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Decisions today need to be data driven. That’s why many companies turn to ETL tools to collect data from their different sources and transform them into usable format. In this blog post, we highlight 10 of the best ETL tools in the market today.
What Is ETL?
ETL stands for Extract, Transform, and Load. It is a process commonly used in data integration and data warehousing to extract data from various sources, transform it into a single, consistent and suitable format, and load it into a target system or data repository.
The Three Steps of the ETL Process:
1. Extract: In the extraction phase, data is gathered and retrieved from multiple sources, such as databases, files, APIs, or web services. The goal is to extract the relevant data needed for analysis or storage.
2. Transform: Once the data is extracted, it often requires transformation to meet the desired format, quality, and structure. Transformation involves cleaning, validating, standardizing, and enriching the data. It may include tasks like data cleansing, data aggregation, data filtering, data validation, data normalization, and data enrichment.
3. Load: The final step is to load the transformed data into a target system or data repository, such as a data warehouse, data mart, or operational database. This involves mapping the transformed data to the appropriate target schema and performing the actual loading process.
The ETL process is crucial for data integration and consolidation from diverse sources into a unified and consistent format. It enables organizations to extract data from different systems, transform it into a usable format, and load it into a centralized repository for analysis, reporting, and decision-making purposes, and thus limiting dirty data. ETL workflows can be automated using specialized tools and technologies to handle large volumes of data efficiently and reliably.
What Are ETL Tools?
ETL tools are software that extract data from different systems, transform it into a usable format, and load it into a centralized repository for analysis, reporting, and decision-making purpose. Specialized ETL tools can automate workflows to handle large volumes of data efficiently and reliably.
How to Evaluate ETL Tools
When evaluating an Extract, Transform & Load tool, you should first guarantee that the product will support your specific use case, as every company has different needs and requirements. Additionally, you also want to see how your top choices stack up with each other on the below to get the most ROI:
Budget – make sure you get the full total cost of ownership of the ETL vendor you choose. Some offer pay-per-use or pay-as-you-go models, while others are more transactional-based.
Security – in a digital world where cybercrime is constantly on the rise, checking security measures from ETL vendors is critical.
Data sources – Can the product extract the data from where it lives (on-premise or in the cloud)? Look at your preferred vendors’ list of connectors to evaluate that it can extract from your data sources and is suitable to the data formats, structured or unstructured, that you require.
Velocity – Depending on your business, you may have a greater need for a tool that can handle a fast-growing volume and complexity of data.
The Best ETL Tools – full list
Below you will find a list of some of the best ETL tools available in the market today. Please note that the ETL tools list is not in any ranking order.
Informatica PowerCenter
G2 rating: 4.4 out of 5
Informatica PowerCenter is an ETL tool that offers a wide range of features such as monitoring, repository management and workflow design. As an Extract, Transform and Load product, it enables enterprises to scale as they meet an increase in data volumes and complexity.
Oracle Data Integrator
G2 rating: 4.0 out of 5
Oracle Data Integrator is a high-performance Bulk Data Management and Data Transformation service. It is an ELT, rather than ETL tool, meaning it reduces intermediate server needs and loading speed as the data is loaded directly into the target system.
Fivetran
G2 rating: 4.2 out of 5
Fivetran is an automated data movement platform that support ELT processes. Connect to 300+ pre-built, no-code source connectors to move data to target destinations.
AWS Glue
G2 rating: 4.2 out 5
AWS is a serverless data integration service that connects to 70+ diverse data sources that is considered one of the top ETL tools on the market. With their graphical user interface, you can build and monitor your ETL pipelines via drag-and-drop, writing code, or connecting your notebook.
Stitch
G2 rating: 4.5 out of 5
Stitch is a simple yet powerful ETL tool that connects to 140+ data sources with no coding needed, and markets itself for businesses of all sizes. It enables users to create zero-maintenance data pipelines in a matter of minutes.
Pentaho Data Integration
G2 rating: 4.3 out of 5
Pentaho Data Integration is a no-code ETL tool that allows customers to accelerate their digital transformation by easily preparing, building, deploying, and analyzing growing volumes of data in an orchestration tool.
Google Cloud Dataflow
G2 rating: 4.2 out 5
Google Dataflow is a serverless, unified stream and batch data processing service that enables customers to transform and enrich data, in real time or historical. The tool offers automated provisioning and management of processing resources.
