Are you searching for the ultimate data pipeline tools to boost your productivity in 2024? Uncover our top 10 expert-selected options.
Data pipeline tools efficiently manage and streamline the flow of information across an enterprise’s data estate, but not all solutions are created equal. Selecting the right one can make all the difference in properly optimizing workflows, enhancing productivity, and underpinning your organization’s data-driven innovation and strategic initiatives.
We evaluated the 10 best data pipeline tools on the market in 2024 to see how they compared in key features, functionalities, benefits, and price. Whether you’re a data engineer, analyst, or business leader seeking to better harness your data resources, this guide will help you choose the tools best suited to your needs.
Here are our picks for the best data pipeline tools for automation in 2024:
Data pipeline tools encompass a broad expanse of varying solutions that include data warehouse platforms, cloud databases, streaming data platforms, data lakes, and cloud data platforms. Our evaluations considered the common features and functionalities of these solutions for the movement, transformation, and management of data across the organization, from origination to destination. The chart below shows how they compare at a glance.
| Core Features | Enterprise Features | Vendor Profile | Integrations | Support | Pricing | |
|---|---|---|---|---|---|---|
| Aiven | 3.5 | 4 | 2.5 | 3 | 4 |
Starts at $290/month 30-day free trial available |
| Amazon Redshift | 4.5 | 4.5 | 4.5 | 4.5 | 4.5 |
$0.25 per hour Two-month free trial available |
| Azure Synapse Analytics | 4.5 | 5 | 4.5 | 5 | 3 |
$883.081/100 data warehouse units (DWUs)/month Free cloud services Free $200 Azure credits for 30 days |
| Confluent | 4 | 3 | 2.5 | 3.5 | 2.5 |
$4.50/hour for enterprise tier Free basic tier available |
| Databricks | 4.5 | 4 | 3.5 | 3.5 | 3.5 |
$327.36 for 1,488 instance hours Free trial available |
| Firebolt | 4.5 | 4.5 | 3 | 3 | 4.5 |
$0.02 per GB $200 in free credits |
| Google BigQuery | 4 | 4 | 5 | 4 | 3.5 |
$0.02/GB/month for all stored data First 1 TiB of query data processed per month is free |
| Redpanda | 3 | 3 | 2.5 | 3.5 | 2 |
$295 per support block hour 15-day free trial available |
| Snowflake | 4 | 4.5 | 4.5 | 4 | 4.5 |
$3 per credit (enterprise level) 30-day free trial available |
| StreamSets | 3.5 | 3.5 | 3.5 | 3.5 | 3 |
Starts at $1,000 per month 30-day free trial available |
Best for Support
Snowflake’s cloud-based data warehousing offering provides a scalable, highly flexible platform for organizations to store, process, and analyze vast amounts of data in the cloud with limited on-premises capabilities. Unlike traditional data warehouses that require significant upfront investment in hardware and infrastructure, Snowflake operates entirely in the cloud for unparalleled agility and cost-effectiveness.
Its unique architecture separates storage and compute resources, enabling organizations to scale each independently according to their specific needs. This approach not only enhances performance and scalability but also optimizes cost efficiency by eliminating over-provisioning.
Snowflake is also renowned for its simplicity and ease of use. With an intuitive user interface and SQL-based query language, users of all types and skill levels—from data analysts to business executives—can perform data management and glean insights across the data pipeline.
Visit Snowflake
| Pros | Cons |
|---|---|
| Strong data security/protection and compliance controls | Limited support for on-premises and hybrid infrastructures |
| High scalability and performance capabilities | Relatively expensive solution |
| Low learning curve and direct management requirements | Limited community support and ecosystem |
Best for Vendor Profile
As part of the Google Cloud Platform, Google BigQuery is the company’s flagship data warehouse solution that provides organizations with a serverless, cloud-native solution for analyzing vast amounts of data quickly and efficiently. BigQuery leverages Google’s infrastructure to provide a fully managed, serverless data warehouse solution, eliminating the need for organizations to manage hardware, infrastructure, or software updates.
