A List of The 19 Best ETL Tools And Why To Choose Them in 2024

As data continues to grow in volume and complexity, the need for an efficient ETL tool becomes increasingly critical for a data professional. ETL tools not only streamline the process of extracting data from various sources but also transform it into a usable format and load it into a system of your choice. This ensures both data accuracy and consistency. 

This is why, in this blog, we’ll introduce you to the top 20 ETL tools to consider in 2024. We’ll walk through the key features, use cases, and pricing for every tool to give you a clear picture of what is available in the market.  Let’s dive in!

What is ETL, and what is its importance?

The essential data integration procedure known as extract, transform, and load, or ETL, aims to combine data from several sources into a single, central repository. The process entails gathering data, cleaning and reforming it by common business principles, and loading it into a database or data warehouse. 

• Extract: This step involves data extraction from various source systems, such as databases, files, APIs, or other data repositories. The extracted data may be structured, semi-structured, or unstructured.
• Transform: During this step, the extracted data is transformed into a suitable format for analysis and reporting. This includes cleaning, filtering, aggregating, and applying business rules to ensure accuracy and consistency.
• Load: This includes loading the transformed data into a target data warehouse, database, or other data repository, where it can be used for querying and analysis by end-users and applications.

Using ETL operations, you can analyze raw datasets in the appropriate format required for analytics and gain insightful knowledge. This makes work more straightforward when researching demand trends, changing customer preferences, keeping up with the newest styles, and ensuring regulations are followed.

Criteria for choosing the right ETL Tool

Choosing the right ETL tool for your company is crucial. These tools automate the data migration process, allowing you to schedule integrations in advance or execute them live. This automation frees you from tedious tasks like data extraction and import, enabling you to focus on more critical tasks. To help you make an informed decision, learn about some of the popular ETL solutions available in the market.

1. Cost: Organizations selecting an ETL tool should consider not only the initial price but also the long-term costs of infrastructure and labor. An ETL solution with higher upfront costs but lower maintenance and downtime may be more economical. Conversely, free, open-source ETL tools might require significant upkeep.
2. Usability: The tool should be intuitive and easy to use, allowing technical and non-technical users to navigate and operate it with minimal training. Look for interfaces that are clean, well-organized, and visually appealing.
3. Data Quality: The tool should provide robust data cleansing, validation, and transformation capabilities to ensure high data quality. Effective data quality management leads to more accurate and reliable analysis.
4. Performance: The tool should be able to handle large data volumes efficiently. Performance benchmarks and scalability options are critical, especially as your data needs grow.
5. Compatibility: Ensure the ETL tool supports various data sources and targets, including databases, cloud services, and data warehouses. Compatibility with multiple data environments is crucial for seamless integration.
6. Support and Maintenance: The level of support the vendor provides, including technical support, user forums, and online resources, should be evaluated. Reliable support is essential for resolving issues quickly and maintaining smooth operations.

Best ETL Tools of 2024

1. LIKE.TG Data

LIKE.TG Data is one of the most highly rated ELT platforms that allows teams to rely on timely analytics and data-driven decisions. You can replicate streaming data from 150+ Data Sources, including BigQuery, Redshift, etc., to the destination of your choice without writing a single line of code. The platform processes 450 billion records and supports dynamic scaling of workloads based on user requirements. LIKE.TG ’s architecture ensures the optimal usage of system resources to get the best return on your investment. LIKE.TG ’s intuitive user interface caters to more than 2000 customers across 45 countries. 

Key features:

• Data Streaming: LIKE.TG Data supports real-time data streaming, enabling businesses to ingest and process data from multiple sources in real-time. This ensures that the data in the target systems is always up-to-date, facilitating timely insights and decision-making.
• Reliability: LIKE.TG provides robust error handling and data validation mechanisms to ensure data accuracy and consistency. Any errors encountered during the ETL process are logged and can be addressed promptly​.
• Cost-effectiveness: LIKE.TG offers transparent and straightforward pricing plans that cater to businesses of all sizes. The pricing is based on the volume of data processed, ensuring that businesses only pay for what they use.

