Equalum [EOL] vs Upsolver comparison

Google and Upsolver are both solutions in the Data Integration category. Additionally, 100% of Google users are willing to recommend the solution, compared to 100% of Upsolver users who would recommend it.

Equalum [EOL]

Read 7 Equalum [EOL] reviews

100% willing to recommend

Upsolver

Read 3 Upsolver reviews

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625 Comparison Views

100% willing to recommend

Equalum [EOL]

Upsolver

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Executive SummaryUpdated on Jan 15, 2026

Equalum EOL and Upsolver compete in data integration and streaming analytics. Upsolver is often seen as superior due to comprehensive features that justify its cost.

Features: Equalum EOL offers real-time data ingestion, transformation, and delivery with a focus on ease of use and operational efficiency. Upsolver supports extensive data preparation and streaming capabilities, complex event processing, automatic scaling, and performance optimization, which helps in managing large-scale data operations.

Ease of Deployment and Customer Service: Equalum EOL is known for rapid deployment and simple integration processes with direct support. Upsolver provides a user-friendly deployment model with comprehensive self-service options and extensive documentation, offering a customer success-centric approach.

Pricing and ROI: Equalum EOL has a lower setup cost, appealing to various business sizes with quick ROI. Upsolver requires a more significant investment but often leads to higher ROI through advanced analytics capabilities and scalability, ideal for enterprises with extensive data management needs.

To learn more, read our detailed Data Integration Report (Updated: January 2026).

Buyer's Guide

Data Integration

January 2026

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Review summaries and opinions

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

Categories and Ranking

Equalum [EOL]

Average Rating

9.2

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Upsolver

Average Rating

8.4

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

Data Integration (38th), Streaming Analytics (20th)

Featured Reviews

Joel Buck

Director of Enterprise Architecture at a pharma/biotech company with 10,001+ employees

There is no better product for CDC and streaming on Kafka

The Equalum user interface is extremely easy to use. I would rank it really highly on user-friendliness. The only issue with the user interface is it doesn't supply everything that you need for somebody who has to work with Equalum. For example, when you get deep into development, there are many areas where you have to go to the command line to do things and the Equalum user interface does not have that functionality. The deployment of their flows needs improvement. It doesn't work with a typical Git branching and CI/CD deployment strategy. If you have multiple projects, all working in one Equalum environment, separating out their work is something that you have to design into your implementation, as opposed to baked into the product.

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reviewer2784462

Software Engineer at a tech vendor with 10,001+ employees

Streaming pipelines have become simpler and onboarding new data sources is now much faster

One of the best features Upsolver offers is the automatic schema evolution. Another good feature is SQL-based streaming transformations. Complex streaming transformations such as cleansing, deduplication, and enrichment were implemented using SQL and drastically reduced the need for custom Spark code. My experience with the SQL-based streaming transformations in Upsolver is that it had a significant positive impact on the overall data engineering workflow. By replacing custom Spark streaming jobs with declarative SQL logic, I simplified development, review, and deployment processes. Data transformations such as parsing, filtering, enrichment, and deduplication could be implemented and modified quickly without rebuilding or redeploying complex code-based pipelines. Upsolver has impacted my organization positively because it brings many benefits. The first one is faster onboarding of new data sources. Another one is more reliable streaming pipelines. Another one is near-real-time data availability, which is very important for us. It also reduced operational effort for data engineering teams. A specific outcome that highlights these benefits is that the time to onboard new sources is reduced from weeks to days. Custom Spark code reduction reached 50 to 40 percent. Pipeline failures are reduced by 70 to 80 percent. Data latency is improved from hours to minutes.

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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

"Equalum has resulted in system performance improvements in our organization. Now, I am ingressing data off of multiple S3 sources, doing data processing, and formatting a schema. This would usually take me a couple of days, but now it takes me hours."

"The main impact for Oracle LogMiner is the performance. Performance is drastically reduced if you use the solution’s Oracle Binary Log Parser. So, if we have 60 million records, initially it used to take a minute. Now, it takes a second to do synchronization from the source and target tables."

"Equalum is real-time. If you are moving from an overnight process to a real-time process, there is always a difference in what reports and analytics show compared to what our operational system shows. Some of our organizations, especially finance, don't want those differences to be shown. Therefore, going to a real-time environment makes the data in one place match the data in another place. Data accuracy is almost instantaneous with this tool."

