In today’s world, data is as valuable as oil. Organizations use this data to derive insights from it. It helps them to serve multiple business use cases, such as generating higher revenues, detecting frauds, preventing customer churn, etc. But what do you need to serve these use cases? You need to create data pipelines. And what is a data pipeline? Data pipelines are processes that copy data from one point (source), transform it if required, and store it at another point (destination).
You will be creating multiple data pipelines to extract and load the data. So there’s a need for a tool that will run and manage these pipelines. These tools automate the data pipelines, thus leading to better productivity. This article provides you with a list of the best data pipeline tools.
Table of contents
- Types of Data Pipeline Tools
- Top 10 Data Pipeline Tools
- How to Build Data Pipelines Using Skyvia Data Flow
- Conclusion
Types of Data Pipeline Tools
There are many data pipeline tools available in the market. These tools are categorized as per the requirements of the team. We will distinguish between the tools as follows:
Open-source versus Licensed Data Pipeline Tools
Open-source data pipeline tools are available to all users. Anyone can install and use them on their systems. As it is open source, it allows users to modify the source code and are free to use.
Some open-source data pipeline tools are as follows:
- Apache Airflow
- Airbyte
- Dagster
The source code of licensed data pipeline tools isn’t available to everyone. You will require a valid subscription to use these tools. These subscriptions are charged on a monthly or annual basis. Some offer a trial version or a version with limited functionality to the users. Below are some licensed data pipeline tools:
- Skyvia
- Hevodata
- Fivetran
Cloud versus On-premise Data Pipeline Tools
Cloud data pipeline tools are fully-managed. The users don’t need to install them. They can access these tools over the web. They avail the power of cloud computing to scale as and when required. These tools run on the cloud and all the data transfer and processing happens over the cloud servers. Examples of cloud data pipeline tools are:
- Skyvia
- Google Dataflow
- StreamSets
Many organizations don’t allow their data to be stored or processed on the cloud. They have to abide by their privacy policies. On-premise data pipeline tools are installed on the servers maintained internally by the organization. These tools are managed by the team internally. It isn’t possible to scale instantly as these tools are installed within an on-premise setup. Some tools are deployed on the cloud as well as on-premise servers. Following are some of the on-premise data pipeline tools:
- Apache Airflow
- Oracle Data Integrator
- Informatica
Stream versus Batch Data Pipeline Tools
Stream data pipeline tools process the data as it arrives – the processing happens on a real-time basis. Some of the tools used to serve stream data pipelines are as follows:
- Apache Spark
- Apache Nifi
- Google Dataflow
Batch data pipeline tools process the data in chunks. These jobs run at regular intervals and take a certain number of records during each iteration. Following are the data pipeline tools that support batch processes:
- Skyvia
- Apache Airflow
- Talend
Top 10 Data Pipeline Tools
Skyvia
Skyvia is a cloud platform for no-coding ELT and ETL data integration, allowing data integration through a wide range of connectors. It runs in a web browser, so there is no need to install any software. Skyvia provides several products that enable you to build integrations of various complexity, create data endpoints, automate backups, and many more. Users can maintain a centralized backup for all the apps via cloud-to-cloud backup. Skyvia offers data synchronization and replication to ensure that the data is up-to-date in a fault-tolerant manner. Every product is priced separately, so you can save money using only the products you need.
Pros:
- Requires no coding.
- More than 180 connectors to cloud sources, databases, and data warehouses.
- Provides SSH and SSL connection support.
- Has a free trial in addition to flexible pricing plans.
- Responsive support team.
Cons:
- Limited transformation options.
- Error handling is basic.
Fivetran
Fivetran is a web-based tool that allows you to create data pipelines over the cloud. It offers integration through connectors. These connectors connect with specific databases or data warehouses. You can apply pre-built transformations on the data to complete your ELT pipelines. After storing the data, you can transform it for better insights. It enables data replication from various SaaS (Software as a Service) sources and databases. The pricing is based on the volume of data consumed – the inserted, updated, or deleted rows.
Pros:
- Integration with over 150+ connectors for better connectivity.
- 24/7 technical support for quick resolutions.
- Fully managed connectors ensure minimal coding and customization. Users need minimal to no coding experience to build data pipelines.
Cons:
- It supports ELT pipelines but not ETL pipelines i.e. data transformation isn’t supported before data is loaded. Users can transform the data after it is loaded into a database.