Use cases: 

• Real-time data integration and analysis
• Customer data integration
• Supply chain optimization

Pricing:

LIKE.TG provides the following pricing plan:

• Free
• Starter- $239/per month
• Professional- $679/per month
• Business Critical- Contact sales

2. Informatica PowerCenter

Informatica PowerCenter is a common data integration platform widely used for enterprise data warehousing and data governance. PowerCenter’s powerful capabilities enable organizations to integrate data from different sources into a consistent, accurate, and accessible format. PowerCenter is built to manage complicated data integration jobs. Informatica uses integrated, high-quality data to power business growth and enable better-informed decision-making.

Key Features:

• Role-based: Informatica’s role-based tools and agile processes enable businesses to deliver timely, trusted data to other companies.
• Collaboration: Informatica allows analysts to collaborate with IT to prototype and validate results rapidly and iteratively.
• Extensive support: Support for grid computing, distributed processing, high availability, adaptive load balancing, dynamic partitioning, and pushdown optimization

Use cases: 

• Data integration
• Data quality management
• Master data management

Pricing:

Informatica supports volume-based pricing. It also offers a free plan and three different paid plans for cloud data management.

3. AWS Glue

AWS Glue is a serverless data integration platform that helps analytics users discover, move, prepare, and integrate data from various sources. It can be used for analytics, application development, and machine learning. It includes additional productivity and data operations tools for authoring, running jobs, and implementing business workflows.

Key Features:

• Auto-detect schema: AWS Glue uses crawlers that automatically detect and integrate schema information into the AWS Glue Data Catalog.
• Transformations: AWS Glue visually transforms data with a job canvas interface
• Scalability: AWS Glue supports dynamic scaling of resources based on workloads

Use cases: 

• Data cataloging
• Data lake ingestion
• Data processing

Pricing:

AWS Glue supports plans based on hourly rating, billed by the second, for crawlers (discovering data) and extract, transform, and load (ETL) jobs (processing and loading data).

4. IBM DataStage

IBM DataStage is an industry-leading data integration tool that helps you design, develop, and run jobs that move and transform data. At its core, the DataStage tool mainly helps extract, transform, and load (ETL) and extract, load, and transform (ELT) patterns. 

Key features:

• Data flows: IBM DataStage helps design data flows that extract information from multiple source systems, transform the data as required, and deliver the data to target databases or applications.
• Easy connect: It helps connect directly to enterprise applications as sources or targets to ensure the data is complete, relevant, and accurate.
• Time and consistency: It helps reduce development time and improves the consistency of design and deployment by using prebuilt functions.

Use cases: 

• Enterprise Data Warehouse Integration
• ETL process
• Big Data Processing

Pricing:

IBM DataStage’s pricing model is based on capacity unit hours. It also supports a free plan for small data.

5. Azure Data Factory

Azure Data Factory is a serverless data integration software that supports a pay-as-you-go model that scales to meet computing demands. The service offers no-code and code-based interfaces and can pull data from over 90 built-in connectors. It is also integrated with Azure Synapse analytics, which helps perform analytics on the integrated data.

Key Features

• No-code pipelines: Provide services to develop no-code ETL and ELT pipelines with built-in Git and support for continuous integration and delivery (CI/CD).
• Flexible pricing: Supports a fully managed, pay-as-you-go serverless cloud service that supports auto-scaling on the user’s demand.
• Autonomous support: Supports autonomous ETL to gain operational efficiencies and enable citizen integrators.

Use cases 

• Data integration processes
• Getting data to an Azure data lake
• Data migrations

Pricing:

Azure Data Factory supports free and paid pricing plans based on user’s requirements. Their plans include:

• Lite
• Standard
• Small Enterprise Bundle
• Medium Enterprise Bundle
• Large Enterprise Bundle
• DataStage

6. Google Cloud DataFlow

Google Cloud Dataflow is a fully optimized data processing service built to enhance computing power and automate resource management. The service aims to lower processing costs by automatically scaling resources to meet demand and offering flexible scheduling. Furthermore, when the data is transformed, Google Cloud Dataflow provides AI capabilities to identify real-time anomalies and perform predictive analysis.

Key Features:

• Real-time AI: Dataflow supports real-time AI capabilities, allowing real-time reactions with near-human intelligence to various events.
• Latency: Dataflow helps minimize pipeline latency, maximize resource utilization, and reduce processing cost per data record with data-aware resource autoscaling.
• Continuous Monitoring: This involves monitoring and observing the data at each step of a Dataflow pipeline to diagnose problems and troubleshoot effectively using actual data samples.

