Comparing Tools For Data Processing Pipelines

If you will ask data professionals about what is the most challenging part of their day to day work, you will likely discover their concerns around managing different aspects of data before they get to graduate to the data modeling stage. 

Data professionals spend most of their time managing data in various forms – be it moving data across various systems, transforming, or processing the data to get meaningful insights. As the volume of data keeps increasing at an accelerated rate, these data tasks become arduous in no time leading to an extensive need for automation. This is what data processing pipelines do for you. 

Automating myriad steps associated with pipeline data processing, helps you convert the data from its raw shape and format to a meaningful set of information that is used to drive business decisions. 

In this post, you will learn about the 10 best data pipeline tools, their pros, cons, and pricing. 

Data pipeline stages

But before delving deeper into the technical aspects of these tools, let’s quickly understand the core components of a data pipeline succinctly captured in the image below:

What does a good data pipeline look like?

A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process.

1. Data Ingestion: Involves raw data collection from origin and storage using architectures such as batch, streaming or event-driven.
2. Data Transformation: Putting data in a standard format post cleaning and validation steps. It also involves data enriching – a crucial step for further data travel.
3. Data Processing: You need to save the processed data through computations such as aggregation, filtering and sorting.
4. Data Storage: To store this processed data to retrieve it over time – be it a data warehouse or a data lake.
5. Data Consumption: You have reached a point where the data is ready for consumption for AI, BI & other analytics.

The origins of a data pipeline connect it to the need for reusability and efficiency. If a typical ML project involves standard pre-processing steps – why not make it reusable? Let’s understand how the other aspects of a data pipeline help the organization achieve its various objectives. 

1. Automation: A data pipeline automates the process of collecting, processing, and storing large volumes of data. It eliminates the need for manual intervention, reducing the risk of errors and increasing efficiency.
2. Scalability: A data pipeline is designed to handle large volumes of data, making it possible to process and analyze data in real-time, even as the data grows.
3. Data quality: A data pipeline can help improve the quality of data by automating the process of cleaning and transforming the data. This ensures that the data is accurate, consistent, and reliable.
4. Cost-effectiveness: A data pipeline can help reduce the cost of managing and analyzing data by automating repetitive tasks and optimizing resource utilization.
5. Real-time insights: A data pipeline can process and analyze data in real-time, allowing organizations to gain insights and make informed decisions quickly.

You can learn more about the benefits of having a data pipeline in place here.

Check also

Building ML Pipeline: 6 Problems & Solutions [From a Data Scientist’s Experience]

How to choose the best data pipeline tool?

It is very easy to get lost in the plethora of options with regard to data pipeline tools with a lot of mature as well as new players on the horizon. These varying choices make the process of choosing the right data pipeline tool overwhelming. Let’s have a look at the factors that one should consider when selecting the ideal data pipeline tool:

1. Your business problem at hand might require processing the streaming data or batch data. The data pipeline tool must be able to support the type of data you need to process for your present business need.

2. The volume of data that needs to be transported plays a big role in determining which tools may be best suited for your use case. This is a difficult decision at the onset, as the volume of data is a factor of time and keeps varying with time, but an initial estimate can be quickly gauged by analyzing this aspect by running a pilot. Also, the industry best practices suggest performing a quick data profiling to understand the data growth. 

3. How frequently you would require to transfer the data is also of key interest. For example, you are expected to find answers to questions like – how often the pipeline should run, to be able to assess this factor more appropriately. 

4. Data quality has been a pain point for businesses impacting their ability to trust the insights derived from it. So, when enterprises look for a data pipeline tool, they tend to gravitate toward one that supports data quality checks. Further, they are interested to know how data quality checks are applied throughout the data pipelines.

5. Most organizations are using multiple cloud-based vendors which makes it important for them to prefer a tool that works seamlessly across all the vendors and meets varying data processing needs. While it is entirely possible that your current business requirements do not make you adopt a multi-cloud approach today, however, the business dynamics change very fast. And when those changes happen, you would want to ensure that you took the right decision during vendor sign-in. 

