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
Databricks Data Intelligence Platform
Score 8.8 out of 10
N/A
Databricks offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service provides a platform for data pipelines, data lakes, and data platforms.
$0.07
Per DBU
Toad Data Point
Score 8.0 out of 10
N/A
Toad Data Point is a cross-platform, self-service, data-integration tool that simplifies data access, preparation and provisioning. It provides data connectivity and desktop data integration, and with the Workbook interface for business users, it provides simple-to-use visual query building and workflow automation.
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.
Medium to Large data throughput shops will benefit the most from Databricks Spark processing. Smaller use cases may find the barrier to entry a bit too high for casual use cases. Some of the overhead to kicking off a Spark compute job can actually lead to your workloads taking longer, but past a certain point the performance returns cannot be beat.
Appropriate for general querying and some DBA work. It's the universal least-offensive solution for most environments - not best of breed, but not subject to unusual/extensive requirements. It just works. On the other hand, some functionality (e.g. data import/export, snippets) are perfunctory and minimal and seem to be either difficult or impossible to automate. If you need to streamline those operations, you'll be forced to rely on third-party solutions that mostly work on top of (instead of with) TOAD.
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.
The workflow is a relatively new feature. Quest is adding additional functionality and the workflows are useful now.
Would be nice if the 'Automate' feature was a bit easier to use.
Would be nice if some of the SQL Editor features in the traditional interface worked better in the new workflow interface (although, these are being fixed with each release).
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 an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.
in terms of graph generation and interaction it could improve their UI and UX
I find Toad Data Point easy to use for both the novice and the experienced business analyst. If all you desire is to access data and create spreadsheets...this is a snap. Toad Data Point actually has cool data analysis features built into it. The newer workflow interface makes automating steps a snap
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.
One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.
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.
The most important differentiating factor for Databricks Lakehouse Platform from these other platforms is support for ACID transactions and the time travel feature. Also, native integration with managed MLflow is a plus. EMR, Cloudera, and Hortonworks are not as optimized when it comes to Spark Job Execution. Other platforms need to be self-managed, which is another huge hassle.
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 is the least common denominator - not particularly optimized for our environment or workflows.
Hangs or slowdowns add anywhere from 5% - 7% for projects utilizing large/complicated data setts. (This could be due to other IT-imposed constraints and not entirely due to TOAD.)
Trying to perform some operations requires reading documentation and experimenting in order to figure out the TOAD-specific approaches and commands.
It just works (when we understand it). Updates don't break things and things don't suddenly start behaving differently. Best of all, we don't mysteriously lose functionality.
