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  5. AWS Glue vs Google Cloud Data Fusion

AWS Glue vs Google Cloud Data Fusion

OverviewDecisionsComparisonAlternatives

Overview

AWS Glue
AWS Glue
Stacks461
Followers819
Votes9
Google Cloud Data Fusion
Google Cloud Data Fusion
Stacks25
Followers156
Votes1

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

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

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Advice on AWS Glue, Google Cloud Data Fusion

Vamshi
Vamshi

Data Engineer at Tata Consultancy Services

May 29, 2020

Needs adviceonPySparkPySparkAzure Data FactoryAzure Data FactoryDatabricksDatabricks

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?

269k views269k
Comments
datocrats-org
datocrats-org

Jul 29, 2020

Needs adviceonAmazon EC2Amazon EC2TableauTableauPowerBIPowerBI

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.

319k views319k
Comments
Pavithra
Pavithra

Mar 12, 2020

Needs adviceonAmazon S3Amazon S3Amazon AthenaAmazon AthenaAmazon RedshiftAmazon Redshift

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?

522k views522k
Comments

Detailed Comparison

AWS Glue
AWS Glue
Google Cloud Data Fusion
Google Cloud Data Fusion

A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.

A fully managed, cloud-native data integration service that helps users efficiently build and manage ETL/ELT data pipelines. With a graphical interface and a broad open-source library of preconfigured connectors and transformations, and more.

Easy - AWS Glue automates much of the effort in building, maintaining, and running ETL jobs. AWS Glue crawls your data sources, identifies data formats, and suggests schemas and transformations. AWS Glue automatically generates the code to execute your data transformations and loading processes.; Integrated - AWS Glue is integrated across a wide range of AWS services.; Serverless - AWS Glue is serverless. There is no infrastructure to provision or manage. AWS Glue handles provisioning, configuration, and scaling of the resources required to run your ETL jobs on a fully managed, scale-out Apache Spark environment. You pay only for the resources used while your jobs are running.; Developer Friendly - AWS Glue generates ETL code that is customizable, reusable, and portable, using familiar technology - Scala, Python, and Apache Spark. You can also import custom readers, writers and transformations into your Glue ETL code. Since the code AWS Glue generates is based on open frameworks, there is no lock-in. You can use it anywhere.
Code-free self-service; Collaborative data engineering; GCP-native; Enterprise-grade security; Integration metadata and lineage; Seamless operations; Comprehensive integration toolkit; Hybrid enablement
Statistics
Stacks
461
Stacks
25
Followers
819
Followers
156
Votes
9
Votes
1
Pros & Cons
Pros
  • 9
    Managed Hive Metastore
Pros
  • 1
    Lower total cost of pipeline ownership
Integrations
Amazon Redshift
Amazon Redshift
Amazon S3
Amazon S3
Amazon RDS
Amazon RDS
Amazon Athena
Amazon Athena
MySQL
MySQL
Microsoft SQL Server
Microsoft SQL Server
Amazon EMR
Amazon EMR
Amazon Aurora
Amazon Aurora
Oracle
Oracle
Amazon RDS for PostgreSQL
Amazon RDS for PostgreSQL
Google Cloud Storage
Google Cloud Storage
Google BigQuery
Google BigQuery

What are some alternatives to AWS Glue, Google Cloud Data Fusion?

Apache Spark

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

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