Datapipe Cloud Analytics for AWS vs Oracle Big Data Cloud Service comparison

Datapipe and Oracle are both solutions in the Cloud Analytics category. Datapipe is ranked #9, while Oracle is ranked #6. Additionally, 100% of Datapipe users are willing to recommend the solution, compared to 100% of Oracle users who would recommend it.

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Executive Summary

We performed a comparison between Datapipe Cloud Analytics for AWS and Oracle Big Data Cloud Service based on real PeerSpot user reviews.

Find out what your peers are saying about IBM, Densify, Spot by NetApp and others in Cloud Analytics.

To learn more, read our detailed Cloud Analytics Report (Updated: November 2024).

Buyer's Guide

Cloud Analytics

November 2024

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Categories and Ranking

IBM Turbonomic

Featured Reviews

SubashSubbiah

Assistant Consultant at a tech services company with 10,001+ employees

It can tell us where performance is lagging on the hardware layer, but the reporting on the application layer is lacking

The automation area could be improved, and the generic reports are poor. We want more details in the analysis report from the application layer. The reports from the infrastructure layer are satisfactory, but Turbonomic won't provide much information if we dig down further than the application layer. I would like them to add some apps for physical device load resourcing and physical-to-virtual calculation. It gives excellent recommendations for the virtual layer but doesn't have the capabilities for physical-to-virtual analysis. Automated deployment is something else they could add. Some built-in automation features are helpful, but we aren't effectively using a few. We want a few more automated features, like autoscaling and automatic performance optimization testing would be useful.

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JeffCooper

Director of Data Science at HealthWorks Analytics

Stable, straightforward to set up and does have good integration with various useful tools

The on-demand pipeline execution is something that we've had some challenges with for on-demand scheduling, however, we have some fairly complex use cases there. That said, we have had some problems getting that to work across a wide variety of use cases. Therefore, depending on the latency and the on-demand nature of it, they could do some improvement there. QuickSight is evolving pretty quickly. While I liked it, it integrates with it, it would help if they did more coordinated releases so that those features in their other products are improved and that those are available too. I'd like to see it coordinated or integrated with more of a data catalog. While there are some features there, the data governance and data cataloging, they touch on that, however, that's an important area of growth. It's becoming more and more important. That's why I would like to see more sophisticated and more complete data cataloging and data governance in that product. I know they're working on that. And of course, sometimes you have to go to half a dozen different AWS products before you get the thing you want. That said, I would like to see more data cataloging, more governance.

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reviewer813444

General Manager with 1-10 employees

Easy to set up with good data integration and data virtualization

We developed the solution based on Hadoop, but using Hadoop as a relationship database, it's hard to maintain and recruit developers. There needs to be better integrations with other solutions and there need to be more tools (including virtualization tools) and utilities between this solution and Hadoop. For example, Oracle has a product called Big Data SQL and it built a bridge between Hadoop and Oracle's database to make things easier for developers. The solution needs to be more stable.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"Turbonomic has helped optimize cloud operations and reduced our cloud costs significantly. Overall, we are at about 40 percent savings, and we spend about three million a year just in Azure. It reduces the size of the VMs, putting them into the right template for usage. People don't realize that you don't have to future-proof a virtual machine in Azure. You just need to build it for today. As the business or service grows, you can scale up or out. About 90 percent of all the costs that we've reduced has been from sizing machines appropriately."

"It helps us get a consolidated view of all customer spending into a single dashboard, allowing us to identify opportunities to improve their current spending."

"I only deal with the infrastructure side, so I really couldn't speak to more than load balancing as the most valuable feature for me. It provides specific actions that prevent resource starvation. It always keeps things in perfect balance."

"I like Turbonomic's automation and AI machine learning features. It shows you what it can do, but it can also act on recommendations automatically. Integration with an APM system makes the AI/ML features truly effective. Understanding what the application is doing and the trends of application behavior can help you make real-world decisions and act on that information."

"With Turbonomic, we were able to reduce our ESX cluster size and save money on our maintenance and license renewals. It saved us around $75,000 per year but it's a one-time reduction in VMware licensing. We don't renew the support. The ongoing savings is probably $50,000 to $75,000 a year, but there was a one-time of $200,000 plus."

