What Are the Best Data Pipeline Tools? A Deep Dive Into Selecting the Most Efficient Solution

Generating and harnessing data is the top priority for almost every modern business. Regardless of what industry or segment your business is in, there are not many problems that can’t be solved or improved with more data. Of course, data alone is only part of the equation. Being able to move the data into the correct platforms is where the real business value can be derived. 

The data used to generate insights usually comes from various platforms. The data, as it makes its way to the correct downstream platform, passes through a series of processes before it is used for analysis. The whole process of taking raw data, transforming it, and making it available for analysis is facilitated by the data pipeline.

What is a data pipeline?

Data pipelines come in various shapes and forms but all of them have the same goal: to move data from one location and move it to another. Let’s take a deeper look at the definition of a data pipeline.

A data pipeline is a series of actions and processes used to transfer raw data from one point to another.

The term “data pipeline” is now ubiquitous in the world of big data. Data pipelines consist of three key elements: a source, a processing step or steps, and a destination. Looking at each component individually can help one to see exactly how it all comes together. Let’s take a look at all three of the components below:

• Source: Data source is the place from which data will be extracted. It can be an application database, IoT system, data coming through APIs, data lake or data warehouse, social media data, and public datasets. The data can be structured, unstructured, or semi-structured.
• Processing: Data processing involves applying transformations to source data to make it ready for analysis. It can be done before or after the data is loaded to the destination.
• Destination: Destination refers to the final storage location where data will be stored. This could be a big data platform, another application database, or another storage or analytics solution. From there, data can be accessed by other systems for analysis.

Data Pipelines vs Data Warehouses

People who are less familiar with data science terms and tools may get confused as to how data pipelines and warehouses fit together. Both components are used in a modern data stack and play crucial roles within it. Explaining what each component does can help add a lot of clarity.

A data warehouse is a centralized collection of data from multiple sources. A data warehouse provides integrated, time-variant, organized, and non-volatile data. A data warehouse is a one-stop-shop for data. It helps organizations generate insights from data and make efficient decisions. There are many different vendors that supply data warehouse software, most of which have various advantages and disadvantages depending on your use case or familiarity with big data platforms.

In contrast, a data pipeline is a process that extracts data from one system, transforms it, and moves it to another system. A data pipeline can connect data warehouses to other systems and facilitate the movement of data into or out of a data warehouse. A data pipeline is the main mechanism used to move data from a primary location, where the data was collected or stored, to the secondary location where the data will be combined with other data feeds.

Unlike a data warehouse, a data pipeline doesn’t store data. The data pipeline supplied a conduit for data to flow throw without actually storing it. As you can see, both of these components are very closely related when it comes to building out a modern data stack.

Data Pipelines vs ETL process

Another possible point of confusion is the similarities between a data pipeline and an ETL process. In an ETL process, data is extracted from a source, transformed, and loaded to a destination. ETL is a subset of the data pipeline, which is a broader concept. An ETL process is not necessarily a data pipeline, however, some data pipelines can support streaming ETL process capabilities. Let’s discuss the differences between ETL and a data pipeline.

1. ETL generally works with batch data, that is, data is moved in chunks and it can be scheduled to run at a particular time. In contrast, a data pipeline can work with batch data as well as streaming data. As mentioned, some pipelines can actually support streaming ETL capabilities too.
2. The destination in ETL is either a database or a data warehouse, while a data pipeline can have multiple destinations such as S3 buckets, data lakes, data warehouses, GCS buckets, or may even initiate a real-time reporting engine.
3. In the ETL process, a data transformation step is necessary, while in the data pipeline, a transformation step can be skipped depending upon the use case.

Challenges in developing data pipelines

A data pipeline provides a robust solution to move and process data, but developing a data pipeline comes with a few challenges. These challenges impact the quality of data at the destination. Let’s discuss common challenges in developing data pipelines: 

1. Data is coming from multiple sources and data integration is running in parallel to ingest large volumes of data. This raises issues like non-standardized data, varying type casting, and duplicate data.
2. Data can be lost during loading due to a power failure or server issues. In most scenarios, data is recovered, but it can be challenging and difficult to identify which can lead to missing or duplicate data in the target.
3. The data pipeline must be flexible to incorporate changes happening in the source system where data is coming from.
4. Selecting the right tools and technologies to design data pipelines is important. There are many options available, but selecting the right tool depends upon the use case and budget for the project.

Types of Data Pipeline Tools

Data pipeline tools can be divided into different categories based on the use case. Let’s explore different types of data pipeline tools.