- Minimal scope for customization of the code.
Apache Airflow
Airflow is an open-source data pipeline orchestration tool. It uses Python programming language to create and schedule the workflows. The workflows are created as DAGs (Directed Acyclic Graphs). So you can orchestrate the pipelines according to your requirements. It allows you to monitor and manage the workflows using its Python-based user interface. We can see logs of the completed and running workflow through its UI. Along with creating data pipelines, you can use Airflow for other tasks. For example, you can perform data transfer, manage infrastructure, build machine learning models, or run any Python code.
Pros:
- Free to use as it is open-source.
- Allows pipelines to be generated dynamically through Python.
- Users can customize the existing operators or define them depending on their use cases.
Cons:
- Building data pipelines require knowledge of Python, as the pipelines are built by writing code.
- Doesn’t provide any dedicated technical support in case of issues. Users need to depend on community support to debug issues.
Airbyte
Airbyte is an open-source data integration platform. It builds ELT data pipelines with the help of its pre-built connectors. It provides integration with over 300+ connectors. These connectors allow you to build data pipelines with different sources and destinations. Apart from this, it provides CDK (Connector Development Kit). This kit is used to build new connectors or edit existing ones. It offers three versions of its product –
Airbyte Open-source, Airbyte Cloud, and Airbyte Enterprise.
Pros:
- It provides multiple versions for its users. So users who need an open-sourced version can opt for that one. Users who don’t want to manage all the operational processes can opt for the licensed versions.
- Huge range of possible integrations with over 300+ connectors.
- Supports integrations with other stacks such as Kubernetes, Airflow, Prefect, etc.
- The licensed edition provides 24/7 technical support for any debugging.
Cons:
- Currently, it only supports ELT pipelines.
- Creating your connectors on CDK requires coding knowledge.
- Many connectors are built by the community and hence aren’t production ready. They are still in the alpha phase and need development before using them in production.
Stitch
Stitch is a cloud-based data pipeline tool. It provides integration with 130+ sources to ingest data into a warehouse. It provides other features such as orchestration, embedding, data transformation, security features, etc. It allows extending the integration by using its import API. So you can push any data to the data warehouse using Stitch API.
Pros:
- Allows pushing the data to multiple destinations from sources.
- Import API allows pushing data to the data warehouse from any source.
- Provides community-driven development and integration with different tools through the Singer project.
Cons:
- No on-premise version.
- Some connectors are accessed using the Enterprise version only.
- Limited customer support.
Talend
Talend Pipeline Designer is a web-based data integration tool. It builds complex data pipelines that process data at rest or in transit. It provides a platform that offers multiple products and services. Some include Data Inventory, Data Stewardship, Pipeline Designer, API Designer, etc. It offers different types of plans depending on your use cases. It offers an open-sourced version under the name of Talend Open Studio.
Pros:
- Allows running data pipelines on-premise as well as on the cloud.
- Provides the ability to design and test APIs for data sharing.
- Capable of handling unstructured data.
Cons:
- No transparent pricing. You need to contact the sales team to get a quote.
- The complex installation process for on-premise version.
- Limited features and connectors for Talend Open Studio.
Integrate.io
Integrate.io is a cloud-based data integration platform. It offers features such as ETL, reverse ETL, ELT, CDC, API generation, etc. Users can create and manage data pipelines with minimal to no coding requirements. It allows users to transform the data and control the data. Before ingesting data to the destination, we can filter data based on certain conditions.
Pros:
- Ability to pull data from any source that offers a REST API.
- Allows creating dependencies between multiple data pipelines.
- Offers API generation for multiple databases, security, network data sources, etc.
Cons:
- A limited number of connectors. The available connectors are more focused on the e-commerce use cases.
- Doesn’t offer an on-premise solution.
Pentaho
Pentaho is now integrated into Hitachi Vantara. It is an end-to-end data integration platform. It offers codeless data pipeline development through its user interface. It offers an open-sourced version with limited functionalities and a licensed version. Along with the ETL functionality, it provides features such as reporting, data mining, creating dashboards and visualizations, OLAP capabilities, etc.
Pros:
- Provides enhanced security features like IP protection and regular patch updates.
- 24/7 technical support along with assigned personnel.
- Support for analyzing and ingesting streaming data.
Cons:
- Minimal support and limited functionality for the open-sourced version.
- It doesn’t offer a web-based UI for accessing the data pipelines.
