SSIS and AWS Glue compete in the data integration tools category. AWS Glue seems to have the upper hand due to its serverless, scalable architecture and flexibility with various use cases.
Features: SSIS integrates into Microsoft environments, supports traditional ETL tasks, and allows custom coding and script tasks, which are bolstered by SQL Server licensure. These features contribute to its high transfer speed and robust data integration capabilities. AWS Glue offers a serverless model with automatic scaling, seamless AWS service integration, and sophisticated transformations, appealing to users needing flexible cloud-based solutions.
Room for Improvement: SSIS could enhance error logging, improve UI responsiveness, and support more data sources. It struggles with real-time processing and limited cloud integration options. AWS Glue needs a better UX, easier setup processes, and improved handling of complex schemas. Its restrictive AWS-only integration and high usage costs pose challenges with large data volumes and execution times.
Ease of Deployment and Customer Service: SSIS suits on-premises deployments and benefits companies preferring on-site setups with Microsoft support available online. However, direct help can be slow or costly. AWS Glue thrives in cloud-based settings, benefiting from comprehensive technical support, documentation, and a collaborative community. Initial AWS environment setups can be complex.
Pricing and ROI: SSIS is cost-effective when included with SQL Server licenses, reducing additional fees for Microsoft-centric businesses. Alternatively, AWS Glue's pay-as-you-go pricing offers flexibility but can lead to steep costs for high workloads or long processing times. Despite these costs, AWS Glue's scalable billing model is beneficial for enterprises with fluctuating workloads.
I advocate using Glue in such cases.
Using SSIS has proven cost-effective as there are no additional fees outside the SQL Server license, and it significantly enhances data management efficiency.
AWS's documentation is reliable, and careful reference often resolves missed upgrade details.
It can easily handle data from one terabyte to 100 terabytes or more, scaling nicely with larger datasets.
For jobs requiring multiple RAM usage, we increase the number of workers accordingly.
AWS Glue is highly stable, and I would rate its stability as nine.
It processes large volumes of data quickly.
A more user-friendly and simpler process would help speed up the deployment process.
With AWS, I gather data from multiple sources, clean it up, normalize it, de-duplicate it, and make it presentable.
Migrating jobs from version 3.0 to 4.0 can present compatibility issues.
SSIS has a difficult learning curve when dealing with complex transformations.
The smallest cost for a project is around €700, while the largest can reach up to €7,000 based on the scale of the usage.
AWS charges based on runtime, which can be quite pricey.
Costing depends on resource usage, and cost optimization may involve redesigning jobs for flexibility.
Utilizing SSIS involves no extra charges beyond the SQL Server license.
AWS Glue's most valuable features include its transformation capabilities, which provide data quality and shape for processing in ML or AI models.
For ETL, I feel the performance is excellent. If I create jobs in a standard way, the performance is great, and maintenance is also seamless.
I think if I'm working with big data, common languages like Python work quite nicely, which is advantageous.
One of the best aspects of SSIS is that it is built into Microsoft SQL Server, so there are no additional costs involved.
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:
AWS Glue Benefits
AWS Glue offers a wide range of benefits for its users. These benefits include:
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.
SSIS is a versatile tool for data integration tasks like ETL processes, data migration, and real-time data processing. Users appreciate its ease of use, data transformation tools, scheduling capabilities, and extensive connectivity options. It enhances productivity and efficiency within organizations by streamlining data-related processes and improving data quality and consistency.
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