As businesses become more digitized, their data sources get more diverse. Thus, integration between your data sources—such as external APIs, SDKs, or streaming mobile application data in real time to the database—needs to be more reliable and seamless. Because of this, many businesses need software solutions to help them keep up with this continuously changing business landscape and keep their customers happy. This is where data pipeline tools come in.
Due to this higher demand for tools and software solutions, several data pipeline tools have been coming up and becoming more popular. From Amazon Web Services (AWS) to SQL, businesses today have many options on the market to manage their data pipelines better. However, this also leaves businesses with a dilemma when it comes to adopting a tool—the dilemma of deciding what is the best, most suitable data pipeline tool for them.
This guide will teach you everything you need about data pipeline tools.
First of all, let’s define what a data pipeline tool is.
What is a data pipeline tool?
A data pipeline tool is an application or software that allows users to integrate and aggregate data from various sources into a unified destination for storage and analysis.
Think of the whole process as users using the application's scripts and graphical user interface (GUI) to build "pipelines" to move data from a source or sources to a destination. Various data extraction and loading processes may take place during this movement. However, your data migration and pipelines depend on the objective or business use case defined by the data engineer or professional.
What are the key advantages of using a data pipeline tool?
Data pipeline tools allow users to move data from source to destination systems.
By using a well-designed and well-structured pipeline architecture, you can rest assured that you're investing in a dependable data migration system. This system not only ensures accurate, reliable, and efficient data movement but also proves to be cost effective and capable of handling large volumes of data at an impressive speed.
Another key advantage of a data pipeline tool is that it facilitates cross-functional collaboration and provides a centralized platform for data management, establishing a single source of truth.
Data pipeline design patterns and architecture
The effectiveness and reliability of data movement and processing rely heavily on the design and architecture of your data pipeline, making these critical aspects to consider.
Because of their importance, we'll explore each separately. Let's begin with design patterns before we discuss data pipeline architecture.
Data pipeline design patterns
Finding a data pipeline tool that works for your business depends on your data pipeline design patterns. And to make it more challenging, each company has its own way of processing data. Thus, even if two businesses are in the same industry, they could have completely different data pipeline design principles for various use cases. This is mainly because the design pattern plays a role in how you can efficiently manage, process, and transfer data within your data system.
What are some of these design patterns you might be wondering about?
Extraction
In extraction design, we focus on how the data is obtained from the source. This could be an incremental load, complete snapshot, streaming, or change data capture (CDC) pattern.
Behavioral
In behavioral design, we focus on how the data is processed and behaves when there are failures or reruns within the pipeline. Will it be an ETL (extract, transform, load) or ELT (extract, load, transform) pipeline? We would also look into the idempotency and self-healing nature of your pipelines. By idempotency, we mean data can be dumped using the same input without producing a different output. At the same time, its self-healing nature ensures that your pipeline automatically recovers from faults and maintains continuous operation.
Structural
The structural pattern answers questions like how your components interact with each other, how your data transformation should be layered, and how they should be organized. You could keep your transformations separate (multi-hop pipelines) or run a multiple-pipeline system.
However, regardless of your pattern, you should choose a tool that lets you build maintainable and reliable pipelines. So, research to understand the features and what works for your business. Acceldata’s resources library, which has excellent books, videos, event debriefs, and guides, is always the best place to get started.
Data pipeline architectures
Similarly, picking a data architecture can be difficult. So, what should you look for?
Depending on your business use case, you could explore these architecture types on conceptual and platform levels. By conceptual, we mean how datasets are processed and transformed. For example, you might choose between data lake or data mesh architecture; event-driven architecture or stream-processing architecture, which is excellent for real-time analytics; or batch-processing architecture. It could also come in a combination of stream-processing and batch-processing architecture, which we call lambda architecture.
At the platform level, we're looking at the tools and frameworks. Your choice will be based on your needs, the organization's goal, or the tool’s scalability, security, and constraints. You also need one that lets you improve data observability by monitoring data across multiple or hybrid data lakes. Also, get one that uses cloud technology like Acceldata to enhance the visibility of your data across multiple sources. This is particularly important today because businesses increasingly collect data from various sources.