Use cases: 

• Data movement
• ETL workflows 
• Powering BI dashboards

Pricing:

Google Cloud Dataflow uses a pay-as-you-go pricing model that provides flexibility and scalability for data processing tasks.

7. Stitch

Stitch is a cloud-first, open-source platform for rapidly moving data. It is a service for integrating data that gathers information from more than 130 platforms, services, and apps. The program centralized this data in a data warehouse, eliminating the need for manual coding. Stitch is open-source, allowing development teams to extend the tool to support additional sources and features.

Key Features:

• Flexible schedule: Stitch provides easy scheduling of when you need the data replicated.
• Fault tolerance: Resolves issues automatically and alerts users when required in case of detected errors
• Continuous monitoring: Monitors the replication process with detailed extraction logs and loading reports

Use cases:

• Data warehousing
• Real-time data replication
• Data migration

Pricing:

Stitch provides the following pricing plan:

• Standard-$100/ month
• Advanced-$1250 annually
• Premium-$2500 annually

8. Oracle data integrator

Oracle Data Integrator is a comprehensive data integration platform covering all data integration requirements:

• High-volume, high-performance batch loads
• Event-driven, trickle-feed integration processes
• SOA-enabled data services

In addition, it has built-in connections with Oracle GoldenGate and Oracle Warehouse Builder and allows parallel job execution for speedier data processing.

Key Features:

• Parallel processing: ODI supports parallel processing, allowing multiple tasks to run concurrently and enhancing performance for large data volumes.
• Connectors: ODI provides connectors and adapters for various data sources and targets, including databases, big data platforms, cloud services, and more. This ensures seamless integration across diverse environments.
• Transformation: ODI provides Advanced Data Transformation Capabilities

Use cases: 

• Data governance
• Data integration
• Data warehousing

Pricing:

Oracle data integrator provides service prices at the customer’s request.

9. Integrate.io

Integrate.io is a leading low-code data pipeline platform that provides ETL services to businesses. Its constantly updated data offers insightful information for the organization to make decisions and perform activities like lowering its CAC, increasing its ROAS, and driving go-to-market success.

Key Features:

• User-Friendly Interface: Integrate.io offers a low-code, simple drag-and-drop user interface and transformation features – like sort, join, filter, select, limit, clone, etc. —that simplify the ETL and ELT process.
• API connector: Integrate.io provides a REST API connector that allows users to connect to and extract data from any REST API. 
• Order of action: Integrate.io’s low-code and no-code workflow creation interface allows you to specify the order of actions to be completed and the circumstances under which they should be completed using dropdown choices.

Use cases:

• CDC replication
• Supports slowly changing dimension
• Data transformation

Pricing:

Integrate.io provides four elaborate pricing models such as:

• Starter-$2.99/credit
• Professional-$0.62/credit
• Expert-$0.83/credit
• Business Critical-custom

10. Fivetran

Fivetran’s platform of valuable tools is designed to make your data management process more convenient. Within minutes, the user-friendly software retrieves the most recent information from your database, keeping up with API updates. In addition to ETL tools, Fivetran provides database replication, data security services, and round-the-clock support. 

Key Features:

• Connectors: Fivetran makes data extraction easier by maintaining compatibility with hundreds of connectors.
• Automated data cleaning: Fivetran automatically looks for duplicate entries, incomplete data, and incorrect data, making the data-cleaning process more accessible for the user.
• Data transformation: Fivetran’s feature makes analyzing data from various sources easier.

Use cases:

• Streamline data processing
• Data integration
• Data scheduling

Pricing:

Fivetran offers the following pricing plans:

• Free 
• Starter
• Standard
• Enterprise

11. Pentaho Data Integration (PDI)

Pentaho Data Integration(PDI) is more than just an ETL tool. It is a codeless data orchestration tool that blends diverse data sets into a single source of truth as a basis for analysis and reporting. 

Users can design data jobs and transformations using the PDI client, Spoon, and then run them using Kitchen. For example, the PDI client can be used for real-time ETL with Pentaho Reporting.

