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AWS Glue vs Google Cloud Data Fusion: What are the differences?
Introduction
In this article, we will compare AWS Glue and Google Cloud Data Fusion, two popular cloud-based data integration and ETL (Extract, Transform, Load) services offered by Amazon Web Services (AWS) and Google Cloud Platform (GCP) respectively. These services are designed to help businesses and organizations efficiently manage their data pipelines and extract insights from large volumes of data.
Key Differences between AWS Glue and Google Cloud Data Fusion
Scalability: AWS Glue offers a highly scalable architecture, capable of handling large volumes of data and providing auto-scaling capabilities to accommodate varying workloads. On the other hand, Google Cloud Data Fusion also provides scalability options, but it is not as customizable as AWS Glue, lacking auto-scaling features.
Data Source Variety: AWS Glue supports a wide range of data sources, including on-premises storages, database systems, file systems, and SaaS applications. Google Cloud Data Fusion, on the other hand, offers a limited selection of data sources, mainly focusing on Google Cloud Platform's own services and a few external databases.
ETL Capabilities: AWS Glue provides extensive ETL capabilities with the help of Apache Spark, allowing users to easily extract, transform, and load their data. It offers a user-friendly visual interface for building ETL workflows and also provides a code editor for advanced scripting. Google Cloud Data Fusion, while also offering ETL functionality, has a more simplified visual interface that may not be as flexible or comprehensive as AWS Glue.
Pricing Model: AWS Glue follows a pay-as-you-go pricing model, where users are billed for the resources consumed during the data processing tasks. Google Cloud Data Fusion, on the other hand, has a fixed pricing structure that includes a base charge, making it easier for users to estimate their costs. The pricing model of AWS Glue may result in more cost variability depending on the specific workload.
Native Integration: AWS Glue seamlessly integrates with other AWS services, such as Amazon S3, Amazon Redshift, and Amazon RDS, making it a preferred choice for businesses already utilizing the AWS ecosystem. Google Cloud Data Fusion, on the other hand, integrates well with Google Cloud Platform services like BigQuery and Cloud Storage, making it a suitable option for organizations using GCP.
Data Catalog Capability: AWS Glue provides a centralized metadata repository known as the "Glue Data Catalog" that helps in cataloging and organizing data assets. It provides features like data lineage and data discovery, which can be useful for data governance and compliance. Google Cloud Data Fusion, on the other hand, lacks a built-in data catalog capability and relies on external solutions or custom implementations for data cataloging.
In Summary, AWS Glue and Google Cloud Data Fusion have key differences in terms of scalability, data source variety, ETL capabilities, pricing model, native integration, and built-in data catalog capabilities.
We need to perform ETL from several databases into a data warehouse or data lake. We want to
- keep raw and transformed data available to users to draft their own queries efficiently
- give users the ability to give custom permissions and SSO
- move between open-source on-premises development and cloud-based production environments
We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.
You could also use AWS Lambda and use Cloudwatch event schedule if you know when the function should be triggered. The benefit is that you could use any language and use the respective database client.
But if you orchestrate ETLs then it makes sense to use Apache Airflow. This requires Python knowledge.
Though we have always built something custom, Apache airflow (https://airflow.apache.org/) stood out as a key contender/alternative when it comes to open sources. On the commercial offering, Amazon Redshift combined with Amazon Kinesis (for complex manipulations) is great for BI, though Redshift as such is expensive.
You may want to look into a Data Virtualization product called Conduit. It connects to disparate data sources in AWS, on prem, Azure, GCP, and exposes them as a single unified Spark SQL view to PowerBI (direct query) or Tableau. Allows auto query and caching policies to enhance query speeds and experience. Has a GPU query engine and optimized Spark for fallback. Can be deployed on your AWS VM or on prem, scales up and out. Sounds like the ideal solution to your needs.
I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?
Hi all,
Currently, we need to ingest the data from Amazon S3 to DB either Amazon Athena or Amazon Redshift. But the problem with the data is, it is in .PSV (pipe separated values) format and the size is also above 200 GB. The query performance of the timeout in Athena/Redshift is not up to the mark, too slow while compared to Google BigQuery. How would I optimize the performance and query result time? Can anyone please help me out?
you can use aws glue service to convert you pipe format data to parquet format , and thus you can achieve data compression . Now you should choose Redshift to copy your data as it is very huge. To manage your data, you should partition your data in S3 bucket and also divide your data across the redshift cluster
First of all you should make your choice upon Redshift or Athena based on your use case since they are two very diferent services - Redshift is an enterprise-grade MPP Data Warehouse while Athena is a SQL layer on top of S3 with limited performance. If performance is a key factor, users are going to execute unpredictable queries and direct and managing costs are not a problem I'd definitely go for Redshift. If performance is not so critical and queries will be predictable somewhat I'd go for Athena.
Once you select the technology you'll need to optimize your data in order to get the queries executed as fast as possible. In both cases you may need to adapt the data model to fit your queries better. In the case you go for Athena you'd also proabably need to change your file format to Parquet or Avro and review your partition strategy depending on your most frequent type of query. If you choose Redshift you'll need to ingest the data from your files into it and maybe carry out some tuning tasks for performance gain.
I'll recommend Redshift for now since it can address a wider range of use cases, but we could give you better advice if you described your use case in depth.
It depend of the nature of your data (structured or not?) and of course your queries (ad-hoc or predictible?). For example you can look at partitioning and columnar format to maximize MPP capabilities for both Athena and Redshift
you can change your PSV fomat data to parquet file format with AWS GLUE and then your query performance will be improved
Pros of AWS Glue
- Managed Hive Metastore9
Pros of Google Cloud Data Fusion
- Lower total cost of pipeline ownership1
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