift resilience has reduced the time it takes us to fix data drift breakages. For example, in our previous Hadoop scenario, when we were creating the Sqoop-based processes to move data from source to destinations, we were getting the job done. That took approximately an hour to an hour and a half when we did it with Hadoop. However, with the StreamSets, since it works on a data collector-based mechanism, it completes the same process in 15 minutes of time. Therefore, it has saved us around 45 minutes per data pipeline or table that we migrate. Thus, it reduced the data transfer, including the drift part, by 45 minutes."
"StreamSets data drift feature gives us an alert upfront so we know that the data can be ingested. Whatever the schema or data type changes, it lands automatically into the data lake without any intervention from us, but then that information is crucial to fix for downstream pipelines, which process the data into models, like Tableau and Power BI models. This is actually very useful for us. We are already seeing benefits. Our pipelines used to break when there were data drift changes, then we needed to spend about a week fixing it. Right now, we are saving one to two weeks. Though, it depends on the complexity of the pipeline, we are definitely seeing a lot of time being saved."
"I like how you can create your own pipeline in your space and reuse those creations. You can collaborate with other people who want to use your code."
"The initial setup is very quick and easy."
"One of the most valuable features of Azure Data Factory is the drag-and-drop interface. This helps with workflow management because we can just drag any tables or data sources we need. Because of how easy it is to drag and drop, we can deliver things very quickly. It's more customizable through visual effect."
"The data mapping and the ability to systematically derive data are nice features. It worked really well for the solution we had. It is visual, and it did the transformation as we wanted."
"It is beneficial that the solution is written with Spark as the back end."
"The two most valuable features of Azure Data Factory are that it's very scalable and that it's also highly reliable."
"Data Factory's best features are connectivity with different tools and focusing data ingestion using pipeline copy data."
"The best part of this product is the extraction, transformation, and load."
"The most valuable features of ODI are the knowledge modules, such as the Loading Knowledge module and the Check Knowledge module, they are helpful. We can check for the constraints in ODI. That helps in figuring out what are the constraints that are the primary keys created in the tables. We can check them with the Check Knowledge module."
"The most valuable features of ODI are the ease of development, you can have a template, and you can onboard transfer very quickly. There's a lot of knowledge modules available that we can use. If you want to connect, for example, a Sibyl, SQL, Oracle, or different products, we don't have to develop them from scratch. They are available, but if it's not, we can go into the marketplace and see if there's a connector there. Having the connector available reduces the amount of hard work needed. We only have to put the inputs and outputs. In some of the products, we use there is already integration available for ODI, which is helpful."
"Integration with all systems is easy with Oracle Data Integrator, and it is easy to use. I have not used any other product, but with Oracle Data Integrator, we can easily connect to an ERP system, an SAP system, or a cloud application."
"The most valuable feature of ODI is the to use of the whole ETL to create a data lake."
"ODI's best features are customization, integration with other versioning tools, and the ability to define new knowledge modules."
"What I found most valuable in Oracle Data Integrator (ODI) is that it integrates well with almost all technologies currently being used in my company."
"ODI's most valuable features are it utilizes the database engine and is very lightweight."
"If you use JDBC Lookup, for example, it generally takes a long time to process data."
"Currently, we can only use the query to read data from SAP HANA. What we would like to see, as soon as possible, is the ability to read from multiple tables from SAP HANA. That would be a really good thing that we could use immediately. For example, if you have 100 tables in SQL Server or Oracle, then you could just point it to the schema or the 100 tables and ingestion information. However, you can't do that in SAP HANA since StreamSets currently is lacking in this. They do not have a multi-table feature for SAP HANA. Therefore, a multi-table origin for SAP HANA would be helpful."
"We create pipelines or jobs in StreamSets Control Hub. It is a great feature, but if there is a way to have a folder structure or organize the pipelines and jobs in Control Hub, it would be great. I submitted a ticket for this some time back."