Key Features: 

• Flexible Data Integration: Users can easily prepare, build, deploy, and analyze their data. 
• Intelligent Data Migration: Pentaho relies heavily on multi-cloud-based and hybrid architectures. By using Pentaho, you can accelerate your data movements across hybrid cloud environments. 
• Scalability: You can quickly scale out with enterprise-grade, secure, and flexible data management.
• Flexible Execution Environments: PDI allows users to easily connect to and blend data anywhere, on-premises, or in the cloud, including Azure, AWS, and GCP. It also provides containerized deployment options—Docker and Kubernetes—and operationalizes Spark, R, Python, Scala, and Weka-based AI/ML models.
• Accelerated Data Onboarding with Metadata Injection: It provides transformation templates for various projects that users can reuse to accelerate complex onboarding projects. 

Use Cases:

• Data Warehousing
• Big Data Integration
• Business Analytics

Pricing:

The software is available in a free community edition and a subscription-based enterprise edition. Users can choose one based on their needs. 

12. Dataddo

Dataddo is a fully managed, no-code integration platform that syncs cloud-based services, dashboarding apps, data warehouses, and data lakes. It helps the users visualize, centralize, distribute, and activate data by automating its transfer from virtually any source to any destination. Dataddo’s no-code platform is intuitive for business users and robust enough for data engineers, making it perfect for any data-driven organization.

Key Features: 

• Certified and Fully Secure: Dataddo is SOC 2 Type II certified and compliant with all significant data privacy laws around the globe. 
• Offers various connectors: Dataddo offers 300+ off-the-shelf connectors, no matter your payment plan. Users can also request that the necessary connector be built if unavailable. 
• Highly scalable and Future-proof: Users can operate with any cloud-based tools they use now or in the future. They can use any connector from the ever-growing portfolio.
• Store data without needing a warehouse: No data warehouse is necessary. Users can collect historical data in Dataddo’s embedded SmartCache storage. 
• Test Data Models Before Deploying at Full Scale: By sending their data directly to a dashboarding app, users can test the validity of any data model on a small scale before deploying it fully in a data warehouse. 

Use Cases:

• Marketing Data Integration(includes social media data connectors like Instagram, Facebook, Pinterest, etc.)
• Data Analytics and Reporting

Pricing:

Offers various pricing models to meet user’s needs.

• Free
• Data to Dashboards- $99.0/mo
• Data Anywhere- $99.0/mo
• Headless Data Integration: Custom 

13. Hadoop

Apache Hadoop is an open-source framework for efficiently storing and processing large datasets ranging in size from gigabytes to petabytes. Instead of using one large computer to store and process the data, Hadoop allows clustering multiple computers to analyze massive datasets in parallel more quickly. It offers four modules: Hadoop Distributed File System (HDFS), Yet Another Resource Negotiator (YARN), MapReduce, and Hadoop Common.

Key Features:  

• Scalable and cost-effective: Can handle large datasets at a lower cost.
• Strong community support: Hadoop offers wide adoption and a robust community.
• Suitable for handling massive amounts of data: Efficient for large-scale data processing.
• Fault Tolerance is Available: Hadoop data is replicated on various DataNodes in a Hadoop cluster, which ensures data availability if any of your systems crash.

Best Use Cases:

• Analytics and Big Data
• Marketing Analytics
• Risk management(In finance etc.)
• Healthcare
• Batch processing of large datasets

Pricing: Free

14. Qlik

Qlik’s Data Integration Platform automates real-time data streaming, refinement, cataloging, and publishing between multiple source systems and Google Cloud. It drives agility in analytics through automated data pipelines that provide real-time data streaming from the most comprehensive source systems (including SAP, Mainframe, RDBMS, Data Warehouse, etc.) and automates the transformation to analytics-ready data across Google Cloud.

Key Features: 

• Real-Time Data for Faster, Better Insights:  Qlik delivers large volumes of real-time, analytics-ready data into streaming and cloud platforms, data warehouses, and data lakes. 
• Agile Data Delivery: Qlik enables the creation of analytics-ready data pipelines across multi-cloud and hybrid environments, automating data lakes, warehouses, and intelligent designs to reduce manual errors. 
• Enterprise-grade security and governance: Qlik helps users discover, remediate, and share trusted data with simple self-service tools to automate data processes and help ensure compliance with regulatory requirements.
• Data Warehouse Automation: Qlik accelerates the availability of analytics-ready data by modernizing and automating the entire data warehouse life cycle.
• Qlik Staige: Qlik’s AI helps customers to implement generative models, better inform business decisions, and improve outcomes.

