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AWS Glue vs Amazon AppFlow: What are the differences?
AWS Glue and Amazon AppFlow are both services provided by Amazon Web Services (AWS) that enable data integration and transformation. Let's explore the key differences between them.
Data Integration Capabilities: AWS Glue is primarily designed for ETL (Extract, Transform, Load) operations, enabling users to automate data ingestion, data transformation, and data loading processes. It offers a range of features such as data cataloging, data transformation, and job orchestration, making it ideal for data integration tasks. On the other hand, Amazon AppFlow focuses on securely transferring data between AWS services and third-party applications, such as Salesforce, Zendesk, and Marketo. It simplifies the process of integrating data with these applications, providing pre-built connectors and giving users the ability to configure and schedule data transfers.
Supported Data Sources and Destinations: AWS Glue supports a wide range of data sources and destinations, including RDS databases, Redshift clusters, S3 buckets, and various on-premises databases. It provides connectors and data ingestion capabilities for a multitude of data sources, ensuring flexibility in data integration workflows. In contrast, Amazon AppFlow is specifically designed for integrating data between AWS services and third-party applications. It offers connectors for popular applications like Salesforce, Slack, and Google Analytics, allowing seamless data transfer between these applications and AWS services.
Transformation Capabilities: AWS Glue provides robust transformation capabilities to clean, transform, and enrich data during the ETL process. It supports Python and Scala as programming languages and provides a visual interface for creating and debugging ETL jobs. Users can leverage Glue's built-in transformations or create custom transformations using their preferred programming language. While Amazon AppFlow also supports basic transformations like data mapping and filtering, its primary focus is on the secure transfer of data between applications. It provides simple mapping and transformation options, making it quick and easy to configure data transfers between different services.
Job Orchestration and Scheduling: AWS Glue enables users to create and manage complex ETL workflows by allowing job orchestration and scheduling. It provides the ability to define dependencies between jobs, create workflows, and schedule them according to specific time triggers or events. In contrast, Amazon AppFlow does not provide extensive job orchestration capabilities. It primarily focuses on data transfer between applications and AWS services, offering simple scheduling options like recurring or on-demand transfers.
Data Cataloging and Metadata Management: AWS Glue includes a fully managed data catalog that automatically discovers and catalogs data from various sources. It organizes and categorizes data, making it easier to discover, search, and query. The catalog also maintains metadata about the data sources, tables, and schemas, enabling efficient data governance and management. On the other hand, Amazon AppFlow does not include a data catalog. It mainly focuses on the secure transfer of data and does not provide extensive metadata management capabilities.
Pricing and Cost Structure: AWS Glue pricing is based on the number of data processing units (DPUs) used, as well as the number of crawler runs, development endpoints, and other related resources. Costs can vary depending on the size and complexity of the data processing tasks. Amazon AppFlow pricing is based on the number of flow runs, which is determined by the frequency of data transfers and the volume of data transferred. The pricing structure is designed to provide flexibility and cost-effectiveness for integrating data with third-party applications.
In summary, AWS Glue is a comprehensive data integration service focused on ETL operations, offering a wide range of data source support, transformation capabilities, job orchestration, and metadata management. On the other hand, Amazon AppFlow is a specialized service primarily focused on securely transferring data between AWS services and third-party applications, providing pre-built connectors, and simple data transfer configurations.
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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