15 best ETL tools of 2023
15 best ETL tools of 2023
Businesses rely on data to make critical decisions for their sales, marketing or any important task they may undertake. However, this data is present in various formats and sizes and stored on various databases or files. To analyze this data you need certain tools to extract this data, transform it into a suitable format and finally load it to a destination storage such as a data warehouse. These tools are referred to as ETL(Extract, Transform, Load) tools.
Today, the majority of businesses use an ETL tool as part of their data integration process. ETL tools are renowned for their efficiency, cost-effectiveness, and scalability to broader data management approaches. In this article, you will understand what ETL is and also explore the different types of ETL tools. In addition, you will learn about some critical factors necessary to choose the best ETL tool. So, read along to discover the best ETL tools that are widely used in the market.
What is ETL?
In the digital era, data plays a very big part. You can store this data in excel sheets or in databases or even as images and videos. However, when you want to analyze or use this data, you need to consolidate it into one format. This is where ETL plays a role.
In simple terms, ETL is an acronym for Extract, Transform and Load. You extract data from a source, transform it into a relevant format and then load this data into a data warehouse or a database. So, companies either create their own standard ETL pipelines or prefer to use automated ETL tools like Fivetran.
The end-to-end ETL process involves the following stages:
- Extraction: This stage involves the extraction of data be it structured or unstructured from various sources such as websites, Customer Relationship Management(CRM) platforms, applications, databases, SaaS software, files, marketing platforms, or any other data source. The data loads into a staging area after extraction is finished and is ready for the next stage - transformation.
- Transformation: The transformation stage prepares the extracted data by cleaning and formatting it. The objective is to get the data ready for querying in the target storage such as a data warehouse like Google BigQuery, Redshift, and Snowflake or databases such as MongoDB, and PostgreSQL.
The extracted data must be transformed into a standard format to fulfill the target database's schema requirements. This is done by running a number of functions and applying a set of rules to the data. The degree of manipulation necessary for ETL transformation totally depends on the extracted data and the demands of the business. Some of the basic transformations involved in this stage are as follows:
- Cleaning: Filling up the missing values with some default values or checking for any date format consistency, etc.
- Deduplication: Identifying & removing duplicate records.
- Format Standardization: Formatting something in accordance with a set of guidelines so that its values and structure are compatible with the intended use case, for example, measurement unit conversion, date/time conversion, etc.
- Key Restructuring: Finding important relationships between tables
- Loading: The transformed data must now be loaded into the desired data destination, such as a database, data warehouse, or data lake. Data can be loaded in 2 different ways: incremental loading or all at once i.e. full loading. A full loading transfers all records from the transformation assembly line to the target warehouse or database. This may result in redundant data, if not checked. Therefore, one of the important factors to take into account throughout the load process is knowing the task that the target environment is expected to perform. When loading data, you could have a detrimental effect on the host system depending on the volume, structure, target, and load type.
What are ETL tools?
Data is only meaningful if it can be converted and used for operational and business insights. Hence, ETL tools are essential for delivering business intelligence to organizations of all sizes. Organizations gather data from numerous sources. ETL Tools help streamline the process of extracting data from multiple sources, transforming it into a defined format, and effectively loading it into the data destination. Now you can use this data to load it into Business Intelligence tools like Tableau, Power BI or others to analyze this data.
ETL tools organize and automate the flows of data from the data sources in order to generate reliable information. The majority of the aforementioned process can be entirely streamlined depending on the ETL tool. Pre-built data connectors offered by ETL tool vendors require little to no coding to extract, transform, and load data to a target system. This eliminates the requirement for challenging data intake, API management, and other duties.
To understand the different types of ETL tools used in the industry, read the next section to find out more.
What are the types of ETL tools?
Depending on various factors, like cloud-based, on-premise, real-time and others, you can basically divide the ETL tools into the following categories:
1. Custom ETL tools
Businesses with internal data engineering and support resources can design and build their own tools and pipelines using languages such as SQL, Python, and Java. Although this strategy provides the most flexibility, it also demands the most work and effort. Additionally, users must carry out their own testing, documentation creation, maintenance, and continued development under this type.
