Justin Reynolds, Contributor
September 28, 2022
NaN minute read
To compete in today’s data-driven market, companies need to quickly transform raw analytics into actionable insights. Unfortunately, most are struggling to make this happen because they lack the necessary infrastructure for data migration and transformation.
As a result, it’s common for data to stagnate in Excel spreadsheets and backend databases instead of reaching business leaders or powering advanced machine learning initiatives.
The good news, however, is that this is changing thanks to the emergence of powerful SaaS-based extract, transform and load (ETL) tools that make it fast and easy to pipeline data. Many of these tools are very easy to leverage, with no-code and low-code features that anyone can use regardless of their technical expertise.
But with so many options to consider, it can be difficult to narrow down a tool that meets your specific needs. In this post, we’ll provide some background information to make your decision easier.
Read on to learn about:
What ETL tools are
The difference between ETL vs ELT solutions
The benefits of ETL tools
When you should make the leap to an ETL tool
What to consider when evaluating ETL Tools
Popular ETL choices for 2022 and beyond
ETL tools extract data from structured and unstructured data sources, transform it into an appropriate format, and then load it into appropriate warehouses. A comprehensive ETL tool can help organizations rapidly obtain data, standardize it, and make it available for developers and business leaders throughout the organization.
At a high level, there are four types of ETL tools:
Real-time ETL services that instantly extract, clean, enrich, and load data into target systems.
Batch ETL tools that use batch processing to pull data from source systems and transfer it into target repositories (reducing resource consumption and saving money along the way).
On-premise ETL tools that enable organizations to keep data on-site in legacy systems and maintain full control over security.
SaaS-based ETL solutions, which are deployed through the cloud and deliver flexibility, cost savings, and agility, making them a popular choice for many businesses.
When it comes to using ETL tools, you have two options: You can build your own solution or use an off-the-shelf SaaS solution.
Designing your own ETL solution is a bit like building a highway before you can drive on it. The process can take months to complete, and it requires extensive ongoing maintenance and updates. Since most enterprises aren’t willing to refocus so many resources to ETL development, many choose to invest in ETL solutions built for today’s analytics needs—let’s look at some of the main benefits of using an ETL vendor.
An ETL vendor can have your new solution up and running in just a few days, and at a fraction of the cost of building your own. The right SaaS ETL solution can help you consolidate all of your company’s data and apply complex transformations to prepare it for analysis without requiring months of in-house engineering time.
ETL vendors also take care of maintenance, perform updates as APIs change, and keep an eye on evolving data structures. This approach can liberate your engineering team, enabling them to tackle higher-level needs and mission-critical projects instead of worrying about lifecycle management. In the event something goes wrong, the vendor will take care of it — giving your team back their nights and weekends.
As your company grows, you’ll fine-tune your models and change the way you manage your data. Sources will change, volume will grow, and you’ll need the capacity for new forms of unstructured and structured data. Good ETL solutions provide the flexibility to configure and adapt to changing conditions while maintaining a single source of truth.
Many modern ETL tools offer powerful interfaces with drag-and-drop functionality. This allows for faster pipelining and mapping between source and target locations, even by team members who aren’t writing code.
Get a simple step-by-step tutorial on how to assemble a modern data stack in 30 minutes (with a YouTube video for guidance).
We’d recommend getting an ETL pretty early on as you assemble your data stack. We knew it was time to look for an ETL solution when we started to ask questions based on what we could answer rather than what we wanted to answer. Read more about Mode's journey with ETL tools. Other signs you are ready for an ETL tool:
There's a need to standardize and extract insights at scale. Every data stack is different, with many combinations of data sources, warehouses, and third-party services. As you grow, at some point you’ll start to feel your workflows get too complex—it will be hard to standardize, protect, and extract insights from data at scale.
There's a backlog of requests from stakeholders. You’ll also feel the pressure from your internal stakeholders—there may be a backlog of requests for answers to their business questions, or concern from security and compliance about how sensitive information is being handled.
If you find yourself held back from creating the best possible data workflows and adopting new technology. Switching to an ETL tool is a big step toward becoming a data-driven business, and reaching the next stage of data maturity means being able to proactively analyze and learn from your data from a single, centralized solution without a huge team working manually to do it.
Companies often make the mistake of comparing ETL services based on integration support and price alone. But there are many additional criteria you can use to evaluate vendors and narrow down your search.
First, it’s important to determine whether your business needs a data syncer or transformer. Most data pipelines fall into one category or the other.
Data syncers have streamlined setups and workflows to quickly transfer data from one source to another. They typically have simpler UIs and focus heavily on sync status. Examples include Blendo, Fivetran, Segment, and Stitch.