6. While the flow of data could be quick through the various steps in the data pipeline, a typical bottleneck could be the transformation steps. Typical ML systems require complex transformations of feature space and your choice of tool should be good at all of them to ensure that the developers don’t get stuck.

7. License costs can vary heavily between tools or even depending on feature 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 costs. Although free tools are available, paid tools are generally preferred for production or large-scale operations.

8. Additionally, you would like to include these questions as part of your assessment criteria – how many data sources and destinations are supported by the tool of choice? Do the tools support technologies that work with your current architecture and your future-state roadmap?

9. Does the tool offer customer support? Customer support allows the users to utilize the tool efficiently and help with any configuration or runtime errors that might crop up during development or worse, during production. Having multiple channels of support is insurance against things going south.

10. Time to production is another metric to consider when choosing a data processing pipeline tool. While many tools would provide extensive features and immense flexibility, others might just cut the mark by allowing infrastructure setup in no time.

Different types of data pipeline tools 

While the market is flooded with a number of data pipeline tools to choose from, the right tool for your organization depends on the business use case. Listed below are some of the common types of data pipeline tools:

Commercial vs open-source data pipeline tools

When a business needs full control over the development process and wants to build highly customizable complex solutions, open-source tools come in handy. Typical examples include:

• Airbyte
• Talend
• Apache Kafka
• Apache Beam
• Apache Nifi

While getting control over the process is an ideal position an organization wants to be in, the time and effort needed to build such systems are immense and frequently exceeds the license fee of a commercial offering. Commercial data pipeline tools not only provide ease of use but also expert support across the data journey. Some such examples include:

• Hevo Data
• Stitch
• Fivetran

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Cloud-based vs on-premise data pipeline tools

Cloud-based data pipeline tools provide the flexibility and scalability of the cloud for data processing operations and are best suited for applications where data accumulates over time. Some of the popular cloud-based vendors are:

• Hevo Data
• Equalum
• AWS DMS

On the other hand, there are vendors offering on-premise data pipeline solutions and are mostly preferred by organizations dealing with highly sensitive data. Some such examples include:

• Informatica
• SAP
• Oracle Data Integrator

Streaming vs batch pipeline data processing tools

Whether it’s about processing social-media feeds or a stream of data from IoT devices, Streaming data processing tools process data in real-time, as it is generated, and can be used for tasks such as detecting anomalies or performing real-time analytics. Some of the most popular vendors providing tools/solutions for streaming data processing are:

• Integrate.io
• StreamSets
• Hevo Data
• Airbyte

Tools for Batch Data Pipelines transfer data in intervals or chunks, and they are commonly viewed as a more traditional method for moving data since they don’t facilitate real-time data processing. Common examples include:

• Talend
• Keboola
• Fivetran

Comparing current market offering

Before you understand the nitty-gritty details of each of the offerings here’s a quick preview of the key features of each tool to help, you understand if it fits your business need.

Now that’s out of the way, let’s get to the details of each offer:

Apache Airflow

Overview

It is one of the most popular open-source python-based data pipeline tools with high flexibility in creating workflows and tasks. It comes with an intuitive web application that allows the user to monitor, schedule, and manage complex workflows with ease. The logical flow of running upstream and downstream tasks is decided using an algorithm commonly known as a Directed Acyclic Graph (DAG).

Categorization

• Open Source 
• Batch data processing

Pros

• Fully customizable and supports complex business use cases.
• Strong community and tech support.

Cons

• It’s not a plug-and-play system and requires a complex setup.
• Requires developers to possess a working knowledge of Python programming language.
• The onus of development, productionizing and monitoring is all on the user aka the developer.

Pricing 

It is free to use and is licensed under Apache License Version 2.0.

Talend

Overview

While Talend’s Open Studio for Data Integration is free-to-download software to start a basic data integration or an ETL project, it also comes powered with more advanced features which come with a price tag.

