As cloud technologies continue to emerge, more and more companies are opting to transfer their data through ETL workflows, given that their current data storage options such as RDBMS are outdated, rigid, and insecure. This has led many businesses to migrate to the cloud, as it offers improved scalability, performance, and fault tolerance. 

However, the vast majority of cloud analytics projects thus far involve data that's already located in the cloud, be it from SaaS applications like Salesforce and Marketo, cloud services such as Google Analytics and Adwords, or raw data already stored in a cloud data lake. Due to the potential risks of moving on-premises data to the cloud, many enterprises have been forced to limit their cloud analytics projects, particularly in regulated industries where data privacy is of utmost importance. 

In light of this data source trend, cloud-only ETL tools have arisen and shifted towards a new processing model known as ELT, which focuses exclusively on simplifying data integration in cloud data warehouses. In this article we will read all about AWS ETL tools. Let’s begin!

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What is AWS ETL?

Amazon Web Services (AWS) offers AWS Glue as an ETL tool. It is a serverless platform and set of tools that can extract data from different sources, perform various transformations such as enriching, cleansing, combining, and normalizing data, and then load and organize the data into databases, data warehouses, and data lakes. 

With Glue, ETL developers can create data pipelines either through a visual interface or coding. Glue also comes with a data catalog that stores data flows and resulting datasets. To run and monitor ETL data flows administrators can use Glue Studio to run and monitor ETL data flows. 

Glue Studio is a traditional ETL tool. It has a visual job editor and a data flow-style user interface. While it allows for high-level graphical definition of flows, its set of transformations is limited. Advanced changes like filters, joins, and mappings necessitate programming or SQL. Glue Studio's connectors are restricted and are only compatible with data sources and destinations hosted on AWS. 

DataBrew, a related but distinct product from AWS Glue, is utilized for data preparation. Through the DataBrew interface, users can explore, analyze, sanitize, and modify raw data interactively. It has a larger library of transformations compared to Glue. The range of DataBrew connectors is restricted, but they do surpass AWS sources and can encompass conventional databases like Oracle or MySQL that operate on AWS.

It's crucial to note that Glue and Glue DataBrew are separate products. Glue is employed for ETL data pipelines, while DataBrew is utilized for data preparation. To combine both, Glue must perform the extraction and loading of data, such as into Redshift, and then employ separate DataBrew preparation tasks to transform data within Redshift. 

Both AWS Glue and Glue DataBrew have certain limitations. 

• They have a restricted selection of data connectors that primarily focus on AWS-owned sources, databases running on AWS, and files from S3 buckets. 
• They cannot securely link to on-premises data sources. 
• There may also be disjoint data integration tasks that need more sophisticated transformations with logic and job execution separated between the two tools. 
• Inconsistent security policies and security vulnerabilities may arise between Glue and Glue DataBrew. 
• In terms of data governance, there are very few features available, mostly related to security, such as encryption, and cataloging via the Glue Catalog.

AWS Data Pipeline vs Glue

Now, let’s take a look at a few differences between AWS Data Pipeline and AWS Glue:

1. Data Sources Supported 

AWS Data Pipeline is capable of handling data from several sources, such as Amazon S3, DynamoDB, RDS, and Redshift. It can also be customized to work with other data sources, such as AWS Elastic File System and on-premises data sources, to perform functions based on Java. AWS Glue is capable of working with various data sources, including Amazon Athena, Amazon EMR, and Redshift Spectrum. 

Additionally, it offers built-in assistance for data residing in Amazon Aurora, Amazon RDS, Amazon Redshift, DynamoDB, and Amazon S3. It can manage JDBC-based data repositories such as MySQL, Oracle, Microsoft SQL Server, and PostgreSQL databases situated in Amazon Virtual Private Cloud, and MongoDB client stores like Amazon too.DocumentDB and MongoDB..

2. Infrastructure Management

AWS Glue is a serverless ETL tool that does not require any infrastructure management. The Apache Spark environment of Glue is responsible for handling tasks such as scaling, provisioning, and configuration. 

But, AWS Data Pipeline is not serverless like Glue. It manages EMR clusters and EC2 instances' lifecycle to execute jobs. Users can define pipelines and have better control over the underlying compute resources.

These differences are essential when comparing AWS Data Pipeline and AWS Glue, as they impact the skills and resources required for ETL activities on the AWS cloud.

3. Transformations

AWS Data Pipeline lacks support for pre-built transformations. Nonetheless, it presents numerous other pre-installed functions, including duplicating data between Amazon S3 and Amazon RDS, or executing a query on Amazon S3 log data. 

