AWS Glue is a managed extract, transform, and load (ETL) service designed to make it easy for customers to prepare and load data for analytics. With it, users can create and run an ETL job in the AWS Management Console. Users point AWS Glue to data stored on AWS, and AWS Glue discovers data and stores the associated metadata (e.g. table definition and schema) in the AWS Glue Data Catalog. Once cataloged, data is immediately searchable, queryable, and available for ETL.
$0.44
billed per second, 1 minute minimum
IBM DataStage
Score 7.7 out of 10
N/A
IBM® DataStage® is a data integration tool that helps users to design, develop and run jobs that move and transform data. At its core, the DataStage tool supports extract, transform and load (ETL) and extract, load and transform (ELT) patterns. A basic version of the software is available for on-premises deployment, and the cloud-based DataStage for IBM Cloud Pak® for Data offers automated integration capabilities in a hybrid or multicloud environment.
N/A
Pricing
AWS Glue
IBM DataStage
Editions & Modules
per DPU-Hour
$0.44
billed per second, 1 minute minimum
No answers on this topic
Offerings
Pricing Offerings
AWS Glue
IBM DataStage
Free Trial
No
Yes
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
—
—
More Pricing Information
Community Pulse
AWS Glue
IBM DataStage
Considered Both Products
AWS Glue
No answer on this topic
IBM DataStage
Verified User
Manager
Chose IBM DataStage
IBM DataStage performes bettere than SSIS in every aspect. IBM DataStage performes better than SAP Data Services in terms of variables and job orchestration flexibility. It is as strong as ODI, but less complex to implement. It allows to write SQL queries as dbt and Glue, but I …
One of AWS Glue's most notable features that aid in the creation and transformation of data is its data catalog. Support, scheduling, and the automation of the data schema recognition make it superior to its competitors aside from that. It also integrates perfectly with other AWS tools. The main restriction may be integrated with systems outside of the AWS environment. It functions flawlessly with the current AWS services but not with other goods. Another potential restriction that comes to mind is that glue operates on a spark, which means the engineer needs to be conversant in the language.
DataStage is somewhat outdated for an ETL. I guess that's what makes it a bit lagged behind its competitors. It can be used for data processing, sure, but its performance seems to be lagging behind or quite slow given the server it is running from. I won’t depend on this application if it's handling a lot of mission-critical banking and business data.
It is extremely fast, easy, and self-intuitive. Though it is a suite of services, it requires pretty less time to get control over it.
As it is a managed service, one need not take care of a lot of underlying details. The identification of data schema, code generation, customization, and orchestration of the different job components allows the developers to focus on the core business problem without worrying about infrastructure issues.
It is a pay-as-you-go service. So, there is no need to provide any capacity in advance. So, it makes scheduling much easier.
Technical support is a key area IBM should improve for this product. Sometimes our case is assigned to a support engineer and he has no idea of the product or services.
Provide custom reports for datastage jobs and performance such as job history reports, warning messages or error messages.
Make it fully compatible with Oracle and users can direct use of Oracle ODBC drivers instead of Data Direct driver. Same for SQL server.
While easy to set up and manage monitoring for large datasets, its complexity can be a barrier for new users. Integration with AWS Ecosystem, Managed Monitoring, Dashboards and monitoring tools for AWS Glue are generally easy to set up and maintain, Automated Data Pipelines. Automates data pipeline creation, making it efficient for certain data integration
Because it is robust, and it is being continuously improved. DS is one of the most used and recognized tools in the market. Large companies have implemented it in the first instance to develop their DW, but finding the advantages it has, they could use it for other types of projects such as migrations, application feeding, etc.
It could load thousands of records in seconds. But in the Parallel version, you need to understand how to particionate the data. If you use the algorithms erroneously, or the functionalities that it gives for the parsing of data, the performance can fall drastically, even with few records. It is necessary to have people with experience to be able to determine which algorithm to use and understand why.
Amazon responds in good time once the ticket has been generated but needs to generate tickets frequent because very few sample codes are available, and it's not cover all the scenarios.
IBM offers different levels of support but in my experience being and IBM shop helps to get direct support from more knowledgeable technicians from IBM. Not sure on the cost of having this kind of support, but I know there's also general support and community blogs and websites on the Internet make it easy to troubleshoot issues whenever there's need for that.
AWS Glue is a fully managed ETL service that automates many ETL tasks, making it easier to set AWS Glue simplifies ETL through a visual interface and automated code generation.
With effective capabilities and easy to manipulate the features and easy to produce accurate data analytics and the Cloud services Automation, this IBM platform is more reliable and easy to document management. The features on this platform are equipped with excellent big data management and easy to provide accurate data analytics.
We are using GLUE for our ETL purpose. it’s ease with other our AWS services makes our ROI, 100% ROI.
One missing piece was compatibility with other data source for which we found a work around and made our data source as S3 only, so our dependencies on other data source is also reducing
It’s hard to say at this point, it delivers, but not quite as I expected. It takes a lot of resources to manage and sort this out (manpower, financial).
Definitely, I don’t have the exact numbers, but given the data it processes, it is A LOT. So props to the developer of this application.
Again, based on my experience, I’d choose other ETL apps if there is one that's more user-friendly.
