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  1. Stackups
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  4. Big Data Tools
  5. AWS Glue vs Cloudflow

AWS Glue vs Cloudflow

OverviewDecisionsComparisonAlternatives

Overview

AWS Glue
AWS Glue
Stacks461
Followers819
Votes9
Cloudflow
Cloudflow
Stacks5
Followers13
Votes0
GitHub Stars323
Forks89

AWS Glue vs Cloudflow: What are the differences?

Introduction:
AWS Glue and Cloudflow are both platforms used for data preparation and processing in the cloud.

1. **Data Processing Models**: AWS Glue supports serverless ETL (extract, transform, load) processes while Cloudflow enables streaming data processing using Kubernetes-based microservices architecture.
2. **Integration with Services**: AWS Glue seamlessly integrates with other AWS services like S3, Redshift, and DynamoDB for data storage and processing, whereas Cloudflow provides integration with multiple data sources including Kafka, HDFS, and Cloud Storage.
3. **Development Environment**: AWS Glue offers a managed environment for data preparation without the need for infrastructure management, while Cloudflow provides a more flexible environment for developers to build and deploy custom data processing pipelines.
4. **Pricing Model**: AWS Glue follows a pay-as-you-go pricing model based on the number of resources used, while Cloudflow offers a subscription-based pricing structure for its platform and services.
5. **Monitoring and Debugging Tools**: AWS Glue provides built-in monitoring and debugging tools for data processing workflows, while Cloudflow offers enhanced monitoring capabilities and real-time debugging features for Kubernetes-based microservices.
6. **Language Support**: AWS Glue supports Python and Scala for data processing tasks, whereas Cloudflow supports Java, Scala, and Kotlin for building data processing applications.

In Summary, AWS Glue focuses on serverless ETL processes and seamless integration with AWS services, while Cloudflow specializes in streaming data processing with Kubernetes-based microservices architecture and versatile language support.

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Advice on AWS Glue, Cloudflow

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
Cloudflow
Cloudflow

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

It enables you to quickly develop, orchestrate, and operate distributed streaming applications on Kubernetes. With Cloudflow, streaming applications are comprised of small composable components wired together with schema-based contracts. It can dramatically accelerate streaming application development—​reducing the time required to create, package, and deploy—​from weeks to hours.

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.
Apache Spark, Apache Flink, and Akka Streams; Focus only on business logic, leave the boilerplate to us; We provide all the tooling for going from business logic to a deployable Docker image; We provide Kubernetes tooling to deploy your distributed system with a single command, and manage durable connections between processing stages; With a Lightbend subscription, you get all the tools you need to provide insights, observability, and lifecycle management for evolving your distributed streaming application
Statistics
GitHub Stars
-
GitHub Stars
323
GitHub Forks
-
GitHub Forks
89
Stacks
461
Stacks
5
Followers
819
Followers
13
Votes
9
Votes
0
Pros & Cons
Pros
  • 9
    Managed Hive Metastore
No community feedback yet
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
Kubernetes
Kubernetes
Apache Spark
Apache Spark
Akka
Akka
Apache Flink
Apache Flink

What are some alternatives to AWS Glue, Cloudflow?

Kubernetes

Kubernetes

Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions.

Rancher

Rancher

Rancher is an open source container management platform that includes full distributions of Kubernetes, Apache Mesos and Docker Swarm, and makes it simple to operate container clusters on any cloud or infrastructure platform.

Docker Compose

Docker Compose

With Compose, you define a multi-container application in a single file, then spin your application up in a single command which does everything that needs to be done to get it running.

Docker Swarm

Docker Swarm

Swarm serves the standard Docker API, so any tool which already communicates with a Docker daemon can use Swarm to transparently scale to multiple hosts: Dokku, Compose, Krane, Deis, DockerUI, Shipyard, Drone, Jenkins... and, of course, the Docker client itself.

Tutum

Tutum

Tutum lets developers easily manage and run lightweight, portable, self-sufficient containers from any application. AWS-like control, Heroku-like ease. The same container that a developer builds and tests on a laptop can run at scale in Tutum.

Portainer

Portainer

It is a universal container management tool. It works with Kubernetes, Docker, Docker Swarm and Azure ACI. It allows you to manage containers without needing to know platform-specific code.

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.

Codefresh

Codefresh

Automate and parallelize testing. Codefresh allows teams to spin up on-demand compositions to run unit and integration tests as part of the continuous integration process. Jenkins integration allows more complex pipelines.

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