Categorization

• Open Source 
• Batch data processing

Pros

• Apache license makes it free to use.
• Relational database connectors such as Teradata, Oracle, and Microsoft SQL servers are available.
• SaaS connectors such as Salesforce, Marketo, and NetSuite are available too.
• Pre-Packaged apps such as Microsoft Dynamics, SAP, and SugarCRM are supported with the free version.

Cons

• Doesn’t include connectors beyond the list shared above.
• No collaboration capability.
• Scheduling capability is missing in the free version.
• Doesn’t include management and monitoring capabilities.
• No built-in data quality functionality.
• No expert support.

Pricing

Talend’s Open Studio is free under the Apache license. Pricing of other modules such as Stitch, Data Management Platform, Big Data Platform, and Data Fabric can be found on their website.

Fivetran

Overview

It is aimed at automating the data movement across the cloud platform of different enterprises, alleviating the pain points of the complexity around the ETL process.

Categorization

• Cloud-based solution 
• Batch data processing

Pros

• Its ease of integration and deployment gives it an edge over its competitors.
• Its zero-configuration, zero-maintenance pipelines make it an ideal choice for companies that are challenged with complicated configurations.

Cons

• Though it offers two types of transformations – basic SQL and transformation for dbt core, however, these transformations expect the user to possess strong SQL knowledge
• Further, the transformations put a constraint on high engineering resources. 

Pricing 

It follows a row-based pricing model which implies you get charged based on the usage. Such a pricing model brings in the complexity around a dependence on monthly usage as against the fixed-rate model which allows for easy budgetary cushions.

Keboola

Overview

It is built with the intent to serve business users with no-code tools that empower them to not only create data integrations on their own but also customize the data pipelines as needed.

Categorization

• Cloud-based solution
• Batch data processing

Pros

• Its “Visual Flow Builder” eliminates the dependencies of business users from the IT teams by providing a drag-and-drop GUI.
• Provides data security using AI & blockchain technologies.
• Automates data collection from varied sources using extraction modules.
• Data extraction, model training, and storage all served under one roof.

Cons

• Some plans are quite expensive for beginners.
• It offers near real-time and is not fully capable of real-time streaming integrations.

Pricing

• Comes with a free tier that includes 120 minutes of computation in the first month and 60 minutes thereafter.
• Credits can be purchased for 14 cents per minute.

Integrate.io

Overview

It’s an e-commerce-focused data warehouse integration platform that requires zero development effort. It supports multi-source integration with capabilities expanding to multiple vendors.

Categorization

• On-premise 
• Stream data processing

Pros

• Easy data pipeline design with a fast and intuitive visual editor enabling non-developers to access and collaborate over projects requiring data pipeline management.
• Basic data pipeline configuration doesn’t require the expertise as that of a developer.

Cons

• There aren’t many options for data sharing 
• Inflexible monitoring and logging make resolving production issues difficult.
• Limited third-party connectors.
• No data sync in real-time.

Pricing

Comes with Starter (USD 15000/year), Professional (USD 25000/year), and Enterprise plans (Custom).

Hevo Data

Overview

It’s an intuitive no-code ETL tool that also supports ELT and reverse ETL processes out of the box. The major highlight of Hevo is that it supports historical and delta data loads while its data integrations are industry-leading with more than 100 of them pre-built. Hevo automatically detects and duplicates the schema at the data destination.

Categorization

• Cloud-based hosting 
• Stream data processing

Pros

• No-code solutions requiring zero development.
• Easy-to-use User Interface.
• One of the best platform support.

Cons

• UI contains many different options which can be overwhelming for new users.
• High CPU requirement at data destination for data loading operations.

Pricing

• Up to a million events/month on the free plan.
• Up to 100 million events/month and a 14-day trial for the starter plan.
• Also offers a customized business plan.

StreamSets

Overview

It is a data integration platform that provides end-to-end solutions to develop, deploy, monitor, and serve data streaming for data operations.

Categorization

• Cloud-based hosting 
• Stream data processing

Pros

• It is best at handling streaming pipelines.
• User-friendly interface with live dashboards and debugging.
• It supports multiple file formats.
• It connects to many DBs.