2. Batch ETL tools
For many firms, the only workable ETL method up until very recently was batch processing in on-premises tools. In the past, processing huge amounts of data required a lot of time and energy and could quickly exhaust a company's computing and storage capacity during business hours. Hence, for businesses, it made more sense to use batch data processing with ETL tools during off-peak hours. These tools performed data extraction, transformation, and loading in batches.
3. Real-time ETL tools
Real-time ETL tools are used to extract, transform, and load data onto the target system in real time. For some data updates, batch processing works fine. However, we now need real-time access to data from various sources more frequently. Real-time demand is forcing us to process data in real-time rather than in batches, using a distributed paradigm and streaming capabilities. As a result, these ETL tools are growing in popularity as businesses look for insights that can be put to use right away. Numerous near real-time ETL tools are offered, both commercially and through open source.
4. On-premise ETL tools
Many businesses use older systems with on-premises data and repository configuration. Data security is the main driver for such an arrangement. Due to this, firms prefer to have an ETL tool installed on-site.
5. Cloud ETL tools
Companies are increasingly using cloud-based ETL tools to ingest data from various web applications or on-premises sources. These tools are deployed in the cloud. As almost all data and applications are supported on the cloud, it becomes easier to extract and load data to the ETL tool. This also saves the resources and other costs to set up the ETL tool. Cloud ETL tools offer high availability, elasticity, and low latency, allowing resources to scale and match the current demands for data processing.
6. Open-source ETL tools
Numerous open-source ETL tools have been developed during the previous decade. As these ETL tools are easily accessible, a huge testing community constantly provides feedback on them to enhance or add to their functionality. Many of these open-source ETL tools offer a graphical interface for building and running pipelines.
7. Hybrid ETL tools
Integrating the features of the above ETL tool types will give you a hybrid ETL tool. So, multiple ETL tasks can be handled at scale by a single ETL platform.
Key factors to evaluate ETL tools
As discussed above, businesses have a lot of flexibility to pick an ETL tool. The optimal ETL tool will vary depending on a number of aspects, including the anticipated use cases, pricing, the location of the source data, the type of data to be moved, the need for scalability, and the amount of knowledge needed to use it. Some businesses prefer to create their own ETL pipelines, and some choose automated tools either on-premise or cloud-based. Choosing the right ETL tool for your use case may get challenging. Hence, we have listed below some of the critical factors you should keep in mind while selecting the best ETL tool for your company.
- Use case: Use case analysis is a crucial factor when choosing your ETL tools. You might not require a solution as robust as large enterprises with complicated datasets if your business is small or if your data analysis needs are low.
- Data connectors: Which data sources is your team most interested in utilizing? When the need arises, how much effort are you willing to put into developing a new integration? The data sources used by many startups and small companies are generally uniform. While choosing the ideal ETL tool for your use case, your data team should choose ETL tools supporting a variety of integration options, be it a data source or a data destination including the ones you will be using.
- Easy-to-use iterface: Who will be using the data, how will it be loaded, and how will it be used? Ensure that the ETL tool has an interactive user interface and is easy to set up. This makes sure that your team be it technical experts or non-technical users, gets an amazing experience while managing data-related tasks and creating data pipelines.
- Scalability: Keep in mind that your data requirements will increase as your company grows. To meet the demands of your expanding business, the ETL tool should support performance optimization capabilities along with other resources needed to scale your pipeline.
- Low latency: Real-time ETL tools are gaining popularity among enterprises as the demand for quick data collection and analysis has grown. Real-time data is crucial since some decisions must be taken immediately. While some source data connectors will have latency requirements, data should move through your ETL process with as little latency as feasible.
- Pricing: Businesses should take into account not just the cost of the tool itself, but also the price of the infrastructure and human resources required to sustain the ETL tool over the long term.
- Built-in monitoring & security: A monitoring system that delivers real-time updates on job progress should or has been integrated into the ETL tools to ensure efficient process execution. The level of security and compliance is equally important to other factors. To ensure that sensitive data is protected and only shared with the right users, companies need to make sure that the ETL tool vendor meets the security requirements.
Top 15 best ETL tools
Now that you have understood the different types of ETL tools and some of the significant factors you should take care of while selecting the best ETL tool, let's take a look at the top ETL tools in the market.
