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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1,319 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 Dec 11, 2025

Informatica Data Engineering Streaming [EOL] and Apache Spark Streaming compete in the data streaming domain. Apache Spark Streaming is often viewed as superior because of its advanced features and better perceived value for cost.

Features: Informatica offers strong data integration capabilities, seamless system connections, and reliable support for complex data workflows. Apache Spark provides powerful real-time data processing, integration with the wider Spark ecosystem, and exceptional versatility and scalability for handling large datasets.

Ease of Deployment and Customer Service: Informatica's deployment is user-friendly with a guided setup, offering a smooth implementation experience. Apache Spark requires more technical expertise but benefits from a large community and detailed documentation that supports self-service problem-solving.

Pricing and ROI: Informatica generally involves higher initial costs with dependable ROI over time due to dedicated support. Apache Spark offers flexible pricing models, leading to better long-term ROI for businesses that fully leverage its capabilities.

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

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Streaming Analytics

January 2026

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Helped 881,707 peers since 2012

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 (10th)

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."

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

"The solution is better than average and some of the valuable features include efficiency and stability."

"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 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."

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

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

More Apache Spark Streaming pros

"It improves the performance."

Cons

"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."

"One improvement I would expect is real-time processing instead of micro-batch or near real-time."

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

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

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

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

"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."

"The initial setup is quite complex."

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."

"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."

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

Information not available

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Top Industries

By visitors reading reviews

Computer Software Company

21%

Financial Services Firm

20%

University

Marketing Services Firm

Financial Services Firm

28%

Computer Software Company

Retailer

Insurance Company

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 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?

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 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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Comparisons

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Compared 8% of the time

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Compared 17% of the time

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Compared 15% of the time

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Compared 14% of the time

Magic xpi Integration Platform vs Informatica Data Engineering Streaming [EOL]

Compared 9% of the time

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Product Reports

Buyer's Guide

Apache Spark Streaming

February 2026

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Buyer's Guide

Streaming Analytics

January 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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