Cons

• Spark integration is sluggish with large datasets.
• Server update locks the entire cluster.

Pricing

• It is free for up to two users.
• USD 1000 a month for a professional plan.
• Enterprise plan is available on demand.

Dagster

Overview

It is a very well-known name for shipping data pipelines with a very high velocity. Its data platform offers rich features such as monitoring different runs at a centralized place along with a view of the timeline of each run and provides a detailed view of every issue with a high degree of confidence. Further, it provides the ability to inspect data assets including lineage, schema, monitoring computations, etc. among others.

Categorization

• Open Source 
• Cloud-based hosting
• Batch data processing

Pros

• It supports end-to-end data management lifecycle at every stage – be it local development, staging, or support for the production environment. 
• Its software-defined assets (announced through Rebundling the Data Platform) and built-in lineage make it an appealing tool for developers.

Cons

• Its inability to support RBAC is seen as a big concern by the community looking for a self-serve architecture.
• It lacks huge community support against its competitors considering its major releases happened in recent times.
• It is yet to test the scale as it witnesses the architectures of the bigger enterprises.

Pricing

Standard tier comes with two plans i.e. Hybrid (3 cents per minute) and Serverless (4 cents per minute) of compute.

Stitch

Overview

It’s an open-source ETL platform hosted on the cloud and interfaces easily with many different databases from almost all popular vendors while also providing the capability to add new ones. 

Categorization

• Cloud-based hosting 
• Batch data processing

Pros

• Its UI is very User-friendly.
• Seamless integration with many data sources and destinations.
• Uses secure protocols for data security.

Cons

• Steep learning curve for beginners.
• It has limited or as good as no option for expert help.

Pricing

A 14-day free trial and then plans start at USD 100 a month for Standard and go up to USD 2500 a month for Premium.

Airbyte

Overview

Airbyte needs a special introduction because it is not only open-source but also highly capable. It provides a SaaS-based data integration platform that supports data extraction from a multitude of sources and storage to varied destinations. It supports tools like Airflow, Prefect, etc. for data orchestration.

Categorization

• Open Source
• Cloud-based hosting 
• Stream data processing

Pros

• Develop custom connects with CDK (Connector Development Kit).
• Enables users to trigger their custom transformations via SQL and dbt.
• Ensures data protection and leaks by ensuring best practices for data storage.

Cons

• Limited connectors.
• No user management UI for user authentication.
• Limited expert support.

Pricing

Free under Apache license while the cloud plan is paid, starting at USD 2.50.

Conclusions

As much as the enterprises are keen to make the most of their valuable asset i.e. data, they also understand that the value stream can only be realized if the data systems are built at scale. A data pipeline lets you seamlessly process the data through automation which inevitably leads to faster and easier scaling of processes. Further, it comes with the added advantage of handling varied data sources with a stronger emphasis on maintaining high-quality data.

The plenty of options in the market make it difficult to choose the right one for your business needs. The new players come with the advantage of understanding the existing market gap by catering to the ongoing pain points of users, but they often lack the product maturity which comes with its due time. 

This article was an attempt to assist you in making the right choice on various factors such as community support, maturity of the tool, pricing, observability, etc. It is intended to give you the current state of the market for data processing pipelines and help you make the trade-off among multiple parameters that affect your decision to choose one over another. 

References

1. https://www.arcion.io/learn/data-pipeline-tools
2. https://www.trustradius.com/data-pipeline
3. https://www.keboola.com/blog/the-7-best-data-pipeline-tools-of-2020
4. https://blog.panoply.io/best-data-pipeline-tools
5. https://hevodata.com/blog/data-pipeline-tools-list/
6. https://www.estuary.dev/data-pipeline-tools/
7. https://research.aimultiple.com/data-pipeline-tools/
8. https://onehourprofessor.com/best-data-pipeline-tools/
9. https://www.fivetran.com/learn/data-pipeline-tools
10. https://www.shipyardapp.com/blog/data-pipeline-tools/