Cloud ETL Tools: The Guide for Data Engineers: NEW (2023)

Cloud-based ETL Basics

Getting Started with ETL (Extract, Transform, & Load)

In an organization, production data exists across many source systems. Eventually, it may need to be accessed and transformed following business needs and loaded into a destination system or database. This process is known as ETL (Extract, Transform, Load) and is a common function for data scientists and engineers. 

For businesses that need to aggregate data from multiple sources, integrate it, and make it accessible for research, ETL strategies are crucial.

Data engineers use ETL tools to manage and streamline the ETL process. In addition to tools for handling data transformations, data quality, and organizing data loads, these tools usually offer a graphical UI for creating data integration processes. 

Some well-known ETL applications include Portable, Microsoft SSIS, Talend, and Apache Nifi.

Types of ETL Processes

Full Load ETL

This ETL process involves extracting and transforming all the data from the source system before it is put into the destination system. This is frequently used when loading the destination system for the first time or when the source data has substantially changed.

Incremental Load ETL

After altering the data since the last load was extracted, transformed, and put into the destination system in incremental load ETL. This method is used when the original data is frequently updated, and the destination system needs to be updated almost instantly.

Delta Load ETL

In contrast to incremental load ETL, which loads the complete new dataset, delta load ETL only takes and changes the data that has changed since the last load. This method is more effective than gradual load ETL when working with big databases.

Real-time ETL 

This type of ETL processes data as soon as it is made accessible, enabling the destination system to receive real-time changes. Applications that require current info, like financial systems or stock trading apps, frequently use this.

Offline ETL 

Offline ETL extracts, transforms, and loads data into the destination system on a scheduled basis. Data is processed in segments. When there is a lesser need for current data and batch processing can manage the amount of data, this is used.

Streaming ETL 

Data is processed in real-time as it is being produced, using streaming ETL. This approach is better when instant data analysis is necessary, such as detecting fraud or ingesting various social media streams.

Batch ETL

Data analysts process data in large batches from different sources, modify it, and place it into the target system in batch ETL. Usually, batch processing is done regularly, like daily, weekly, or monthly. The large amounts of data that this ETL procedure can handle are appropriate. 

Data flow ETL

Data flow diagrams are created as part of ETL to show how information moves between source and destination systems. Usually, complicated data environments with numerous sources and destinations use this type of ETL procedure.

What Are The Top Uses of ETL?

Although companies have used ETL for many years, it has evolved. The ETL scope has broadened to include new use cases due to more diverse data sources and destinations. 

The following are six top ETL data integration use cases:

Data Warehousing

Data science teams aim to keep a single source of truth, and in turn, they look to data warehousing. A data warehouse consolidates enormous quantities of data collected within various systems. Businesses increasingly rely on cloud data warehouses like Amazon Redshift and Snowflake to handle massive amounts of data successfully. (Tools like Portable help get it there!)

Due to its ability to combine data from various sources into a singular repository, ETL is a crucial part of the data warehousing process. The source data is prepared for the phases of data warehouse architecture through ETL. It also supports automated processes to build and maintain self-regulating data pipelines.

Application Integration

A typical company works with hundreds of applications; the primary challenge is making these applications function together.

Application collaboration enables on-premises and online apps, like Salesforce and Microsoft Dynamics CRM, to work in harmony. Data can be rapidly extracted from all apps and consolidated into a single view using ETL.

Legacy System Modernization

Archaic relational databases have become incompatible with various newer technologies. Businesses increasingly employ ETL to upgrade their technology stack.

Data transfer to cloud databases like Oracle, Azure, or No SQL databases like MongoDB is a typical modernization initiative.

Business intelligence

Business intelligence (BI) employs ETL to prepare data for analysis by converting it into a simple file to evaluate. Data from various sources is extracted, transformed, and loaded using ETL into a data repository or another analysis system. ETL makes assessing and reporting data easier by ensuring it's correct and consistent.

Data Quality

By adding transformations to the data to ensure it is correct and consistent, ETL enhances data quality. This process removes duplicates, appends data, and enforces validation standards to ensure the data is valid.

Data Transformation: 

Converting data using ETL from its original version into one that data engineers can quickly load into the destination system. Examples are performing calculations, transforming data categories, and data aggregation.

