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  5. AWS Glue vs Matillion

AWS Glue vs Matillion

OverviewDecisionsComparisonAlternatives

Overview

Matillion
Matillion
Stacks51
Followers71
Votes0
GitHub Stars0
Forks0
AWS Glue
AWS Glue
Stacks462
Followers819
Votes9

AWS Glue vs Matillion: What are the differences?

Introduction

AWS Glue and Matillion are both popular tools used for data integration and ETL (Extract, Transform, Load) processes. While they serve similar purposes, there are key differences between the two. In this article, we will outline the top six differences between AWS Glue and Matillion.

  1. Deployment Method: AWS Glue is a fully managed service provided by Amazon Web Services (AWS), which means that it is hosted and maintained by AWS. On the other hand, Matillion is a software that needs to be installed and managed on your own infrastructure or cloud environment. This difference in deployment method affects the scalability and maintenance of the tools.

  2. Pricing Model: AWS Glue has a pay-as-you-go pricing model, where you only pay for the resources you use and the data processing time. In contrast, Matillion has a fixed subscription-based pricing model, where you have to pay for a license based on the number of users or the size of your data. This difference in pricing models can impact the cost-effectiveness of the tools, depending on your specific requirements and usage patterns.

  3. Integration with Ecosystem: AWS Glue is tightly integrated with the AWS ecosystem, meaning that it seamlessly integrates with other AWS services, such as Amazon S3, Amazon Redshift, and Amazon Athena. This makes it easier to build end-to-end data pipelines within the AWS environment. On the other hand, Matillion has broader integration capabilities with various cloud platforms, databases, and data warehouses, allowing you to connect to multiple data sources and destinations beyond the AWS ecosystem.

  4. Data Transformation Capabilities: AWS Glue provides a visual interface for creating data transformation jobs using its built-in Apache Spark engine. It also supports custom transformations using Python or Scala code. Matillion, on the other hand, offers a powerful drag-and-drop interface with a wide range of pre-built components for data transformation. This makes it easier for non-technical users to design and execute complex data transformations without writing any code.

  5. Ease of Use and Learning Curve: AWS Glue can be more complex to set up and configure, especially if you are new to the AWS ecosystem. It requires understanding of AWS services and their configurations. Matillion, on the other hand, has a user-friendly interface with intuitive workflows and a shorter learning curve. It provides a more beginner-friendly experience for users without extensive knowledge of AWS or coding skills.

  6. Community Support and Documentation: AWS Glue is backed by the extensive AWS community and documentation, which provides a wealth of resources and support for troubleshooting and learning. Matillion also has a supportive user community, but with a smaller user base compared to AWS. The documentation and resources for Matillion might not be as extensive as AWS Glue, potentially leading to more limited support options.

In summary, AWS Glue and Matillion differ in their deployment method, pricing model, integration capabilities, data transformation options, ease of use, and community support. The choice between the two depends on your specific needs, familiarity with the AWS ecosystem, and budget considerations.

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Advice on Matillion, 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

Matillion
Matillion
AWS Glue
AWS Glue

It is a modern, browser-based UI, with powerful, push-down ETL/ELT functionality. With a fast setup, you are up and running in minutes.

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

Edit, Transform and Load Data intuitively; Load Data from Dozens of Sources; 50% reduction in ETL development and maintenance effort ; Rich orchestration environment; Work as a team; Cheap; Billing via AWS.
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
0
GitHub Stars
-
GitHub Forks
0
GitHub Forks
-
Stacks
51
Stacks
462
Followers
71
Followers
819
Votes
0
Votes
9
Pros & Cons
No community feedback yet
Pros
  • 9
    Managed Hive Metastore
Integrations
Amazon S3
Amazon S3
Zendesk
Zendesk
MongoDB Stitch
MongoDB Stitch
Amazon Redshift
Amazon Redshift
Cassandra
Cassandra
Salesforce Sales Cloud
Salesforce Sales Cloud
Mixpanel
Mixpanel
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 Matillion, AWS Glue?

Google BigQuery

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

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.

Amazon Redshift

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon EMR

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

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.

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