Data transformers provide greater functionality, but they’re a bit more complex to use. In other words, they not only sync data but can also transform and enrich it. Transformers also typically have more complex pricing structures based on compute time or data throughput. They tend to support a more diverse set of data sources, too, including APIs, log files, S3 buckets, and other unstructured sources. Examples include dbt.
Pro tip: Syncers are better for moving data out of third-party sources into centralized warehouses. Transformers are ideal when working with massive log files and generic file stores, such as S3.
It’s critical to assess each vendor’s security and compliance policies — especially if your business needs to adhere to frameworks like HIPAA and GDPR. After all, not all ETL providers offer the same level of compliance, data access management, visibility, and data governance.
As Fivetran explains, data pipelines may be virtual, but ETL still requires strong security measures. As such, it’s important to make sure the data centers that your ETL provider uses contain robust authorization and monitoring controls to limit access.
It’s also a good idea to work with a provider offering a layered security defense, which uses multiple mechanisms to protect data. To illustrate, Snowflake provides a great description of its layered security model, which combines network security, identity access management (IAM), and data encryption.
For most companies, ETL serves as a foundational data layer supporting all product, marketing, and sales decisions. That being the case, data quality is imperative for success.
If the data that you’re using isn’t trustworthy — meaning data is missing or incorrect — it will lead to incorrect decisions that negatively impact the entire company. What’s more, you might not notice that you have a data quality issue for a long time, which could cause you to make poor decisions.
As such, ETL testing support should be a top consideration when comparing tools and services. ETL testing focuses on data verification, validation, and qualification. When comparing ETL tools, it’s a good idea to assess both their native testing controls and support for third-party testing services.
When selecting an ETL provider, you should determine whether the tool syncs with the type of data that you need. As it turns out, ETL tools offer different levels of third-party integrations — and some are more comprehensive than others.
In other words, the tool needs to do more than just move data from point to point. It also needs to be able to pull data from the correct source locations.
As an example, suppose you’re looking to pull advertisement data from Facebook. It’s important to investigate what type of data the ETL tool can procure. One tool may pull daily ad interactions while another might measure weekly engagement.
The main purpose of using an ETL tool is to make data analysis fast and easy. However, not all tools offer a great user experience — and some can be downright difficult to work with.
When comparing ETL vendors, you’ll want to demo and trial different platforms to get a sense of how easy and intuitive it is to interact with data. Make sure the tool makes it easy to query data and that the UI is something you’ll feel comfortable working with every day.
When relying on ETL tools for critical data management, it’s vital to have strong and responsive support. After all, even the best platforms fail from time to time.
As you’ll see in the reviews below, vendors tend to have varying levels of support. It’s worth asking hard-hitting questions when negotiating with vendors and trying different options to ensure you make the best decision.
When browsing the ETL market, it’s unlikely that any vendor will have the full list of integrations your business needs.
With this in mind, it helps to talk to each vendor and inquire about their roadmap and integration strategy. Just because a vendor doesn’t support a certain integration right now, doesn’t mean they won’t have it six months to a year down the road.
With ETL, pricing structure matters more than the list price. If your business is growing, it’s necessary to use ETL tools that will enable you to scale data consumption without breaking the bank. Some companies charge based on the amount of data that you process, which can get very expensive.
Now that you have a better idea of why ETL tools are important and some of the features to look for when shopping for a solution, it’s time to examine some of our favorite ETL software on the market right now.
Etleap is an ideal ETL tool for business teams that need to start pipelining data quickly. With Etleap, you’ll receive out-of-box ETL infrastructure you can use to set up robust data pipelines in just a few hours. Etleap also offers a managed service, which makes life easier for engineers.
With Etleap, you can parse, de-identify, structure, and clean data, and model that data with SQL. The solution will also manage dependencies. In addition, the solution lets you leverage automation and eliminate repetitive tasks with pipeline scheduling and orchestration.
It’s extremely easy to set up data pipelines with Etleap. If you encounter any issues, Etleap support should be able to quickly identify and resolve them.
If you have some experience with SQL, you should be able to jump in and start using Etleap. In other words, you don’t have to be an expert data engineer to use this service.
Etleap has a decent amount of connectors and is constantly developing more for customers.
Etleap has limited source control management capabilities for tracking changes.
Pipelines may stall from time to time, which can impact operations and data flows.
You may experience limited support from Etleap if you’re in the Pacific time zone.
Segment — which was acquired by Twilio in 2020 — is a platform that collects events from mobile and web apps.
One of Segment’s most powerful features is data capture and delivery, which lets you collect analytics from customer touchpoints and distribute them to different tools and warehouses. It also features data filtering and schema locking, customer personas, and a robust integrations catalog.
Segment has a free plan, a team service that starts at $120 per month, and a business plan with custom pricing.