Best Cloud ETL Tools Integrations & Connectors

1. Portable

Portable is the best data integration tool for teams with long-tail data sources. Portable is an ETL platform that offers connectors for over 300 unusual data sources. In brief, Portable has long-tail ETL connectors that Fivetran does not.

Top Features

• Customized data source connections can be built on demand, maintained, and free of charge.

• Seven days a week, twenty-four hours a day, direct help is offered.

• A broad selection of immediately usable long-tail data connections.

Pricing

• There are no volume, connectors, or destination restrictions for manual data processing under Portable's free plan. 

• The monthly fixed fee for automated data transfers at Portable is $200. 

• Feel free to get in touch with sales if you have enterprise business needs and SLA requirements.

G2 Rating

5.0 out of 5

2. Apache Airflow

An open-source system called Apache Airflow enables programmatic process authoring, scheduling, and tracking. It was created in Python and employs a top-down configuration method to set up processes as directed acyclic graphs (DAGs) of tasks.

The company AirBnB developed Airflow in 2014, and it has since grown to be one of the most well-liked open-source initiatives in the data engineering space.

Top Features

• A built-in system for delivering warning emails when activities fail is included in Airflow, along with a web-based user interface for tracking the progress of processes and tasks.

• Dynamic creation of directed acyclic networks.

• Workflow authoring.

• Open-source.

• Rigidity and Scalability.

Pricing

Under the terms of the Apache License 2.0, Airflow is a free and open-source software program.

G2 Rating

4.3 out of 5

3. Airbyte

The new open-source data integration tool Airbyte copies data from apps, APIs, and datasets to data warehouses, lakes, and other destinations while operating in the security of your cloud.

To help users keep track of their data pipelines and guarantee that data is moving efficiently, Airbyte offers tracking and alerting capabilities.

It also makes creating your connectors simple and offers an ever-growing inventory of maintenance-free connectors.

Top Features

• Utilize or modify more than 300 common connectors.

• In just 30 minutes, you can build custom connections using our CDK.

• Replications should be configured to meet your unique needs.

• Affordable prices.

• Offers the finest assistance.

Pricing

Offers three options: free, cloud, and enterprise.

• Cloud, beginning at $2.50 per credit.

• Please get in touch with sales about Enterprise plans.

G2 Rating

4.3 out of 5

4. Stitch

Talend comes with the data transport utility Stitch. It manages basic data transformations and retrieval using a built-in GUI, Python, Java, or SQL. Talend Data Quality and Talend Monitoring are additional features.

Top Features

• Large Scalability

• Modify Nested JSON

• Text notifications and ongoing auditing

• Replication Frequency 

• Warehouse views

• Highly Scalable

Pricing

• Available 14-day no-risk tryout

• The standard plan costs $100 per month and includes up to 5 million live rows, one destination, and ten sources (limited to "Standard" sources)

• You can get an advanced plan with up to 100 million rows and three destinations for $1,250 per month.

• You can get a premium service with up to 1 billion rows and five destinations for $2,500 per month.

G2 Rating

4.5 out of 5

5. Fivetran

A cloud-based data integration tool called Fivetran helps businesses automate the transfer of data from various sources to a central data repository or another location.

Due to the completely controlled, zero-maintenance design used by Fivetran, operations like data deduplication, data translation, and quality reviews are all carried out autonomously.

Top Features

• Comprehensive integration

• Rapid deployment

• Important notifications are always up to date

• Personalized setup and Raw data access

• Connect any BI tools

• Directly mapped schema and integration monitoring

Pricing

The three versions of Fivetran cost between $1 and $2 each.

• Each credit for the Starter version costs $1.

• The regular edition charges $1.5 for each credit.

• Each credit in the Enterprise version costs $2.

G2 Rating

4.2 out of 5

6. Informatica PowerCenter

An effective and efficient ETL metadata-driven utility, Informatica PowerCenter, aids companies in managing their data integration requirements. It is extensively used in a variety of sectors, including banking, healthcare, and retail.

Because of its scalable, high-performance architecture, PowerCenter can rapidly manage and analyze huge volumes of data. The ideal execution percentage, according to Informatica, is 100%. Compared to earlier ETL operations, the instructions and software accessibility are considerably easier.

Top Features

• Agile methods and role-based tools.

• High availability, dynamic partitioning, adaptive load sharing, and pushdown optimization.

