Cloudera Enterprise Data Hub vs. Dataiku vs. Oracle Autonomous Data Warehouse

Cloudera is critical for constructing an organizational data center
while maximizing the value of that volume of data.



Cloudera is great for comprehending data and querying for valuable
replies.



Cloudera supports data transfer from a variety of external databases and
third-party platforms.

Incentivized

Dataiku DSS is very well suited to handle large datasets and projects which requires a huge team to deliver results. This allows users to collaborate with each other while working on individual tasks. The workflow is easily streamlined and every action is backed up, allowing users to revert to specific tasks whenever required. While Dataiku DSS works seamlessly with all types of projects dealing with structured datasets, I haven't come across projects using Dataiku dealing with images/audio signals. But a workaround would be to store the images as vectors and perform the necessary tasks.

Incentivized

II would recommend Oracle Autonomous Data Warehouse to someone looking to fully automate the transferring of data especially in a warehouse scenario though I can see the elasticity of the suite that is offered and can see it is applicable in other scenarios not just warehouses.

Incentivized

• One of the oldest distributors of enterprise standard Hadoop.
• Distribution is based on open source Hadoop even though customizations are done on top of that.
• Faster updates and bug fixes to the products as they have Apache committers.
• Central configuration and control of your Hadoop platform (but still needs improvements).

Incentivized

• Low-code platform.
• Open source version includes most valuable modules.
• User friendly documentation.

Incentivized

• Very easy and fast to load data into the Oracle Autonomous Data Warehouse
• Exceptionally fast retrieval of data joining 100 million row table with a billion row table plus the size of the database was reduced by a factor of 10 due to how Oracle store[s] and organise[s] data and indexes.
• Flexibility with scaling up and down CPU on the fly when needed, and just stop it when not needed so you don't get charged when it is not running.
• It is always patched and always available and you can add storage dynamically as you need it.

• Not fully Open Source, couple of components of the distributions are privately owned, meaning with public contributions are not welcome
• Improvements to Cloudera manager can only be recommended. its very hard to get it done once recommended as the full control is with them.
• Should make components more aligned to Open Source rather than making it closed sourced.
• Custom Features of open source software tools supported only by Cloudera are tricky. Cant commit changes to tools like Hue.
• Improvements to Cluster Management tool is required, which are already available to its competitors.

Incentivized

• The visualization feature of flow still has a lot room to improve, when the flow is complex.
• The "non-coding" template/building block for deep learning lack of many important configurable parameters.
• Lack of the unified way to allow applying the "design pattern" on the Python codes (if we want to develop our own module or building blocks.

Incentivized

• Level of integration or compatibility to connect it to different applications can be improved
• The support service is slow
• The issue is with the record number limitation of not being able to bring back more than one million records or not being able to export larger datasets to Excel

Incentivized

Likely to renew the use in case the requirements for Cloudera remain valid. The rapid change in customer requirements and solutions that must be validated, integrated or tested changes. As the maturity of the solution increases, the requirements to renew use decrease. From a solution feature perspective by itself would probably grade 10.

Because

• It is really simple to provision and configure.
• Does not require continous attention from the DBA, autonomous features allows the database to perform most of the regular admin tasks without need for human intervention.
• Allows to integrate multiple data sources on a central data warehouse, and explode the information stored with different analytic and reporting tools.

Incentivized

The user experience is very good. Everything feels intuitive and "flows" (sorry excuse the pun) so nicely, and the customization level is also appropriate to the tool. Even as a newer data scientist, it felt easy to use and the explanations/tutorials were very good. The documentation is also at a good level

Incentivized

The amazing part of Dataiku DSS is their customer service. Based on urgency and technical level, you get a reply from the Dataiku engineer when you raise a query. So far, my queries have been pretty complex to solve, so I have received solutions even from the CTO of the company as well, which is why I would describe their customer support as very good.

Incentivized

Understanding Oracle Cloud Infrastructure is really simple, and Autonomous databases are even more. Using shared or dedicated infrastructure is one of the few things you need to consider at the moment of starting provisioning your Oracle Autonomous Data Warehouse.

Incentivized

Cloudera is a
great choice because it provides fast streaming data for tracking, breaks down
silos by providing unified self-service platforms for data-driven insights,
secures machine learning, AI solutions, and stores self-service data, enabling
our analysts to concentrate on more important tasks like displaying critical
information.

Incentivized

Dataiku was selected for me, but I am happy about that. I like Dataiku for the user experience, it feels less code-y and I like to demo things to non technical stakeholders because they can still follow along. When you open some other notebooks, you can see that peoples eyes start to glaze over

Incentivized

Our organization adopted Oracle almost 20 years ago and there were a few options at that time. Oracle was the leading database tech company at that time and it was a safe choice to us. And they have been evolved and always ahead of new technologies, high performance, and professional business support. We didn't find a good reason to replace Oracle with any other competitors.

Incentivized

• Cloudera products are the most widely. It is more business friendly as data is more secure. The sensitive data that you operate on is local to you and your project rather than processing this data on Cloud.
• Cloudera is definitely faster as wait time is reduced if on Cloud.
• A lot range of products are covered. So it is definitely good for businesses and had good returns on investments.

• So far it has had a positive impact. Multiple departments are coming to us with their business problems.
• I can't specifically say about ROI as I'm a developer, though I have heard this solution is economical compared to other AI/ML enterprise tools.
• By using this tool, my client has let go of software that was used earlier, and we have created a simpler framework to replace that software.

Incentivized

• Overall the business objective of all of our clients have been met positively with Oracle Data Warehouse. All of the required analysis the users were able to successfully carry out using the warehouse data.
• Using a 3-tier architecture with the Oracle Data Warehouse at the back end the mid-tier has been integrated well. This is big plus in providing the necessary tools for end users of the data warehouse to carry out their analysis.
• All of the various BI products (OBIEE, Cognos, etc.) are able to use and exploit the various analytic built-in functionalities of the Oracle Data Warehouse.

Incentivized