"It's got it all, from end-to-end. It's the glue. There are a lot of other products out there, good products, but there's always a little bit of something missing from the other products. Equalum did its research well and understood the requirements of large enterprise and governments in terms of one tool to rule them all, from a data migration integration perspective."

"Equalum provides a single platform for core architectural use cases, including CDC replication, streaming ETL, and batch ETL. That is important to our clients because there is no other single-focus product that covers these areas in that much detail, and with this many features on the platform. The fact that they are single-minded and focused on CDC and ETL makes this such a rich solution. Other solutions cover these things a little bit in their multi-function products, but they don't go as deep."

"It's a really powerful platform in terms of the combination of technologies they've developed and integrated together, out-of-the-box. The combination of Kafka and Spark is, we believe, quite unique, combined with CDC capabilities. And then, of course, there are the performance aspects. As an overall package, it's a very powerful data integration, migration, and replication tool."

"I found two features in Equalum that I consider the most valuable. One is that Equalum is a no-code tool. You can do your activities on its graphical interface, which doesn't require complex knowledge of extracting, changing, or loading data. Another feature of Equalum that I like the most is that it monitors the data transfers and tells you if there's any issue so that you can quickly check and correct it. Equalum also tells you where the problem lies, for example, if it's a hardware or communication issue."

"All our architectural use cases are on a single platform, not multiple platforms. You don't have to dump into different modules because it is the same module everywhere."

"The most prominent feature of Upsolver is its function as an ETL tool, allowing data to be moved across platforms and different data technologies."

"It was easy to use and set up, with a nearly no-code interface that relied mostly on drag-and-drop functionality."

"A specific outcome that highlights these benefits is that the time to onboard new sources is reduced from weeks to days, custom Spark code reduction reached 50 to 40 percent, pipeline failures are reduced by 70 to 80 percent, and data latency is improved from hours to minutes."

"Customer service is excellent, and I would rate it between eight point five to nine out of ten."

Cons

"If you need to use the basic features of Equalum, for example, you don't even need data integration, then many competitors in the market can give you basic features. For instance, if you need batch ETL, you can pick among solutions in the market that have been around longer than Equalum. What needs improvement in Equalum is replication, as it could be faster. Equalum also needs better integration with specific databases such as Oracle and Microsoft SQL Server."

"They need to expand their capabilities in some of the targets, as well as source connectors, and native connectors for a number of large data sources and databases. That's a huge challenge for every company in this area, not just Equalum."

"Right now, they have a good notification system, but it is in bulk. For example, if I have five projects running and I put a notification, the notification comes back to me for all five projects. I would like the notification to come back only for one project."

"There is not enough proven integration with other vendors. That is what needs to be worked on. Equalum hasn't tested anything between vendors, which worries our clients. We need more proven vendor integration. It is an expensive product and it needs to support a multi-vendor approach."

"Their UI could use some work. Also, they could make it just a little faster to get around their user interface. It could be a bit more intuitive with things like keyboard shortcuts."

"The deployment of their flows needs improvement. It doesn't work with a typical Git branching and CI/CD deployment strategy."

"I should be able to see only my project versus somebody else's garbage. That is something that would be good in future. Right now, the security is by tenants, but I would like to have it by project, e.g., this project has this source and flows in these streams, and I have access to this on this site."

"I think that Upsolver can be improved in orchestration because it is not a full orchestration tool."

"There is room for improvement in query tuning."

"Upsolver excels in ETL and data aggregation, while ThoughtSpot is strong in natural language processing for querying datasets. Combining these tools can be very effective: Upsolver handles aggregation and ETL, and ThoughtSpot allows for natural language queries. There’s potential for highlighting these integrations in the future."

"On the stability side, I would rate it seven out of ten. Using multiple cloud providers and data engineering technologies creates complexity, and managing different plugins is not always easy, but they are working on it."

Pricing and Cost Advice

"As soon as you have more than six users, Equalum is lower in cost [than Talend] and if the group gets bigger, it's quite a big delta. If more users want to use it, you don't end up with an increase in licensing costs, so that makes it very easy. And if you need more licenses or more sources, it's a very simple upgrade methodology."

