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  3. AWS Glue vs Google Cloud Dataflow

AWS Glue vs Google Cloud Dataflow

OverviewDecisionsComparisonAlternatives

Overview

Google Cloud Dataflow
Google Cloud Dataflow
Stacks223
Followers497
Votes19
AWS Glue
AWS Glue
Stacks465
Followers819
Votes9

AWS Glue vs Google Cloud Dataflow: What are the differences?

AWS Glue and Google Cloud Dataflow are both cloud-based services for processing and transforming big data. However, there are several key differences between these two platforms.

  1. Data Processing Model: AWS Glue uses an extract, transform, load (ETL) approach, making it ideal for batch processing and data warehousing. On the other hand, Google Cloud Dataflow employs a dataflow programming model, enabling both batch and stream processing.

  2. Language Support: AWS Glue supports Python and Scala, allowing users to write custom transformations and extract data from a variety of sources. In contrast, Google Cloud Dataflow provides native support for Java, Python, and other popular programming languages.

  3. Managed Service: AWS Glue is a fully managed service, which means that AWS takes care of infrastructure provisioning and maintenance. In contrast, Google Cloud Dataflow requires users to manage cluster resources, providing more control but also requiring more setup and management effort.

  4. Data Source Integration: AWS Glue integrates seamlessly with other AWS services like Amazon S3, Amazon RDS, and more. Google Cloud Dataflow offers native integration with Google Cloud Storage, BigQuery, and various other Google Cloud services.

  5. Performance and Scalability: Google Cloud Dataflow leverages the power of Google's infrastructure, enabling high scalability and improved performance for processing large datasets. AWS Glue also offers good performance, but it may not match the scale of Google Cloud Dataflow for extremely large workloads.

  6. Pricing Model: AWS Glue pricing is based on data processing and storage usage, with separate costs for crawlers, jobs, and development endpoints. Google Cloud Dataflow pricing is based on the total number of processing units (vCPU + memory) used for data processing.

In summary, AWS Glue is suited for ETL and data warehousing use cases with its managed service and AWS integration, while Google Cloud Dataflow is more versatile with its support for both batch and stream processing, as well as its scalability and performance capabilities.

Advice on Google Cloud Dataflow, AWS Glue

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

Google Cloud Dataflow
Google Cloud Dataflow
AWS Glue
AWS Glue

Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.

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

Fully managed; Combines batch and streaming with a single API; High performance with automatic workload rebalancing Open source SDK;
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.
Statistics
Stacks
223
Stacks
465
Followers
497
Followers
819
Votes
19
Votes
9
Pros & Cons
Pros
  • 7
    Unified batch and stream processing
  • 5
    Autoscaling
  • 4
    Fully managed
  • 3
    Throughput Transparency
Pros
  • 9
    Managed Hive Metastore
Integrations
No integrations available
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

What are some alternatives to Google Cloud Dataflow, AWS Glue?

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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