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AWS Glue vs OpenRefine: What are the differences?
Introduction
In this article, we will explore the key differences between AWS Glue and OpenRefine, two popular tools used for data preparation and transformation. Both tools aim to enhance data quality and facilitate data analysis, but they differ in their underlying architecture, capabilities, and integration options.
Architecture: AWS Glue is a fully managed extract, transform, and load (ETL) service that runs on the cloud, whereas OpenRefine is a desktop-based open-source tool. AWS Glue offers scalability, high availability, and automatic infrastructure management, while OpenRefine requires local installation and manual configuration.
Integration with Cloud Services: AWS Glue integrates seamlessly with other AWS services, such as Amazon S3, Amazon Redshift, and Amazon RDS, making it ideal for working within a cloud-based ecosystem. On the other hand, OpenRefine does not have native integration with cloud services and primarily focuses on local data processing.
Scalability: With AWS Glue, users benefit from automatic scaling based on the volume and complexity of their data processing tasks. It can easily handle large-scale data transformations and handle parallel processing for faster execution. OpenRefine, being a desktop tool, may face limitations in terms of processing power and scalability, particularly for big datasets.
Data Catalog: AWS Glue provides a centralized metadata repository called the Glue Data Catalog that facilitates the management and organization of various data assets. This catalog allows automatic discovery and cataloging of data sources, generating metadata, and enabling data lineage tracking. OpenRefine does not have a built-in data catalog and relies on manual processes for organizing and maintaining data assets.
Pre-built Transformations and Code Generation: AWS Glue offers a range of pre-built transformations and transformations, making it easier to perform common data preparation tasks. It also supports code generation in Python or Apache Spark, allowing users to enhance and customize their data transformations. While OpenRefine offers a wide array of data cleaning and transformation functions, it may require more manual scripting for complex transformations.
Pricing Model: AWS Glue follows a pay-as-you-go pricing model based on the number of data processing units and data catalog objects used. The cost can vary depending on the scale of data processing and usage of additional services. OpenRefine, being an open-source tool, is available free of cost but requires manual deployment and setup.
In Summary, AWS Glue and OpenRefine differ in their architecture (cloud-based vs. desktop-based), integration with cloud services, scalability, data catalog capabilities, availability of pre-built transformations, and pricing models.
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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