Keboola is a data platform as a service that helps you build and automate all your data pipelines.
By automating ETL, ELT, and reverse ETL pipelines, you save precious data engineering time so you can focus on more revenue-generating tasks.
Keboola is fully self-service, offering intuitive no-code tools to the business experts who don’t know how to use code to create data integrations by themselves as well as a feature-rich developer toolbox for engineers so they can fully customize their data pipelines.
250 connectors help you build data pipelines with out-of-the-box components in a couple of clicks. In case you stumble upon a data source/destination that is not covered by Keboola’s pre-built connectors, you can use the Generic Extractor and Generic Writer to extract and load data from any endpoint.
Easy to use. Business experts can use Keboola as a self-service ETL tool without needing to rely on the IT department. The Visual Flow Builder empowers them to build ETL pipelines within a drag-and-drop GUI. The no-code transformations allow them to clean the data with a couple of clicks. The data engineers can build pipelines in the same friendly GUI, or use developer tools, such as code-driven data transformations (SQL, Python, R, or Julia), a devoted CLI for data pipelines, or connect their dbt code.
Drag ‘n’ drop flow builder. Building a data pipeline doesn’t get easier than drag ‘n’ drop. Simply select components you want to use, add placeholders if you are not sure about the next step, and when the data pipeline is ready just hit “run” and watch your data flow into the selected destinations.
No vendor lock-in Monthly fees keep your relationship with Keboola flexible. Unlike the majority of vendors, it is easy to take your data and scripts out of Keboola and migrate them to a different solution.
Stellar G2 reviews. A 4.7 rating on g2.com makes Keboola one of the highest rated data pipeline tools on the market.
Stitch is an ETL platform which helps you to connect your sources (incoming data) to your destinations (databases, storages and data warehouses). It is designed to enhance your current system by smoothing out the edges of ETL processes on data pipelines and accelerate time from data ingestion to insights.
Stitch has one of the most extensive integrations of all vendors. It covers a vast range of sources and destinations.
Relies on the Singer framework, which allows you to customize parts of the pipeline yourself.
It offers cron job-like orchestration, as well as logging and monitoring. This allows you to keep an eye on the health of your data pipeline.
Stitch offers a 14-day free trial version, so you can try the platform yourself before committing.
A lot of integrations (sources and destinations) require a higher payment plan, meaning that your scaling may be hindered by steeper costs.
No automated table snapshot, backup, or recovery. If there is an outage or something goes wrong, you could suffer data loss.
Limited transformation functionalities. Unlike its sources and destination integrations, Stitch is lacking when it comes to transformation support. It also requires additional staging storage to compute data transformations.
It does not offer 24/7 live support.
Who is it for?
Companies who prefer a synching data pipeline with a lot of integrations (Stitch offers a high number of integrated sources and destinations), but have low requirements for transformations and do not plan to scale horizontally to new integrations.
Segment is a customer data platform which helps you to unify your customer information across your technological touchpoints, from websites to mobile apps. With its clickable user-interface, Segment offers an easy-to-use platform for managing integrations between sources and destinations. Its platform is centered around users; all of the data transformations, enrichment and aggregations are executed while keeping the user at the center of the equation.
Identity stitching. One of the major advantages of Segment is that it offers identity stitching. It uses an identity graph, where information about a customer’s behavior and identity can be combined across many different platforms (e.g. Google, Facebook...) and clients (e.g. desktop, phone…). This enables you to centralize customer information.
Personas. Segment automatically builds up personas based on your data. Personas can be used to streamline marketing and sales operations, increase personalization, and just nail that customer journey in general!
Price. Segment does have a free tier, but it’s unusable for anyone who has more than two data sources. Many of its worthwhile features are locked behind higher-tiered plans, and customers complain about how expensive it has become (a lot).
Non-user based analytics. Segment has devoted a lot of its development to user analytics. If your needs exceed those of customer-centric analyses (e.g. revenue reports, internet of things, etc.) Segment might not offer the best support for your use case.
Who is it for?
Segment is ideal for companies who would benefit massively from stitching their customer information across platforms (and have the budget to do so).
Fivetran is an ETL platform which technically automates ETL jobs. It is a SaaS data integration tool that enables you to extract and load data from different data sources through data mappings. It supports an extensive list of incoming data sources, as well as data warehouses (but not data lakes).
Extensive security measures make your data pipeline safe from prying eyes.
Supports event data flow, which is great for streaming services and unstructured data pipelines.
It allows you to access the data pipeline with custom code (Python, Java, C#, Go…), thus making it possible to build your connections.
Limited data sharing options.
No open source. Fivetran does not showcase (parts of) its codebase as open-source, making it more difficult to self-customize.
Vendor lock-in. Annual contracts make it harder to separate yourself from Fivetran. In addition, it’s currently impossible to take your data, schemas and queries and easily migrate them to another platform.
Limited to non-existent data transformation support. It does not transform data before loading it into the database, but you can transform it afterwards using SQL commands.
Requires additional staging storage to compute data transformations.
Who is it for?
Fivetran is an ETL tool geared more towards data engineers, data analysts and technical professionals. It is great for companies who plan to deploy the tool among their technical users, but not for those who want to democratize data pipelines across the board.
Integrate.io is a no-code data warehouse integration platform designed specifically for ecommerce. Through its graphical interfaces, users can combine data from all of the sources and send them to one single destination.
The visual editor is intuitive and fast, making data pipeline design easy. This also allows non-technical users to access data pipelines and collaborate across departments.
It does not require coding ability to use the default configuration.
Limited data sharing options.
