Sep 09, 2021
We have experiences only in Pentaho Data Integrator (open source competitor of the Oracle Data Integrator).
But OLAP exporting wasn't in our scope until now.
Find out what your peers are saying about Databricks, Microsoft, Apache and others in Streaming Analytics.
I can say we have noticed a strong return on investment largely due to improved scalability and reduced operational friction in asynchronous workflows.
Practically, the biggest support channels are its community ecosystem, documentation, GitHub discussions, and engineering forums.
The Apache community provides support for the open-source version.
There is plenty of community support available online.
I can get solutions quickly, and any tickets I submit to Oracle are responded to and resolved rapidly.
The technical support of Oracle is very good; they support the Oracle Data Integrator (ODI) solution effectively.
Customers have not faced issues with user growth or data streaming needs.
For traffic spikes, Apache Kafka naturally helps by buffering events, allowing consumers to catch up instead of immediately overwhelming downstream services.
I need to enable my solution with high availability and scalability.
The scalability and the ability to handle multiple workloads of several parallel ETL jobs could use improvement.
Testing changes in lower environments before production rollout and verifying replication health and cluster stability is essential.
Apache Kafka is stable.
This feature of Apache Kafka has helped enhance our system stability when handling high volume data.
In terms of performance stability, I have not experienced any downtimes, crashes, or performance issues with the Oracle Data Integrator (ODI).
The performance angle is critical, and while it works in milliseconds, the goal is to move towards microseconds.
Running and maintaining an Apache Kafka cluster at scale involves handling partitions, replications, retention policies, rebalancing, and monitoring, which requires strong expertise.
Apache Kafka groups could introduce themes or profiles of configuration to help manage this complexity without needing expertise.
If I use a source system like Oracle and a target system like Teradata, ODI will still run, but it struggles a bit with different infrastructures.
It would be excellent not to have to go into different areas to perform different activities but rather have a user-defined interface where we can configure a job, run it, monitor it, link packages, and link subprocesses all in one frame.
Adding AI capabilities would make Oracle Data Integrator (ODI) even better.
From a price perspective, if you are asking about Apache Kafka, I would rate it a nine.
The open-source version of Apache Kafka results in minimal costs, mainly linked to accessing documentation and limited support.
Its pricing is reasonable.
ODI is cheaper compared to Informatica PowerCenter and IBM DataStage.
The pricing aspect of Oracle Data Integrator (ODI) is reasonable; it brings significant value to the table.
Apache Kafka is effective when dealing with large volumes of data flowing at high speeds, requiring real-time processing.
Apache Kafka is particularly valuable for managing high levels of transactions.
Regarding durability and reliability, messages are persisted, so temporary consumer failures do not automatically lead to data loss, which is valuable in financial workflows where losing events is unacceptable.
The main benefits that Oracle Data Integrator (ODI) brings to the table include data quality, data completeness functionality, metadata management, and the reverse engineering feature, which allows integrating the metadata of diversified data sources with a single click.
Oracle Data Integrator (ODI) is powerful and strong if my system uses Oracle components for environments like OLTP, enterprise data warehouse, or data marts.
Oracle Data Integrator (ODI)'s ELT architecture has helped optimize my data movement and transformation significantly.
| Product | Mindshare (%) |
|---|---|
| Apache Kafka | 3.9% |
| Apache Flink | 8.2% |
| Databricks | 7.9% |
| Other | 80.0% |
| Product | Mindshare (%) |
|---|---|
| Oracle Data Integrator (ODI) | 2.5% |
| Informatica Intelligent Data Management Cloud (IDMC) | 3.7% |
| SSIS | 3.6% |
| Other | 90.2% |
| Company Size | Count |
|---|---|
| Small Business | 32 |
| Midsize Enterprise | 20 |
| Large Enterprise | 51 |
| Company Size | Count |
|---|---|
| Small Business | 26 |
| Midsize Enterprise | 12 |
| Large Enterprise | 44 |
Apache Kafka provides scalable, high-throughput, real-time data processing. Appreciated for its open-source nature and integration capabilities, Kafka supports distributed messaging and high-volume handling with essential features like message retention, replication, and partitioning.
Apache Kafka is a powerful tool for managing efficient data streams and high volumes of asynchronous messages. Its ease of setup and robust integration options make it popular among industries requiring real-time data streaming and processing. Key features such as message retention and consumer groups cater to demanding applications, while fault-tolerant design ensures reliability. Despite its advantages, Kafka can improve in areas like duplicate management, documentation, and intuitive interfaces. Challenges in configuration and monitoring tools suggest areas for enhancement, alongside reducing complexity and resource dependency.
What are the key features of Apache Kafka?Industry applications for Apache Kafka include real-time data streaming for IoT, big data management, and analytics. In finance, it supports fraud detection and transaction monitoring. Healthcare uses Kafka for patient data handling and logistics leverage its data distribution capabilities to optimize operations. Its ability to manage large-scale asynchronous communication makes it vital across sectors demanding high data throughput and reliability.
Oracle Data Integrator offers flexible EL-T architecture, optimizing processing with database capabilities. It supports diverse data sources, automates deployment, and provides efficient data transformations, making it suitable for data warehousing and complex data environments.
Oracle Data Integrator leverages EL-T architecture to enhance processing by utilizing database strengths. It integrates with a wide array of technologies, including RDBMS, cloud, and big data. The software's Knowledge Modules enable customizable integration strategies, accelerating development. With a user-friendly interface and automation features, it simplifies metadata management and supports real-time data warehousing. Key areas such as UI performance, integration, and real-time data capabilities require enhancements. Challenges include error handling, initial setup, and compatibility with platforms like Git, Azure, and IoT services. Improvements in metadata management, scalability, and user-friendliness are needed.
What are the most important features of Oracle Data Integrator?Organizations utilize Oracle Data Integrator primarily in data warehousing, handling data from ERP systems, EBS, Fusion, and cloud databases. It aids in creating data lakes, OLTP migrations, digital health initiatives, and automation tasks, ensuring seamless integration with databases like MySQL and SQL Server.
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