• Tools with graphics and without code.

Pricing

• Professional Edition: This expensive model, which charges $8000 annually for each user, necessitates a license.

• Personal Edition: You are free to use it whenever you need to.

G2 Rating

4.4 out of 5

7. Microsoft SQL Server Integration Services (SSIS)

Microsoft SQL Server's SSIS is software for creating high-performance data integration and workflow systems. SSIS provides an extensive range of integrated transformations that let users enhance and change data as it moves through the ETL process.

Top Features

• Tasks and connectors for Azure data sources.

• Tasks and connections to Hadoop/HDFS.

• Basic data processing tools.

• Built-in connections to data sources.

• There are built-in tasks and transformations.

Pricing

SSIS is a component of SQL Server, which has a range of versions varying from free (Express and Developer editions) to $14,256 per core (Enterprise).

G2 Rating

3.7 out of 5

8. AWS Glue

Data transfer between data storage is made easy by Amazon Web Services (AWS) Glue, a completely controlled extract, transform, and load (ETL) solution. It offers a straightforward and scalable paradigm for structuring ETL operations, and it can instantly find and categorize data to facilitate searching and querying.

AWS Glue is made to make it simple and affordable for businesses to transfer and integrate their data across a variety of sources and destinations. 

Top Features

• Serverless technology and large scalability.

• Automated code generation.

• Monitoring and troubleshooting.

• Integration with other AWS services.

• Adaptive data integration for well-liked data stores and open-source forms.

Pricing

Users of Amazon Glue only pay for the resources they actually use because it is a pay-as-you-go service. When using AWS Glue, there are no setup fees or minimal expenses. $0.44 per hour of computer editing

G2 Rating

4.2 out of 5

9. Skyvia

A cloud-based tool for cloud data integration and management called Skyvia helps businesses link and control data across cloud and on-premises systems and applications.

It offers administration and tracking features that enable users to keep track of their ETL workflows and address problems as they appear.

Top Features

• More data sources and destinations are enabled, including Salesforce, Dynamics, Zoho, SQL Server, MySQL, and Oracle.

• Data replication and synchronization are necessary for real-time data integration.

• Data backup and restoration capabilities

• Characteristics for data validation and quality

Pricing

• The most affordable plan costs $15 per month.

• The standard package costs $79 a month.

• The monthly fee for the Professional package is $399.

• For the Enterprise plan, get in touch with client support.

G2 Rating

4.8 out of 5

10. Oracle Data Integrator

A high-Performance Bulk Data Transport and Data Transform tool is Oracle Data Integrator (on-premises & Cloud Service). The Oracle data integration framework, which also consists of Oracle GoldenGate and Oracle Data Quality, contains this as one of its components.

Delivers cutting-edge extract, load, and transform (ELT) technology, even when used with diverse systems, to boost speed and lower data integration costs.

Data storage, data transmission, and real-time data integration are just a few of the use cases for which ODI is designed to assist developers in developing data integration solutions.

Top Features

• Database, HDFS, ERP, CRM, B2B system, flat file, XML, JSON, LDAP, JDBC, and ODBC integration are all included. Java must also be set up.

• Proprietary Licensing.

• Design And Development Environment.

Pricing

A single processor deployment costs around $36,400.

G2 Rating

4.0 out of 5

11. StarfishETL

Enterprises can connect to, extract data from, transform, and load it from a variety of sources using StarfishETL, an ETL and data integration tool. To create and manage data integration tasks, it has a simple drag-and-drop UI.

It allows data extraction from a variety of pre-built interfaces for popular data sources like MySQL, SQL Server, and Oracle, as well as from unstructured data sources like CSV and Excel files.

Top Features

• Data Archiving.

• Data Cleaning & Enhancement.

• Data Lake & Warehouse Prep.

• Full-Service Integration.

• Notification Management.

Pricing

• The cost of the Starfish program is determined by the use of online and cloud migration services. 

• Cloud migration costs $495 per month, whereas conventional migration costs start at $1495.

• The cost choices for CRM connection are different. 

• Depending on the scale of the company, they can range up to $1,000 per month.

G2 Rating

4.7 out of 5

12. Talend Open Studio

Users can create, build, and carry out data integration and data transformation procedures using the open-source data integration tool Jaspersoft ETL (formerly known as Talend Open Studio for Data Integration).

