AWS Glue vs. Mu Sigma muRx

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
AWS Glue
Score 7.5 out of 10
N/A
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
muRx
Score 0.0 out of 10
N/A
Mu Sigma headquartered in Northbrook offers muRx, a problem space modeling, analytics planning and data preparation tool designed to support business decisioning and BI processes.N/A
Pricing
AWS GlueMu Sigma muRx
Editions & Modules
per DPU-Hour
$0.44
billed per second, 1 minute minimum
No answers on this topic
Offerings
Pricing Offerings
AWS GluemuRx
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
AWS GlueMu Sigma muRx
Considered Both Products
AWS Glue
Chose AWS Glue
Informatica Intelligent Cloud Integration Services and Informatica PowerCenter
Chose AWS Glue
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.
Chose AWS Glue
AWS Glue is easier to use and has more and better features compared to it. And more documentation and tutorials and labs are widely available on the internet about AWS Glue which in turn helps in easier implementation of the spark jobs. Auto scaling is an added advantage. It's …
Chose AWS Glue
The main reason we choose AWS Glue over Talend open studio 1) Does not support Spark 2) Run only on java 3) not really feasible solution for heavy workloads 4) most of the cases need customer support 5) no proper documentation is available
Chose AWS Glue
AWS Glue is a managed service. It was easier for us to integrate it into our stack since we are already an AWS shop. It saved us the headache of managing a 3rd part service.
Chose AWS Glue
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 …
Chose AWS Glue
Glue comes in form of a managed service. However, the AWS data pipeline puts additional responsibility to manage the infrastructure. We were not requiring fine-grained control of the hardware which the AWS data pipeline provides. We also want to park our data on DynamoDB. AWS …
Chose AWS Glue
We are already in AWS services, so AWS glue is the first choice for us. But for the comparison of ETL job making and process time, it's way faster for other services.
Chose AWS Glue
Glue is easier especially if you are already in AWS. It easily integrates to other AWS services. Compliments well with Amazon Athena, S3, and Lake Formation. Compared to Snowflake, it is also much much cheaper and you don't have to build outside AWS. Support is also good if you …
muRx

No answer on this topic

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User Ratings
AWS GlueMu Sigma muRx
Likelihood to Recommend
7.0
(0 ratings)
-
(0 ratings)
Usability
7.0
(0 ratings)
-
(0 ratings)
Support Rating
7.0
(0 ratings)
-
(0 ratings)
User Testimonials
AWS GlueMu Sigma muRx
Likelihood to Recommend
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).
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Pros
  • 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.
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Cons
  • 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
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Usability
I personally found it very usable for a data engineer's day job, particularly for performing ETL and managing the data pipelines.
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Support Rating
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.
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Alternatives Considered
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.
Read full review
No answers on this topic
Return on Investment
  • 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
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ScreenShots