AWS Glue vs Informatica Enterprise Data Lake comparison

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Executive Summary

We performed a comparison between AWS Glue and Informatica Enterprise Data Lake based on real PeerSpot user reviews.

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To learn more, read our detailed Cloud Data Integration Report (Updated: March 2024).
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Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"I appreciate AWS Glue for its cost-effectiveness.""The most valuable feature of AWS Glue is its ease of use and good documentation. Additionally, we can do all the transformations that we need.""AWS Glue is quite better than other tools, but you have to learn it properly before you start using it.""I like the fact that AWS Glue works with Python scripts.""What I like best about AWS Glue is its real-time data backup feature. Last week, there was a production push, and what used to take almost ten days to send out around fifty-six thousand emails now takes only two hours.""The most valuable feature of AWS Glue is that it provides a GUI format with a drag-and-drop feature.""The facility to integrate with S3 and the possibility to use Jupyter Notebook inside the pipeline are the most valuable features.""The most valuable feature for me is the visual interface of AWS Glue."

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"The process of using the tool's scalability option is well documented."

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Cons
"On occasion, the solution's dashboard reports that a project failed due to runtime but it actually succeeded.""Glue could perform better. It sometimes takes too long to test a Glue job. Google Cloud Platform offers more Python scripts than AWS.""The price of the solution could improve.""I have encountered challenges with multi-region support.""There should be more connectors for different databases.""It would be better if it were more user-friendly. The interesting thing we found is that it was a little strange at the beginning. The way Glue works is not very straightforward. After trying different things, for example, we used just the console to create jobs. Then we realized that things were not working as expected. After researching and learning more, we realized that even though the console creates the script for the ETL processes, you need to modify or write your own script in Spark to do everything you want it to do. For example, we are pulling data from our source database and our application database, which is in Aurora. From there, we are doing the ETL to transform the data and write the results into Redshift. But what was surprising is that it's almost like whatever you want to do, you can do it with Glue because you have the option to put together your own script. Even though there are many functionalities and many connections, you have the opportunity to write your own queries to do whatever transformations you need to do. It's a little deceiving that some options are supposed to work in a certain way when you set them up in the console, but then they are not exactly working the right way or not as expected. It would be better if they provided more examples and more documentation on options.""I would like to see stable libraries at the moment they are not there.""The technical support for this solution could be improved. In future, we would like to connect more services like Athena or Kinesis to help control more loads of data."

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"Informatica Enterprise Data Lake's setup process was complex since it doesn't support a lot of real-time systems."

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Pricing and Cost Advice
  • "The pricing is a bit higher than other solutions like Athena and EC2. If the pricing becomes more scaled or flexible, it will be good because you have to pay 44 cents just for one DPU for an hour. If you increase DPUs to 5 or 10, the pricing gets multiplied. There are also some time limits like 0 to 10 minutes or 10 to 20 minutes. If the pricing is according to the minutes, it would be better because you have to limit your job to 10 minutes or 20 minutes."
  • "It is not expensive. AWS Glue works on the serverless architecture. We get charged for the time the server is up. For our use case, we have to use it once in a day, and it is not expensive for us."
  • "Its price is good. We pay as we go or based on the usage, which is a good thing for us because it is simple to forecast for the tool. It is good in terms of the financial planning of the company, and it is a good way to estimate the cost. It is also simple for our clients. In my opinion, it is one of the best tools in the market for ETL processes because of the fact that you pay as you use, which separates it from other big tools such as PowerCenter, Pentaho Data Integration, and Talend."
  • "Technical support is a paid service, and which subscription you have is dependent on that. You must pay one of them, and it ranges from $15,000 to $25,000 per year."
  • "This solution is affordable and there is an option to pay for the solution based on your usage."
  • "AWS Glue is quite costly, especially for small organizations."
  • "AWS Glue uses a pay-as-you-go approach which is helpful. The price of the overall solution is low and is a great advantage."
  • "The overall cost of AWS Glue could be better. It cost approximately $1,000 a month. There is paid support available from AWS Glue."
  • More AWS Glue Pricing and Cost Advice →