- No transparent pricing. It now provides a 30-day trial to try the enterprise edition.
StreamSets
StreamSets is a data ops platform for building and managing data pipelines. It offers two licensed editions – a professional edition with limited functionalities and an enterprise edition with extensive support and functionalities. It offers 100+ connectors for building data pipelines with databases and cloud providers. It provides two types of engines to run data pipelines. The data collector engine runs pipelines in the batch, stream, or CDC modes. The transformer engine runs pipelines involving transformations on the entire data sets.
Pros:
- Allows integration with multiple cloud providers and on-premise solutions.
- Support for batch as well as streaming data pipelines.
- Multiple transformer engines such as Spark and Snowflake.
Cons:
- No on-premise solution.
- The users might require Kubernetes knowledge as the data pipelines run on top of it.
Apache Spark
Spark is an open-source data transformation engine. It can integrate with a wide range of frameworks covering multiple use cases. Users can build data pipelines that process real-time as well as batch data. It can perform Exploratory Data Analysis (EDA) on huge amounts of data. It can run SQL queries by connecting with different storage.
Pros:
- It is free to use as it is open-source.
- Offers support for multiple languages such as Python, Scala, Java, R, and SQL.
Cons:
- It requires extensive coding experience to implement data pipelines.
- Debugging is difficult as there is no dedicated technical support.
How to Build Data Pipelines Using Skyvia Data Flow
You can create a data pipeline with the help of Data Flow in Skyiva. Data Flow represents a diagram of connected components that form a flow. Each component works with input and output data. Let’s check the Data Flow creation process based on the example.
Task Description
You have three data storages: a source database, Hubspot, and a target database. The source database has a Customers table that stores customer-related data, including the CompanyName and ContactName fields you are interested in. Hubspot stores deals-related information. It has the Number Of Opened Deals field in the Companies table. And finally, you have a target database with a Contact table that should store CompanyName, ContactName, with the according Number Of Opened Deals in it.
Prerequisites
We assume you already have a Skyvia account and have created a connection to each data source you plan to work with.
To create a connection between Skyvia and a data source:
- Open Skyvia and go to New -> Connection.
- Choose the Connector from the list.
- Fill out the required credentials.
- Click Create.
Solution
Once you’ve created all the connections, you can start building a data pipeline with the help of Data Flow.
To create a new Data Flow, go to New -> Data Flow. Our example has three components: Source, Lookup, and Target. To add a component to the Data Flow, drag it from the components list left to the Data Flow diagram. Each component has its input and output. Link components by connecting input and output circles on the diagram.
Source
Source extracts data from a data source and starts the Data Flow.
1. Click Source on the Data Flow diagram and choose source connection from the Connection dropdown.
2. Each connection has several actions available. Choose Execute Command to grab the data by executing an SQL query: SELECT CompanyName, ContactName FROM Customers.
3. You may check the output results by clicking on the output arrow.
Lookup
Lookup matches input records with records from another data source and adds columns of the matched records to the scope.
Source output consists of two columns: CustomerName and ContactName. Let’s add the Number Of Opened Deals column from the HubSpot Companies table to the scope with the help of the Lookup component.
1. Click on Lookup on the diagram and choose connection from the Connection dropdown. For example, HubSpot.
2. Choose Lookup from the Actions dropdown.
3. From the Table dropdown, select the table you are interested in. Number Of Opened Deals is stored in a Companies table.
4. Select Key for records matching such as Company name.
5. Select the columns you want to add to the output results from the Result Columns dropdown. In select a single column — Number Of Opened Deals.
6. Open Parameters to map keys. We map Company name in HubSpot to CompanyName in the source database.
7. You can click on the output arrow to check the changes in the output results.
Target
Target component defines the data source where you want to load your data and the load method.
- Click Target on the Data Flow diagram and choose target connection from the Connection dropdown.
- Click Actions to define the load method.
- Select the table to load data from the Table dropdown. In our case, it’s Contact.
- Open Parameters to map Lookup output columns with the Contact table columns.
Conclusion
In this blog, we provided a list of data pipeline tools. Some are on-premise, open-source, cloud-based, and support real-time and batch processes. Depending on your use cases, you can decide which tool suits you best. If you want a free tool, then go for open-source tools. If you don’t want to manage the tool, then cloud-based tools are the way forward. Check out streaming data pipeline tools if you want to process the data in real time.
