Need advice about which tool to choose?Ask the StackShare community!
AWS Data Pipeline vs AWS Glue: What are the differences?
What is AWS Data Pipeline? Process and move data between different AWS compute and storage services. AWS Data Pipeline is a web service that provides a simple management system for data-driven workflows. Using AWS Data Pipeline, you define a pipeline composed of the “data sources” that contain your data, the “activities” or business logic such as EMR jobs or SQL queries, and the “schedule” on which your business logic executes. For example, you could define a job that, every hour, runs an Amazon Elastic MapReduce (Amazon EMR)–based analysis on that hour’s Amazon Simple Storage Service (Amazon S3) log data, loads the results into a relational database for future lookup, and then automatically sends you a daily summary email.
What is AWS Glue? Fully managed extract, transform, and load (ETL) service. A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.
AWS Data Pipeline belongs to "Data Transfer" category of the tech stack, while AWS Glue can be primarily classified under "Big Data Tools".
Some of the features offered by AWS Data Pipeline are:
- You can find (and use) a variety of popular AWS Data Pipeline tasks in the AWS Management Console’s template section.
- Hourly analysis of Amazon S3‐based log data
- Daily replication of AmazonDynamoDB data to Amazon S3
On the other hand, AWS Glue provides the following key features:
- Easy - AWS Glue automates much of the effort in building, maintaining, and running ETL jobs. AWS Glue crawls your data sources, identifies data formats, and suggests schemas and transformations. AWS Glue automatically generates the code to execute your data transformations and loading processes.
- Integrated - AWS Glue is integrated across a wide range of AWS services.
- Serverless - AWS Glue is serverless. There is no infrastructure to provision or manage. AWS Glue handles provisioning, configuration, and scaling of the resources required to run your ETL jobs on a fully managed, scale-out Apache Spark environment. You pay only for the resources used while your jobs are running.
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?
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
Currently, we are using AWS data pipelines to load data from RDS to Redshift. But we are facing a lot of issues like running for long hours and failing frequently with no space left on device issues. Also, the EC2 instance needs to be modified whenever we face space issues. So to overcome this, we are exploring AWS Glue. Is this advisable to migrate our ETL to AWS Glue? Any suggestions are very much helpful for us.
Thanks, Anitha KG
Glue Jobs are basically a serverless version of Spark running on AWS, this means you don't have to size your cluster. The cool part is that you can setup a crawler on top of your RDS database, and using this metadata information to query RDS from a Glue job, and only then load it to Redshift after some transformation. Here the reference: https://aws.amazon.com/blogs/database/how-to-extract-transform-and-load-data-for-analytic-processing-using-aws-glue-part-2/
Using DataPipeline you will always fail the vertical scaling of your EC2 machine. Another solution, if you want to use DataPipeline, is how your process the data, for example, you can make chunked requests to RDS, save the result to S3, only then loading to Redshift. This solution will require much more effort and orchestration skills.
Pros of AWS Data Pipeline
- Easy to create DAG and execute it1
Pros of AWS Glue
- Managed Hive Metastore9
Sign up to add or upvote prosMake informed product decisions
What is AWS Data Pipeline?
What is AWS Glue?
Need advice about which tool to choose?Ask the StackShare community!
What companies use AWS Data Pipeline?
What companies use AWS Glue?
Sign up to get full access to all the companiesMake informed product decisions
What tools integrate with AWS Glue?
Sign up to get full access to all the tool integrationsMake informed product decisions