Use Cases: 

• Business intelligence and analytics
• Augmented analytics
• Visualization and dashboard creation

Pricing:

It offers three pricing options to its users:

• Stitch Data Loader
• Qlik Data Integration
• Talend Data Fabric

15. Airbyte

Airbyte is one of the best data integration and replication tools for setting up seamless data pipelines. This leading open-source platform offers a catalog of 350+ pre-built connectors. Although the catalog library is expansive, you can still build a custom connector to data sources and destinations not in the pre-built list. Creating a custom connector takes a few minutes because Airbyte makes the task easy.

Key Features:

• Multiple Sources: Airbyte can easily consolidate numerous sources. You can quickly bring your datasets together at your chosen destination if your datasets are spread over various locations.
• Massive variety of connectors: Airbyte offers 350+ pre-built and custom connectors.  
• Open Source: Free to use, and with open source, you can edit connectors and build new connectors in less than 30 minutes without needing separate systems.
• It provides a version-control tool and options to automate your data integration processes.

Use Cases:

• Data Engineering
• Marketing
• Sales
• Analytics
• AI

Pricing:

It offers various pricing models:

• Open Source- Free
• Cloud—It offers a free trial and charges $360/mo for a 30GB volume of data replicated per month.
• Team- Talk to the sales team for the pricing details
• Enterprise- Talk to the sales team for the pricing details

16. Portable.io

Portable builds custom no-code integrations, ingesting data from SaaS providers and many other data sources that might not be supported because other ETL providers overlook them. Potential customers can see their extensive connector catalog of over 1300+ hard-to-find ETL connectors. Portable enables efficient and timely data management and offers robust scalability and high performance.

Key Features:

• Massive Variety of pre-built connectors: Bespoke connectors built and maintained at no cost. 
• Visual workflow editor: It provides a graphical interface that is simple to use to create ETL procedures.
•  Real-Time Data Integration: It supports real-time data updates and synchronization. 
• Scalability: Users can scale to handle larger data volumes as needed. 

Use Cases:

• High-frequency trading
• Understanding supply chain bottlenecks
• Freight tracking
• Business Analytics

Pricing:

It offers three pricing models to its customers:

• Starter: $290/mo
• Scale: $1,490/mo
• Custom Pricing

17. Skyvia

Skyvia is a Cloud-based web service that provides data-based solutions for integration, backup, management, and connectivity. Its areas of expertise include ELT and ETL (Extract, Transform, Load) import tools for advanced mapping configurations. 

It provides wizard-based data integration throughout databases and cloud applications with no coding. It aims to help small businesses securely manage data from disparate sources with a cost-effective service. 

Key Features: 

• Suitable for businesses of all sizes: Skyvia offers different pricing plans for businesses of various sizes and needs, and every company can find a suitable one.
• Always available: Hosted in reliable Azure cloud and multi-tenant fault-tolerant cloud architecture, Skyvia is always online.
• Easy access to on-premise data: Users can connect Skyvia to local data sources via a secure agent application without re-configuring the firewall, port forwarding, and other network settings.
• Centralized payment management: Users can Control subscriptions and payments for multiple users and teams from one place. All the users within an account share the same pricing plans and their limits.
• Workspace sharing: Skyvia’s flexible workspace structure allows users to manage team communication, control access, and collaborate on integrations in test environments.

Use Cases:

• Inventory Management
• Data Integration and Visualization
• Data Analytics

Pricing: 

It Provides five pricing options to its users:

• Free
• Basic: $70/mo
• Standard: $159/mo
• Professional: $199/mo
• Enterprise: Contact the team for pricing information. 

18. Singer

Singer is an open-source standard for moving data between databases, web APIs, files, queues, etc. The Singer spec describes how data extraction scripts—called “Taps”—and data loading scripts—“Targets”—should communicate using a standard JSON-based data format over stdout. By conforming to this spec, Taps and Targets can be used in any combination to move data from any source to any destination.

