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AWS Glue vs Amazon Redshift Spectrum: What are the differences?
Introduction
AWS Glue and Amazon Redshift Spectrum are two powerful tools offered by Amazon Web Services (AWS) that can be used for data analysis and processing. While both services provide capabilities for querying and analyzing large datasets, there are key differences between AWS Glue and Amazon Redshift Spectrum that make them suitable for different use cases.
Data Storage and Querying: One key difference between AWS Glue and Amazon Redshift Spectrum is the way they store and query data. AWS Glue is a fully managed extract, transform, and load (ETL) service that can handle structured and semi-structured data. It provides a centralized metadata repository and supports batch and real-time data processing. On the other hand, Amazon Redshift Spectrum is a feature of Amazon Redshift, a data warehousing service. Redshift Spectrum enables users to query data directly from their external data sources, such as Amazon S3, without the need to load the data into Redshift first.
Data Processing Engine: Another important difference is the underlying data processing engine used by each service. AWS Glue uses Apache Spark, a powerful open-source analytics engine, to process and transform the data. Spark provides a distributed computing model that can handle large datasets and supports a wide range of data processing tasks. In contrast, Amazon Redshift Spectrum uses a massively parallel processing (MPP) architecture to process queries on large datasets stored in Amazon S3. Redshift Spectrum leverages the same query optimizer and execution engine as Amazon Redshift, providing high-performance query processing capabilities.
Cost Structure: The cost structure of AWS Glue and Amazon Redshift Spectrum also differs. AWS Glue pricing is based on the number of Data Processing Units (DPUs) used per hour, as well as the number of crawlers, classifiers, and development endpoints provisioned. On the other hand, Amazon Redshift Spectrum pricing is based on the amount of data scanned by queries. Users are charged per terabyte of data scanned, with separate pricing for standard and Athena data formats. The cost implications of using each service should be carefully evaluated based on the specific use case and data processing requirements.
Data Transformation Capabilities: AWS Glue provides a rich set of data transformation capabilities, including data cleansing, deduplication, and normalization. These transformations can be applied during the ETL process to improve data quality and consistency. In contrast, Amazon Redshift Spectrum focuses primarily on querying and analyzing data rather than data transformation. While Redshift Spectrum provides a limited set of data manipulation functions, its main strength lies in the ability to directly query external data sources stored in Amazon S3.
Performance and Scaling: When it comes to performance and scaling, AWS Glue and Amazon Redshift Spectrum have different strengths. AWS Glue's use of Apache Spark allows for distributed processing and parallel execution, making it well-suited for handling large datasets and complex transformations. On the other hand, Amazon Redshift Spectrum's MPP architecture enables parallel query execution across multiple Redshift Spectrum nodes, providing high-performance querying capabilities. The choice between the two services depends on the specific performance and scaling requirements of the workload.
Integration with Other AWS Services: Both AWS Glue and Amazon Redshift Spectrum integrate well with other AWS services, but in different ways. AWS Glue integrates with various AWS services, such as Amazon S3, Amazon RDS, and Amazon Redshift, to facilitate data ingestion and transformation. It also supports custom connectors for connecting to on-premises data sources. On the other hand, Amazon Redshift Spectrum seamlessly integrates with Amazon Redshift, allowing users to query external data sources stored in Amazon S3 without the need for data movement or ETL.
In Summary, AWS Glue and Amazon Redshift Spectrum are two AWS services with distinct differences. AWS Glue is a fully managed ETL service that provides data processing and transformation capabilities, while Amazon Redshift Spectrum is a feature of Amazon Redshift that enables querying of data directly from external sources. The choice between the two services depends on factors such as data storage and querying requirements, data processing engine preference, cost structure, data transformation needs, performance and scaling requirements, and integration with other AWS services.
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 Amazon Redshift Spectrum
- Good Performance1
- Great Documentation1
- Economical1
Pros of AWS Glue
- Managed Hive Metastore9
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