Fivetran is a cloud-based automated ETL (Extract, Transfer, Load) tool that assists in moving data from different sources to data storage, like a data warehouse or a database. To consolidate their data, users can connect to more than 100 data sources by leveraging Fivetran's powerful connectors.
Key Features of Fivetran:
- Supports 100+ data connectors having use cases in Marketing Analytics, Databases, Sales Analytics, Product Analytics, Finance & Ops Analytics, Support Analytics and Engineering Analytics.
- It quickly adjusts to the API and schema changes to ensure data consistency and integrity.
- It continuously synchronizes data across target warehouses and data sources.
- In order to give you a low-code solution Fivetran offers prebuilt data models that can be used to swiftly deliver new reports and respond to ad hoc queries while saving crucial engineering time.
- Fivetran supports the most extensive privacy, security, and compliance program.
- All Fivetran clients have 24/7 access to Support Specialists that collaborate directly with you to quickly troubleshoot any technical concerns.
Pricing: 14-day free trial and consumption-based pricing model - based on your source type and monthly active rows.
Talend is a free ETL tool mostly used for creating data pipelines. It includes Talend Studio, which facilitates the design of the data flow and transformation logic.
Key Features of Talend:
- It is compatible with different cloud environments and hybrid clouds.
- Talend is developed on the Eclipse platform that provides a library of connectors for connecting to data sources, including databases, flat files, and web services.
- Through the drag-and-drop GUI, data components from Excel, Oracle, Salesforce, and other data sources can be connected to run jobs.
- It features built-in connectors that allow it to access data from a variety of environments, including databases, SaaS platforms, and other applications.
- It effortlessly connects with Google Cloud Platform, Snowflake, Microsoft Azure, Amazon Web Services, and other platforms.
- Advantage of a robust open-source community that actively contributes to documentation and offers help.
While being enterprise-grade, the Matillion ETL solution is exceptional in that it enables use cases specific to small and medium-sized businesses. Matillion's cloud-native ETL platform helps make sense of the data at every stage of the data lifecycle.
Key Features of Matillion:
- Users can either define data transformations in SQL or use a straightforward point-and-click interface to do so.
- Allows you to accelerate transformations by loading data beforehand.
- You can execute batch and CDC pipelines with the Matillion Data Loader without having any coding experience. Therefore, most of your employees won't have any trouble accessing the business intelligence it provides.
- Equipped with sufficient connectors that support a variety of cloud data warehouses, including Snowflake, Amazon Redshift, Microsoft Azure Synapse, and Google BigQuery.
Pricing: 14-day free trial and pay-as-you-go pricing model offered.
With hundreds of connectors and a comprehensive solution for ETL, ELT, API Generation, and Data Insights, Integrate.io is a market-leading low-code data integration platform that enables users to quickly create and manage automated, secure pipelines. The platform allows you to effortlessly aggregate data to warehouses, and databases while being extremely scalable with any data volume or use case.
Key Features of Intergrate.io:
- You can pull data from any source that supports RestAPI.
- Offers more than 100 major SaaS application packages and data repositories, including MongoDB, MySQL, Amazon Redshift, Google Cloud Platform, and Facebook.
- All significant e-commerce platforms, including Shopify, NetSuite, BigCommerce, and Magento, are supported.
- Provides a wealth of customization options due to its extensive-expression language, sophisticated API, and webhooks.
- It offers solutions for reverse ETL and business intelligence (BI) to disseminate cleaned data to different stakeholders.
- The Field Level Encryption feature enables you to encrypt and decrypt individual data fields using their unique encryption key.
Pricing: 14-day free trial and flexible pricing plans offered.
With the SnapLogic Intelligent Integration Platform, SnapLogic genuinely continues to innovate when it comes to data integration. It is more than just an ETL tool. The SnapLogic IIP is a multi-tenant cloud service that offers scalability, robust parallel processing, full data management features, and various pre-built connectors.
Key Features of Snaplogic:
- Since the SnapLogic iPaaS platform is entirely Rest-based, customers have a great deal of flexibility when connecting to various sources and creating ETL pipelines.
- Includes numerous built-in connectors and simple feed tracking into a system.
- Java-based custom Snaps can be created by developers.