Top Features

• Process designer using drag and drop

• Activity monitoring

• The dashboard evaluates job performance and execution

• Direct integration with CRM and ERP programs like Salesforce.com, SAP, and SugarCRM

Pricing

Depending on the capacity, standard plans can cost between $100 and $1,250 per month; yearly installments are discounted.

G2 Rating

4.4 out of 5

13. Apache Falcon

Apache Falcon can manage and orchestrate big data platforms like Apache Hadoop, Apache Pig, and Apache Hive. It is designed to help businesses manage and schedule their data pipelines quickly and effectively, keep track of those pipelines, and trace their data history.

ETL processes can be defined and configured by Falcon users using either a GUI user interface or Apache Oozie programming.

Top Features

• Falcon provides a complete image of the data lineage that can be used to comprehend the origin and flow of the data as well as spot problems with its quality.

• Easy to use.

• Multi-faceted.

• Streamline processes.

Pricing

Since Apache Falcon is open-source and cost-free to use, there are no ongoing membership or license fees.

G2 Rating

4.5 out of 5

14. Rivery

The DataOps system, which handles data ingestion, transformation, and orchestration, is the foundation of Rivery. As a low-code ETL software, Rivery offers many essential features, from ready-made data connectors to fast data model Kits. 

Top Features

• More than 200 sources of data

• There are more than 15 authorized data destinations.

• 24/7 client assistance

• Starter packages for ELT, Reverse ETL, and transformation tools come with already-constructed "rivers" that link well-known data sources and destinations.

Pricing

• Each RPU credit for the starter plan costs $0.75.

• RPU credits for professional programs start at $1.20.

• To inquire about the Enterprise package, speak with the sales team.

G2 Rating

4.6 out of 5

15. Pentaho Kettle

A potent open-source tool for data integration and change is Pentaho Kettle, also known as Pentaho Data Integration (PDI).

Data extraction from one or more sources, transformation to satisfy particular criteria, and loading into a destination are all parts of the Extract, Transform, and Load (ETL) model on which Pentaho Kettle is based.

Top Features

• Job and Transformation design.

• Scalability.

• Error handling and recovery.

• Batch scheduling and monitoring.

• Extensibility.

Pricing

Currently, Pentaho Kettle offers a 30-day free trial period. Pricing information is not provided.

G2 Rating

4.3 out of 5

On-Premises vs. Cloud vs. Serverless ETL

ETL Solution	Example Tools	Pros	Cons
On-Premises: An on-premises ETL tool is typically installed on a single machine that ingests raw data, transforms it, and loads it into a destination.	MySQL, Talend, and Pentaho	Complete command over hardware, security, and network configuration; Cost-effective for processing huge amounts of data; It is possible to use relational systems like MySQL; Greater customization and flexibility; Integration with Python and Java programming languages; Accepts the JSON data format	High initial and ongoing costs; Limited scalability; Data processing can be slow for large datasets; Businesses are in charge of updating and managing their ETL systems.
Cloud ETL: A cloud ETL solution is hosted on a cloud platform and manages data integration between different cloud-based systems and services.	Portable, Stitch, Matillion, Fivetran	Scalable and flexible with pay-as-you-go pricing; Connects to a wide range of data sources through APIs and SaaS connectors; Integrates with cloud data analytics and data warehousing services; Has a robust ecosystem of third-party tools and services; Can handle large-scale data processing	Data privacy and security concerns; Potential issues with data integration and management across different platforms and services; Dependency on cloud service providers
Serverless ETL: A serverless ETL solution scales resources on demand and is hosted on a cloud platform.	AWS Glue, Azure Data Factory, Google Cloud Dataflow	Scalable and cost-effective; No infrastructure management; AWS Glue allows for high-performance data ingestion; Supports various programming languages; Integrates with other AWS services like Lambda, S3, and Athena	Limited support for specific data types and sources; Limited customization choices; Lack of infrastructure management;

In conclusion, Serverless ETL offers scalability and cost-effectiveness with less infrastructure administration, Cloud ETL offers scalability and cost-effectiveness, and on-premises ETL offers total control over data and infrastructure.

Considering their needs for data integration, financial constraints, and security issues, organizations should select the strategy that best suits their goals.

Why Is Cloud-Based ETL Better?