"Sometimes, when we have large amounts of data that is very efficiently stored in Hadoop or Kafka, it is not very efficient to run it through StreamSets, due to the lack of efficiency or the resources that StreamSets is using."
"The logging mechanism could be improved. If I am working on a pipeline, then create a job out of it and it is running, it will generate constant logs. So, the logging mechanism could be simplified. Now, it is a bit difficult to understand and filter the logs. It takes some time."
"The solution needs to be more connectable to its own services."
"Azure Data Factory can improve the transformation features. You have to do a lot of transformation activities. This is something that is just not fully covered. Additionally, the integration could improve for other tools, such as Azure Data Catalog."
"Data Factory's performance during heavy data processing isn't great."
"Integration of data lineage would be a nice feature in terms of DevOps integration. It would make implementation for a company much easier. I'm not sure if that's already available or not. However, that would be a great feature to add if it isn't already there."
"It can improve from the perspective of active logging. It can provide active logging information."
"Data Factory's cost is too high."
"One area for improvement is documentation. At present, there isn't enough documentation on how to use Azure Data Factory in certain conditions. It would be good to have documentation on the various use cases."
"User-friendliness and user effectiveness are unquestionably important, and it may be a good option here to improve the user experience. However, I believe that more and more sophisticated monitoring would be beneficial."
"It has been very good. Just recently, I've faced an issue, but I solved it somehow. While integrating with a file, I faced an issue where I wanted output files, and I had used the text field limited quotations, but at the end of the file, there was a line breakage for the last column. So, we just removed the text field because it was not working correctly for us."
"The solution lacks some functions and features."
"ODI could improve by being more user-friendly. Informatica, which is also an ETL tool, similar to ODI, but Informatica is very user-friendly, easy to use, and simple to integrate, compared to ODI. ODI has many features, put them all together, and sometimes we get confused about which ones to use, which ones not to use."
"The initial setup is complex, especially if you also have to install a console."
"The price needs to be lowered. It's too expensive."
"An area for improvement in Oracle Data Integrator (ODI) is real-time integration. Currently, my company has a workaround to implement real-time integration, an area on which Oracle must focus more. Real-time integration should be easier in Oracle Data Integrator (ODI). Another area for improvement in Oracle Data Integrator (ODI) is integration with more publishers and subscribers rather than just database integrations."
"The interface of ODI could be improved. For example, navigating and finding functions can be difficult. For example, you have to know which step you need to go to look at where your job status is. The logical step is a bit complex compared to other tools. It's much easier to get a graphical view, but with ODI, it's graphical, plus you have to know all the other pieces that fit around it. You have to think about the logical and physical aspects."
"An area for improvement would be the lack of SQL compatibility - ODI has no ability to interact with SQL unstructured types and data types."
StreamSets offers an end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps, and power the modern data ecosystem and hybrid integration.
Only StreamSets provides a single design experience for all design patterns for 10x greater developer productivity; smart data pipelines that are resilient to change for 80% less breakages; and a single pane of glass for managing and monitoring all pipelines across hybrid and cloud architectures to eliminate blind spots and control gaps.
With StreamSets, you can deliver the continuous data that drives the connected enterprise.
Azure Data Factory is a managed cloud service built for extract-transform-load (ETL), extract-load-transform (ELT), and data integration projects. This is a digital integration tool as well as a cloud data warehouse that allows users to create, schedule, and manage data in the cloud or on premises. The use cases of the product include data engineering, operational data integration, analytics, ingesting data into data warehouses, and migrating on-premise SQL Server Integration Services (SSIS) packages to Azure.
The tool allows users to create data-driven workflows for initiating data movement and data transformation at scale. Data can be ingested from disparate data stores via pipelines. Companies can utilize this product to build complex ETL processes for transforming data visually with data flows. Azure Data Factory also offers services such as Azure HDInsight Hadoop, Azure Databricks, Azure Synapse Analytics, and Azure SQL Database. These services are created to facilitate data management and control for organizations, providing them with better visibility of their data for improved decision-making.