Data pipeline tools: open-source and commercial options
Now that you understand the basics of data pipeline tools, let's explore some of the top ones available. These tools can be classified based on various factors, such as batch or real time, on-premises or cloud, or open-source or commercial. However, this section of the post will focus primarily on the open-source and commercial classification options.
Open-source data pipeline tools
Open-source data pipeline tools offer a unique advantage. The source code or underlying technology is available to the public. Thus, you can customize it to your specific needs, provided you have the expertise. Some popular tools in this category include the following:
- Apache Airflow: This open-source tool has a web-based UI that provides users with a simple interface for building, scheduling, and monitoring data pipelines through DAG (Directed Acyclic Graph). It also has a strong community and supports customization through plugins. On the flip side, it has a steep learning curve and setup can be complex.
- Dagster: Like Airflow, Dagster is a client code-first, open-source framework tool for building, monitoring, and orchestrating data pipelines and workflows. Unlike the other tools, it focuses more on orchestrating data assets than tasks. The learning curve is also steeper, as one needs an understanding of Python and the tool framework.
- Airbyte: This open-source tool is known for its simplicity in handling data integration tasks and has strong community backing. It is also best suited for batch data pipelines and supports self-hosted and cloud-hosted solutions. However, it only supports ELT pipelines, and some connectors are still in development.
Commercial data pipeline tools
Commercial data pipeline tools come at a cost. However, they offer ease of use with rapid feature deployment. They are also proficient in managing extensive data processing and have robust security measures. Lastly, unlike open-source options, commercial tools offer dedicated support teams that provide assistance, guidance, and responsive issue resolution. One tool in this category is Acceldata, which provides a comprehensive suite of features with reliable support. Other examples of tools in this category are:
- Estuary: This data pipeline tool is commonly known for its real-time Change Data Capture (CDC), ETL, and data integration capabilities, making it great for creating streaming data pipelines. It also has a user-friendly GUI-based web interface that supports SQL and JavaScript transformations. The only con would be the learning curve, mainly because it is a relatively new tool with new and specific functionalities and interfaces.
- Hevo: This low-code/no-code tool is known for its efficiency and intuitive interface. It also doesn't require deep technical expertise and provides data transformation capabilities tailored for precise analytics. However, automating and scheduling data migration tasks can sometimes be challenging.
- Google Cloud Dataflow: This serverless, unified stream and batch data processing is fast and cost-effective with real-time AI capabilities. Since it has a serverless approach, operational overhead is reduced. As a downside, the tool's deep integration with the Google Cloud Platform may restrict its adaptability for users with varied infrastructure requirements.
Leveraging Acceldata for Data Pipeline Management
Data pipeline tools are the backbone of modern data-driven organizations. You can gain comprehensive visibility into their data by leveraging Acceldata for your robust data transformation features and data processing. Acceldata also offers APIs and integrations that allow you to integrate its service with the apps and software solutions you already use. This means you don’t have to worry about learning an entirely new system and abandoning your old one to manage and process your data more effectively.
Similar to popular tools like AWS, Acceldata's data pipeline architecture is serverless (or cloud based). Cloud-based services can better process larger quantities of data and data from multiple sources. Acceldata can help businesses gain comprehensive visibility into their data, pipelines, and processing at any time and point in their data lifecycle. Ultimately, using amazing tools like Acceldata gives you a strategic position for success in the digital age.
Conclusion
To recap, we explored data pipelines, their importance, and several commonly available design patterns and architectures. We also looked at the tools and how each of them can help you create and leverage data pipelines in your organization. One of these tools is Acceldata, a comprehensive data pipeline management platform that comes with various robust features so you can better and seamlessly optimize your data pipeline engineering and integration processes. By simply applying and exploring any of these tools, you can have improved efficiency and better build reliable and effective data pipleines to drive business success.
So get started today with a 30-day free trial of Acceldata today, or send us a message to set up a request and see how we can help you today!
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