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
Tableau Prep
Score 6.8 out of 10
N/A
Tableau Prep enables users to get to the analysis phase faster by helping them quickly combine, shape, and clean their data. According to the vendor, a direct and visual experience helps provide users with a deeper understanding of their data, smart features make data preparation simple, and integration with the Tableau analytical workflow allows for faster speed to insight. Tableau Prep allows users to connect to data on-premises or in the cloud, whether it’s a database or a…
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 …
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.
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 …
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
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.
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 …
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 …
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.
Tableau Prep is a good tool to use in tandem with Tableau. You can use Tableau by just plugging in an excel sheet or other form of dataset, but the tools that Tableau Prep has specifically makes the data set work better with Tableau. The extra time to optimize is worth it.
Tableau Prep is covered in our license whereas Alteryx required additional fees and a separate license. Tableau Prep is similar to Tableau Desktop which a lot of our team is comfortable with so it wasn't difficult to get a feel for Tableau Prep. However, Alteryx would have been …
We use both Tableau Prep and Alteryx and, although Tableau Prep integrates better with Tableau desktop, I find that more often then not I open up Alteryx to do most of my data prep prior to bringing my data into analysis as it is just so much more robust. Not that this makes …
Before Prep, we had to do all the data joining and connecting in a Tableau Workbook. Not only did this cause workbooks connected with live data to run frustratingly slowly, a new connection and set-up had to be established every time a new workbook as created, even if you were …
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).
Tableau Prep is a very powerful tool when it comes to creating workflows to monitor each stage for data analysis and quality issues. Also, it is convenient to explore the data with the wealth of operators and checking work at each stage of the process. Also, some areas that would help is if there are more types of automation provided.
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.
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.
I have not really had to reach out for any kind of customer support for Tableau Prep, so I can't really say. However, the support that Tableau has given for their other products has been great, so I would assume it would be the same here. They are also constantly adding new features and providing software updates, and that is always a plus.
Live connections to cloud services (Google Sheets for example) and cloud hosted databases (cloud hosted SIS for example) for scheduled flows are not supported
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.
Tableau Prep is covered in our license whereas Alteryx required additional fees and a separate license. Tableau Prep is similar to Tableau Desktop which a lot of our team is comfortable with so it wasn't difficult to get a feel for Tableau Prep. However, Alteryx would have been a totally new platform.