- There are more than 100 Snap Packs available from SnapLogic, and each one contains a number of related Snaps, or data access connectors.
- Data lakes, data warehouses, and databases are supported by SnapLogic.
Pricing: Free data trial available. Pricing plans are available on request.
7. Pentaho Data Integration
Pentaho Data Integration, formerly known as Kettle, is best recognized for its Spoon graphical user interface. Pentaho Data Integration is popular for having features like Data Integration, Reporting, Data Mining, OLAP services, and more in addition to its ETL capabilities.
Key Features of Pentaho Data Integration:
- This open-source ETL tool creates XML files to represent pipelines and uses its ETL engine to carry out pipeline execution.
- It can be set up in a cloud or on-premises.
- Pentaho mainly relies on hybrid and multi-cloud systems.
- It supports data access for IoT tools.
- It offers features for data processing and data integration from many data sources.
- It enables the creation of reports in various formats including Excel, Text, CSV, PDF, HTML, and XML.
Pricing: Pricing details are offered on request.
Using Singer, a company can transfer data more efficiently thanks to its open-source scripting tool. Singer establishes the connection between data extraction and data loading scripts, enabling data to be extracted or loaded from/to any source. The scripts use JSON so they are able to support rich data types, enforce data structures with JSON Schema, and be used with any programming language.
Key Features of Singer:
- It is a CLI-based application that needs some coding expertise.
- The scripts for this tool come in two varieties: taps and targets. A script called a tap pulls data from your sources. The data streams are received by a target script, which then stores them at the final location.
- 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 that are already there.
- It allows you to stream data from over 110 sources to roughly 10 destinations.
- Singer accesses the source to extract data, which is then written to a standard stream using a JSON-based format.
Apache Hadoop allows large data sets to be processed in a distributed fashion across computer clusters. It is a general-purpose distributed computing system that can be used to store, manage, and analyze the data of any structure.
Key Features of Hadoop:
- Over 20 key open-source projects make up the intricate ecosystem of Hadoop. These projects include Pig, MapReduce, and Spark, which are used to carry out ETL operations.
- Hadoop is horizontally and vertically scalable.
- It can process and store structured, semi-structured and unstructured data.
- By duplicating data across multiple DataNodes in a Hadoop cluster, Hadoop ensures data availability even if one of your systems crashes.
- It manages its storage via a distributed file system. Due to the concurrent processing of such a large number of file blocks, Hadoop is fast.
- Hadoop is well-known for its data locality characteristic, which involves bringing compute logic closer to the data rather than the other way around. This property of Hadoop lowers a system's bandwidth usage.
Dataddo is a no-code, cloud-based ETL tool designed for professionals with any degree of technical competence. It offers a broad variety of connectors, completely customized metrics and a central system for managing all data pipelines at once. Dataddo connects with your existing workflows and data architecture without any issues.
Key Features of Dataddo:
- Continuous pipeline maintenance is not necessary thanks to Dataddo's fully managed APIs.
- You can concentrate on integrating your data thanks to its user-friendly UI and straightforward setup process.
- You can send data from any business application to any business intelligence platform such as Tableau, Power BI, and Looker Studio.
- Upon request, more connectors can be connected within 10 business days.
- Customizable metrics provided by the Dataddo for developing or connecting sources
Pricing: Offers a free plan for 3 dataflow connections and a custom pricing plan depending on the number of dataflows that starts at $99/month.
11. AWS Glue
AWS Glue is a cloud data integration tool that works with both visual and code-based clients. The serverless platform has other tools that perform additional functionalities, such as the AWS Glue Data Catalog for discovering data across the company and the AWS Glue Studio for visually developing, executing, and managing ETL pipelines.
Key Features of AWS Glue:
- AWS Glue's core competencies include data analysis & categorization.
- You can automatically deduce database and table schema from your data in Amazon S3 using AWS Glue crawlers, and then save the related metadata in the AWS Glue Data Catalog.
- One of AWS's fully managed ETL services, AWS Glue, can be seamlessly linked with the company's other products, including Redshift, S3 Lambda, and RDS.
- In order to assist users in moving their data into the cloud, it can also connect to on-premises data sources.