Segment lets you send data to multiple destinations simultaneously, such as Mixpanel and Google Analytics. Plus, some plugins integrate with different frameworks and programming languages.
Segment offers a debugger feature that’s very useful for developers. When implementing new events, you can add them to the code on a local machine and see them in Segment while testing.
This platform integrates nicely with programs like Blaze, Amplitude, and Redshift.
It can be difficult to set up multiple instances of the same sources (like web events) with different destinations. It’s not impossible, but creating a complete graph of data connections requires repetitive configuration.
Segment is expensive compared to other similar tools and services.
The service can be technical and requires a fair amount of development resources. You need the right team and strategy to make this work.
Fivetran is another ETL service that’s ideal for beginners and non-technical teams that need to quickly connect to data sources.
With Fivetran, you can access a massive amount of integrations and comprehensive reporting and data analytics features. However, the platform doesn’t feature a data quality control mechanism, which is a drawback.
As for pricing, Fivetran has several different plans that range from about $120 (starter model) to $240 per month (enterprise). Customers only pay for rows updated or inserted by Fivetran’s connectors (i.e., monthly active rows).
Fivetran makes it very easy to handle data from multiple sources.
It integrates well with Snowflake and PostgreSQL data warehouse destinations. It also integrates with PostgreSQL upstream data sources.
The service provides rich data integration capabilities with out-of-box connectors for many SaaS-based data sources and databases.
Fivetran doesn’t support complex data transformations.
The company doesn’t release new connectors very frequently. It works much better if you have a modern data stack and work with Fivetran’s preferred partners.
Fivetran’s pre-made reports can be inconsistent over connectors.
Stitch is a simple but powerful ETL tool for data teams that integrates with more than 130 data sources. Teams can use Stitch to centralize data for tighter security, governing, and analysis.
One of Stitch’s key features is the Stitch Import API, which lets you push data to Stitch from any location. Stitch also lets you extract data from any source through the Singer open source framework.
Stitch pricing ranges from $100 to $2,500 per month, depending on the plan you pick.
You can easily perform data replication in Stitch from an external source to an internal database on a schedule.
The platform is entirely self-serve, meaning there’s no need to deal with customer representatives or account managers.
Stitch features a wide range of built-in source and destination integrations.
Stitch doesn’t provide transparency with integration configurations, which makes it tough to evaluate issues.
There’s no single sign-on (SSO) or multi-factor authentication (MFA) component.
The company may not be able to guarantee data quality. There’s also no row-level filtering option.
Databricks SQL is a serverless data warehouse that runs on the Databricks Lakes platform. With this platform, you can run all SQL and BI applications with open formats and APIs, a unified governance model, and your own preferred tools.
Databricks works with SQL and popular tools like Fivetran, Tableau, and Power BI. As an added bonus, its serverless compute feature eliminates the need to manage and configure cloud service infrastructure on the Lakehouse.
Databricks is easy to use, with a small learning curve, so you can start focusing on the problem at hand right away and get immediate value.
The platform supports ACID transactions.
It also supports streaming live tables.
The platform changes often, which can make it difficult to stay up to date. It can also be difficult to manage jobs at scale without the right CI/CD systems in place.
It can be challenging to debug code and use interactive applications outside of Databricks-approved tools.
Regular maintenance can sometimes lead to failure and downtime in Databricks. However, proper scheduling and retry mechanisms are useful workarounds.
Data Virtuality combines ETL with data virtualization, resulting in a flexible self-service data integration tool with SQL functionality.
The platform integrates with over 200 data connectors, which come ready-to-use and fully optimized. In addition, the company ensures data quality and provides access to metadata repositories for easier master data management. Customers also have transparency through data lineage across ETL and data virtualization processes.
Data Virtuality pros
It provides access to out-of-box API integration without having to code. This is also an excellent option if you’re looking for Amazon API access for advertising and seller APIs.
One of Data Virtuality’s biggest strengths is its pre-built connectors that provide speedy access to data. The concept of a virtual data layer that supports sophisticated SQL procedures also makes Data Virtuality worth checking out.
The Data Virtuality team is helpful and responsive, and eager to work with customers.
Data Virtuality cons
Documentation may be difficult to navigate and incomplete.
You need to have experience and training with logical data warehouse design to get the most out of this platform.
The Pipes Web User Interface (UI) may be difficult to work with for some users. Custom pipe definitions can also be hard to master.
Integrate.io offers a low-code ETL and reverse ETL service for data pipelines. However, if you like to code, you can also access a powerful API and rich expression language along with webhooks for advanced customization.
This is a great option if you’re looking for an ETL platform with strong enterprise-grade security. It’s certified SOC 2-compliant and contains advanced security features like hashing, two-factor authentication, and data masking.
This platform is great for connecting data across silos, and you don’t have to be a data scientist to use it, either.
