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ETL tools

Data increasingly becomes the most critical asset, but without proper collection, processing, storage, and analysis, its overall value is reduced. With the growth of disparate data sources, the need for consolidation into centralized systems grows accordingly. Although we don’t have precise usage statistics for recent years, we are highly confident that ETL tools have cemented their position as the standard solution in this context. 

However, the diversity of available tools makes a choice difficult. At Data Crafts, we have followed their evolution and know which ones are better suited for what.

With our field experience, we help clients make informed decisions and reach their goals faster by building their data architectures. But let’s try and make an objective comparison of ETL solutions you can get today so that you can choose the most suitable option for yourself.

What Are ETL Tools?

As a rule, when loading data, it becomes necessary to store it in one system and transfer it to another for further processing and analysis. To integrate raw data from a source system into a target database like a data lake or data warehouse, two data integration processes are commonly employed.

The first process is ETL (Extract, Transform, Load). It involves extracting data from different sources, transforming it to fit the target schema, and loading it into the target system.

The second approach is ELT (Extract, Load, Transform). It offers greater flexibility and scalability as the transformation of data can be performed as per specific requirements, and it can handle diverse data sources and volumes more efficiently.


The ELT process outlines three critical stages:

  1. Extract — data extraction without changes and loading into the staging area;
  2. Load — loading new and changed data from the staging area into the storage;
  3. Transform — grouping and converting data into a format that matches the storage structure. In the process, the data is cleaned, checked for completeness, and reports are generated for further error correction

However, ETL is still popular when data is relatively small and the transformation process is more complex. The purpose of this approach is to minimize errors and bugs and obtain reliable data reporting. And it is handled via ETL tools – special software, a set of libraries that help:

  • Consolidate data according to a single system of values and detail, ensuring its quality and reliability;
  • Provide an audit trail so that, after conversion, it is possible to understand from which source data and amounts each line of data was collected

With their help, specialists collect, modify (for instance, using calculations and concatenations), and then insert the data into storage.

What Types of ETL Software Tools Are There?

Previously, local ETLs were often created in-house, but custom solutions either quickly became obsolete or lacked complex features and capabilities. They were expensive, required maintenance, only supported batch processing, and did not scale well.

If more than one-time conversion is required, it is advisable to consider readymade software. We would divide such solutions into three categories:

  • SaaS tools: Fivetran, Stitch, and Hevo;
  • Open-source: Airbyte, Pentaho Data Integration;
  • Coding: Singer, custom development

It is advisable to choose an option based on the tasks set, as well as the existing platform. At the same time, it is worth considering the speed, extensibility, and scalability of the tool.

With a huge, confusing selection of available ETL tools, the following overview of the tools that we believe stand out the most should make your choice easier.

ETL Tools Comparison

With the advent of new technologies and big data, the number of sources of information has grown, which has created a demand for effective data integration solutions. The following ones stand out the most, in our opinion.


Fivetran automatically adapts to schema and API changes, providing easier and more reliable access to data.


The tool helps develop robust and automated processes and allows you to quickly add new data sources. You won’t need any training or custom code, but you will have SQL access to all data with the support of Azure, BigQuery, Redshift, Snowflake, and other databases. Also, full data replication is enabled by default.


Hevo sets up and runs in minutes, but allows real-time data transfer from any source.


It also provides accurate alerts and detailed monitoring. Powerful algorithms can detect the schema of the incoming data and reproduce it in storage without your participation. Heva enables complex mechanisms for cleaning, modifying, and improving data before and after data migration to storage. And it complies with all SOC II, HIPAA, and GDPR requirements.


The most affordable automated services in our ETL tool comparison. A lengthy free trial allows you to unload all historical data and set up incremental replication of new data. It offers a fairly simple interface that allows you to specify which tables and fields (or all) you want to replicate, how often, and in what way.


The tool also supports full overwriting and incremental replication by key. If it is necessary to transform data before transferring it to the storage, you can create a view with the necessary data directly in the source database and replicate it in the same way.

We must say that when we compare ETL tools, such a simple solution comes out as quite sufficient in most cases. Stitch supports various storage environments, autonomously performs data type conversions depending on the particular storage (for example, it converts JSON and enum to varchar in the case of Redshift), and also monitors the structure of the original database and changes in tables.

Lastly, Stitch boasts a responsive support team, which helped us solve a variety of issues right in the chat window on many occasions. In terms of budget, this is still the first candidate for a replacement in the chosen architecture.

