Apache Iceberg vs. AWS Glue

Apache Iceberg

Apache Iceberg

2 Reviews and Ratings

AWS Glue

AWS Glue

37 Reviews and Ratings

Overview
Product	Rating	Most Used By	Product Summary	Starting Price
Apache Iceberg	Score 7.9 out of 10	N/A	Apache Iceberg is an open table format for huge analytic datasets. Iceberg adds tables to compute engines including Spark, Trino, PrestoDB, Flink, Hive and Impala using a high-performance table format that works just like a SQL table. Iceberg is used in production where a single table can contain tens of petabytes of data and even these huge tables can be read without a distributed SQL engine. It was designed to solve correctness problems in eventually-consistent cloud object stores.	N/A
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

Pricing

Apache Iceberg

AWS Glue

Editions & Modules

No answers on this topic

per DPU-Hour: $0.44
billed per second, 1 minute minimum

Offerings

Pricing Offerings
Apache Iceberg	AWS Glue
Free Trial
No	No
Free/Freemium Version
Yes	No
Premium Consulting/Integration Services
No	No

Entry-level Setup Fee

No setup fee

No setup fee

Additional Details

—

—

More Pricing Information

Community Pulse
	Apache Iceberg	AWS Glue
Considered Both Products	Apache Iceberg No answer on this topic	AWS Glue Verified User Anonymous Chose AWS Glue Informatica Intelligent Cloud Integration Services and Informatica PowerCenter Incentivized Helpful? Ashutosh Mishra 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. Incentivized Helpful? Verified User Anonymous 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 … Incentivized Helpful? Verified User Anonymous 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 Incentivized Helpful? Verified User Anonymous 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. Incentivized Helpful? Verified User Anonymous 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 … Incentivized Helpful? Apurv Doshi Practice Head - Labs (Innovation and R&D) 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 … Incentivized Helpful? Verified User Anonymous 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. Incentivized Helpful? Verified User Anonymous Chose AWS Glue Azure Databricks and Snowflake Incentivized Helpful? SC Sonny Carlos Head of Cloud and Data Business 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 … Incentivized Helpful?

Best Alternatives
	Apache Iceberg	AWS Glue
Small Businesses	DBeaver Score 9.2 out of 10	IBM SPSS Modeler Score 7.1 out of 10
Medium-sized Companies	DBeaver Score 9.2 out of 10	IBM InfoSphere Information Server Score 8.0 out of 10
Enterprises	DBeaver Score 9.2 out of 10	IBM InfoSphere Information Server Score 8.0 out of 10
All Alternatives	View all alternatives	View all alternatives

User Ratings
	Apache Iceberg	AWS Glue
Likelihood to Recommend	- (0 ratings)	7.0 (0 ratings)
Usability	- (0 ratings)	7.0 (0 ratings)
Support Rating	- (0 ratings)	7.0 (0 ratings)

User Testimonials
	Apache Iceberg	AWS Glue
Likelihood to Recommend	No answers on this topic	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 Apurv Doshi Practice Head - Labs (Innovation and R&D) Read full review
Pros	No answers on this topic	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 SC Sonny Carlos Head of Cloud and Data Business Read full review
Cons	No answers on this topic	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 Verified User Anonymous Read full review
Usability	No answers on this topic	I personally found it very usable for a data engineer's day job, particularly for performing ETL and managing the data pipelines. Incentivized Verified User Anonymous Read full review
Support Rating	No answers on this topic	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 Verified User Anonymous Read full review
Alternatives Considered	No answers on this topic	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 Verified User Anonymous Read full review
Return on Investment	No answers on this topic	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 Verified User Anonymous Read full review
ScreenShots