The solution’s serverless architecture enables BigQuery to automatically scale to handle petabytes of data without requiring manual intervention, ensuring optimal performance and cost efficiency. However, like Snowflake, the solution’s lack of a fully-realized on-premises version means that data professionals must exert a significant degree of finesse to support on-premises to cloud data movement.
Visit Google BigQuery
| Pros | Cons |
|---|---|
| Seamless integration with Google Cloud ecosystem | Limited customization options |
| Cost-effective solution | Lack of integrations |
| High performance, serverless architecture | Relatively high level of complexity |
Best Overall
Amazon Redshift is a fully managed cloud-based data warehousing solution offered by Amazon Web Services (AWS). Designed to handle large-scale data analytics workloads with ease, Redshift enables organizations to efficiently store, query, and analyze vast amounts of data in a cost-effective manner. The data warehouse leverages a columnar storage architecture and massively parallel processing (MPP) capabilities to deliver high performance and scalability, allowing users to execute complex queries on petabytes of data with low latency.
With its combination of performance, scalability, and ease of use, Amazon Redshift empowers organizations to derive actionable insights from their data and drive informed decision-making at scale.
Visit Amazon Redshift
| Pros | Cons |
|---|---|
| High performance solution | Lack of multi-cloud support |
| Strong data security and compliance controls | Can be expensive depending on configuration |
| Integrates seamlessly within AWS ecosystem | Self-managed/configured solution (requires deep technical expertise) |
Best for Core Features
A relatively newish player among the lot, Firebolt offers a cloud data warehouse built for high-performance analytics and real-time insights. The solution has quickly gained a sizable following for its ability to handle extremely large-scale datasets while delivering blazing-fast query speed; these performance gains are powered by its unique architecture combining a highly optimized columnar storage engine with a novel indexing technique.
Because of its innovative data architecture design, Firebolt is capable of lightning-fast query performance—even on datasets spanning petabytes of data. Customers can therefore in turn execute complex analytics queries in milliseconds, providing real-time insights to drive informed decision-making on the dime.
Visit Firebolt
| Pros | Cons |
|---|---|
| Powerful, fast analytical features and tools | Limited integration capabilities |
| Built in data security controls | Steep learning curve |
| Relatively easy to scale up/down | Complex (if advanced query and data analysis capabilities are required) |
Best for Integrations
Part of the Microsoft Azure Cloud portfolio of solutions, Azure Synapse Analytics—formerly known as Azure SQL Data Warehouse—is a fully-featured, comprehensive analytics service designed to streamline data integration, big data, and data warehousing workflows. The platform integrates seamlessly with the broader Azure ecosystem, providing organizations with a unified platform to ingest, prepare, manage, and serve analytical insights from diverse data sources.
Synapse Analytics leverages the power of massively parallel processing (MPP) to handle large-scale data workloads efficiently, enabling users to analyze petabytes of data in a matter of seconds. MPPs are a type of data warehouse that leverage multiple nodes/servers for processing, allowing for dramatic performance and scalability gains.
Visit Azure Synapse Analytics
| Pros | Cons |
|---|---|
| Built-in automations for data science/engineering tasks | Limited user interface can be difficult to use |
| Seamless integration within Microsoft Azure ecosystem | Relatively expensive solution |
| SQL-based functionality for data warehousing | Difficult to access data engineering/management components outside of Azure Synapse |
Best for Ease of Use
Databricks is a leading unified analytics platform built on top of Apache Spark, designed to help organizations accelerate innovation by bringing together data engineering, data science, and business analytics teams. At its core, Databricks provides a collaborative workspace where users can leverage Spark’s distributed computing capabilities to process and analyze large-scale datasets efficiently.
With its unified interface and notebook-style approach to data pipeline design, Databricks enables seamless collaboration between data engineers, data scientists, and analysts, allowing teams to work together to extract valuable insights from their data and drive business outcomes.
Visit Databricks
| Pros | Cons |
|---|---|
| Easy to get started with and use | Features centralized around notebooks can be difficult to implement in production |
| Available directly through Databricks and via Microsoft Azure Cloud | Can be an expensive solution for large data workloads |
| Intuitive, notebook-based pipeline engineering interface | Data lake architecture can make update, merge and delete operations difficult |
Best for Pricing
Confluent is a leading platform designed to help organizations harness the power of event streaming for real-time data processing and analytics. Built by the creators of Apache Kafka, Confluent provides a complete event streaming platform that enables companies to capture, store, process, and analyze streaming data in real-time.