"I like Turbonomic's built-in reporting. It provides a ton of information out of the box, so I don't have to build panels for the monthly summaries and other reports I need to present to management. We get better performance and bottleneck reporting from this than we do from our older EMC software."

"We have seen a 30% performance improvement overall."

"With over 2500 ESX VMs, including 1500+ XenDesktop VDI desktops, hosted over two datacentres and 80+ vSphere hosts, firefighting has become something of the past."

More IBM Turbonomic pros

"The initial setup is pretty straightforward."

"The solution's most valuable aspect is the fact that it is open source."

"The solution's most valuable aspects are its data integration and data virtualization."

Cons

"I would love to see Turbonomic analyze backup data. We have had people in the past put servers into daily full backups with seven-year retention and where the disk size is two terabytes. So, every single day, there is a two terabyte snapshot put into a Blob somewhere. I would love to see Turbonomic say, "Here are all your backups along with the age of them," to help us manage the savings by not having us spend so much on the storage in Azure. That would be huge."

"Additional interfaces would be helpful."

"Since the introduction of a HTML 5 based interface, our main - but minor - criticism of a less than intuitive operation managers' GUI would be the area of improvement."

"We're still evaluating the solution, so I don't know enough about what I don't know. They've done a lot over the years. I used Turbonomics six or seven years ago before IBM bought them. They've matured a lot since then."

"Recovering resources when they're not needed is not as optimized as it could be."

"The deployment process is a little tricky. It wasn't hard for me because I have pretty in-depth knowledge of Kubernetes, and their software runs on Kubernetes. To deploy it or upgrade it, you have to be able to follow steps and use the Kubernetes command line, or you'll need someone to come in and do it for you."

"In Azure, it's not what you're using. You purchase the whole 8 TB disk and you pay for it. It doesn't matter how much you're using. So something that I've asked for from Turbonomic is recommendations based on disk utilization. In the example of the 8 TB disk where only 200 GBs are being used, based on the history, there should be a recommendation like, "You can safely use a 500 GB disk." That would create a lot of savings."

"The reporting needs to be improved. It's important for us to know and be able to look back on what happened and why certain decisions were made, and we want to use a custom report for this."

More IBM Turbonomic cons

"I'd like to see it coordinated or integrated with more of a data catalog."

"The solution needs to improve the functionality of the auto-deduplication."

"We've had some issues with stability."

Pricing and Cost Advice

"I have not seen Turbonomic's new pricing since IBM purchased it. When we were looking at it in my previous company before IBM's purchase, it was compatible with other tools."

"In the last year, Turbonomic has reduced our cloud costs by $94,000."

"The pricing is in line with the other solutions that we have. It's not a bargain software, nor is it overly expensive."

"We felt the pricing was very fair for the product. It is in no way prohibitive for larger deployments, unlike other similar product on the market."

"You should understand the cost of your physical servers and how much time and money you are spending year over year on expanding your virtual farm."

"It's worth the time and money investment if you can afford it."

"The product is fairly priced right now. Given its capabilities, it is excellently priced. We think that the product will become self-funding because we will be able to maximize our resources, which will help us from a capacity perspective. That should save us money in the long run."

"We see ROI in extended support agreements (ESA) for old software. Migration activities seem to be where Turbonomic has really benefited us the most. It's one click and done. We have new machines ready to go with Turbonomic, which are properly sized instead of somebody sitting there with a spreadsheet and guessing. So, my return on investment would certainly be on currency, from a software and hardware perspective."

More IBM Turbonomic pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Computer Software Company

13%

Manufacturing Company

10%

Insurance Company

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

What is your experience regarding pricing and costs for Turbonomic?

It offers different scenarios. It provides more capabilities than many other tools available. Typically, its price is...

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What needs improvement with Turbonomic?

The implementation could be enhanced.

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What is your primary use case for Turbonomic?

We use IBM Turbonomic to automate our cloud operations, including monitoring, consolidating dashboards, and reporting...

