Data Engineering

How to Choose the Right ETL Tool for Your Business

5 minutes read

Modified on July 26, 2022

eCommerce

Modern Businesses have a data-driven approach. Hence companies rely on data replication from multiple sources using ETL tools. You can write your ETL code, or you can adopt a ready-made ETL service to do the work for you. What factors are to be considered while selecting a service? Let us see how to evaluate each of them.

We have listed down 10 major factors that will help you choose the right ETL service for your business:

Support: Data sources and Destinations
Extensibility & Compatibility
Usability
Scalability
Security
Customer Support
Stability
Batch & Stream Data Ingestion
Data transformations
Pricing

Support: Data Sources and Destinations

ETL services require destinations in which you can store your analytics data. Destinations are mainly data-warehouses such as Google Big Query, Amazon Redshift, and Snowflake; or a data lake such as Google Cloud Storage, Amazon S3, or Microsoft Azure. Some ETL tools permit you to push data only in one data warehouse while others allow multiple destinations. There are some ETL services that allow data replication to multiple places simultaneously.

It is quite challenging to find an ETL platform that supports all the SaaS tools, databases, and other data sources your company is using. Thus, you prefer the one that allows replication of your most essential data sources.

Extensibility and Compatibility

As an organization will grow, the chance of the chosen ETL tool supporting new data sources will be less. The ETL tool should have the capacity to add additional data sources. There must be other third-party tools that your clients use. The ETL service should be compatible with those tools through APIs, webhooks, or other software.

Usability

You have to check the simplicity of the ETL tool’s interface, whether it is easy to set up integrations, to schedule and monitor replication tasks. The tool should support data replication on different schedules. The granularity, flexibility and customization should let your business become productive.

Scalability

With the growth of the business, the data volumes will also increase. Thus, choose a tool that can meet your growing needs without deteriorating service. A data pipeline architecture supports a large volume of data.

Security

Security is the most crucial element of a system. For a cloud-based data pipeline, keep into account the following factors:

The security controls should be user-configurable.
There should be an API key management.
Whether the vendor encrypts data at motion and rest, otherwise, you should be able to enable encryption.
Whether HTTPS is used for web-based data sources.
What schedule is used to delete your data after it reaches the destination?
What does the vendor offer for the integration of data sources and destinations?
Whether it uses Secure Shell (SSH) for strong authentication

HIPAA, SOC 2, and GDPR compliance are three of the most common measures according to national and international data security standards. Try to check out the details of the certifications possessed by the platform.

Customer Support

The ETL tool’s support service should also be to resolve issues instantly or allow you to fix those yourself. The customer support team might be available whenever you require their help. Try to assess how much you have to rely on them or the availability of support channels like phone, email, online chat, or web form.
The documentation should be written with the relevant technical expertise required to use the tool.

Stability and Reliability

Try to analyze how much downtime you can allow and check the service level agreement (SLA). It will describe what percentage of uptime they guarantee. To evaluate a platform for stability and reliability, ensure that the extracted data is accurate and reaches the destination in a reasonable timeframe.

Batch and Stream Processing

Batch and Stream ingestion are two processes in building a data pipeline architecture. Most ETL tools do batch extraction from data sources, but others do stream processing for real-time events. One needs to know which one is ideal for which analysis.

Data Transformations

Nowadays, most companies offer data warehouses on cloud platforms. The transformations occur after the data has been loaded in the warehouse, using a modeling tool like dbt or Talend Data Fabric, or just SQL.

Pricing

ETL tools may charge based on the amount of data replicated, the number of data sources used, or the number of users using the software. Some ETL service providers have different pricing plans on their websites while others will customize according to your use case. Select the one which will allow a free trial for new users, free historical data loads, and replication from new data sources. Also, consider scalability to understand how your costs will vary with data volume.

After the ten significant factors are considered in selecting an ETL tool, start the trial by setting up and replicating data to your destination. Test for:

Usability: Add a destination, a few sources, and perform a few integrations. Analyze the resulting logs. Examine a few integrations to learn how easily you can use the tool.
Synchronization and integration: Learn how reliable the ETL tool is in sending the extracted data at the required frequency; or how easily it adds, and removes tables, columns, and rows.
Scheduling: Check if you are getting the required data in the destination on a schedule.
Accuracy: For accuracy, test a few data sets from various data sources.

Daton: Simplifying ELT

ETL platforms help companies by removing the trouble of writing their own ETL code and building data pipelines from scratch. Daton is a simple data pipeline that can populate popular data warehouses like Snowflake, Bigquery, Amazon Redshift for fast and easy analytics using 100+ data sources. The best part is that Daton is easy to set up without the need for any coding experience and it is the cheapest data pipeline available in the market.

Extensibility and scalability are important factors that you must consider while choosing ETL services. As an enterprise expands, the probability of the ETL tool supporting new data sources will decrease. The ETL tool must be able to add more data sources and be compatible with the other third-party tools through webhooks or APIs. Also, check for the downtime you may consider. Go through the Service Level Agreement (SLA) that describes the amount of uptime the ETL tool offers. The accurate data must reach the destination within the specified time frame. However, you must also consider other factors like customer support, pricing, etc., before choosing the correct ETL service.

During your trial with the ETL tool, the first test for usability is done by adding destinations and sources and then, performing integrations. Check the integrations to understand their usability. Next, check for synchronization. Find out if it is reliable to send the data extracted at the specified frequency. Also, find out if it can easily add or remove tables, rows, and columns. Another essential fact to verify is whether you can retrieve the data at the destination on a particular schedule. Perform a few tests on some data sources and check the accuracy level of the tool. Hence, the factors to be investigated while testing the ETL tool are usability, synchronization, scheduling and accuracy.

Security is vital for any system. For any pipeline of cloud-based data, you should keep the following factors in mind to ensure its security: The system must have security controls that users can configure. An API key management tool must be there. Try to know whether the encryption of data by the vendor is done at motion and rest; otherwise, provisions for enabling encryption need to be present. Check if HTTPS has been used for web-based data sources. Verify the schedule to remove your data once it has reached the destination. Find out the offers by the vendor concerning the integration of data sources and their destinations. Verify if the tool uses Secure Shell (SSH) to ensure strong authentication and prevent malicious attacks.

Different ETL tools have different prices. The cost of ETL tools may depend on the amount of data that has undergone replication, the total number of data sources involved, or the number of users working with the help of the software. So, various ETL service providers come with different pricing plans and specify them on their official sites. Other ETL providers have provisions for customized plans to suit your needs. Try to choose an ETL service provider according to your requirements and check if it offers a free trial for new users. You may look for one with free replication from different data sources and historical data loads. Also, keep scalability in mind to know how your costs may vary if data increases.

Daton is a cloud data pipeline which helps in simplifying the concept and usage of ETL tools. ETL platforms aid companies in omitting the need to write ETL code and build data pipelines from the initial stage. Daton can simply be defined as a data pipeline that can populate several data warehouses, such as Bigquery, Snowflake, and Amazon Redshift, for quick and hassle-free analysis with the help of multiple data sources. There can be more than a hundred data sources involved. The best part about Daton is that it is easy to set up, and there is no need to have any prior coding experience. Daton is one of the most budget-friendly data pipelines you can find in the market. It aims to meet the demands of the companies, and its low cost helps them to operationalize data. It is mainly developed for brands.