AWS Glue vs. IBM Cognos Analytics

When the data which requires ETL has different formats, schema, and volume, this service suits them best. So, when the volume is not consistent (typical use-case of healthcare and online shopping), AWS Glue can be the prime choice. When the data is available in both batch and streaming mode, the developer needs to generate a separate codebase. This increases the source code management efforts. So, prefer to go with Glue when the nature of the data is the same (either batched or streamed).

Incentivized

I use predictive analytics techniques, which can help me predict my future sales based on collected data, giving me insight into my market's trends.This market data can be analyzed, giving me the opportunity to gain in-depth insight into my market's competition and positioning it competitively, aided by developing strategies to improve my marketing approach.

Incentivized

• After data cleansing, the team also implemented the best practices for using AWS platform services as a Data Lake, such as job bookmarking for AWS Glue jobs, proper delimiter for the AWS Glue crawlers, partitioning in AWS S3, and transformation to parquet file for compression and faster querying time in Amazon Athena.
• Data modernization through combining data from multiple sources into a functioning datasets, rebuilding DW, and resctructuring data sources.
• Aims to lessen customer complaints, eliminate manual data extraction requests via SR from different data sources, and Increase accuracy, consistency and speed up reconciliation process.

Incentivized

• Data Visualization: Plenty of options exist for multiple use cases, and dashboards are easy to implement and customize.
• Integration with IBM Watson: makes it easy to use Watson AI features (NLP etc.) on your data.
• Its advanced analytics functionalities with powerful pattern detection/prediction models.

Incentivized

• It’s integration with other cloud vendors is bit difficult
• If it can support non SQL based databases as well, it would be powerful.
• Real time data synchronisation in data source is missing

Incentivized

• API integration is not upto the mark with very limited options.
• Laptops get overheated when the tool is used from moderate to heavy use. Also, there is a lag in the tool times.
• Licensing & Maintenance can go from cheap to expensive depending on the scope.
• Lot of scope to improve the customer support & its not upto the industry standards.

Incentivized

It took my BI team one year to become productive at developing useful content on the IBM Cognos platform. After this year, the reports being developed for a client were stale and no longer relative to the ever changing needs of the business client. Given the same opportunity, I would select a platform that allows the team to quickly produce BI content. Fail fast and recover quickly!

I personally found it very usable for a data engineer's day job, particularly for performing ETL and managing the data pipelines.

Incentivized

We have a strong user base (3500 users) that are highly utilizing this tool. Basic users are able to consume content within the applied security model. We have a set of advanced users that really push the limits of Cognos with Report and Query Studio. These users have created a lot of personal content and stored it in 'My Reports'. Users enjoy this flexibility.

Reports can typically be viewed through any browser that can access the server, so the availability is ultimately up to what the company utilizing it is comfortable with allowing, though report development tends to be more picky about browsers and settings as mentioned above. It also has an optional iPad app and general mobile browsing support, but dashboards lack the mobile compatibility. What keeps it from getting a higher score is the desktop tools that are vital to the development process. The compatibility with only Windows when the server has a wide range of compatibility can be a real sore point for a company that outfits its employees exclusively with Mac or Linux machines. Of course, if they are planning on outsourcing the development anyways, it's a rather moot point

Overall no major complaints but it doesn't handle DMR (Dimensionally Modeled for Relational) very well. DMR modelling is a capability that IBM Cognos Framework Manager provides allowing you to specify dimensional information for relational metadata and allows for OLAP-style queries. However, the capability is not very efficient and, for example, if I'm using only 2 columns on a 20-column model, the software is not smart enough to exclude 18 columns and the query side gets progressively larger and larger until it's effectively unusable.

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.

Incentivized

Why is their web application not working as fast as you think it should? They never know, and it is always a a bunch of shots in the dark to find out. Trying to download software from them is like trying to find a book at the library before computers were invented.

Incentivized

Onsite training provided by IBM Cognos was effective and as expected. They did not perform training with our data which was a bit difficult for our end-users.

The online courses they offer are thorough and presented in such a way that someone who isn't already familiar with the general design methodologies used in this field will be capable of making a good design. The training environments are provided as a fully self contained virtual machine with everything needed already to create the environments. We've had some persisting issues with the environments becoming unavailable, but support has been responsive when these issues arise and straightening them out for us

The implementation was handled very well. The initial implementation exposed a lot of disagreement between our campuses and departments as to how we define data. This was not entirely unexpected, but I thought that we did a nice job as a team to work through some of these challenges.

The cataloging of data objects is the best in the case of AWS Glue. We use AWS Glue in all of our data pipelines to sync external and internal data sources and to automatically produce SQL-based ETL based on AWS Glue catalog objects. Integration with Amazon products is the other advantage.

Incentivized

Power BI is stronger for quick ad-hoc analysis and dashboards, but IBM Cognos Analytics is better when consistency, precision, and mass distribution matter. Tableau is best for interactive analysis, while IBM Cognos Analytics is better for standardized, repeatable enterprise reporting. Sigma shines for customizable dashboards and drill-down analysis while IBM Cognos Analytics holds an edge in data discovery and visualization.

Incentivized

The Cognos architecture is well suited for scalability. However, the architecture must be designed with scalability in mind from day one of the implementation. We recently upgraded from 10.1 to 10.2.1 and took the opportunity to revamp our architecture. It is now poised for future growth and scalability.

• Positive Impact :- after ETL we can able to do some kind of automation
• Negative :- At some point of time it can hamper the cost but not really

Incentivized

• The platform can be pretty pricey.
• Convoluted contracts and they fine you for any breach during audits.
• Things are changed between releases without any warning and break.
• It is second to none at being able to customize list and crosstab type reports.
• You can easily set up a report to be emailed out every day and even have it export the report to a shared folder.
• On-prem environments can be scaled up pretty much as much as you would like.

Incentivized