Overview

AWS Glue

Stacks465

Followers819

Votes9

Corral

Stacks0

Followers4

Votes0

GitHub Stars694

Forks40

AWS Glue vs Corral: What are the differences?

1. Cost Model: AWS Glue is a fully managed service with usage-based pricing while Corral has a fixed pricing model based on the number of nodes. This difference can affect the cost efficiency for different types of workloads.

2. Integration with Other AWS Services: AWS Glue seamlessly integrates with other AWS services such as S3, RDS, and Redshift, making it easier to build data pipelines. On the other hand, Corral may not have as extensive integration with AWS services.

3. ETL Functionality: AWS Glue provides a fully managed ETL service with built-in transforms and crawlers for data ingestion and transformation. In comparison, Corral may require more manual coding and configuration for ETL processes.

4. Data Catalog: AWS Glue includes a managed metadata catalog that automatically discovers and profiles data, making it easy to search and query the data. Corral may not have a similar built-in data catalog functionality.

5. Security and Compliance Features: AWS Glue offers robust security features such as encryption, access control, and auditing for compliance requirements. In contrast, Corral may have limited security and compliance features.

6. Scalability: AWS Glue can automatically scale resources based on the workload demand, providing seamless scalability. Corral may have limitations in terms of scalability and may require manual intervention for scaling.

In Summary, AWS Glue offers a more comprehensive and fully managed ETL service with integration with various AWS services, while Corral may have a more fixed pricing model and limited integration capabilities.

Advice on AWS Glue, Corral

Aditya

Mar 13, 2021

Review

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

220k views220k

Comments

Vamshi

Data Engineer at Tata Consultancy Services

May 29, 2020

Needs adviceon

PySpark

Azure Data Factory

Databricks

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

Jul 29, 2020

Needs adviceon

Amazon EC2

Tableau

PowerBI

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

Detailed Comparison

AWS Glue	Corral
A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.	Corral is a MapReduce framework designed to be deployed to serverless platforms, like AWS Lambda. It presents a lightweight alternative to Hadoop MapReduce.
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
GitHub Stars -	GitHub Stars 694
GitHub Forks -	GitHub Forks 40
Stacks 465	Stacks 0
Followers 819	Followers 4
Votes 9	Votes 0
Pros & Cons
Pros 9 Managed Hive Metastore	No community feedback yet
Integrations
Amazon Redshift Amazon S3 Amazon RDS Amazon Athena MySQL Microsoft SQL Server Amazon EMR Amazon Aurora Oracle Amazon RDS for PostgreSQL	AWS Lambda

What are some alternatives to AWS Glue, Corral?

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

Distributed SQL Query Engine for Big Data

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 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

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 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™ 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

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

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.