The interface is simple, and it’s easy to learn different features and components. If you ever get stuck, you can reach support through a convenient chat window.
You may experience slow syncing when running two jobs in parallel on different cluster nodes.
There aren’t any connectors for SAP or Azure.
AWS Glue is a serverless ETL and data integration service for data discovery, preparation, and merging. It’s a great option for use cases involving semistructured data or evolving schemas that require a highly scalable tool. Of note, the AWS Glue Schema Registry now supports the JSON Schema.
Amazon charges an hourly rate for AWS Glue, with per-second billing for crawlers and ETL jobs. The company also charges a monthly fee for AWS Glue Data Catalog.
AWS Glue pros
AWS Glue comes with a Spark code editor for producing custom code.
The platform is handy if you’re looking for a code-based interface option for data integration. It also enables serverless integration.
The solution works seamlessly with other AWS tools. If you’re using AWS for other data management processes, this could be a natural fit.
AWS Glue cons
The platform may be difficult to set up and debug. Expect a big learning curve with Glue; it’s not for beginners.
You can easily integrate AWS Glue with any type of Spark or Hadoop cluster, making it very flexible.
Glue can only execute jobs using Python or Scala. It doesn’t support different languages, including Java and Go.
Azure Synapse Analytics is a popular high-performance analytics service from Microsoft that enables customers to query data using serverless or dedicated infrastructure at scale.
One of the platform’s noteworthy features is code-free hybrid data integration, which enables you to build ETL and ELT processes in a code-free visual environment and ingest data from over 95 native connectors.
You can manage data pipelines in the same analytics platform as your cloud data warehouse for a simplified approach to ETL.
Azure Synapse Analytics pros
Azure Synapse makes it easy to build a data pipeline.
This service comes with a flexible usage-based payment model.
It integrates seamlessly with otherAzure Data Services products, including Azure Databricks, ADLS, ADX, Active Directory, and Power BI.
Azure Synapse Analytics cons
You need to know how to use Azure Active Directory and Azure Storage to benefit from this platform.
It can sometimes be difficult to diagnose failures in Azure Synapse Analytics.
Some users may encounter platform issues, and it can be difficult to access support at times.
Apache Airflow is an open-source ETL tool for creating, scheduling, and tracking workflows.
Companies use Airflow to efficiently build and maintain ETL pipelines. It lets users create workflows in standard Python and supports a range of integrations with platforms — like Google Cloud, AWS, Azure, and others.
Apache Airflow has the support of a strong open source community.
Directed Acyclic Graphs (DAGs) are straightforward to implement, and they’re efficient, too.
The platform handles dependency management very well.
The platform can be unreliable for jobs with longer run times.
Users don’t have the option to change the code in the UI, which can be frustrating.
It can be difficult to make changes once you deploy a pipeline. Users typically need to build a new one instead.
Pentaho — which is now under the control of Hitachi Ventara — lets you rapidly build and deploy data pipelines for data integration and analytics.
The platform provides broad connectivity to almost any data source from a single dashboard, making it one of the most flexible offerings on the market. Plus, it comes with an easy drag-and-drop interface for simple pipelining as well as dataflow templates
This platform is accessible as an enterprise or community version.
Pentaho comes with a friendly UI, meaning virtually anyone can use it. You don’t need strong programming skills to access Pentaho.
Issue log reports can be confusing, which can make troubleshooting in Pentaho challenging.
Pentaho is consistent across different database nodes.
Troubleshooting in Pentaho can be difficult, as issue log reports aren’t always clear.
The platform has limited integration with the JDBC connector.
Pentaho can take a long time when loading large datasets.
Editor’s note: These are just a few of our favorite tools and services. There are countless options to choose from when it comes to ETL — like Oracle Data Integrator, Azure Data Factory, IBM DataStage, Informatica, and many others. For the best results, we encourage you to explore the market, try different options, and pick tools that align with your exact needs and workflows.
Mode enables data leaders to finally realize a true analytics center of excellence. With a single, low-code platform that integrates with your tech stack and provides value to your line of business teams, you’re on a path to truly iterative analytics that drive better business outcomes.
Our platform integrates with virtually any third-party ETL service like AWS Athena, Aurora, and Redshift, as well as other leading services like Snowflake, Google BigQuery, and PostgreSQL, among others. We can connect your organization’s raw data, data scientists, analysts, and decision-makers over a central platform, reducing time-to-answers and making it easy to share actionable insights.
In addition, we have an exciting new integration with dbt which enables our customers to get better views on data freshness.
And if you’re ready to accelerate your analytics with Mode, take advantage of our free 14-day trial today.
Get a simple step-by-step tutorial on how to assemble a modern data stack in 30 minutes (with a YouTube video for guidance).