  • "The licenses attached to the solution are highly priced."
  • More Informatica Enterprise Data Lake Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:AWS Glue and Azure Data factory for ELT best performance cloud services.
    Top Answer:We reviewed AWS Glue before choosing Talend Open Studio. AWS Glue is the managed ETL (extract, transform, and load) from Amazon Web Services. AWS Glue enables AWS users to create and manage jobs in… more »
    Top Answer:AWS Glue's main use case is for allowing users to discover, prepare, move, and integrate data from multiple sources. The product lets you use this data for analytics, application development, or… more »
    Top Answer:The process of using the tool's scalability option is well documented.
    Top Answer:The licenses attached to the solution are highly priced. Informatica has licensing models for every product and for every feature, like the web service feature, which is something my company doesn't… more »
    Top Answer:Governance, data dictionary, and data cataloging are not available in Informatica Enterprise Data Lake. A lot of businesses are facing issues related to understanding the area revolving around… more »
    Ranking
    1st
    Views
    12,051
    Comparisons
    8,582
    Reviews
    30
    Average Words per Review
    398
    Rating
    7.9
    39th
    out of 94 in Data Integration
    Views
    447
    Comparisons
    426
    Reviews
    1
    Average Words per Review
    832
    Rating
    7.0
    Comparisons
    Also Known As
    Informatica Intelligent Data Lake, Intelligent Data Lake
    Learn More
    Overview

    AWS Glue is a serverless cloud data integration tool that facilitates the discovery, preparation, movement, and integration of data from multiple sources for machine learning (ML), analytics, and application development. The solution includes additional productivity and data ops tooling for running jobs, implementing business workflows, and authoring.

    AWS Glue allows users to connect to more than 70 diverse data sources and manage data in a centralized data catalog. The solution facilitates visual creation, running, and monitoring of extract, transform, and load (ETL) pipelines to load data into users' data lakes. This Amazon product seamlessly integrates with other native applications of the brand and allows users to search and query cataloged data using Amazon EMR, Amazon Athena, and Amazon Redshift Spectrum.

    The solution also utilizes application programming interface (API) operations to transform users' data, create runtime logs, store job logic, and create notifications for monitoring job runs. The console of AWS Glue connects all of these services into a managed application, facilitating the monitoring and operational processes. The solution also performs provisioning and management of the resources required to run users' workloads in order to minimize manual work time for organizations.

    AWS Glue Features

    AWS Glue groups its features into four categories - discover, prepare, integrate, and transform. Within those groups are the following features:

    • Automatic schema discovery: AWS Glue crawlers connect to the organization's source or target data source through a prioritized list of classifiers to determine the schema for users' data. This feature creates metadata in companies' AWS Glue Data Catalog.

    • Schemas for data stream management: The AWS Glue Schema Registry enables users to validate and control the evolution of streaming data through registered Apache Avro schemas for no additional charge.

    • Automatic scaling based on workload: This feature dynamically scales resources up and down based on workload. The feature controls job resources, removing them depending on how much the workload can be split up.

    • FindMatches: This feature is for machine learning-based data deduplication and cleansing, and works by finding records that are imperfect matches of each other to remove useless data copies.

    • Edit, debug, and test ETL code: This feature helps users who have chosen to interactively develop their ETL code by providing development endpoints for editing, debugging, and testing the code it generates for them.

    • AWS Glue DataBrew: An interactive, point-and-click visual interface for specialists to clean and normalize data without the need to write any code.

    • AWS Glue Interactive Sessions: This feature simplifies the development of data integration jobs by enabling data engineers to interactively prepare and explore data.

    • AWS Glue Studio Job Notebooks: This AWS Glue feature provides serverless notebooks with minimal setup, allowing developers to start working in a timely manner.