It enables users to integrate an array of intricate expressions and functions into pipeline definitions that are manually coded. A pipeline can contain up to 100 objects, but AWS Glue offers support for more if needed. The transformation workflow is automated, with scheduling, assignment, and re-assignment of transformation activities handled automatically. Task runners are responsible for executing the transformation activities, as well as the Extract and Load functions, based on the defined schedule. 

AWS Glue provides 16 pre-built transformations, such as Join, Map, and SplitRows. Moreover, AWS Glue DataBrew provides more than 250 pre-built transformations that can automate data preparation duties, such as identifying anomalies, standardizing formats, and rectifying invalid values. There are predefined scripts for common data transformation tasks, simplifying the overall process of building and executing a job. Developers can also use their own scripts for greater flexibility beyond the pre-built options. AWS Step Functions allow the creation of workflows. It is possible to create workflows through AWS Glue blueprint or manually build a workflow component-by-component using the AWS Management Console or AWS Glue API.

4. Pricing

Pricing is a crucial factor to take into account when choosing between AWS Data Pipeline and AWS Glue for your organization. Here's a brief summary of the pricing for both services beyond their free tiers: 

For AWS Data Pipeline, the charge is $1 per month per pipeline if it is used more than once a day, and $0.68 per month per pipeline if it is used once or less per day. Additionally, you have to pay for EC2 and other resources you consume. On the other hand, AWS Glue charges $0.44 per hour for each Data Processing Unit, billed per second of use. 

Data Processing Units are used when running crawlers or jobs. Furthermore, there is a fee of $1 for every 100,000 objects controlled in the data catalog and $1 for every million requests made to the data catalog. 

When deciding between AWS Data Pipeline and AWS Glue, it's important to consider the type, frequency, and number of objects involved in your ETL activity, as these factors can significantly impact your costs.

What are AWS ETL Tools?

AWS ETL (Extract, Transform, Load) tools are a suite of services provided by Amazon Web Services (AWS) that facilitate the process of extracting, transforming, and loading data between different systems. These tools can help organizations to integrate data from various sources into a single destination, enabling them to gain insights into their data and make informed decisions. 

The AWS ETL tools include:

1. AWS Data Pipeline: This is a fully managed solution that enables users to transfer data between different AWS facilities and on-premises data sources. Users can create data-driven workflows, called pipelines, to automate the movement of data between various sources and destinations, including Amazon S3, Amazon RDS, DynamoDB, and Redshift.
2. AWS Glue: This is a serverless, entirely managed ETL service that streamlines the process of transferring data between data repositories. Glue automatically discovers data sources, infers schemas, and generates ETL code to transform the data. Users can create and run ETL jobs with Glue to transform data from various sources and load it into different destinations.
3. AWS Glue DataBrew: DataBrew is a data preparation service that uses a visual approach to simplify the cleaning and standardization of data. With over 250 pre-installed transformations available, it enables users to filter anomalies, standardize formats, and correct invalid values to clean and transform data. Users can also create custom transformations using Python scripts. 

AWS ETL tools have several benefits, including:

1. Scalability: They are designed to handle large volumes of data, making them suitable for organizations with big data requirements.
2. Automation: The tools automate the extraction, transformation, and loading of data, which reduces the requirement for manual involvement.
3. Integration: They can integrate with a wide range of data sources and destinations, including on-premises systems, making it easier to manage data integration across different environments.
4. Flexibility: They offer a range of options for data transformation, including pre-built transformations and the ability to write custom scripts. 

Overall, AWS ETL tools simplify the process of managing ETL workflows on the cloud, enabling organizations to gain insights from their data quickly and efficiently.

Factors that Drive AWS ETL Tool Decisions

When choosing an AWS ETL tool, there are several factors that organizations need to consider. These factors can influence the choice of tool and the success of the ETL process. Here are some of the factors to consider:

1. Complexity of the data: The complexity of the data can influence the choice of tool. If the data is relatively simple, AWS Data Pipeline can be a good choice. However, if the data is more complex, with a variety of formats and structures, AWS Glue may be a better choice.
2. Scalability: The size and scale of the data can also impact the choice of tool. Suppose the data volume is relatively low and the processing demands are not significant, AWS Data Pipeline may be adequate. However, if the data volume is large and processing requirements are more complex, AWS Glue may be a better choice due to its scalability and ability to handle larger data volumes.
3. Automation: AWS Glue is more automated than AWS Data Pipeline, as it can automatically discover and catalog data sources and handle complex data transformations.
4. Integration with other AWS services: AWS Data Pipeline can be integrated with a wide range of AWS services. Some of them include Amazon S3, DynamoDB, RDS, and Redshift. AWS Glue can also integrate with these services, but it also includes additional features such as support for Apache Spark.
5. Cost: Finally, cost is an important consideration. Both AWS Data Pipeline and AWS Glue have different pricing models, and organizations need to evaluate the costs associated with each tool based on their specific needs and usage. 

