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 Data Pipeline Tools.
Before we dive into the details, here is a snapshot of what this post covers:
What is a Data Pipeline Tool?
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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 tools 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.
Types of Data Pipeline Tools
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 Data Pipeline tools allow you to move data, usually a very large volume, at a regular interval or 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 Pipeline 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 data 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 Decision
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 should allow you to intuitively build a pipeline and set up your infrastructure in 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.
Here is a list of use cases for the different Data Pipeline Tools mentioned in this article:
Hevo, No-code Data Pipeline Solution
Hevo is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines from 150+ sources that are flexible to your needs.
For the rare times things do go wrong, Hevo ensures zero data loss. To find the root cause of an issue, Hevo also lets you monitor your workflow so that you can address the issue before it derails the entire workflow. Add 24*7 customer support to the list, and you get a reliable tool that puts you at the wheel with greater visibility. Check Hevo’s in-depth documentation to learn more.
Hevo offers a simple, and transparent pricing model. Hevo has 3 usage-based pricing plans starting with a free tier, where you can ingest upto 1 million records.
What makes Hevo amazing:
- Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.
- Schema Management: Hevo can automatically detect the schema of the incoming data and maps it to the destination schema.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
Hevo was the most mature Extract and Load solution available, along with Fivetran and Stitch but it had better customer service and attractive pricing. Switching to a Modern Data Stack with Hevo as our go-to pipeline solution has allowed us to boost team collaboration and improve data reliability, and with that, the trust of our stakeholders on the data we serve.
– Juan Ramos, Analytics Engineer, Ebury
Check out how Hevo empowered Ebury to build reliable data products here.
Sign up here for a 14-Day Free Trial!
Business Challenges That Data Pipelines Mitigates:
Data Pipelines face the following business challenges and overcome them while serving your organization:
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.
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.
Scalability
Traditional systems cannot handle large volumes of data, which can strain their performance. Data pipelines that are cloud-based provide scalable infrastructure and optimized performance.
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 factors that drive Data Pipeline Tools decisions.
It also provided the difference between Batch vs. Real-Time Data Pipeline, Open Source vs. Proprietary Data Pipeline, and On-premise vs. Cloud-native Data Pipeline Tools.
Now you can also read about Hevo’s Inflight Transformation feature and know how it improves your ELT data pipeline productivity. A Data Pipeline is the mechanism by which ETL processes occur. Now you can learn more about the best ETL tools that simplify the ETL process.
Visit our Website to Explore Hevo
Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand.
Share your experience of finding the Best Data Pipeline Tools in the comments section below!
Sourav is enthusiastic about data science and loves to write on diverse topics related to data, software architecture, and integration.