    • Complex ETL pipeline building: This feature allows the product to be invoked on a schedule, on demand, or based on an event, allowing users to start multiple jobs in parallel or specify dependencies to build complex ETL pipelines.

    • AWS Glue Studio: This AWS Glue feature allows users to visually transform data through a drag-and-drop interface. The product automatically generates the code for ETL processes for users' data.

    AWS Glue Benefits

    AWS Glue offers a wide range of benefits for its users. These benefits include:

    • Users of other AWS products can easily onboard with AWS Glue, as it is integrated across a wide range of the company's services.

    • The solution is serverless, which allows for a lower total cost of ownership.

    • AWS Glue offers more power for users, as it automates much of the effort in building, maintaining, and running ETL jobs.

    • The product allows customers to easily discover and search across all their AWS datasets through AWS Glue Data Catalog.

    • AWS Glue does not require additional payment for managing and enforcing schemas for data streams.

    • The solution facilitates the authority of scalable ETL jobs for beginners and non-coding experts through a drag-and-drop interface.

    Reviews from Real Users

    Mustapha A., a cloud data engineer at Jems Groupe, likes AWS Glue because it is a product that is great for serverless data transformations.

    Liana I., CEO at Quark Technologies SRL, describes AWS Glue as a highly scalable, reliable, and beneficial pay-as-you-go pricing model.

    The Intelligent Data Lake enables raw big data to be systematically transformed into fit-for-purpose data sets for a variety of data consumers. Data scientists and analysts can quickly find the data they’re looking for using semantic and faceted search. They can see data profiles, lineage, and other relationships to know whether they can trust the data and whether it’s fit-for-use in their analytic projects. 

    Sample Customers
    bp, Cerner, Expedia, Finra, HESS, intuit, Kellog's, Philips, TIME, workday
    Information Not Available
    Top Industries
    REVIEWERS
    Computer Software Company47%
    Financial Services Firm18%
    Pharma/Biotech Company12%
    Consumer Goods Company6%
    VISITORS READING REVIEWS
    Financial Services Firm20%
    Computer Software Company13%
    Insurance Company7%
    Manufacturing Company7%
    VISITORS READING REVIEWS
    Financial Services Firm15%
    Computer Software Company11%
    Manufacturing Company11%
    Healthcare Company7%
    Company Size
    REVIEWERS
    Small Business29%
    Midsize Enterprise13%
    Large Enterprise58%
    VISITORS READING REVIEWS
    Small Business15%
    Midsize Enterprise12%
    Large Enterprise73%
    VISITORS READING REVIEWS
    Small Business11%
    Midsize Enterprise11%
    Large Enterprise77%
    Buyer's Guide
    Cloud Data Integration
    March 2024
    Find out what your peers are saying about Amazon, MuleSoft, Matillion and others in Cloud Data Integration. Updated: March 2024.
    765,386 professionals have used our research since 2012.

    AWS Glue is ranked 1st in Cloud Data Integration with 37 reviews while Informatica Enterprise Data Lake is ranked 39th in Data Integration with 1 review. AWS Glue is rated 7.8, while Informatica Enterprise Data Lake is rated 7.0. The top reviewer of AWS Glue writes "Provides serverless mechanism, easy data transformation and automated infrastructure management". On the other hand, the top reviewer of Informatica Enterprise Data Lake writes "A scalable tool that needs a lot of maintenance due to its unstable nature". AWS Glue is most compared with AWS Database Migration Service, Informatica PowerCenter, SSIS, Informatica Cloud Data Integration and Talend Open Studio, whereas Informatica Enterprise Data Lake is most compared with Palantir Foundry, Oracle Data Integrator (ODI), IBM InfoSphere DataStage and Azure Data Factory.

    See our list of best Cloud Data Integration vendors.

    We monitor all Cloud Data Integration reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.