Azure Data Factory allows companies to create schedules for moving and transforming data into their pipelines. This can be done hourly, daily, weekly, or according to the specific needs of the organization. The steps through which the data-driven workflows work in Azure Data Factory are the following:
1. Connecting to required sources and collecting data. After connecting to the various sources where data is stored, the pipelines move the data to a centralized location for further processing.
2. Transforming and enriching the data. Once the data is moved to a centralized data store in the cloud, the pipelines transform it through services like HDInsight Hadoop, Azure Data Lake Analytics, Spark, and Machine Learning.
3. Delivering the transformed data to on-premise sources or keeping it in cloud storage sources for usage by different tools and applications.
Azure Data Factory Concepts
The solution consists of a series of interconnected systems that provide data integration and related services for users. The following concepts create the end product for users:
Azure Data Factory Benefits
Azure Data Factory offers clients many several benefits. Some of these include:
Reviews from Real Users
According to Dan M., a Chief Strategist & CTO at a consultancy, Azure Data Factory is secure and reasonably priced.
A Senior Manager at a tech services company evaluates the tool as reasonably priced, scales well, good performance.
Oracle Data Integrator (ODI) is a data integration software solution that provides a unified infrastructure to streamline data and application integration projects. It uses a powerful design approach to data integration, which separates the declarative rules from the implementation details. The solution is based on a unique ELT (Extract Load Transform) architecture, eliminating the need for a standalone ETL server and proprietary engine.
Oracle Data Integrator Features
ODI has many valuable key features. Some of the most useful ones include:
Oracle Data Integrator Benefits
There are many benefits to implementing ODI. Some of the biggest advantages the solution offers include:
Reviews from Real Users
Below are some reviews and helpful feedback written by PeerSpot users currently using the Oracle Data Integrator (ODI) solution.
Brian D., Business Process and Strategy Specialist Advisor at NTTData, says, “The Knowledge Module (KM) is my favorite feature of ODI. This is where I learned how to use variables to make jobs dynamic. I took that knowledge and created a KM that would go into iTunes and pull the sales of eBooks. Making something that is reusable, like a KM, is important to not only reduce build time but also maintenance in the future.”
Ashok S., Applications Support Manager at a marketing services firm, mentions, "The most valuable features of ODI are the ease of development, you can have a template, and you can onboard transfer very quickly. There's a lot of knowledge modules available that we can use. If you want to connect, for example, a Sibyl, SQL, Oracle, or different products, we don't have to develop them from scratch. They are available, but if it's not, we can go into the marketplace and see if there's a connector there. Having the connector available reduces the amount of hard work needed. We only have to put the inputs and outputs. In some of the products, we use there is already integration available for ODI, which is helpful."
Azure Data Factory is ranked 1st in Data Integration Tools with 41 reviews while Oracle Data Integrator (ODI) is ranked 4th in Data Integration Tools with 12 reviews. Azure Data Factory is rated 8.0, while Oracle Data Integrator (ODI) is rated 8.2. The top reviewer of Azure Data Factory writes "There's the good, the bad and the ugly....unfortunately lots of ugly". On the other hand, the top reviewer of Oracle Data Integrator (ODI) writes "We can make all the EPM tools work together as one and we can create a puzzle that will increase the performance and capability of all EPM tools". Azure Data Factory is most compared with Informatica PowerCenter, Microsoft Azure Synapse Analytics, Informatica Cloud Data Integration, Alteryx Designer and Denodo, whereas Oracle Data Integrator (ODI) is most compared with Oracle Integration Cloud Service, SSIS, Informatica PowerCenter, Oracle GoldenGate and Talend Open Studio. See our Azure Data Factory vs. Oracle Data Integrator (ODI) report.
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