Fivetran vs Airbyte vs Hevo Data – Comparison of Top ETL Tools
Platforms like Fivetran, Airbyte, & Hevo Data are among the leading products in the category of ETL solutions. Every year, numerous new and appealing technologies join the market, transforming the data integration environment. It’s a very competitive market, and selecting the best solutions requires careful evaluation, especially if there are significant high-level feature overlaps.
The finest ETL tool for your company is one that integrates into your present-day data stack & is tailored to your specific needs. All three, Fivetran, Airbyte, & Hevo Data feature robust automated data transfer capabilities that support a wide range of data inputs. These tools are all powerful no-code solutions that enable data teams to develop data solutions rapidly. However, when it comes to the specifics of data integration, pricing, & expertise, the three systems are somewhat different. Let us dive right into it.
What are ETL Tools?
ETL stands for extraction, transformation, and loading. ETL tools are platforms that help with the extraction, transformation and loading of data. They offer a collection of functions and tools that will allow your data team to harvest data from multiple sources, turn it into a uniform format, & load it into a specific system for reporting and analysis. ETL tools often provide a user-friendly interface via which you can build and execute data integration workflows. These solutions streamline and automate the entire ETL procedure, making it more effective, reliable, and flexible while also offering features like data profiling, error management, and scheduling. ETL tools are critical for teams that deal with huge amounts of data from numerous sources because they help maintain data quality, maintain consistency in data, & provide enhanced decision-making through streamlined and integrated data.
How Do ETL Tools Work?
The ETL process consists of three stages that allow data to be integrated from the source to the final destination.
a. EXTRACT information from a primary source,
b. TRANSFORM data by replicating, merging, and assuring quality before,
c. LOADING it into the desired database.
This section will go through each of these processes in depth.
Extraction –
a. Determine the data sources.
Specify the data sources you wish to extract data from. Databases, files, APIs, online services, spreadsheets, & other resources are examples.
b. Get connected to the data sources.
Utilise the connectivity features of the ETL tool to connect to the selected data sources. This usually involves offering connection information including server addresses, login credentials, and access rights.
c. Establish the extraction parameters.
Provide the data extraction requirements, like the tables, columns, time periods, or filters to be used.
d. Begin data extraction.
Run the extraction process with the ETL tool. It will gather the necessary data from the indicated sources & temporarily store it for further use.
Transformation –
a. Data profiling.
Conduct data profiling in order to analyse and comprehend the retrieved data. This entails investigating its structure, quality, trends, & interactions.
b. Data cleansing.
Use data cleansing procedures to assure the integrity & consistency of your data. In this step, duplicates are removed, mistakes are corrected, missing data are handled, and formats are standardised.
c. Data transformation.
Use transformations to alter data into a structure appropriate for the destination system. This involves merging, separating, combining, or pivoting data, along with applying computations or business rules
d. Data enrichment.
Improve the data by adding new information from other sources or mixing it with current data throughout the ETL process.
e. Data validation.
To ensure correctness and dependability, validate the modified data against preset rules & business logic.
f. Error handling.
Find and deal with any errors or discrepancies that arise throughout the transformation process. This might include logging problems, ignoring or correcting faulty data, or contacting stakeholders.
Loading –
a. Specify the target structure.
Define the target system’s structure & schema where the altered data will be uploaded. Tables, columns, data types, & any other pertinent parameters must be specified.
b. Data mapping.
Link the modified data characteristics to their target system counterparts. During the loading process, this guarantees the data is correctly positioned and mapped.
c. Begin data loading.
Start the loading procedure, which loads the converted data into the intended system. Inserting, modifying, or adding entries in databases, creating files, or stocking data warehouses or data lakes are all examples of this.
d. Indexing and optimization.
Apply any required indexing or optimisation techniques to the loaded data to improve query speed and allow for more efficient data retrieval.
e. Verification & validation.
In the target system, validate the loaded data to confirm it’s precision & integrity. This might include comparing the imported data to the source or doing data reconciliation.
f. Monitoring & error handling.
Keep an eye on the loading procedure and deal with any issues or inconsistencies that arise. This includes reporting faults, retrying unsuccessful loads, & informing stakeholders regarding the loading process’s progress.
What are the Differences between ETL and ELT Tools?