Open-Source vs Private Data Pipeline Tools

Open-source tools are made publicly available and can be customized by users. These tools are usually free, but to enhance their functionality, expert developers are required or an enterprise license is required to unlock some features. Open-source tools include:

• Airbyte
• Apache Kafka
• Talend

On the other hand, private/closed-source data pipeline tools cater to specific business use cases and are fully managed. These tools offer state-of-the-art solutions for data pipelines and don’t require customization. Private data pipeline tools include:

• Arcion
• Hevo Data
• Stitch
• Fivetran

On-premises vs Cloud-native data pipeline tools

Due to security and data privacy constraints, many businesses, especially those in highly-regulated industries, have on-premise systems to store their data. Sometimes, these companies also require on-premise data pipeline tools as well. The main reason for deploying on-premise data pipeline tools instead of those based in the cloud is to provide better security and control. On-premise tools include:

• SAP
• Informatica
• Oracle Data Integrator

Cloud-native data pipeline tools make use of the cloud to transfer and process data. By using the power of the cloud, these tools can sometimes be more scalable and cost-efficient than running data pipelines on-premise. Many provide extremely secure infrastructure to store data contrary to the popular belief that cloud deployments are less secure than on-premise ones. Available cloud-native tools include:

• Equalum
• AWS DMS
• Hevo Data

Batch vs Realtime Data Pipeline Tools

Batch Data Pipeline Tools move data in chunks or batches in intervals. These tools don’t support real-time data processing and tend to be considered a more legacy approach to moving data. Batch data pipeline tools include:

• Talend
• IBM InfoSphere DataStage
• Informatica PowerCenter

Real-time data pipeline tools perform ETL on data and deliver the results for decision-making in real time. Data is ingested from streaming sources such as IoT devices and sensors in self-driving cars. Most processes that are using AI and ML to power business decisions or predict consumer/user behavior will use these types of data pipelines. Real-time data pipeline tools include:

• StreamSets
• Hevo Data
• Confluent

Factors that Drive Data Pipeline Tool Decisions

Data pipeline tools help with extracting data from a data source, applying data transformations, and moving the data into one or multiple data storage locations. There are tools available to carry out each step in the data pipeline process and many tools cover every aspect of it. The decision to choose the right tool can be overwhelming, but with the right insight, it doesn’t have to be. Let’s look at the factors that you should consider when selecting data pipeline tools:

1. Type of Data: The data pipeline tool you decide on may depend upon the type of data that’s going to be ingested by the pipeline. Is it real-time or batch data? 
2. Data Size: The amount of data that you will be transporting can determine which tools may be best suited for your use case. How much data will be processed in a single run? In the case of large data sets, how much time does the tool take to process the amount of data you’ll be moving? 
3. Data Transfer Frequency: The frequency in which data transfer will be happening should also be of consideration. In the case of batch data, how often should the pipeline run?
4. Data Quality: Does the tool support data quality checks and how will they be applied to ensure good data quality? Data quality is an important aspect of data pipelines.
5. Cloud support: If needed, the data pipeline tool chosen should provide multi-cloud support. Multi-cloud support means data tools can move data between different clouds and can work with data residing on different cloud platforms. Cloud and multi-cloud support can be important for those looking for cloud-based solutions.
6. Data Transformation: How is data transformation done? Do you require the data to be processed quickly? What’s the processing time the data tool should take?
7. Cost: Costs can vary heavily between tools or even depending on license requirements. Be sure to know the amount of data you will be moving and how quickly that data needs to be moved, in order to calculate potential infrastructure license costs as well. Although free tools are available, paid tools are generally required for production or large-scale operations.
8. Data sources and destinations: How many data sources and destinations are supported by your tool of choice? Do the tools supported technologies work with your current architecture and your future-state roadmap? Data source refers to the location from where data will be extracted, and data destination refers to the place it will be stored.
9. Customer Support: Does the tool offer customer support? Customer support can help users to utilize the tool efficiently and help with any configuration or runtime errors that are experienced. Having multiple channels of support generally leads to a better experience.

How Arcion Can Help?

Choosing the correct tool to build your data pipelines with is crucial for your business's scalability and reliability needs. Data pipelines are a key ingredient in creating successful real-time decision-making platforms, migrate to a different database systems, or just simply increasing data availability to unlock new use cases. A great data pipeline solution should ensure smooth data flow between a source database or system and one or multiple target systems.

Arcion is a go-to solution for many enterprises who are looking to select a data pipeline tool that is scalable, reliable, and extremely easy to configure and use. With Arcion, you can adopt a no-code CDC platform and inject data into real-time decision-making systems in minutes. It provides robust data pipelines that offer high availability capable, leverage log-based CDC, and auto-scalable features. Available with multiple deployment options, Arcion can migrate data to and from on-prem data sources, cloud-based data sources or a mix of both. 

Arcion's CDC capabilities monitor the changes in the source system and replicates those changes in the destination system through multiple types of CDC. The types supported include log-based, delta-based, and checksum-based CDC. Arcion’s CDC process goes beyond just DML changes including DDLs, automatic schema evolution & schema conversion, in-flight column transformation, and several other non-DML changes. 

