Organizations of all sizes and industries now have access to ever-increasing amounts of data, far too vast for any human to comprehend. All this information is practically useless without a way to efficiently process and analyze it, revealing the valuable data-driven insights hidden within the noise.
The ETL (extract, transform, load) process is the most popular method of collecting data from multiple sources and loading it into a centralized data warehouse. During the ETL process, information is first extracted from a source such as a database, file, or spreadsheet, then transformed to comply with the data warehouse’s standards, and finally loaded into the data warehouse.
Best 10 ETL Tools in 2020
ETL is an essential component of data warehousing and analytics, but not all ETL software tools are created equal. The best ETL tool may vary depending on your situation and use cases. Here are 7 of the best ETL software tools for 2020 and beyond:
Top 7 ETL Tools Comparison
Xplenty is a cloud-based ETL and ELT (extract, load, transform) data integration platform that easily unites multiple data sources. The Xplenty platform offers a simple, intuitive visual interface for building data pipelines between a large number of sources and destinations.
More than 100 popular data stores and SaaS applications are packaged with Xplenty. The list includes MongoDB, MySQL, PostgreSQL, Amazon Redshift, Google Cloud Platform, Facebook, Salesforce, Jira, Slack, QuickBooks, and dozens more.
Scalability, security, and excellent customer support are a few more advantages of Xplenty. For example, Xplenty has a new feature called Field Level Encryption, which allows users to encrypt and decrypt data fields using their own encryption key. Xplenty also makes sure to maintain regulatory compliance to laws like HIPPA, GDPR, and CCPA.
Thanks to these advantages, Xplenty has received an average of 4.4 out of 5 stars from 83 reviewers on the G2 website. Like AWS Glue, Xplenty has been named one of G2’s “Leaders” for 2019. Xplenty reviewer Kerry D. writes: “I have not found anything I could not accomplish with this tool. Support and development have been very responsive and effective.”
2. AWS Glue
AWS Glue is a fully managed ETL service from Amazon Web Services that is intended for big data and analytic workloads. As a fully managed, end-to-end ETL offering, AWS Glue is intended to take the pain out of ETL workloads and integrates well with the rest of the AWS ecosystem.
Notably, AWS Glue is serverless, which means that Amazon automatically provisions a server for users and shuts it down when the workload is complete. AWS Glue also includes features such as job scheduling and “developer endpoints” for testing AWS Glue scripts, improving the tool’s ease of use.
AWS Glue users have given the service generally high marks. It currently holds 4.1 out of 5 stars on the business software review platform G2, based on 36 reviews. Thanks to this warm reception, G2 has named AWS Glue a “Leader” for 2019.
Alooma is an ETL data migration tool for data warehouses in the cloud. The major selling point of Alooma is its automation of much of the data pipeline, letting you focus less on the technical details and more on the results.
Public cloud data warehouses such as Amazon Redshift, Microsoft Azure, and Google BigQuery were all compatible with Alooma in the past. However, in February of 2019 Google acquired Alooma and restricted future signups only to Google Cloud Platform users. Given this development, Alooma customers who use non-Google data warehouses will likely switch to an ETL solution that more closely aligns with their tech stack.
Nevertheless, Alooma has received generally positive reviews from users, with 4.0 out of 5 stars on G2. One user writes: “I love the flexibility that Alooma provides through its code engine feature… [However,] some of the inputs that are key to our internal tool stack are not very mature.”
Talend Data Integration is an open-source ETL data integration solution. The Talend platform is compatible with data sources both on-premises and in the cloud, and includes hundreds of pre-built integrations.
While some users will find the open-source version of Talend sufficient, larger enterprises will likely prefer Talend’s paid Data Management Platform. The paid version of Talend includes additional tools and features for design, productivity, management, monitoring, and data governance.
Talend has received an average rating of 4.0 out of 5 stars on G2, based on 47 reviews. In addition, Talend has been named a “Leader” in the 2019 Gartner Magic Quadrant for Data Integration Tools report. Reviewer Jan L. says that Talend is a “great all-purpose tool for data integration” with “a clear and easy-to-understand interface.”
Stitch is an open-source ELT data integration platform. Like Talend, Stitch also offers paid service tiers for more advanced use cases and larger numbers of data sources. The comparison is apt in more ways than one: Stitch was acquired by Talend in November 2018.
The Stitch platform sets itself apart by offering self-service ELT and automated data pipelines, making the process simpler. However, would-be users should note that Stitch’s ELT tool does not perform arbitrary transformations. Rather, the Stitch team suggests that transformations should be added on top of raw data in layers once inside the data warehouse.
G2 users have given Stitch generally positive reviews, not to mention the title of “High Performer” for 2019. One reviewer compliments Stitch’s “simplicity of pricing, the open-source nature of its inner workings, and ease of onboarding.” However, some Stitch reviews cite minor technical issues and a lack of support for less popular data sources.
6. Informatica PowerCenter
Informatica PowerCenter is a mature, feature-rich enterprise data integration platform for ETL workloads. PowerCenter is just one tool in the Informatica suite of cloud data management tools.
As an enterprise-class, database-neutral solution, PowerCenter has a reputation for high performance and compatibility with many different data sources, including both SQL and non-SQL databases. The negatives of Informatica PowerCenter include the tool’s high prices and a challenging learning curve that can deter smaller organizations with less technical chops.