Vendor lock-in. Annual contracts make it harder to separate yourself from Integrate.io
Limited logging and monitoring. Not all logs are available and it is hard to inspect the platform when things go wrong.
It does not offer as many 3rd party connectors as other platforms.
Lacks real-time data synchronization
Who is it for?
Companies who are looking for a cloud-based solution which is easy to use, but does not require a lot of modifications or scaling.
With its clickable user interface, Etleap allows analysts to create their own data pipelines from the comfort of the user interface (UI). Though sometimes clunky, the UI offers a wide range of customization without the need to code.
Strong security standards keep your data safe.
No need to code in order to use the transformation features.
Covers a wide variety of incoming source types, such as event streams, files, databases, etc.
Limited destinations - Amazon Redshift, S3 Data Lakes, BigQuery, Snowflake and a few more.
No REST API connector.
The user interface is not as friendly.
Who is it for?
Analysts and data engineers who want to speed up their data pipeline deployment without sacrificing the technical rigor to do so. Not so apt for non-technical users, since it requires an understanding of underlying engineering standards to use the platform.
7. Free and open-source tools (FOSS)
Free and open-source tools (FOSS for short) are on the rise. Companies opt for FOSS software for their data pipelines because of its transparent and open codebase, as well as the fact that there are no costs for using the tools.
Among the most notable open source data pipeline solutions are:
pandas - with its Excel-like tabular approach, pandas is one of the best and easiest solutions for manipulating and transforming your data, just like you would in a spreadsheet.
Apache Airflow - a cron job on steroids. Apache Airflows collects big data sets, and allows you to schedule, orchestrate, and monitor the execution of your entire data pipeline. It was designed to make handling large and complex data workflows easier, however it’s not the most scaling friendly tool out there.
Postgres - one of the most popular SQL databases. Postgres adds to the usual feature set of SQL databases by extending its data type support (covers unstructured data with JSON fields) and offering built-in functions which speed up analytics.
Metabase - a lightweight application layer on top of your SQL database, which speeds up querying and automates report generation for the non-technical user.
Free. There are no vendor costs.
Fully customizable. Open source means that you can inspect the code and see what it does on a granular level, then tailor it to suit your specific use case.
No vendor lock-in. No contractual obligation to keep with a vendor who doesn’t fulfill your needs.
Community support. FOSS has a community of fans who offer plenty of support on StackOverflow and other channels.
Fun. FOSS solutions allow for a lot of tinkering, which - we’re ready to admit it - is fun.
Solution lock-in. Customized solutions are hard to disentangle when moving to a different tool or platform, especially when home-brewed solutions do not follow the best engineering practices.
High maintenance costs. Every change to the data pipeline requires you to invest engineering hours… and data pipelines change a lot. From APIs altering their endpoints to software upgrades deprecating libraries, FOSS solutions are guilty of high maintenance costs.
Lack of technical support. When things go wrong, there’s no one to call who can help you resolve your technical mess. You must be more self-reliant and budget for errors.
Scaling. As your company grows, so do your needs. The engineering solutions differ drastically depending on the scale of your data operations. For example, implementing the infrastructure for a distributed message broker makes sense when you are processing high volumes of streaming data, but not when you are collecting marketing spend via APIs. FOSS solutions require you to develop in-house expertise in scaling infrastructure (costly) or outsource it to contractors instead (also costly).
Time-to-insights opportunity costs. The average time it takes to build your entire data pipeline is north of 9 months. Vendor solutions shorten the timeline from months to weeks, so you skip the opportunity costs accumulated when waiting for your BI infrastructure to be ready to answer questions.
Who is it for?
Data-scarce companies who do not plan to scale.
Small data pipelines, which are developed as prototypes within a larger ecosystem.
Hobbyists and tinkerers.
Which tool should you choose?
Go for a tool that'll stay with you no matter your company's growth stage, and that will bring data engineering, data science, and data analytics operations under the same roof.
Only starting your business journey?
Keep in mind your data teams will need a platform that fits all use cases and will ensure data quality and bring control and visibility across the full data stack. To make it easier, we summarized the use cases from above to show the clear winner that will create a single source of truth for your data analytics and business intelligence dashboards.
The 7 best solutions presented above are just the tip of the iceberg when it comes to the options available for your data pipelines in 2023.
Build your first data pipeline in minutes with Keboola
Building a data pipeline can take a great deal of manual work - from data discovery to acquisition, organizing, cleaning, and transformations.
With Keboola, you can skip the tedious work (and the errors that come with it), accelerate data pipeline building and increase the overall productivity of your team.
The user-friendly interface will empower even non-technically savvy colleagues to build their first data pipelines and gain data insights without the help of data engineers.
A data pipeline is a series of steps that allow data to move from one location to another. It consists of three elements: A source, processing steps, and a destination.
2. Data pipeline vs ETL process
Data pipeline refers to any set process that moves data from one system to another, whereas the ETL process refers to the movement of data from its raw format to its final cleaned format ready for analytics in three exact steps: Extract, transform, load.
3. On-premise vs cloud-native data pipeline tools
On-premise data pipeline tools extract data from on-premise sources, process it and transfer it to the local server. This gives businesses more control as the data process is completely integrated into the organization’s internal system.
Cloud-native data pipeline tools are built, managed and deployed in cloud computing environments. Cloud-native data pipeline tools can sometimes be more scalable and cost-efficient than running them on-premise.
4. Batch vs realtime data pipeline tools
Batch data pipeline tools will first store data received and then process it in a batch. Realtime data pipeline tools will process the data immediately.