Apache Spark Streaming vs Informatica Data Engineering Streaming [EOL] comparison

Apache and Informatica are both solutions in the Streaming Analytics category. Additionally, 94% of Apache users are willing to recommend the solution, compared to 100% of Informatica users who would recommend it.

Apache Spark Streaming

Read 17 Apache Spark Streaming reviews

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1,361 Comparison Views

94% willing to recommend

Informatica Data Engineerin...

Read 1 Informatica Data Engineering Streaming [EOL] review

100% willing to recommend

Apache Spark Streaming

Informatica Data Engineerin...

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Executive SummaryUpdated on Mar 15, 2026

Informatica Data Engineering Streaming and Apache Spark Streaming provide data streaming solutions, with Apache Spark seeming to have an advantage due to its strong features, scalable architecture, and community support.

Features: Apache Spark Streaming offers real-time processing, fault tolerance, and scalability, making it suitable for large-scale data tasks. Informatica supports complex transformations with a user-friendly interface and efficient workflow management, ensuring seamless IT environment integration.

Ease of Deployment and Customer Service: Informatica provides straightforward deployment with extensive support for customer onboarding. Apache Spark, requiring more technical expertise, benefits from a strong open-source community and comprehensive documentation, offering flexibility and learning opportunities.

Pricing and ROI: Informatica Data Engineering Streaming offers competitive pricing with potentially faster ROI due to straightforward implementation. Apache Spark, being open-source, has lower initial software costs but may require investment in skilled personnel for optimal deployment. Its scalability and adaptability contribute to higher long-term ROI.

To learn more, read our detailed Streaming Analytics Report (Updated: March 2026).

Buyer's Guide

Streaming Analytics

March 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

Apache Spark Streaming

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Streaming Analytics (9th)

Informatica Data Engineerin...

Average Rating

8.0

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

No ranking in other categories

Featured Reviews

Himansu Jena

Sr Project Manager at Raj Subhatech

Efficient real-time data management and analysis with advanced features

There are various ways we can improve Apache Spark Streaming through best practices. The initial part requires attention to batch interval tuning, which helps small intervals in micro batches based on latency requirements and helps prevent back pressure. We can use data formats such as Parquet or ORC for storage that needs faster reads and leveraging feature predicate push-down optimizations. We can implement serialization which helps with any Kyro in terms of .NET or Java. We have boxing and unboxing serialization for XML and JSON for converting key-pair values stored in browser. We can also implement caching mechanisms for storing and recomputing multiple operations. We can use specified joins which help with smaller databases, and distributed joins can minimize users. We can implement project optimization memory for CPU efficiency, known as Tungsten. Additionally, load balancing, checkpointing, and schema evaluation are areas to consider based on performance and bottlenecks. We can use Bugzilla tools for tracking and Splunk to monitor the performance of process systems, utilization, and performance based on data frames or data sets.

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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."

"Apache Spark Streaming's most valuable feature is near real-time analytics, as developers can build APIs easily for a code-steaming pipeline and the solution has an ecosystem of integration with other stock services."

"With Apache Spark Streaming, you can have multiple kinds of windows; depending on your use case, you can select either a tumbling window, a sliding window, or a static window to determine how much data you want to process at a single point of time."

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

"With Apache Spark Streaming's integration with Anaconda and Miniconda with Python, I interact with databases using data frames or data sets in micro versions and create solutions based on business expectations for decision-making, logistic regression, linear regression, or machine learning which provides image or voice record and graphical data for improved accuracy."

"Apache Spark's capabilities for machine learning are quite extensive and can be used in a low-code way."

"I appreciate Apache Spark Streaming's micro-batching capabilities; the watermarking functionality and related features are quite good."

"Apache Spark Streaming has features like checkpointing and Streaming API that are useful."

More Apache Spark Streaming pros

"It improves the performance."

Cons

"We would like to have the ability to do arbitrary stateful functions in Python."

"Monitoring is an area where they could definitely improve Apache Spark Streaming. When you have a streaming application, it generates numerous logs. After some time, the logs become meaningless because they're quite large and impossible to open."

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

"We would like to have the ability to do arbitrary stateful functions in Python."

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

"The problem is we need to use it in a certain manner. After that, we need to apply another pipeline for the machine learning processes, and that's what we work on."

"The solution itself could be easier to use."

"When dealing with various data types including COBOL, Excel, JSON, video, audio, and MPG files, challenges can arise with incomplete or missing values."

More Apache Spark Streaming cons

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

Pricing and Cost Advice

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

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

"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."

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

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

21%

Computer Software Company

Comms Service Provider

Marketing Services Firm

Financial Services Firm

26%

Retailer

Computer Software Company

Government

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	9
Midsize Enterprise	2
Large Enterprise	7

No data available

Questions from the Community

What needs improvement with Apache Spark Streaming?

One of the improvements we need is in Spark SQL and the machine learning library. I don't think there is too much to work on, but the issue is when we want to use machine learning, we always need t...

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What is your primary use case for Apache Spark Streaming?

We work with Apache Spark Streaming for our project because we use that as one of the landing data sources, and we work with it to ensure we can get all of the data before it goes through our data ...

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What advice do you have for others considering Apache Spark Streaming?

One thing I would share with other organizations considering Apache Spark Streaming is the necessity of having effective data storage. We want to ensure we acquire and manage our data storage effec...

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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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Product Reports

Buyer's Guide

Apache Spark Streaming

March 2026

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Buyer's Guide

Streaming Analytics

March 2026

Informatica Data Engineering Streaming [EOL] report

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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 Data Engineering Streaming [EOL] is a real-time streaming data processing platform that enables organizations to efficiently leverage continuous data insights from large volumes of diverse data sources.

Informatica Data Engineering Streaming [EOL] offers advanced capabilities for processing streaming data, enabling businesses to harness and analyze information as it's generated. It supports scalability and provides high-performance processing essential for data-driven decision-making. This platform empowers dynamic adaptation to ever-evolving data needs, ensuring timely insights.

What are the key features of Informatica Data Engineering Streaming [EOL]?

Scalability: Designed to handle large data volumes with optimized performance.
Seamless Integration: Connects effortlessly with multiple data sources and destinations.
Real-Time Processing: Enables immediate analysis of streaming data as it arrives.
Data Connectivity: Supports a wide range of connectors for enhanced flexibility.
Fault Tolerance: Ensures consistent operation despite hardware failures.

What benefits should users consider in reviews?

Enhanced Efficiency: Streamlines data processing workflows for faster results.
Cost Savings: Reduces infrastructure and operational costs by optimizing resources.
Improved Decision-Making: Provides timely insights for informed business strategies.
Increased Agility: Adapts quickly to changes in data streams and analytics requirements.

In specific industries, Informatica Data Engineering Streaming [EOL] is implemented to transform manufacturing processes by enabling real-time monitoring and predictive maintenance. In finance, it supports fraud detection and transaction processing by delivering real-time data insights. Retail businesses utilize it to enhance customer experience through personalized recommendations and inventory management.

Informatica

Sample Customers

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

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