Apache Iceberg vs. AWS Glue

Overview
ProductRatingMost Used ByProduct SummaryStarting 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 IcebergAWS Glue
Editions & Modules
No answers on this topic
per DPU-Hour
$0.44
billed per second, 1 minute minimum
Offerings
Pricing Offerings
Apache IcebergAWS Glue
Free Trial
NoNo
Free/Freemium Version
YesNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Apache IcebergAWS Glue
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User Ratings
Apache IcebergAWS 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 IcebergAWS 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).
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Pros
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  • 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
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  • 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
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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
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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
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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.
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Return on Investment
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  • 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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