Businesses today generate massive amounts of data. This data is scattered across different systems used by the business: cloud applications, databases, SDKs, etc. To gain valuable insight from this data, deep analysis is required. As a first step, companies would want to move this data to a single location for easy access and seamless analysis.
This article introduces you to data pipeline tools and the factors that drive a data pipeline tools decision. It also provides the difference between batch vs real-time data pipeline, open source vs proprietary data pipeline, and on-premise vs cloud-native options.
Table of Contents
What Is a Data Pipeline Tool?
Dealing with data can be tricky. To be able to get real insights from data, you would need to perform ETL:
- Extract data from multiple data sources that matter to you.
- Transform and enrich this data to make it analysis-ready.
- Load this data to a single source of truth, more often a Data Lake or Data Warehouse.
Each of these steps can be done manually. Alternatively, each of these steps can be automated using separate software tool,s too.
However, during the process, many things can break. The code can throw errors, data can go missing, incorrect/inconsistent data can be loaded, and so on. The bottlenecks and blockers are limitless.
Often, a data pipeline tool is used to automate this process end-to-end efficiently, reliably, and securely. Data pipeline software has many advantages, including the guarantee of a consistent and effortless migration from various data sources to a destination, often a data lake or data warehouse.
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Get Started with Hevo for FreeTop Data Pipeline Tools to Consider in 2025
| Data Pipeline Tool | Key Features |
| Hevo Data | Best recommended for teams looking for a platform that offers automatic schema detection and evolution. – Startups that need analysis-ready data can also leverage Hevo for their business. – It is also recommended for companies that need data to be moved error-free with no loss. |
| Informatica PowerCenter | This is for companies that need an enterprise ETL tool that is used in building data warehouses. |
| IBM Infosphere Datastage | Companies that need to integrate a huge amount of data across multiple target applications with the help of parallel frameworks. |
| Talend | Companies that are looking for a platform that combines a vast range of governance and Data Integration capabilities. |
| Pentaho | Companies looking to deploy information on the cloud on single-node or clusters of computers |
| Apache Kafka | Companies looking for a tool that excels in batch and real-time data processing. |
| Apache Airflow | Companies looking for an open-source tool that can programmatically schedule, author, and monitor workflows. – It is also beneficial for companies looking to schedule their automated workflows through a command-line interface. |
| Airbyte | An open-source platform for building and running custom data integrations. – Provides pre-built connectors for various sources and destinations. |
| Stitch | Cloud-based data integration platform focusing on ease of use and scalability. |
| Fivetran | Cloud-based data integration platform known for its reliability and comprehensive connector library. |
| AWS Glue | A fully managed ETL service on AWS, offering data integration, transformation, and cleaning capabilities. |
| Apache NiFi | An open-source data integration and processing platform designed for high-volume data flows. |
Types of Data Pipeline Tools
The tech experts from Molecula state that depending on the purpose, different types of data pipeline tools are available. The popular types are as follows:
1) Batch vs. Real-time Data Pipeline Tools
Batch pipeline tools allow you to move data, usually a very large volume, at a regular interval or in batches. This comes at the expense of real-time operation. More often than not, these type of tools is used for on-premise data sources or in cases where real-time processing can constrain regular business operation due to limited resources. Some of the famous batch data tools are as follows:
The real-time ETL tools are optimized to process data in real-time. Hence, these are perfect if you are looking to have analysis ready at your fingertips day in-day out. These tools also work well if you are looking to extract data from a streaming source, e.g. the data from user interactions that happen on your website/mobile application. Some of the famous real-time pipeline tools are as follows:
2) Open Source vs. Proprietary Data Pipeline Tools
Open source means the underlying technology of the tool is publicly available and therefore needs customization for every use case. This type of data pipeline tool is free or charges a very nominal price. This also means you would need the required expertise to develop and extend its functionality as needed. Some of the known open source data pipeline tools are:
The proprietary data pipeline tools are tailored as per specific business use, therefore require no customization and expertise for maintenance on the user’s part. They mostly work out of the box. Here are some of the best proprietary data pipeline tools that you should explore:
3) On-premises vs. Cloud-native Data Pipeline Tools
Previously, businesses had all their data stored in On-premise systems. Hence, a data lake or data warehouse also had to be set up on-premise. These data pipeline tools clearly offer better security as they are deployed on the customer’s local infrastructure. Some of the platforms that support on-premise data pipelines are:
Cloud-native data pipeline tools allow the transfer and processing of cloud-based data to data warehouses hosted in the cloud. Here, the vendor hosts the data pipeline, allowing the customer to save resources on infrastructure. Cloud-based service providers put a heavy focus on security as well. The platforms that support cloud data pipelines are as follows:
The choice of a data pipeline that would suit you is based on many factors unique to your business. Let us look at some criteria that might help you further narrow down your choice of data pipeline tool.
