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AWS Glue vs Apache Flink: What are the differences?
Introduction
This Markdown code provides a comparison between AWS Glue and Apache Flink, highlighting their key differences. Both AWS Glue and Apache Flink are powerful tools used in big data processing and analytics. However, they have distinct features and functionalities that set them apart.
Real-Time Processing: AWS Glue primarily focuses on batch processing, providing batch data integration and transformation. On the other hand, Apache Flink is designed to handle both batch and stream processing. It offers advanced capabilities for real-time data processing, making it suitable for applications that require low-latency data ingestion and analytics.
Ease of Use: AWS Glue offers a fully managed service with an automated infrastructure that simplifies data ingestion, transformation, and cataloging tasks. It provides a user-friendly visual interface for creating ETL (Extract, Transform, Load) jobs without the need for extensive coding knowledge. In contrast, Apache Flink requires more manual configuration and coding expertise, making it more suitable for advanced users or developers familiar with distributed systems.
Connectivity: AWS Glue integrates seamlessly with other AWS services, such as Amazon S3, Amazon Redshift, and Amazon RDS, allowing easy data transfer and transformation within the AWS ecosystem. It also supports connector libraries for various data sources. Apache Flink, on the other hand, provides a wide range of connectors for diverse data systems, including file systems, message queues, and databases, enabling connectivity with various external systems.
Data Processing Model: AWS Glue follows a serverless data processing model, where the underlying infrastructure is abstracted, and users are billed based on the resources consumed during job execution. Apache Flink, on the other hand, offers a distributed data processing framework, allowing users to deploy and manage their own Flink clusters. This gives users more control and flexibility over resource allocation and scaling for different workloads.
Advanced Analytics: Apache Flink provides advanced analytics capabilities for complex event processing, machine learning, and graph processing. It offers a rich set of APIs and libraries for stream processing, state management, and iterative processing, enabling sophisticated data analysis and real-time decision-making. AWS Glue, while providing basic transformation and enrichment capabilities, does not offer the same level of advanced analytics functionality as Apache Flink.
Community and Ecosystem: Apache Flink has a vibrant open-source community with extensive documentation, active support forums, and a wide range of third-party integrations. This makes it easy for users to leverage community-contributed libraries, connectors, and tools for various use cases. AWS Glue, being a managed service within the AWS ecosystem, has a smaller community but benefits from the broader AWS community and ecosystem, providing access to a wide range of AWS services and integrations.
In summary, AWS Glue primarily focuses on batch processing with a user-friendly interface and seamless integration within the AWS ecosystem, while Apache Flink offers advanced capabilities for real-time processing, requires more manual configuration, and has a vibrant open-source community with extensive analytical 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.
We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.
In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.
In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.
The first solution that came to me is to use upsert to update ElasticSearch:
- Use the primary-key as ES document id
- Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.
Cons: The load on ES will be higher, due to upsert.
To use Flink:
- Create a KeyedDataStream by the primary-key
- In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
- When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
- When the Timer fires, read the 1st record from the State and send out as the output record.
- Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State
Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.
Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"
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 Apache Flink
- Unified batch and stream processing16
- Easy to use streaming apis8
- Out-of-the box connector to kinesis,s3,hdfs8
- Open Source4
- Low latency2
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