Apache Spark Streaming vs Informatica Data Engineering Streaming comparison

Apache and Informatica are both solutions in the Streaming Analytics category. Apache is ranked #10 with an average rating of 8.2, while Informatica is ranked #16 with an average rating of 8.0. Apache holds a 2.7% mindshare in SA, compared to Informatica’s 1.4% mindshare. Additionally, 91% of Apache users are willing to recommend the solution, compared to 100% of Informatica users who would recommend it.

Apache Spark Streaming

Read 11 Apache Spark Streaming reviews

1,689 Views
1,207 Comparison Views

91% willing to recommend

Informatica Data Engineerin...

Read 1 Informatica Data Engineering Streaming review

410 Views
381 Comparison Views

100% willing to recommend

Apache Spark Streaming

Informatica Data Engineerin...

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Executive Summary

We performed a comparison between Apache Spark Streaming and Informatica Data Engineering Streaming based on real PeerSpot user reviews.

Find out what your peers are saying about Databricks, Amazon Web Services (AWS), Microsoft and others in Streaming Analytics.

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Streaming Analytics

March 2025

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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

Apache Spark Streaming

Ranking in Streaming Analytics

10th

Average Rating

8.0

Reviews Sentiment

7.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Informatica Data Engineerin...

Ranking in Streaming Analytics

16th

Average Rating

8.0

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of April 2025, in the Streaming Analytics category, the mindshare of Apache Spark Streaming is 2.7%, down from 3.8% compared to the previous year. The mindshare of Informatica Data Engineering Streaming is 1.4%, up from 0.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

AbhishekGupta

Engineering Leader at Walmart

Easy integration, beneficial auto-scaling, and good open-sourced support community

The service structure of Apache Spark Streaming can improve. There are a lot of issues with memory management and latency. There is no real-time analytics. We recommend it for the use cases where there is a five-second latency, but not for a millisecond, an IOT-based, or the detection anomaly-based. Flink as a service is much better. Apache Spark Streaming does not have auto-tuning. A customer needs to invest a lot, in terms of management and maintenance.

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Reviewer43443

BI Practice Lead at a tech services company with 51-200 employees

Helps with real-time data processing and improves decision-making overall

It improves decision-making overall for the company. Informatica is usually the tool for setting up the data, streaming the data into your data warehouse from your source, transforming the data, and preparing and modeling it into some desired format. It improves the performance. You need to know how to use it and how to implement it, but it improves performance.

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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

"Apache Spark Streaming was straightforward in terms of maintenance. It was actively developed, and migrating from an older to a newer version was quite simple."

"It's the fastest solution on the market with low latency data on data transformations."

"As an open-source solution, using it is basically free."

"Apache Spark Streaming's most valuable feature is near real-time analytics. The developers can build APIs easily for a code-steaming pipeline. The solutions have an ecosystem of integration with other stock services."

"Spark Streaming is critical, quite stable, full-featured, and scalable."

"Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows."

"The solution is very stable and reliable."

"The platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams."

More Apache Spark Streaming pros

"It improves the performance."

Cons

"Integrating event-level streaming capabilities could be beneficial."

"The initial setup is quite complex."

"It was resource-intensive, even for small-scale applications."

"There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused."

"The cost and load-related optimizations are areas where the tool lacks and needs improvement."

"In terms of improvement, the UI could be better."

"The service structure of Apache Spark Streaming can improve. There are a lot of issues with memory management and latency. There is no real-time analytics. We recommend it for the use cases where there is a five-second latency, but not for a millisecond, an IOT-based, or the detection anomaly-based. Flink as a service is much better."

"We don't have enough experience to be judgmental about its flaws."

More Apache Spark Streaming cons

"Skill requirement is required. There is a learning curve."

Pricing and Cost Advice

"Spark is an affordable solution, especially considering its open-source nature."

"I was using the open-source community version, which was self-hosted."

"On a scale from one to ten, where one is expensive, or not cost-effective, and ten is cheap, I rate the price a seven."

"People pay for Apache Spark Streaming as a service."

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

28%

Computer Software Company

19%

Manufacturing Company

University

Financial Services Firm

24%

Manufacturing Company

15%

Computer Software Company

14%

Educational Organization

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

What do you like most about Apache Spark Streaming?

Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows.

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What needs improvement with Apache Spark Streaming?

We don't have enough experience to be judgmental about its flaws, as we've only used stable features like batch micro-batch. Integration poses no problem; however, I don't use some features and can...

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What is your primary use case for Apache Spark Streaming?

We use Spark Streaming in a micro-batch region. It's not a full real-time system, but it offers high performance and low latency.

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What needs improvement with Informatica Data Engineering Streaming?

Skill requirement is required. There is a learning curve.

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What is your primary use case for Informatica Data Engineering Streaming?

We implement business intelligence solutions, including data warehousing tools, data integration to load data into warehouses, and then creating reports.

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What advice do you have for others considering Informatica Data Engineering Streaming?

Overall, I would rate the solution an eight out of ten. Usually, Informatica is for big clients because of its pricing, and it also requires some skill sets. It requires investment into a proper da...

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Compared 18% of the time

Apache Flink vs Informatica Data Engineering Streaming

Compared 17% of the time

Starburst Enterprise vs Informatica Data Engineering Streaming

Compared 12% of the time

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Compared 11% of the time

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Product Reports

Buyer's Guide

Streaming Analytics

March 2025

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Buyer's Guide

Streaming Analytics

March 2025

Informatica Data Engineering Streaming report

Download Informatica Data Engineering Streaming product report

Also Known As

Spark Streaming

Big Data Streaming, Informatica Intelligent Streaming, Intelligent Streaming

Overview

Spark Streaming makes it easy to build scalable fault-tolerant streaming applications.

Apache

Informatica Intelligent Streaming allows organizations to prepare and process streams of data and uncover insights while acting in time to suit business needs. It can scale out horizontally and vertically to handle petabytes of data while honoring business service level agreements (SLAs). Intelligent Streaming provides pre-built high-performance connectors such as Kafka, HDFS, NoSQL databases, and enterprise messaging systems and data transformations to enable a code-free method of defining your data integration logic.

Informatica

Sample Customers

UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, eBay Inc.

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