Factors That Drive Data Pipeline Tool Decisions
With so many data pipeline tools available in the market, one should consider a couple of factors while selecting the best-suited one as per the need.
- Easy Data Replication: The tool you choose to build or buy should allow you to set up your infrastructure within minimal time
- Maintenance Overhead: The tool should have minimal overhead and work out of the box.
- Data Sources Supported: It should allow you to connect to numerous and various data sources. You should also consider support for those sources you may need in the future.
- Data Reliability: It should transfer and load data without error or dropped packet.
- Realtime Data Availability: Depending on your use case, decide if you need data in real-time or in batches will be just fine.
- Customer Support: Any issue while using the tool should be solved quickly and for that choose the one offering the most responsive and knowledgeable customer sources
- Scalability: Check whether the data pipeline tool can handle your current and future data volume needs.
- Security: Access if the tool you are choosing can provide encryption and other necessary regulations for data protection.
- Documentation: Look out if the tool has proper documentation or community to help when any need for troubleshooting arises.
- Cost: Check the costs of license and maintenance of the data pipeline tool that you are choosing, along with its features to ensure that it is cost-effective for you.
Business Challenges That Data Pipelines Mitigate
Data pipelines face the following business challenges and overcome them while serving your organization:
1. Operational Efficiency
It is difficult to orchestrate and manage complex data workflows. You can improve the operational efficiency of your workflow using data pipelines through automated workflow orchestration tools.
2. Real-Time Decision-Making
Sometimes, there is a delay in decision-making because of traditional batch processing. Data pipelines enable real-time data processing and speed up an organization’s decision-making.
3. Scalability
Traditional systems cannot handle large volumes of data, which can strain their performance. Data pipelines, which are cloud-based, enable you to scale your infrastructure and optimize your performance.
4. Data Integration
The organizations usually have data scattered across various sources, which poses challenges. Data pipelines, through the ETL process, can ensure the consolidation of data in a central repository.
Conclusion
The article introduced you to data pipeline tools and the key factors that influence decisions, including batch vs real-time, open source vs proprietary, and on-premise vs cloud-native options.
For a seamless data integration experience, Hevo offers automated schema mapping, real-time data sync, and no-code pipeline setup. It’s a powerful solution to streamline your ETL processes and improve overall data pipeline efficiency.
Sign up for Hevo’s 14-day free trial and experience seamless data migration.
FAQs
1. What is a data pipeline tool?
A data pipeline tool automates the process of moving data from one system to another. It extracts data from sources, transforms it into a usable format, and loads it into a destination, such as a database or data warehouse, ensuring efficient data flow and integration.
2. Is a data pipeline the same as ETL?
A data pipeline covers a broader process of moving data across systems, whereas ETL means the extraction, transformation, and loading of data, and it’s one of the many types of data pipelines.
3. What is the most common data pipeline?
The most common data pipeline is the ETL pipeline (Extract, Transform, Load), where data is extracted from a source, transformed into a suitable format, and then loaded into a destination like a data warehouse for analysis.
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