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AWS Data Pipeline vs AWS Glue: What are the differences?

Introduction

AWS Data Pipeline and AWS Glue are both services provided by Amazon Web Services for managing and processing data. While both services offer similar features and capabilities, there are key differences between AWS Data Pipeline and AWS Glue. In this article, we will explore these differences and understand when to use each service.

  1. Data Transformation Capabilities: AWS Data Pipeline provides a range of built-in activities and pre-defined templates for data transformation tasks. These activities allow for easy data manipulation, such as filtering, aggregating, and joining, through simple configuration settings. On the other hand, AWS Glue offers a more powerful and flexible approach to data transformation. With AWS Glue, you can define and create complex ETL (Extract, Transform, Load) jobs using the built-in Spark framework, enabling more sophisticated data transformations.

  2. Dependency and Scheduling: AWS Data Pipeline is designed for orchestrating and managing the execution of batch workflows. It allows you to define dependencies between activities and schedule them accordingly. AWS Data Pipeline uses a visual interface to define and manage these workflows. In contrast, AWS Glue focuses on data cataloging and data preparation. While it does provide scheduling capabilities, it does not offer the same level of dependency management as AWS Data Pipeline.

  3. Data Catalog and Metadata Management: AWS Glue includes a fully managed data catalog that automatically discovers and categorizes metadata about your data sources. This catalog allows you to search, query, and explore your data assets effortlessly. AWS Glue also provides the ability to create and manage custom metadata, making it easier to understand and manage your data. In comparison, AWS Data Pipeline does not have built-in capabilities for data cataloging and metadata management.

  4. Data Format Support: AWS Glue supports a wide range of data formats out of the box, including popular file formats like JSON, CSV, Parquet, and Avro. It can automatically infer the schema of your data and generate code to perform data processing tasks. AWS Data Pipeline, on the other hand, has more limited support for data formats, primarily focusing on relational databases, Hadoop Distributed File System (HDFS), and Amazon S3.

  5. Data Source Connectivity: AWS Glue offers seamless connectivity to various data sources, including Amazon S3, relational databases (RDS), and Redshift. It provides native connectors for these data sources, simplifying the process of data extraction and loading. AWS Data Pipeline also supports these data sources but requires additional configuration and setup to establish connections.

  6. Pricing Model: AWS Glue follows a pay-as-you-go pricing model, where you are charged based on the number of compute units used and the amount of data processed. AWS Data Pipeline, on the other hand, is priced based on the number of pipeline executions and the duration of these executions. The pricing model for AWS Data Pipeline is more focused on the workflow orchestration and automation aspects.

In Summary, AWS Data Pipeline and AWS Glue differ in their data transformation capabilities, dependency and scheduling features, data catalog and metadata management, data format support, data source connectivity, and pricing model. Depending on your specific requirements and use case, you can choose the appropriate service to suit your needs.

Advice on AWS Data Pipeline and AWS Glue

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.

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Replies (3)
John Nguyen
Recommends
on
AirflowAirflowAWS LambdaAWS Lambda

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.

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Recommends
on
AirflowAirflow

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.

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Recommends

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.

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Vamshi Krishna
Data Engineer at Tata Consultancy Services · | 4 upvotes · 245.1K views

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?

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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?

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Replies (4)

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

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Carlos Acedo
Data Technologies Manager at SDG Group Iberia · | 5 upvotes · 237.5K views
Recommends
on
Amazon RedshiftAmazon Redshift

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.

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Alexis Blandin
Recommends
on
Amazon AthenaAmazon Athena

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

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Recommends

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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Anitha Gangadharam
Business Analyst at Amazon · | 1 upvotes · 3.3K views
Needs advice
on
AWS Data PipelineAWS Data Pipeline
and
AWS GlueAWS Glue

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

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Replies (1)
Nicola Corda
Data Engineer at Heycar · | 1 upvotes · 3.3K views
Recommends
on
AWS GlueAWS Glue

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.

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Pros of AWS Data Pipeline
Pros of AWS Glue
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  • 9
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What is AWS Data Pipeline?

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?

A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.

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Blog Posts

Aug 28 2019 at 3:10AM

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What are some alternatives to AWS Data Pipeline and AWS Glue?
Airflow
Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
AWS Step Functions
AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly.
Apache NiFi
An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.
AWS Batch
It enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. It dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of the batch jobs submitted.
Azure Data Factory
It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud.
See all alternatives