Despite these drawbacks, Informatica PowerCenter has earned a loyal following, with 44 reviews and an average of 4.3 out of 5 stars on G2—enough to be named a G2 “Leader” for 2019. Reviewer Victor C. calls PowerCenter “probably the most powerful ETL tool I have ever used”; however, he also complains that PowerCenter can be slow and does not integrate well with visualization tools such as Tableau and QlikView.
7. Oracle Data Integrator
Oracle Data Integrator (ODI) is a comprehensive data integration solution that is part of Oracle’s data management ecosystem. This makes the platform a smart choice for current users of other Oracle applications, such as Hyperion Financial Management and Oracle E-Business Suite (EBS). ODI comes in both on-premises and cloud versions (the latter offering is referred to as Oracle Data Integration Platform Cloud).
Unlike most other software tools on this list, Oracle Data Integrator supports ELT workloads (and not ETL), which may be a selling point or a dealbreaker for certain users. ODI is also more bare-bones than most of these other tools, since certain peripheral features are included in other Oracle software instead.
Oracle Data Integrator has an average rating of 3.9 out of 5 stars on G2, based on 12 reviews. According to G2 reviewer Christopher T., ODI is “a very powerful tool with tons of options,” but also “too hard to learn…training is definitely needed.”
No two ETL software tools are the same, and each one has its benefits and drawbacks. Finding the best ETL tool for you will require an honest assessment of your business requirements, goals, and priorities.
Given the comparisons above, the list below offers a few suggested groups of users that might be interested in each ETL tool:
- AWS Glue: Existing AWS customers; companies who need a fully managed ETL solution.
- Xplenty: Companies who use ETL and/or ELT workloads; companies who prefer an intuitive drag-and-drop interface that non-technical employees can use; companies who need many pre-built integrations; companies who value data security.
- Alooma: Existing Google Cloud Platform customers.
- Talend: Companies who prefer an open-source solution; companies who need many pre-built integrations.
- Stitch: Companies who prefer an open-source solution; companies who prefer a simple ELT process. Companies who don’t require complex transformations.
- Informatica PowerCenter: Large enterprises with large budgets and demanding performance needs.
- Oracle Data Integrator: Existing Oracle customers; companies who use ELT workloads.
Who is ETL Developer?
An ETL developer is a type of software engineer that manages the Extract, Transform, and Load processes, implementing technical solutions to do so.
ETL developer is a software engineer that covers the Extract, Transform, and Load stage of data processing by developing/managing the corresponding infrastructure.
What are the different types of ETL tools?
The five critical differences of ETL vs ELT:
- ETL is Extract, Transform and Load while ELT is Extract, Load, and Transform of data.
- In ETL data moves from the data source, to staging, into the data warehouse.
- ELT leverages the data warehouse to do basic transformations. No data staging is needed.
- ETL can help with data privacy and compliance, cleansing sensitive & secure data even before loading into the data warehouse.
- ETL can perform sophisticated data transformations and can be more cost effective than ELT.
ETL and ELT is easy to explain, but understanding the big picture—i.e., the potential advantages of ETL vs. ELT—requires a deeper knowledge of how ETL works with data warehouses, and how ELT works with data lakes.
How HotelTonight Streamlined their ETL Process Using IronWorker. For a detailed discussion of Harlow’s ETL process at work, check out Harlow’s blog at: http://engineering.hoteltonight.com/ruby-etl-with-ironworker-and-redshift
ETL Role: Data Warehouse or Data Lake?
There are essentially two paths to strategic data storage. The path you choose before you bring in the data will determine what’s possible in your future. Although your company’s objectives and resources will normally suggest the most reasonable path, it’s important to establish a good working knowledge of both paths now, especially as new technologies and capabilities gains wider acceptance.
We’ll name these paths for their destinations: The Warehouse or the Lake. As you stand here are the fork in the data road considering which way to go, we’ve assembled a key to what these paths represent and a map to what could be waiting at the end of each road.
This well-worn path leads to a massive database ready for analysis. It’s characterized by the Extract-Transform-Load (ETL) data process. This is the preferred option for rapid access to and analysis of data, but it is also the only option for highly regulated industries where certain types of private customer data must be masked or tightly controlled.
Data transformation prior to loading is the key here. In the past, the transformation piece or even the entire ETL process would have to be hand-coded by developers, but it’s more common now for businesses to deploy pre-built server-based solutions or cloud-based platforms with graphical interfaces that provide more control for process managers. Transformation improves the quality and reliability of the information through data cleansing or scrubbing, removing duplicates, record fragments, and syntax errors.
This new path how only recently begun to open up for wider use thanks to the massive storage and processing power of cloud providers. Raw, unstructured, incompatible data streams of all types can pool together for maximum flexibility in handling that data at a later point. It is characterized by the Extract-Load-Transform (ELT) data process.
The delay in transformation can afford your team a much wider scope of possibilities in terms of data mining. Data mining introduces many of the tools at the edge of artificial intelligence, such as unsupervised learning algorithms, neural networks, and natural language processing (NLP), to serendipitously discover new insights hidden in unstructured data. At the same time, securing the talent and the software you need to refine raw data into information using the ELT process can still be a challenge. That is beginning to change as ELT becomes better understood and cloud providers make the process more affordable.
Choosing the Right Path
To go deeper into all of these terms and strategies, consult our friends over at Xplenty: ETL vs ELT: Top Differences. You’ll find a nuts and bolts discussion and supporting illustrations that compare the two approaches in categories such as “Costs”, “Availability of tools and experts” and “Hardware requirements.” The most important takeaway is that the way we handle data is evolving along with the velocity and volume of what is available. Making the best call early on will have significant repercussions across both your market strategy and financial performance in the end.