Overall, the choice of AWS ETL tool depends on the specific requirements of the organization, the complexity of the data, scalability, automation, integration with other AWS services, and cost.

Which is the best tool for ETL in AWS?

By this stage of the article, you have already read all about AWS ETL Tools. However, third-party AWS ETL tools may offer benefits over AWS Glue and internal pipelines, such as the ability to integrate with non-AWS data sources through graphical interfaces and more appealing pricing structures. So, how do you determine the best ETL tool for your organization? Let’s compare some of the top ETL tools available in the market.

1. Fivetran

Fivetran is a cloud-based data integration platform that specializes in automating data pipelines. It enables organizations to connect data from disparate sources, including databases, applications, and cloud services, to a data warehouse for analysis and reporting. 

It automates the data ingestion process by providing pre-built connectors for hundreds of data sources, eliminating the need for manual coding or scripting. Fivetran streamlines the process of establishing connections to multiple data sources, extracting data, transforming it, and loading it into a targeted destination, which decreases the manual effort required to establish and maintain data pipelines. 

In addition, it provides pre-built connectors for more than 150 data sources, including popular SaaS applications such as Salesforce, HubSpot, and Shopify, making it simple to integrate data from a broad range of sources without the need for custom code. Fivetran offers real-time data replication, ensuring that the data in your data warehouse is constantly up-to-date. Its user-friendly interface simplifies the process of creating and managing data pipelines. 

Additionally, it provides a comprehensive dashboard for monitoring pipeline performance and troubleshooting issues. Fivetran is designed to handle significant volumes of data and can be scaled to meet the needs of growing organizations. Furthermore, it integrates with major cloud data warehouses like Amazon Redshift, Google BigQuery, and Snowflake.

2. AWS Glue    

AWS Glue is a widely used ETL tool that is managed completely by AWS. It streamlines the data preparation process before analysis. The AWS Glue is designed to be intuitive and straightforward, allowing users to create and execute ETL jobs with ease through a few simple clicks in the AWS Management Console. You only need to set up AWS Glue to access your data stored in AWS. Once done, it will automatically identify your data and save its metadata in the AWS Glue Data Catalog. This makes your data searchable and queryable instantly and ready for ETL.

Pros:

• AWS Glue is a serverless ETL tool that simplifies the process of data preparation for analysis. Since it is serverless, there is no need to manage resources, although this also means less control over the resources. 
• The billing is based on usage and can be more cost-effective than long-running solutions like EMR. 
• AWS Glue is easy to use, and set up can be done quickly through a wizard-style interface. Common transforms can be easily set up, and the recent release of Glue Studio makes the process even simpler with a GUI for job creation. 
• Code writing is not required, as Glue can automatically generate code for common use cases. However, if users are interested in writing transforms from scratch, it is possible to do so.

Cons:

• Compute resources cannot be fully controlled as Glue offers a limited selection of three instance types for general, memory-intensive, and machine learning tasks. There are not many options for customization, and if you require specific compute profiles, the available options may not be satisfactory.
• Glue is based on Spark and supports only Python or Scala scripts, which means that if you have scripts written for another platform or language, it may be difficult to adapt them to Glue.
• Python modules can be included in Glue scripts, but Spark itself cannot be extended (as far as known). This could be a problem for users who are migrating from a self-managed, customized Spark cluster.

3. AWS Data Pipeline  

AWS Data Pipeline is a web service provided by Amazon that allows users to easily create automated workflows for data transformation and movement. This means that users don't need to build a complicated ETL or ELT platform to utilize their data. By utilizing pre-configured templates and configurations provided by Amazon, users can perform most operations using computing resources from Amazon's services such as EMR. This service makes it simpler for users to extract, load, and transform their data.

Pros:

• The user-friendly interface of AWS Data Pipeline comes with predefined templates for many AWS databases, making it simple to use.
• The ability to generate clusters and resources on-demand helps users to reduce costs.
• Users can schedule jobs to run at specific times. This provides flexibility.
• A robust security suite safeguards data both in motion and at rest. AWS's access control feature enables precise control over access rights.
• Its fault-tolerant architecture handles system stability and recovery, sparing users from such activities.

Cons:

• The data pipeline is designed mainly for AWS services, making integration with third-party services challenging.
• Managing data pipelines and on-premises resources can be overwhelming due to the many installations and configurations.
• The data pipeline's way of representing preconditions and branching logic might be complex to beginners. Other tools such as Airflow can be used to simplify complex chains.

4. Stitch Data      

Stitch is a platform that allows users to easily and affordably replicate data. It supports over 90 data sources and provides compliance with SOC 2, HIPAA, and GDPR. It is cloud-based and can be easily scaled, allowing for reliable integration with new data sources. Additionally, Stitch offers support for Amazon Redshift and S3 destinations.   