"They have a very simple approach to licensing. They don't get tied up with different types of connectivity to different databases. If you need more connectors or if you need more CPU, you just add on. It's component-based pricing."

"Equalum was reasonably priced. It is not like those million dollar tools, such as Informatica."

"Equalum licensing costs vary, but I won't be able to give information on its fees."

"Equalum is rather expensive compared to its competitors. So, you have to make up that cost in time savings, and we usually do that. If we are saving money, it is because we are reducing our development time."

"Upsolver is affordable at approximately $225 per terabyte per year."

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Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	2
Midsize Enterprise	1
Large Enterprise	4

No data available

Questions from the Community

Why should I use Equalum instead of LogMiner?

You'd want to use the Equalium Oracle Binary Log Parser because it's just better than the LogMiner. Sure, LogMiner is made by Oracle and probably the team knows some insight to make it efficient th...

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Is Equalum compatible with all databases?

I'm using Equalum's data replication software for Oracle because that's the one database it's designed for. While it may sound limiting, when you find out how many solutions it can provide for you ...

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Can I use Equalum for free?

No, it's not free but you can benefit from a free trial, though. There's an option to try their platform for a limited amount of time, so that may be useful to help you decide if you want to contin...

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What is your experience regarding pricing and costs for Upsolver?

My experience with pricing, setup cost, and licensing was a very good experience, but it is not a direct experience because it was not my responsibility. It was in charge of the customer. However, ...

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What needs improvement with Upsolver?

I think that Upsolver can be improved in orchestration because it is not a full orchestration tool. I believe it could be better in this regard. The cost needs attention at a very large scale. I th...

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What is your primary use case for Upsolver?

My main use case for Upsolver is during an IT consulting project for a large enterprise running a cloud-native data platform on AWS. I used Upsolver to ingest and process high-volume stream data fr...

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Product Reports

Buyer's Guide

Equalum [EOL]

January 2026

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Buyer's Guide

Data Integration

January 2026

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Overview

Equalum EOL offers robust data replication services, focusing on CDC replication, streaming and batch ETL. It provides a user-friendly interface with no-code capabilities, integrating seamlessly with technologies like Kafka and Spark.

Equalum EOL is designed to facilitate data integration and migration processes through advanced CDC replication, streaming, and batch ETL capabilities. Its intuitive interface supports complex data processes without requiring extensive technical skills. Enhanced with automated self-healing and performance monitoring, the platform provides fast data streaming services. Equalum integrates effectively with Kafka and Spark, allowing unique functionality enhancements. The graphical interface is advantageous for users needing command-line features for workflow deployment while offering intuitive monitoring for quick issue resolution.

What are the most important features of Equalum EOL?

CDC Replication: Ensures seamless data synchronization across systems.
Streaming ETL: Provides real-time data processing for timely insights.
Batch ETL: Supports large-scale data aggregation for comprehensive analysis.
No-Code Interface: Simplifies complex processes for non-technical users.
Integration with Kafka and Spark: Enhances data processing capabilities.
Automated Self-Healing: Reduces downtime and system latency.

What benefits should users look for in reviews of Equalum EOL?

Improved Data Sync: Efficient data replication minimizes lag.
Performance Monitoring: Keeps data processes running smoothly.
Flexible Integration: Adapts to various data sources and platforms.
Real-Time Data Access: Facilitates on-the-fly decision-making with streaming ETL.

Equalum EOL is implemented across industries for data replication tasks such as moving databases and SQL Server data to Oracle. It effectively integrates legacy and siloed data for applications, transactions, and BI dashboards, operating both on-premises and in the cloud. With features like the micro-batching of Kafka topics and transforming large XML files into Snowflake, Equalum EOL supports consultants offering SaaS and on-premises solutions, catering to real-time Change Data Capture needs in cloud environments.

Google

Upsolver's mission is to turn engineering-intensive data lakes into easy-to-use repositories that serve 50X more data professionals compared to the status-quo today. Upsolver cuts 95% from the tedious process of preparing event data for analytics and machine learning, using a visual SQL-based interface, a cloud-native data platform and deep technology for stream-processing, indexing and eventually-consistent file systems.

Upsolver

Sample Customers

SIEMENS, GSK, Wal-Mart, T Systems

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