Cloud-based ETL is the ideal ETL option for most businesses because it provides several benefits.

Affordability 

Compared to conventional on-premises ETL solutions, cloud-based ETL solutions are usually less costly. Organizations can escape the up-front expenses of hardware, software licenses, and upkeep by using cloud-based ETL. Instead, they use a pay-as-you-go model and only pay for the tools they actually use.

Scalability 

Cloud-based ETL solutions adapt to evolving organizational requirements with scalability. A wide variety of processing resources are available from cloud companies, and they can be rapidly provided or de-provisioned as required. Because of this, businesses can easily manage high data volumes or sudden increases in their data management needs.

Usability 

A graphical user interface is often provided by cloud-based ETL systems, which makes it easier to create ETL processes. As a result, businesses can easily build and administer data pipelines without worrying about supporting technology.

Flexibility 

Compared to on-premises options, cloud-based ETL solutions give greater flexibility. Organizations can rapidly spin up new environments without additional hardware or software and try new configurations.

Security 

Data encryption, access controls, and threat monitoring are just a few of the strong security measures that cloud companies give. Therefore, businesses can rely on cloud-based ETL tools to protect their data.

What to Look for in Enterprise & Free Open-Source ETL Tools?

There are 11 qualities to consider when assessing enterprise or free, open-source ETL tools:

1. Scalability: Think about the ETL tool's scalability, particularly if you plan to handle large amounts of data. Look for tools with the capacity to add more processing power as required and the ability to expand horizontally.

2. Connectivity: The ETL application should handle a wide range of data sources and destinations, including databases, online services, and APIs.

3. Performance: With little interruption or error, the ETL application should be able to handle data swiftly and effectively.

4. Security: ETL applications should have strong security features, such as encryption, access limits, and data masking to safeguard sensitive customer data.

5. Customization: The ETL application should be extensible and customizable, allowing you to adapt it to your unique business requirements.

6. Support: The ETL application should have a devoted support staff or online discussion board where you can get assistance with any problems or inquiries that crop up.

7. Cost: The final factor to take into account is the price of the ETL tool, which should include any registration fees, continuing upkeep fees, and possible hidden costs.

8. Documentation: The user manuals, implementation instructions, and troubleshooting advice for the ETL utility should all be comprehensive and current.

9. Automation: You should be able to plan data integrations and transformations so that they execute routinely using the ETL tool's automation features.

10. Low/No-Code: The ETL tool should be able to build data flows and changes with little to no coding knowledge on the part of business users.

11. User Interface: The ETL tool's user interface needs to be simple and easy to use so that users can quickly set up data sources, transformations, and locations.

Top Picks for Data Warehouses 

Amazon Redshift 

Amazon Web Services offers a cloud-based data storage tool called Amazon Redshift (AWS). It makes it simple for users to load data from different sources and transform it before analyzing it.

Redshift is the best option for large-scale data warehousing requirements because it provides sophisticated data replication and distribution capabilities. 

Redshift is an extremely adaptable and extensible option for businesses looking to store, manage, and analyze massive amounts of information in the cloud because it seamlessly integrates with other cloud services as part of the Amazon ecosystem.

Google BigQuery 

Users of Google BigQuery can swiftly and effectively analyze enormous amounts of data thanks to its cloud-based data warehousing technology. For querying big datasets, it offers a simple SQL-like interface and supports both structured and unstructured data.

Users can keep and process data with BigQuery in a scalable and economical manner, paying only for the data they actually use. BigQuery can handle large-scale data warehousing requirements without needing a lot of administration or infrastructure, thanks to its serverless design and automatic scaling.

BigQuery, which is a component of the Google Cloud Platform, easily interacts with other cloud services, making it a strong and adaptable option for businesses seeking to store and analyze data in the cloud.

Snowflake 

Users of the Snowflake cloud-based data warehousing tool can store, handle, and evaluate significant amounts of data. It offers connections with major cloud service providers like Google Cloud, Microsoft Azure, and Amazon AWS and gives a completely controlled service.

Snowflake uses a distinctive architecture that divides storage from compute resources, allowing users to scale their workloads up or down as necessary. Large amounts of data in different forms, such as structured, semi-structured, and unstructured data, can be stored and analyzed by users.

