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AWS Glue vs Pig: What are the differences?
Introduction: When it comes to data processing in the cloud, AWS Glue and Pig are both popular tools. However, they have distinct differences in terms of their functionalities and use cases.
1. Data Processing Paradigm: AWS Glue is a managed ETL service that offers a serverless data integration solution, making it easier to extract, transform, and load data. On the other hand, Pig is a high-level platform for creating MapReduce programs and runs on the Apache Hadoop platform, allowing users to process large datasets efficiently.
2. Programming Language: AWS Glue uses PySpark, which is a high-level API for Apache Spark written in Python, enabling developers to write ETL jobs using Python scripts. In contrast, Pig uses its own scripting language called Pig Latin, designed to simplify the process of writing complex data processing tasks.
3. Data Catalog: AWS Glue provides a centralized metadata repository where users can store, search, and access metadata for all the data assets in their AWS account. Pig does not have a built-in data catalog, requiring users to manage metadata manually or use external tools for metadata management.
4. Scalability: AWS Glue automatically scales resources based on the workload, allowing users to process vast amounts of data efficiently without worrying about infrastructure management. While Pig can also scale to handle large datasets, users may need to manually configure the cluster size and resources for optimal performance.
5. Integration with AWS Services: AWS Glue seamlessly integrates with other AWS services such as Amazon S3, Amazon Redshift, and Amazon RDS, making it easy to extract and load data from these services. Pig, on the other hand, can integrate with AWS services but may require additional configuration and setup for seamless data transfer.
6. Real-time Processing: AWS Glue supports real-time data processing through integration with Apache Kafka, enabling users to stream and process data in real-time. Pig is primarily designed for batch processing and may not be as well-suited for real-time processing without additional tools or configurations.
In Summary, AWS Glue is a managed ETL service with native support for Python scripting, automatic scaling, and seamless integration with AWS services, while Pig is a platform for creating MapReduce programs using Pig Latin, requiring manual scalability and lacking built-in data catalog capabilities.
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
Pros of AWS Glue
- Managed Hive Metastore9
Pros of Pig
- Finer-grained control on parallelization2
- Proven at Petabyte scale1
- Open-source1
- Join optimizations for highly skewed data1
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