The zero code approach allows users to easily adapt Arcion and build their data pipelines without any custom code. Arcion can be set up and configured strictly through configuration files or by using Arcion’s intuitive and easy-to-use UI to set up pipelines in a matter of minutes. Arcion also makes the user’s journey smooth by providing support through extensive documentation, tutorials, blogs, and customer support.

Let’s take a deeper dive into each aspect of Arcion and how it can solve many factors that drive data pipeline tool decisions we discussed above.

Sub-second Latency From Distributed & Highly Scalable Architecture

Many other existing CDC solutions don’t scale for high-volume, high-velocity data, resulting in slow pipelines, slow delivery to the target systems. Arcion is the only CDC solution with end-to-end multi-threaded architecture that auto-scales vertically & horizontally. Any process Arcion runs from Source to Target is parallelized using patent-pending techniques to achieve maximum throughput. There isn’t a single step within the pipeline that is single threaded. Arcion users get ultra low latency CDC replication and can always keep up with the forever increasing data volume on Source.

100% Agentless Change Data Capture

Arcion is the only CDC vendor in the market that offers 100% agentless CDC to all its supported 20+ connectors. The agentless CDC applies to all the complex enterprise databases including Oracle, SQL Server, Sybase, DB2 Mainframes, IBM Informix, SAP Hana, etc. Modern open source databases like Postgres, MySQL, MongoDB, Cassandra, etc., and a variety of data warehouses like Netezza, SAP Hana, BigQuery and Snowflake, and many others. Arcion reads directly from database logs, never reading from the database itself. Previously, data teams faced administrative nightmares and security risks associated with running agent-based software on production cloud environments. You can now replicate data in real time, at scale, with guaranteed delivery — but without the inherent performance issues or the security concerns.

Effortless Setup & Maintenance

Arcion's no-code deployment allows data team to deploy production-ready pipelines in minutes. Because the agentless nature, Arcion removes DevOps bottlenecks & dependencies during deployment and maintenance. As the end-to-end data replication tool, there is no need to incorporate Kafka, Spark Streaming, Kinesis, or other streaming tools required. It instantly simplifies the data pipeline architecture and saves significant maintenance efforts and resources.

Data Consistency Guaranteed

Arcion provides transactional integrity and data consistency through its CDC technology. To further this effort, Arcion also has built-in data validation support that works automatically and efficiently to insure data integrity is always maintained. It offers a solution for both scalable data migration and replication while making sure that zero data loss has occurred.

Pre-Built 20+ Enterprise Data Connectors

Arcion has a library of pre-built data connectors. These connectors can provide support for almost 20 enterprise databases, data warehouses, and cloud-native analytics platforms (see full list). Unlike other ETL tools, Arcion provides full control over data while still maintaining a high degree of automation. Data can be moved from one source to multiple targets or multiple sources to a single target depending on your use case.

Other Data Pipeline Tools

Fivetran

Fivetran is a SaaS-based data integration tool. It automates the data integration process by providing fully managed ETL and low-maintenance pipelines. Fivetran enables users to use data mapping to link their data sources and destinations. It is compatible with a wide range of incoming data sources and data warehouses (but not data lakes).

Pros

1. Fivetran supports streaming data services and unstructured data.
2. It provides full control over the data pipeline using custom code and is compatible with a number of languages such as Python, Java, C#, and Go.
3. It ensures fast analysis by using automated data pipelines and providing defined schemas and ERDs

Cons

1. Fivetran doesn’t allow you to migrate data, schema, and queries to other platforms.
2. Data transformation is not supported before load. Data can only be transformed after loading it into a database using SQL.
3. Customization can be a bit challenging as Fivetran's codebase is not entirely open source.

Pricing

1. Starter: $120 (up to 10 users)
2. Standard option: $60 (single user)
3. Standard: $180 (unlimited users)
4. Enterprise: $240 (unlimited users)
5. Business critical: tailored to client specifications

Airbyte

Airbyte is a SaaS-based open-source data integration platform. Airbyte allows users to extract data from more than 120 sources, and data can be stored and replicated to a number of destinations. In Airbyte, orchestration can be done using in-built functions or through orchestration tools like Airflow, Prefect, etc.

Pros

1. Airbyte provides a Connector Development Kit that can be used to create custom connectors.
2. Airbyte provides custom transformation features via SQL and dbt. Users can trigger their own dbt packages at the destination level immediately after EL. 
3. Airbyte doesn’t store data in any temporary location during extraction. This prevents data leaks and ensures data protection.

Cons

1. In comparison with other tools, Airbyte offers a smaller number of connectors to extract and load data
2. The user management system is a key component that allows users to maintain the logs of users working with the product. Airbyte has no user management system available yet.
3. Airbyte has limited options to facilitate user queries. Discourse is the only active support channel at the moment.

