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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.
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.
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.
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.
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.
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.
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.
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.
You could also use AWS Lambda and use Cloudwatch event schedule if you know when the function should be triggered. The benefit is that you could use any language and use the respective database client.
But if you orchestrate ETLs then it makes sense to use Apache Airflow. This requires Python knowledge.
Though we have always built something custom, Apache airflow (https://airflow.apache.org/) stood out as a key contender/alternative when it comes to open sources. On the commercial offering, Amazon Redshift combined with Amazon Kinesis (for complex manipulations) is great for BI, though Redshift as such is expensive.
You may want to look into a Data Virtualization product called Conduit. It connects to disparate data sources in AWS, on prem, Azure, GCP, and exposes them as a single unified Spark SQL view to PowerBI (direct query) or Tableau. Allows auto query and caching policies to enhance query speeds and experience. Has a GPU query engine and optimized Spark for fallback. Can be deployed on your AWS VM or on prem, scales up and out. Sounds like the ideal solution to your needs.
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?
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?
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
First of all you should make your choice upon Redshift or Athena based on your use case since they are two very diferent services - Redshift is an enterprise-grade MPP Data Warehouse while Athena is a SQL layer on top of S3 with limited performance. If performance is a key factor, users are going to execute unpredictable queries and direct and managing costs are not a problem I'd definitely go for Redshift. If performance is not so critical and queries will be predictable somewhat I'd go for Athena.
Once you select the technology you'll need to optimize your data in order to get the queries executed as fast as possible. In both cases you may need to adapt the data model to fit your queries better. In the case you go for Athena you'd also proabably need to change your file format to Parquet or Avro and review your partition strategy depending on your most frequent type of query. If you choose Redshift you'll need to ingest the data from your files into it and maybe carry out some tuning tasks for performance gain.
I'll recommend Redshift for now since it can address a wider range of use cases, but we could give you better advice if you described your use case in depth.
It depend of the nature of your data (structured or not?) and of course your queries (ad-hoc or predictible?). For example you can look at partitioning and columnar format to maximize MPP capabilities for both Athena and Redshift
you can change your PSV fomat data to parquet file format with AWS GLUE and then your query performance will be improved
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