- ETL pipelines are built in Python and executed using PySpark and Apache Spark.
- Custom SQL queries are now supported by AWS Glue for more hands-on data interactions.
- You can connect to more than 70 different types of data sources, manage your data in a single data catalog, and visually build, operate, and keep an eye on ETL pipelines that load data into your data lakes.
Pricing: Offers Free plan and custom pricing model depending on the number of data processing units used.
12. Azure Data Factory
Azure Data Factory is the cloud ETL service provided by Azure for serverless data integration & transformation. It provides a code-free user interface for simple authoring as well as single-pane-of-glass monitoring and management.
Key Features of Azure Data Factory:
- Azure Data Factory also interfaces with PostgreSQL, MySQL, MongoDB, and Azure Cosmos DB.
- It is a fully managed service that links to a variety of on-premises and cloud data sources.
- It can copy, transform, and enrich data before writing it to Azure data services as a destination.
- Supports Hadoop, Spark, and machine learning for transformation processes.
- It supports PDF and CSV as output formats.
Pricing: Offers a pay-as-you-go pricing model.
13. Google Cloud Dataflow
A fully managed data processing service, Google Cloud Dataflow is designed to optimize computing power and automate resource utilization.
Key Features of Google Cloud Dataflow:
- Through flexible scheduling and dynamic resource scaling to ensure consumption meets needs, the service is intended to reduce processing costs.
- As the data is transformed, Google Cloud Dataflow also provides AI capabilities to power predictive analysis and real-time anomaly detection.
- Developers can construct batch and continuous ETL tasks.
- It gives developers Java and Python APIs that they may use to connect to Google Cloud sources, perform transformations, and write data to various Google Cloud destinations.
- Within the Google Cloud Platform ecosystem, Apache Beam pipelines are executed by Google Dataflow. For representing and sending data sets, both batch and streaming, Apache provides Java, Python, and Go SDKs. This gives users the ability to define their data pipelines using the relevant SDK.
Pricing: Offers free trial and pay-as-you-use model on the basis of resources used.
Stitch, a provider of ETL as a service, is built on the open-source Singer core. Without the need for code, Stitch quickly transfers data into a data warehouse to help you find your answers more quickly. Additionally, it is scalable, allowing you to expand its features as your needs change.
Key Features of Stitch:
- The platform provides automated pipelines and self-service ELT built on open source platform called Singer.
- It supports simple transformations only, user-defined transformations are not supported.
- More than 130 data connectors are available.
- Supports a considerable number of internally developed or community-supported data sources.
- Users can choose plans based on their usage and needs thanks to its volume-based pricing model.
- Offers compliance tools for internal and external data governance.
Pricing: 14-days free trial and volume-based pricing model offered.
15. Informatica PowerCenter
Informatica PowerCenter is possibly the most sophisticated ETL product on the market, It is popular among big businesses and highly rated by market researchers. It is a component of the broad range of products that make up the Informatica Platform.
Key Features of Informatica PowerCenter:
- It parses complex data types like JSON, XML, PDF, and data from the Internet of Things.
- The platform delivers high availability, efficient speed, and pre-built transformations for user-friendliness as well as the capacity to grow to meet computational demands.
- It supports multiple clouds, hybrid clouds, as well as unique ETL rules.
- Many third-party databases, such as SQL, Oracle, and Teradata, are supported.
- For cloud data lakes and warehouses, it provides many connectors, including those for AWS, Azure, Google Cloud, and Salesforce.
- Suitable for major businesses with hefty budgets and strict performance requirements.
- It can automatically verify modified data to uphold predefined standards.
Pricing: Free trial and consumption-based pricing model offered.
In a nutshell, a good ETL tool extracts data, upholds data quality guidelines, conforms data into a consistent format so that data from various sources may be merged, and delivers data that is ready for use to create applications or to make decisions.
In this article, you understood the various types of ETL tools available in the market. You also learned about some of the key factors to keep in mind while choosing the right ETL tool. In addition, you explored the best ETL tools widely leveraged in the industry. Depending on your needs, you can use one of these to increase the efficiency and productivity of your business workflow. Fivetran's pre-built models can extract and clean your data in minutes using a simple drag-and-drop interface, saving countless hours of manual work.
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