But it has its disadvantages, like the following:

  • Closed code — you are limited by the capabilities that the vendor supports;
  • There may be specific connectors (or connection methods) that the vendor does not support;
  • The regular use of Stitch may come out quite pricey

When it comes to the above ETL tools we compare, Fivetran is for those who need a lot of out-of-the-box integrations and the flexibility of multiple data stores. Hevo is better when data transformations are added using Python from existing data sources. And Stitch is for those who need a simple process without complex transformations.

Alternatively, there is a class of modern and user-friendly open-source data integration flow management solutions, such as Airbyte and Singer. Let’s take a look.


This is a project that is rapidly gaining popularity. This is a modern standard for building data integration flows from various applications, databases, and APIs into analytical warehouses and data lakes.


It features the most extensive set of connectors available for use in a matter of minutes, as well as an accessible, scalable architecture. The list includes all the popular database management systems and applications with various installation options (e.g., GCP, K8s, Docker, AWS, and Azure).

As metadata storage, you can use an external system (Postgres), a web interface, a set of workhorses (Workers), the number of which can be flexibly adjusted, as well as a full-fledged scheduler with the ability to closely adjust the frequency of data replication.

Airbyte empowers various sync strategies, including the following scenarios:

  • Full unloading of the entire amount of data and overwriting on the receiving end;
  • Full unloading of the entire amount of data and its addition to the receiving end;
  • Incremental reading of records and addition of them to the receiving end;
  • Incremental reading of records, addition to the receiving end, as well as the formation of a replicated version of the view;
  • Manual replication of data from the source

This is a suitable option when you need to host an application at your facilities in accordance with security and compliance requirements. It can be useful in flattening arrays and nested collections. In general, in the ETL open-source tools comparison, this solution is out of the competition.

At the same time, you pay only for the resources used (in the case of using the cloud), while there is also no fee for the number of connectors and the amount of replicated rows, like in Hevo and Fivetran.


An open-source JSON-based tool from Stitch. It can help create modular pipelines that are easier to maintain. The tool offers a simple approach to standardizing data actions.


Singer promotes two types of ETL scenarios handled by the two respective algorithms:

  1. Singer Taps — scripts or code fragments that are connected to sources and then display data in the JSON format;
  2. Singer Targets — routes incoming data to destinations

With Singer, you can create data pipelines across systems. Popular faucets available include MySQL, Amazon S3, Google Sheets, Salesforce, and Facebook Ads. You can then transfer the data to commonly used targets such as Google PostgreSQL, CSV, and BigQuery.

Also, you can use your taps and targets if you need more integrations. Because Singer uses JSON, you can define data schemas and extended data types to suit your needs.

But it has its downsides. You will have to process every data source and target. It also has incomplete documentation on GitHub. And you will have to manage the characteristics of the data flow down to the smallest detail.

In Singer, each connector is a separate open-source project. So you never know what you’ll get until you try it. Lastly, these are standalone binaries, so you’ll have to build everything around them to get them to work.

Lastly, Singer doesn’t provide data transformation and community support features. A lack of standardization can make it difficult to meet your needs.

ETL tools comparison matrix

To make an ultimate choice, you should consider the following aspects:

  • Environment, degree of support for the platform and data sources;
  • Extract and load performance;
  • Capabilities of the data conversion and processing function;
  • Presence of management and planning features;
  • Features and integrations

If you run cloud storage, you need a cloud tool. Moreover, it should automatically specify the source, and then copy data to the destination. It must be able to read and understand the schema of the source data and be aware of the limitations of the target platform.

The point of ETL tools is to avoid writing code. The benefits of cloud computing are greatly reduced if you have to bring in skilled people every time you replicate your data.

You don’t have to worry about how your data pipeline will recover from a failure. The platform should be able to retry any synchronization without creating duplicate or inconsistent data.

Of course, this is just our subjective opinion, but Airbyte offers the most advanced data integration mechanism based on business specifics. It is a well-established tool that allows you to consolidate data efficiently across databases, data lakes, and other storage solutions. Thanks to its open-source code, it can be easily integrated with existing or developing data systems.

Bottom Line

The field of data engineering services is changing dynamically, and new standards are emerging. ETL tools can provide a high return on investment, and deliver incredible performance. But why wait when you can use these opportunities to grow your business? If expertise or desire is not enough, our specialists are ready to help.

We will unify your data sources and enrich your business potential with solutions based on your particular needs. Book a 30-minute free consultation with our expert to help you implement effective analytics!

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