Databricks vs Informatica Data Engineering Streaming comparison

Databricks and Informatica are both solutions in the Streaming Analytics category. Databricks is ranked #1 with an average rating of 8.2, while Informatica is ranked #16 with an average rating of 8.0. Databricks holds a 14.3% mindshare in SA, compared to Informatica’s 1.3% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 100% of Informatica users who would recommend it.

Databricks

Read 84 Databricks reviews

7,296 Views
4,867 Comparison Views

96% willing to recommend

Informatica Data Engineerin...

Read 1 Informatica Data Engineering Streaming review

435 Views
415 Comparison Views

100% willing to recommend

Databricks

Informatica Data Engineerin...

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Executive Summary

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

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

To learn more, read our detailed Streaming Analytics Report (Updated: December 2024).

Buyer's Guide

Streaming Analytics

December 2024

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Categories and Ranking

Databricks

Ranking in Streaming Analytics

1st

Average Rating

8.2

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Data Science Platforms (1st)

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 December 2024, in the Streaming Analytics category, the mindshare of Databricks is 14.3%, up from 9.9% compared to the previous year. The mindshare of Informatica Data Engineering Streaming is 1.3%, up from 0.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

Dunstan Matekenya

Data Scientist at a financial services firm with 10,001+ employees

Process large-scale data sets and integrates with Apache Spark with notebook environment

Databricks integrates natively with Apache Spark, which I use as a processing engine for large-scale datasets. This native integration is one of its strengths. Another strength is that the platform makes it very easy to manage resources. For example, setting up a cluster of five or fifteen nodes is straightforward with Databricks. The notebook environment is also excellent, making it easy to perform various tasks.

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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

"The setup was straightforward."

"Databricks makes it really easy to use a number of technologies to do data analysis. In terms of languages, we can use Scala, Python, and SQL. Databricks enables you to run very large queries, at a massive scale, within really good timeframes."

"The capacity of use of the different types of coding is valuable. Databricks also has good performance because it is running in spark extra storage, meaning the performance and the capacity use different kinds of codes."

"Databricks is based on a Spark cluster and it is fast. Performance-wise, it is great."

"It is fast, it's scalable, and it does the job it needs to do."

"Databricks' Lakehouse architecture has been most useful for us. The data governance has been absolutely efficient in between other kinds of solutions."

"The most valuable feature is the ability to use SQL directly with Databricks."

"The built-in optimization recommendations halved the speed of queries and allowed us to reach decision points and deliver insights very quickly."

More Databricks pros

"It improves the performance."

Cons

"The stability of the clusters or the instances of Databricks would be better if it was a much more stable environment. We've had issues with crashes."

"The product cannot be integrated with a popular coding IDE."

"Databricks' performance when serving the data to an analytics tool isn't as good as Snowflake's."

"I would like it if Databricks made it easier to set up a project."

"There should be better integration with other platforms."

"The solution could be improved by adding a feature that would make it more user-friendly for our team. The feature is simple, but it would be useful. Currently, our team is more familiar with the language R, but Databricks requires the use of Jupyter Notebooks which primarily supports Python. We have tried using RStudio, but it is not a fully integrated solution. To fully utilize Databricks, we have to use the Jupyter interface. One feature that would make it easier for our team to adopt the Jupyter interface would be the ability to select a specific variable or line of code and execute it within a cell. This feature is available in other Jupyter Notebooks outside of Databricks and in our own IDE, but it is not currently available within Databricks. If this feature were added, it would make the transition to using Databricks much smoother for our team."

"Costs can quickly add up if you don't plan for it."

"There would also be benefits if more options were available for workers, or the clusters of the two points."

More Databricks cons

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

Pricing and Cost Advice

"We have only incurred the cost of our AWS cloud services. This is because during this period, Databricks provided us with an extended evaluation period, and we have not spent much money yet. We are just starting to incur costs this month, I will know more later on the full cost perspective."