Pros:

• Easy to set up and use: Stitch offers a user-friendly interface. You can set up a pipeline in just a few minutes.
• Large number of data source integrations: Stitch supports more than 100 integrations, which makes it easier to integrate with a wide range of data sources.
• Cost-effective: Stitch offers flexible pricing plans, which makes it easy to start small and scale as your business grows.
• Maintains SOC 2, HIPAA, and GDPR compliance: Stitch is designed with data privacy and security in mind and maintains compliance with major regulations.

Cons:

• Limited customization: Stitch offers limited customization options, which can be a drawback for businesses with complex data integration needs.
• Limited transformations: Stitch offers limited transformation options, which may require additional transformation tools to be used.
• Limited data destination options: Stitch is primarily designed for Amazon Redshift and S3 destinations, which may not be suitable for all businesses.
• Limited monitoring options: Stitch offers limited monitoring options, which can be a drawback for businesses that require real-time monitoring and alerts.

5. Talend    

Talend is an open-source ETL (Extract, Transform, Load) tool. It is used to extract data from various sources, transform it according to business requirements, and load it into a target data store. It offers a range of pre-built connectors and data integration components to simplify the ETL process. Talend also provides a graphical interface for designing data integration jobs and workflows, making it easy for non-technical users to create and maintain ETL processes. Talend supports a wide range of data integration scenarios. This includes batch processing, real-time integration, and big data integration.

Pros:

• Open Source: Talend is an open-source tool, meaning that it is freely available and customizable, which can save organizations money compared to proprietary ETL tools.
• Wide Range of Connectors: It has a wide range of connectors and integrations to various databases, cloud services, and applications. This makes it easy to integrate data from different sources.
• User-Friendly Interface: Talend offers a user-friendly interface with drag-and-drop features that simplify the development process.
• Scalability: Talend is highly scalable, allowing it to handle large amounts of data efficiently. 

Cons:

• Learning Curve: Talend can have a steep learning curve for those who are new to ETL tools or data integration processes.
• Limited Support: Although Talend has a large user community, support options are limited compared to proprietary ETL tools.
• Performance: Talend may experience performance issues when dealing with very large data sets or complex data transformations.
• Complexity: Talend can be complex to set up and configure, especially when integrating with different databases or applications.

6. Informatica        

Informatica is a highly popular data processing tool used for ETL (Extract, Transform, Load) processing. Regarded as one of the top-performing solutions for data processing and governance, this software is widely utilized in areas such as data warehousing, business intelligence, and data integration among business applications. Informatica offers built-in features that allow it to easily connect with various source systems, including databases, file systems, and SaaS-based applications, using configurations, adapters, and pre-built connectors.

Pros:

• The software offers well-designed graphical user interfaces for several tasks, including monitoring sessions, scheduling jobs, designing ETL processes, debugging, and administration. 
• It supports the management of queued messages, third-party application data, mainframe and file-based data, as well as XML and unstructured data. 
• The web-based centralized platform serves as the only point of authority for an enterprise application, ensuring excellent protection quality while reducing administrative costs. 
• Selecting a grid solution offers a cost-effective solution to meet the high demand for processing, with the ability to scale linearly and provide high availability.     

Cons:

• The Workflow Monitor does not have a sorting option available. Within the monitor, it is not feasible to differentiate between essential and non-essential folders. Similarly, relocating an item from one folder to another is not supported within the Repository Manager. Importing XML export files is also not an option.
• Additionally, the development of mappings and workflows is not feasible.

7. Integrate .io   

This ETL solution in the cloud allows direct connection to Amazon Redshift, eliminating the need for an intermediary server. This allows for local work or the utilization of cloud-based computing tools. The platform allows for business data transformations without the requirement of writing extensive code. Users can also aggregate data from various sources and upload it to a single storage location. In terms of security, the solution utilizes various measures such as FLE, hashing, 2FA, SSL/TLS encryption, and data masking. It has also received SOC 2 accreditation.

Pros:

• The platform has straightforward data transformation capabilities.
• It provides simple workflows to define task dependencies.
• It offers streamlined integrations for Salesforce to Salesforce.
• There are comprehensive data security and compliance measures in place.
• The solution supports a wide range of data sources and destinations.
• The customer support is strong.

Cons:

• Only basic error logging is available for troubleshooting purposes.
• The interface can become complicated when dealing with more complex pipelines.
• The company's e-commerce background may not be ideal for some customers.
• Certain aspects of the platform are not fully integrated.         

Conclusion

In this article you read about the different AWS ETL Tools and the factors that drive making the decision about which tool you should choose. You also learned about third party ETL tools with their pros and cons, you can make a well calculated decision on which tool suits your needs the best.