Key Features:

• Unix-inspired: Singer taps and targets are simple applications composed of pipes—no daemons or complicated plugins needed.
• JSON-based: Singer applications communicate with JSON, making them easy to work with and implement in any programming language. 
• Efficient: Singer makes maintaining a state between invocations to support incremental extraction easy.
• Sources and Destinations: Singer provides over 100 sources and has ten target destinations with all significant data warehouses, lakes, and databases as destinations. 
• Open Source platform: Singer.io is a flexible ETL tool that enables you to create scripts to transfer data across locations. You can create your own taps and targets or use those already there.

Use Cases:

• Data Extraction and loading.
• Custom Pipeline creation.

Pricing: Free

19. Matillion

Matillion is one of the best cloud-native ETL tools designed for the cloud. It can work seamlessly on all significant cloud-based data platforms, such as Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse, and Delta Lake on Databricks. Matillion’s intuitive interface reduces maintenance and overhead costs by running all data jobs in the cloud.

Key Features:

• ELT/ETL and reverse ETL
• PipelineOS/Agents: Users can dynamically scale with Matillion’s PipelineOS, the operating system for your pipelines. Distribute individual pipeline tasks across multiple stateless containers to match the data workload and allocate only necessary resources.
• High availability: By configuring high-availability Matillion clustered instances, users can keep Matillion running, even if components temporarily fail. 
• Multi-plane architecture: Easily manage tasks across multiple tenants, including access control, provisioning, and system maintenance.

Use Cases:

• ETL/ELT/Reverse ETL 
• Streamline data operations
• Change Data Capture

Pricing:

It provides three packages: 

• Basic- $2.00/credit
• Advanced- $2.50/credit
• Enterprise- $2.70/credit

20. Apache Airflow

Apache Airflow is an open-source platform bridging orchestration and management in complex data workflows. Originally designed to serve the requirements of Airbnb’s data infrastructure, it is now being maintained by the Apache Software Foundation. Airflow is one of the most used tools for data engineers, data scientists, and DevOps practitioners looking to automate pipelines related to data engineering.

Key Features:

• Easy useability: Just a little knowledge of Python is required to deploy airflow. 
• Open Source: It is an open-source platform, making it free to use and resulting in many active users.
• Numerous Integrations: Platforms like Google Cloud, Amazon AWS, and many more can be readily integrated using the available integrations. 
• Python for coding: beginner-level knowledge of Python is sufficient to create complex workflows on airflow.
• User Interface: Airflow’s UI helps monitor and manage workflows.
• Highly Scalable: Airflow can execute thousands of tasks per day simultaneously.

Use Cases:

• Business Operations
• ELT/ETL
• Infrastructure Management
• MLOps

Pricing: Free

Comparison of Top 20 ETL Tools

Future Trends in ETL Tools

• Data Integration and Orchestration: The change from ETL to ELT is just one example of how the traditional ETL environment will change. To build ETL for the future, we need to focus on the data streams rather than the tools. We must account for real-time latency, source control, schema evolution, and continuous integration and deployment.
• Automation and AI in ETL: Artificial intelligence and machine learning will no doubt dramatically change traditional ETL technologies within a few years. Solutions automate data transformation tasks, enhancing accuracy and reducing manual intervention in ETL procedures. Predictive analytics further empowers ETL solutions to project data integration challenges and develop better methods for improvement.
• Real-time Processing: Yet another trend will move ETL technologies away from batch processing and towards introducing continuous data streams with real-time data processing technologies.
• Cloud-Native ETL: Cloud-native ETL solutions will provide organizations with scale, flexibility, and cost savings. Organizations embracing serverless architectures will minimize administrative tasks on infrastructure and increase their focus on data processing agility.
• Self-Service ETL: With the rise in automated ETL platforms, people with low/no technical knowledge can also implement ETL technologies to streamline their data processing. This will reduce the pressure on the engineering team to build pipelines and help businesses focus on performing analysis. 

Conclusion

ETL pipelines form the foundation for organizations’ decision-making procedures. This step is essential to prepare raw data for storage and analytics. ETL solutions make it easier to do sophisticated analytics, optimize data processing, and promote end-user satisfaction. You must choose the best ETL tool to make your company’s most significant strategic decisions. Selecting the right ETL tool depends on your data integration needs, budget, and existing technology stack. The tools listed above represent some of the best options available in 2024, each with its unique strengths and features. Whether looking for a simple, no-code solution or a robust, enterprise-grade platform, an ETL tool on this list can meet your requirements and help you streamline your data integration process.

FAQ on ETL tools