"The cost is around $600,000 for 50 users."

"We implement this solution on behalf of our customers who have their own Azure subscription and they pay for Databricks themselves. The pricing is more expensive if you have large volumes of data."

"The price of Databricks is reasonable compared to other solutions."

"The licensing costs of Databricks depend on how many licenses we need, depending on which Databricks provides a lot of discounts."

"We only pay for the Azure compute behind the solution."

"Whenever we want to find the actual costing, we have to send an email to Databricks, so having the information available on the internet would be helpful."

"I would rate the tool’s pricing an eight out of ten."

More Databricks pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

16%

Computer Software Company

11%

Manufacturing Company

Healthcare Company

Financial Services Firm

21%

Computer Software Company

16%

Manufacturing Company

15%

Educational Organization

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

Which do you prefer - Databricks or Azure Machine Learning Studio?

Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with ...

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How would you compare Databricks vs Amazon SageMaker?

We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...

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Which would you choose - Databricks or Azure Stream Analytics?

Databricks is an easy-to-set-up and versatile tool for data management, analysis, and business analytics. For analytics teams that have to interpret data to further the business goals of their orga...

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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

Dataiku vs Databricks

Compared 11% of the time

Informatica PowerCenter vs Databricks

Compared 8% of the time

Amazon SageMaker vs Databricks

Compared 8% of the time

Dremio vs Databricks

Compared 8% of the time

Microsoft Power BI vs Databricks

Compared 7% of the time

More Databricks Competitors

Google Cloud Dataflow vs Informatica Data Engineering Streaming

Compared 35% of the time

Apache Flink vs Informatica Data Engineering Streaming

Compared 20% of the time

Starburst Enterprise vs Informatica Data Engineering Streaming

Compared 12% of the time

More Informatica Data Engineering Streaming Competitors

Product Reports

Buyer's Guide

Databricks

December 2024

Download Databricks product report

Buyer's Guide

Streaming Analytics

December 2024

Informatica Data Engineering Streaming report

Download Informatica Data Engineering Streaming product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

Big Data Streaming, Informatica Intelligent Streaming, Intelligent Streaming

Learn More

Databricks

Informatica

Overview

Databricks is utilized for advanced analytics, big data processing, machine learning models, ETL operations, data engineering, streaming analytics, and integrating multiple data sources.

Organizations leverage Databricks for predictive analysis, data pipelines, data science, and unifying data architectures. It is also used for consulting projects, financial reporting, and creating APIs. Industries like insurance, retail, manufacturing, and pharmaceuticals use Databricks for data management and analytics due to its user-friendly interface, built-in machine learning libraries, support for multiple programming languages, scalability, and fast processing.

What are the key features of Databricks?

User-friendly interface: Simplifies operations and usability.
Built-in machine learning libraries: Facilitates machine learning tasks.
Support for multiple programming languages: Enhances flexibility.
Scalability: Efficiently handles growing data needs.
Fast processing: Improves performance and speed.
Automated optimization: Reduces manual efforts.
Data visualization: Provides insightful visuals.
Collaborative features: Enhances teamwork.
Delta Lake performance: Boosts data management.
Seamless cluster management: Simplifies system operations.

What are the benefits or ROI to look for in Databricks reviews?

Efficiency in handling large datasets: Ensures smooth processing.
Interactive workspace environment: Improves user collaboration.
Integration with platforms: Provides connectivity benefits.
Performance optimization: Enhances overall system performance.
Support for data governance and security: Ensures data integrity and protection.

Databricks is implemented in insurance for risk analysis and claims processing; in retail for customer analytics and inventory management; in manufacturing for predictive maintenance and supply chain optimization; and in pharmaceuticals for drug discovery and patient data analysis. Users value its scalability, machine learning support, collaboration tools, and Delta Lake performance but seek improvements in visualization, pricing, and integration with BI tools.

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.

Sample Customers

Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware

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