Overview

AWS Glue

Stacks461

Followers819

Votes9

Amazon AppFlow

Stacks9

Followers42

Votes0

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.

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Advice on AWS Glue, Amazon AppFlow

Vamshi

Data Engineer at Tata Consultancy Services

May 29, 2020

Needs adviceon

PySpark

Azure Data Factory

Databricks

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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datocrats-org

Jul 29, 2020

Needs adviceon

Amazon EC2

Tableau

PowerBI

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.

319k views319k

Comments

Pavithra

Mar 12, 2020

Needs adviceon

Amazon S3

Amazon Athena

Amazon Redshift

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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Detailed Comparison

AWS Glue	Amazon AppFlow
A fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics.	It is a fully managed integration service that enables you to securely transfer data between Software-as-a-Service (SaaS) applications like Salesforce, Marketo, Slack, and ServiceNow, and AWS services like Amazon S3 and Amazon Redshift, in just a few clicks. With AppFlow, you can run data flows at nearly any scale at the frequency you choose - on a schedule, in response to a business event, or on demand. You can configure data transformation capabilities like filtering and validation to generate rich, ready-to-use data as part of the flow itself, without additional steps. AppFlow automatically encrypts data in motion, and allows users to restrict data from flowing over the public Internet for SaaS applications that are integrated with AWS PrivateLink, reducing exposure to security threats.
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.; Developer Friendly - AWS Glue generates ETL code that is customizable, reusable, and portable, using familiar technology - Scala, Python, and Apache Spark. You can also import custom readers, writers and transformations into your Glue ETL code. Since the code AWS Glue generates is based on open frameworks, there is no lock-in. You can use it anywhere.	Point and click user interface; Native SaaS integrations; Enterprise grade data transformations; High scale data transfer; Data privacy defaults through PrivateLink; Custom encryption keys; IAM policy enforcement; Flexible data flow triggers; Easy to use field mapping; Built in reliability
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Integrations
Amazon Redshift Amazon S3 Amazon RDS Amazon Athena MySQL Microsoft SQL Server Amazon EMR Amazon Aurora Oracle Amazon RDS for PostgreSQL	Google Analytics Slack Dynatrace Datadog Zendesk Marketo Snowflake Amplitude Veeva

What are some alternatives to AWS Glue, Amazon AppFlow?

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.