ETL & ELT are two typical data integration techniques. ETL (Extract, Transform, & Load) is the process of transforming information on a distinct processing server before moving it to the data Centre. ELT, or Extract, Load, & Transform, on the contrary, executes data transformations inside the data warehouse itself. ETL and ELT are both data integration approaches. Their primary responsibility is to move data from one location to another. Each, however, has distinct properties and is appropriate for a variety of data demands. Your choice between ETL & ELT will impact how you store, analyse, and process data. So, before deciding between the two ways, every factor must be considered. This section will provide a full comparison of these two platforms.
1. Order Processing:
In ETL tools, data is taken from the original source, transformed into the appropriate format, and subsequently loaded into the destination system in sequential order.
However, with ETL tools, data is processed in a sequential manner, with data first retrieved from the original source, subsequently transformed to the appropriate format, and lastly loaded into the destination system.
2. Storage of data:
ETL tools are frequently used alongside conventional data warehousing techniques. They make it easier to transform information into a standard format before it is loaded into a distinct data warehouse or data centre. The modified data is saved in a structured format for further reporting and analysis.
ELT tools, on the other hand, are widely employed in association with data storage structures such as data lakes. These tools allow for the direct loading of unprocessed data into the intended system, which can contain unstructured as well as structured information in its native form. Within the target system, transformation & analytics can be carried out on raw data.
3. Transformation of data:
ETL tools provide thorough data transformation. They provide a variety of built-in features like mappings, & transformations for cleansing, aggregating, merging, and reshaping data to meet business requirements. Typically, ETL tools feature graphical interfaces for defining and configuring complicated transformation algorithms.
But ELT tools, on the other hand, frequently focus on minimum or light changes during the loading step. While they may provide rudimentary transformation capabilities, the major focus is on using the target system’s processing capabilities. SQL queries or specialised processing engines are used to accomplish transformation activities within the target system.
4. Scalability:
Because of the processing needs during the transformation step, ETL tools can encounter scalability issues. As the volume of data grows, ETL operations will require more computational resources to conduct the transformations, resulting in scalability limits.
ELT tools benefit from inherent scalability, especially when coupled with cloud-based systems. Cloud infrastructure enables the intended system to dynamically increase processing resources, allowing it to handle larger amounts of data and simultaneous transformations more effectively.
5. Requirements for Skill:
ETL tools frequently need specialised skills and an understanding of the tool’s capabilities. You must be familiar with data integration ideas, data mapping, & transformations, in addition to the interface and functionality of the specific ETL tool.
However, ELT solutions are often compatible with well-known data processing & analytics technologies. Understanding SQL along with data manipulation languages is useful for maximising the target system’s processing capabilities. Experience with cloud-based systems and related applications will also be advantageous.
6. Processing adaptability:
ETL tools provide you with a lot of power and flexibility across the data transformation procedure. To meet your company’s unique business requirements, you can set up intricate workflows, define data dependencies, and create custom transformation logic.
However, in terms of loading and transformation order, ELT tools prioritise flexibility. The loading phase occurs initially, enabling the data to be accessible in the intended system instantly. Transformation can subsequently be conducted on demand, giving you the freedom to experiment with various transformation scenarios and tweak them as required.
Critical Features of ETL Tools
In the long term, ETL tools are simpler and more efficient to utilise than hand-coding. In reality, ETL tools are critical for processing massive amounts of raw data. Before you purchase an ETL tool, you should analyse its capabilities and features to see if it will fulfil your data management needs. Here are some crucial characteristics of an ETL solution:
1. Connection Library: Modern ETL systems provide a large connection library that includes file types, databases, & cloud platforms. Make certain to buy a tool that can natively integrate with your data sources.
2. Usability: Handling ETL mappings which are custom-coded is a complicated operation that demands extensive technical knowledge. Conserve your developer resources & move data from developers’ control to the hands of business users, require an enterprise ETL platform that provides an easy, code-free atmosphere for extracting, transforming, and loading data.
3. Transformation Of Data: Data transformation requirements range from basic lookups & combines to more complex tasks including denormalizing data or transforming raw data into formatted tables. You can choose an ETL tool that provides a variety of simple and complicated transformations to meet your data manipulation needs.
4. Data Quality & Profiling: If you want clean, correct data stored in your repository you can use ETL tools. Seek an ETL tool with built-in data integrity and profiling tools to verify the consistency, correctness, and completeness of company data.
5. Automation: Automations are required to handle the daily hundreds of ETL operations that large businesses have to perform. To simplify the data management operations, look for an elaborate ETL automation platform with end-to-end automation features, including task scheduling and workflow orchestration.