Additionally, it works with various analytics and visualization tools and provides a querying interface for data similar to SQL. Overall, Snowflake is a top choice for data stores because of its adaptability, scalability, and usability.

Microsoft Azure Synapse Analytics 

With the help of a unified analytics tool, customers of Microsoft Azure Synapse Analytics' cloud-based data warehousing solution can analyze large amounts of data.

Users can create pipelines for data ingestion, transformation, and processing using a range of tools and languages with Azure Synapse Analytics. To safeguard confidential data, it also provides cutting-edge security features like data encryption and access controls.

Large amounts of both structured and unstructured data can be stored and analyzed by users using Synapse Analytics, which also offers robust tools for data integration, data preprocessing, and data warehousing.

Overall, the versatility, scalability, and security features of Azure Synapse Analytics make it a top choice for data warehouses.

Oracle Autonomous Data Warehouse 

A cloud-based data warehouse tool called Oracle Autonomous Data Warehouse uses machine learning to handle many data warehousing tasks, such as deployment, tuning, and scaling.

It offers a high-performance, scalable, and secure option for organizing and analyzing huge amounts of data. Autonomous Data Warehouse lowers the need for manual administration and upkeep thanks to its self-driving and self-securing capabilities. It is a great option for businesses seeking to streamline their data warehousing operations.

The smooth connection that Autonomous Data Warehouse offers with other Oracle Cloud services enables users to create end-to-end data solutions in the cloud quickly.

How To Set Up Data Pipelines

Creating a data pipeline consists of three stages that move data from various sources to a single location where it can be transformed, improved, and analyzed to produce insightful results.

A fundamental process for creating data pipelines is below.

Stage 1: Establish Data-Driven Workflows

Simplify your data sources

With the aid of tools like Apache Kafka, Apache Storm, and Apache Flink, you can manage and improve data as it comes, ensuring that you always have the most recent information.

Define the schema

Define the structure for your data, including data categories, connections, and any changes required. This will aid in the consistency and accuracy of your statistics.

Stage 2: Optimize Data in Real-Time

Copy data into a separate data lake

Altering data formats and transformations without a backup is problematic. Replicate data securely into a separate staging environment that allows for historical analysis. 

Modify data formats and create additional metadata

Due to the increase in unstructured data and the diversity of sources, real-time data processing should be possible.

Select a data integration tool

Choose a tool for data integration that will help you transfer data from source to destination. There are numerous choices, including Portable, Apache NiFi, Talend, and Amazon Glue.

Stage 3: Achieve Better Business Results 

Implement data quality controls

Utilize data quality measures to guarantee your data's accuracy, completeness, and consistency. This involves data cleansing, profiling, and authentication.

Facilitate real-time business reporting

After all that work to streamline data analysis, it's time for stakeholders to use it. Present your data in an easy-to-understand and useful way - ensure they know how to create dashboards and pull reports autonomously.

Data visualization tools like Tableau, Power BI, and QlikView are a few examples that can help you create accurate and visually appealing dashboards that provide insights into your data and support better decision-making.

Portable: The Best ETL Tool for Data Analytics Teams

Portable is a highly adaptable and user-friendly ETL solution for data analytics teams seeking an effective data integration tool.

With an emphasis on its scalability and reliability, here are reasons why Portable is regarded as the top ETL tool for data analytics teams.

300+ ETL Connectors

With Portable, data analysts can quickly link to and extract data from more than 300 unique SaaS using various pre-built ETL connectors. Easily transform data from different sources, such as CRMs, complex data stores, and other datasets, into data integration pipelines, and automating ETL tasks are made simple as a result.

No-Code Solution 

Portable's user-friendly interface makes it cost-effective to construct ETL workflows without needing any coding or programming abilities. This means that data analysts can rapidly and easily build complicated data integration pipelines without engaging IT or tech teams.

Automation 

With the help of Portable's strong automation features, data analysts can plan out a continuous data integration platform. Save time and prevent delays brought on by routine procedures can be decreased.

Integration with CRMs and Data Warehouses

Merge data from various CRMs and data centers thanks to Portable's native ETL pipeline capabilities. This can increase data quality and make it possible for more thorough research.

User-Friendly Interface 

Analysts can easily construct, control, and monitor ETL workflows using Portable's drag-and-drop UI and intuitive workflow builder. This helps users handle complicated data-merging initiatives without substantial training or technological know-how.