Pricing

1. Open-source: Free
2. Cloud: 2.50/Credit
3. Custom Cloud: Customize Plan

Stitch

Stitch is an open-source cloud-based ETL platform. It supports a large number of sources and destinations. Stitch provides an open-source tool kit for writing scripts that enable customers to build new sources. Stitch is a transparent and flexible platform to manage data pipelines.

Pros

1. Stitch provides a user-friendly interface to help users easily navigate through the product.
2. It saves time through fast integration with different data sources and destinations.
3. Stitch provides state-of-the-art data protection. It uses HTTPS protocols to protect web-based data sources.

Cons

1. For beginners, Stitch can be a complicated tool, and it takes time to learn it.
2. Stitch provides limited customer support.  

Pricing

Stitch offers a free 14-day trial, and then users can demand a price quote as per their requirements. 

Hevo

Hevo Data is a no-code data pipeline tool that supports ETL, ELT, and reverse ETL processes. It provides more than 100 pre-built data integrations and supports historical and incremental data loads. Hevo detects the schema and replicates it at the destination automatically.

Pros

1. Hevo offers no-code solutions. To use Hevo no hard-core development or programming expertise is required.
2. The UI of any application is the first introduction. Hevo User-friendly UI so that users can easily interact and navigate with the platform.
3. Hevo offers great customer support.

Cons

1. Users require time to learn and get started with Hevo.
2. The process of loading data from source to destination requires high CPU consumption at the destination end.

Pricing

1. Free: Up to 1 million events/month
2. Starter: Up to 300 million events/month.
3. Business: Customized Plan

Customers can get on-demand price quotes.

StreamSets

StreamSets provides a modern data integration platform that empowers DataOps technologies. It provides a complete end-to-end solution to build, run, monitor, and deliver continuous data for DataOps. It was built in 2015 with the philosophy of enabling data teams to spend less time fixing issues and be focused on actually utilizing data.

Pros

1. StreamSets efficiently handle streaming and record-based data.
2. It provides a user-friendly interface with live dashboards, which helps users to fix errors in real time.
3. StreamSets supports multiple file formats and connectivity options.

Cons

1. StreamSets integration with spark functions becomes troublesome with large datasets.
2. When settings in one processor are updated, the entire data flow is paused.

Pricing

1. Free: Up to two users are free.
2. Professional: $1000/month
3. Enterprise: Customized Plan

Equalum

Equalum is a cloud-based data integration platform that provides change data capture, and real-time and batch ingestions. It supports both structured and semi-structured data.

Pros

1. Equalum has drag-and-drop options with a no-code UI.
2. Equalum provides extensive deployment options, including on-premises, SaaS, and hybrid.
3. It supports streaming data ingestions and streaming ETL
4. Equalum has an end-to-end monitoring and alerting system that helps to keep tabs on each step of the pipeline.

Cons

1. In Equalum, even if a problem occurs in one project, bulk notifications are sent to all projects.

Pricing

Equalum offers a free trial. The price is provided by the vendor on demand.

AWS DMS

The AWS Data Migration Service is a cloud-based service used to migrate data from on-prem or cloud-based data stores to AWS cloud. It supports a variety of data types, like relational databases, NoSQL databases, and other types of data stores. AWS DMS makes sure that source applications are operational during data transfer to reduce application downtime.

Pros

1. AWS DMS with its pay-as-you-go model is a cost-effective option.
2. It is an easy-to-use platform and users can get familiarized with it quickly.
3. AWS DMS supports a wide range of source databases.

Cons

1. There is no support for HTTP/HTTPS or SOCKS proxy, which is crucial for data protection.
2. The documentation and help to use AWS DMS are limited.

Pricing

AWS DMS operates on a pay-as-you-go model. It charges on a per-hour basis and provides a wide range of pricing options.

Conclusion

Data pipelines help businesses unleash the full potential of their data by getting the data where it needs to be, efficiently. Data pipelines can dramatically increase the productivity of data teams and unlock real-time use cases for business intelligence. Enabling real-time business intelligence and decision-making capabilities can help with the growth of the business. 

There are plenty of tools available for developing data pipelines. As we discovered, each tool has its own advantages and disadvantages. Before making any decision, find out what value data brings to your business and how efficiently this data can be utilized. With that decided, you can then select the data pipeline tool that best suits your requirements. 

Now that you are aware of what a data pipeline is, the different types of data pipelines, and the tools available to build them, we hope your decisions on data pipelines in the future are well-informed. Looking to use the most flexible and easy to configure tool on the market? Download Arcion Self-hosted for free and unlock the power of your data through zero data loss and zero downtime pipelines in minutes.