Integrate.io
G2 rating: 4.3 out of 5
Integrate.io is an ETL tool that connects 150+ data sources and destinations through a unified no-code data integration platform that supports your entire data journey. Integrate.io allows yout bo build your ETL data pipeline via a drag and drop interface and claims to have the fastest data replication in the industry, for a single source of reporting truth.
Azure Data Factory
G2 rating: 4.5 out of 5
Azure Data Factory is a serverless data integration service that allows customers to simplify hybrid data integration at an enterprise scale. It has 90+ built-in connectors to integrate data sources with and allows users to build ETL and ELT pipelines, code-free.
IBM Cloud Pak for Integration
G2 rating: 4.5 out of 5
IBM Cloud Pak for Integration is an AI-powered integration software solution. A no-code integration tool, it connects with cloud and on-premise apps and uses AI to automatically map data formats and accelerate data transformation to optimize your integration processes.
Limitations of ETL Tools
ETL tools extract, transform and load your data, but have limitations if you want to use them to feed your billing systems with usage data. They don’t provide usage-data specific features like billing connectors or data correction, and don’t give you the visibility and flexibility to charge customers based on usage. You can read more about the differences between ETL tools and usage data management products like DigitalRoute.
FAQ’s
What is the difference Between ETL and ELT?
Both ETL and ELT are used to extract data from various sources, transform it into a suitable format, and load it into a target system or data warehouse. However, the key difference lies in the order in which the transformation step occurs.
ELT (Extract, Load, Transform) is a newer approach that flips the order of the transformation step compared to ETL. In ELT, data is first extracted from the source systems and loaded into the target system or data warehouse without any significant transformation. The data is stored in its raw or minimally processed form. Once the data is loaded, the transformation step is performed directly within the target system using the processing power and capabilities of the database or data warehouse. This approach leverages the parallel processing capabilities of modern data platforms and takes advantage of their ability to handle large volumes of data efficiently.
Other differences include:
Tooling – ETL often requires a dedicated ETL tool or middleware, while ELT leverages the processing capabilities of the target system or data warehouse itself.
Flexibility – ELT provides more flexibility in terms of performing ad-hoc or exploratory analysis directly on the raw data, as transformations can be applied on-demand within the target system.
Scalability – ELT can take advantage of the scalability and parallel processing capabilities of modern data platforms, allowing for faster processing of large volumes of data.
What Are the Benefits of ETL Tools?
Data Integration: ETL tools provide a unified platform for integrating data from various heterogeneous sources such as databases, files, APIs, cloud services, and more. They offer connectors and adapters that simplify the process of extracting data from different systems, ensuring compatibility and seamless integration.
Data Transformation and Cleansing: ETL tools enable powerful data transformation capabilities, allowing you to cleanse, filter, validate, aggregate, and enrich data as it moves from source to target. These tools provide a range of built-in functions, expressions, and transformations that facilitate complex data manipulations and ensure data quality and consistency.
Automation and Workflow Management: ETL tools enable the automation of data integration processes through the use of workflows and scheduling capabilities. You can design and schedule the sequence of data extraction, transformation, and loading tasks, reducing manual effort and ensuring data processing occurs at the desired intervals.
Data Security: ETL tools provide mechanisms to handle data security and privacy concerns. They can encrypt data during transmission and at rest, support authentication and authorization mechanisms, and help in adhering to data protection regulations and compliance requirements.
What Are the 4 Types of ETL Tools?
According to Hubspot, there are 4 types of ETL tools:
Enterprise software ETL tools
Open-source ETL tools
Cloud-based ETL tools
Customer ETL tools
Is SQL an ETL Tool?
SQL (Structured Query Language) is not an ETL (Extract, Transform, Load) tool itself, but it can be used as part of an ETL process.
SQL is a programming language used for managing relational databases, querying and manipulating data, and defining the structure and relationships of the data.
While SQL is not specifically designed as an ETL tool, it can be used effectively in the transformation and loading stages of the ETL process. SQL queries can be written to extract data from source systems, perform various transformations on the data, and load it into the target system. SQL also supports functions, joins, aggregations, and other operations that are commonly used in data transformation.