Fivetran vs Airbyte vs Hevo Data
You have probably already come across three of the top ETL systems, Fivetran, Airbyte, & Hevo Data, as there are now numerous more data sources than ever. We’ll guide you through the advantages and disadvantages of all three platforms in this comparison. We’ll describe the features of each platform in detail and even provide a basic framework to help you decide when to utilise each tool for data management. Investigating the real capabilities of the systems under consideration is crucial.
Features of Fivetran vs Airbyte vs Hevo Data
1. Ready-Made Source Connectors
The fact that an ETL tool integrates with the data sources you want is one of the most crucial factors to consider when choosing such tools. When you take into account the offering, you’re actually just buying access to the connections the vendor currently has in their catalogue as the majority of suppliers don’t add several new data sources every year. The number of connectors a supplier offers is a good indicator of how well they can assist your analytics team in centralising data.
More than 160 data sources are available in Fivetran’s connection catalogue. When your workforce is first setting up your data stack, these connections are often the biggest databases, file-based sources, & business apps that are useful. For instance, Salesforce Marketing Cloud, Oracle PeopleSoft, & Amazon Ads.
170+ data sources are included in Airbyte’s open-source offering. Although the cloud offering is more recent, it does have certain limitations on which sources can be used in the product. However, it is realistic to anticipate that all 170+ sources will soon be accessible in the cloud product.
Hevo Data provides 150+ data connections, although the free plan only includes 50+ of them.
2. Develop custom connectors
Understanding how each of these three distinct data integration solutions will be useful in these situations is crucial. Are you required to write code to keep the connection clean when anything breaks, to repair it?
When creating customised connections and maintaining them in case something goes wrong, Fivetran advises customers to employ cloud functionalities. Users must study the API records, deploy a cloud function, test everything, and finally maintain the pipeline if something goes wrong during the course of the development process. This is a typical situation when data teams contact Portable for assistance because it might involve a substantial amount of work.
Because Airbyte is completely open-source, engineers are free to create distinctive connectors. Developers still have to familiarise themselves with the Airbyte protocol along with the CDK framework, study the source documentation, and then configure the integration in such instances. Custom connections must be maintained by the user who constructed them, who is also responsible for resolving any problems that might emerge.
You must create your own connections for any lengthy data sources utilising the REST API or Hevo’s Webhooks data source. You are in charge of continuous maintenance.
3. Maintenance and Support
When data connectors break or develop, your searches and dashboards become a complete mess. Understanding how both ETL providers will assist you in the event of a problem as well as the features each platform offers for alerting, tracking, & connection maintenance is crucial.
When something goes wrong, Fivetran provides a ticketing mechanism for customers to report it. Although difficulties are frequently resolved promptly because of Fivetran’s big clientele, help occasionally comes off as impersonal.
Customers are urged to utilise the documentation, Slack & Discourse forums to solve problems, as Airbyte has over 12,000 customers of the open-source software.
Hevo Data, however, provides customers with 24/7 help.
Pricing & Plans of Fivetran vs Airbyte vs Hevo Data
1. Fivetran
For data pipelines, Fivetran charges based on monthly active rows.
a. A free 14-day trial of the Fivetran platform is available.
b. Monthly Active Rows are economical at low quantities but prohibitively expensive at big levels.
c. This platform offers the Starter, Standard, & Enterprise plans. However, you must get in touch with their sales team if you want further information about the costs of any of these schemes.
2. Airbyte
Airbyte uses a credit-based pricing scheme.
a. Users have to pay credits before use; nevertheless, the business provides initial free credits when establishing an account.
b. Pricing rises with data quantities. They offer two plans, “Growth” with $2.50 per credit and “High Volume”, in which you have to talk to their sales rep.
c. For engineers that seek a free option to manage infrastructure themselves, Airbyte offers open-source software.
3. Hevo Data
Hevo charges on the basis of events.
a. One million events are free (with a cap of 50+ data connections)
b. Starter plan begins at $239/month
c. Business: After speaking with their salesperson, you will be provided with a customised quotation
You must make a critical selection while purchasing an ETL solution based on your own requirements. We’ve detailed the benefits and drawbacks of Fivetran, Airbyte, and Hevo Data in order to clarify the situations in which each option makes sense, but you may still require more assistance. To learn more about this, speak to our ETL specialist.