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Comparisons

VMware Aria Operations vs IBM Turbonomic

Compared 19% of the time

Azure Cost Management vs IBM Turbonomic

Compared 13% of the time

VMware vSphere vs IBM Turbonomic

Compared 9% of the time

Cisco Intersight vs IBM Turbonomic

Compared 8% of the time

Cloudability vs IBM Turbonomic

Compared 6% of the time

More IBM Turbonomic Competitors

No data available

Product Reports

Buyer's Guide

IBM Turbonomic

December 2024

Download IBM Turbonomic product report

Buyer's Guide

Cloud Analytics

November 2024

Download Datapipe Cloud Analytics for AWS product report

Buyer's Guide

Cloud Analytics

November 2024

Download Oracle Big Data Cloud Service product report

Also Known As

Turbonomic, VMTurbo Operations Manager

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Oracle

Overview

IBM Turbonomic is a performance and cost optimization platform for public, private, and hybrid clouds used by companies to assure application performance while eliminating inefficiencies by dynamically resourcing applications through automated actions.

IBM Turbonomic leverages AI to continuously analyze application resource consumption, deliver insights and dashboards, and make real-time adjustments. Common use cases include cloud cost optimization, cloud migration planning, data center modernization, FinOps acceleration, Kubernetes optimization, sustainable IT, and application resource management. By integrating with various cloud platforms, on-premise infrastructures, and containers, IBM Turbonomic provides a holistic view of the environment, ensuring that resources are allocated efficiently.

The solution is designed to support complex IT environments, offering actionable insights and automated actions that help IT teams proactively manage application performance and infrastructure resources. Turbonomic customers report an average 33% reduction in cloud and infrastructure waste without impacting application performance, and return-on-investment of 471% over three years.

IBM Turbonomic manages resources across hybrid and on-premises data centers to ensure efficiency and financial impact awareness. It automates resource allocation, balances memory dynamically, and provides robust performance metrics. Users benefit from its ability to prevent resource starvation and offer cost-saving recommendations through continuous management. Executive Dashboards provide insights for cost justification and resource management, making IT operations simpler with AI-driven automation and actionable recommendations.

What are the most important features of IBM Turbonomic?

Automated Resource Allocation: Dynamically allocates resources for optimal performance.
Dynamic Memory Balancing: Adjusts memory allocation to prevent resource shortages.
Right-Sizing: Sizes VMs accurately for Azure and on-prem environments.
Single Platform Management: Manages resources across hybrid and on-premises data centers.
Proactive Performance Optimization: Continuously optimizes performance to prevent issues.
Kubernetes Support: Enhances container management with robust support.
Cost-Saving Recommendations: Provides actionable tips for reducing expenses.

What benefits should users look for in IBM Turbonomic reviews?

Cost Reduction: Helps minimize expenses through efficient resource management.
Performance Improvement: Enhances application performance by preventing resource starvation.
Detailed Insights: Offers comprehensive analytics to justify costs and resource utilization.
Simplified IT Processes: Uses AI automation to streamline operations.

IBM Turbonomic is implemented across multiple industries, including finance for optimizing peak payroll processing, IT for enhanced virtual server management, and healthcare for reliable performance monitoring. Organizations benefit from its robust automation, ensuring maximum efficiency and cost savings.

Datapipe's cloud analytics solution for AWS environments is an innovative data-mining and intelligence tool that can govern and analyze 100% of your AWS usage across the enterprise.

This intelligence powers you in optimizing your architecture and organization, save costs, run more efficiently, and enhance your security.

Oracle Big Data Cloud Service is an automated service that provides a highpowered environment tailor-made for advancing businesses' analytical capabilities. With automated lifecycle management and one-click security, Big Data Cloud Service is designed to optimally and securely run a wide variety of big data workloads and technologies while simplifying operations. Big Data Cloud Service includes several high-end analytics components, ranging from Oracle Big Data Spatial and Graph to R and embedded components like Apache Spark and Impala.

Sample Customers

IBM, J.B. Hunt, BBC, The Capita Group, SulAmérica, Rabobank, PROS, ThinkON, O.C. Tanner Co.

Citrix, CloudPassage, mongoDB, Datastax

GE Digital, Wiggle, RecVue

Find out what your peers are saying about IBM, Densify, Spot by NetApp and others in Cloud Analytics. Updated: November 2024.

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