Naturally, Confluent’s creators designed the platform’s core around Apache Kafka, an open-source distributed event streaming platform known for its scalability, fault-tolerance, and high-throughput capabilities. Confluent extends Kafka’s functionality with additional features and tools to simplify the development, management, and operation of event-driven architectures.
Visit Confluent
| Pros | Cons |
|---|---|
| Strong centralized management capabilities and unified dashboard | Lack of open source plugins and integration options |
| Reliable, highly scalable capabilities for supporting streaming data | Monitoring capabilities are lacking |
| Rich feature set and built-in debugging tools | Somewhat lacking in support resources and documentation |
Best for Performance
Redpanda has developed an advanced event streaming platform designed to deliver high-performance, real-time data processing at scale. Billed as an alternative to Apache Kafka (based on a C++ clone of Kafka), the solution provides a modern, cloud-native solution for building event-driven applications.
At its core, Redpanda uses a distributed architecture built on top of the Raft consensus algorithm, providing strong consistency guarantees and fault-tolerance while ensuring low-latency data processing. This enables the solution to scale beyond the capabilities of standard Kafka implementations.
Visit Redpanda
| Pros | Cons |
|---|---|
| Low latency Kafka-compatible solution | Can be costly for enterprise-levels deployments |
| Simplified data streaming architecture (when compared to Kafka) | High level of complexity when it comes to debugging, testing, and monitoring |
| Strong vertical and horizontal scaling capabilities | Difficult to customize solution |
Best for Enterprise Features
StreamSets is a leading data integration platform that enables organizations to efficiently ingest, transform, and move data across various systems and environments. With its intuitive and powerful graphical interface, StreamSets simplifies the complexities of data integration by providing a visual drag-and-drop interface for designing data pipelines.
These pipelines can connect to a wide range of data sources and destinations, including databases, cloud storage services, message queues, and more, making it a versatile solution for handling diverse data integration use cases.
Acquired by German software behemoth Software AG in 2022, StreamSets is now one of the company’s many offerings in the cloud data integration space. And as part of a broad portfolio of software products, StreamSets has expanding, albeit limited connectivity/integrations across the company’s data solutions.
Visit StreamSets
| Pros | Cons |
|---|---|
| Easy to use and integrate with other platforms | Complexity in setting up logging and monitoring |
| Simple to create data pipelines/jobs | Manual setup/configuration of Docker instances required |
| Support for both on-premises and cloud-based data pipelines | Documentation is lacking |
Best for Premium Support
Aiven offers a leading platform for managed, open-source data infrastructure services in the cloud. The solution simplifies the deployment, management, and scaling of popular open-source technologies such as Apache Kafka, PostgreSQL, Elasticsearch, and more. Aiven’s managed services are designed to provide high availability, security, and performance, allowing organizations to focus on building applications rather than managing infrastructure.
It’s worth noting that Aiven’s database-as-a-service cloud is more of a platform that supports multiple open source data solutions, versus a data warehouse platform unto itself. With support for multiple cloud providers and regions, Aiven offers flexibility and scalability to meet the needs of businesses of all sizes.
Visit Aiven
| Pros | Cons |
|---|---|
| Easy to get started and deploy | Difficult to set up and tune, despite being a managed solution |
| Straightforward, intuitive interface and management front-end | API and query performance/monitoring capabilities lacking |
| Powerful scale up/down capabilities | Relatively expensive option |
Data pipeline tools offer a range of features to facilitate data ingestion, processing, scaling, reliability, and ongoing monitoring.
Data pipeline tools should allow for collecting and sorting data from various sources—databases, APIs, files, streams, or external systems, for example—while supporting multiple ingestion methods to accommodate diverse data formats and sources. They should also support both real-time streaming or batch processing with minimal latency, maximum throughput, and optimal data freshness.
Once ingested, data needs to be processed to extract valuable insights. A competent data pipeline tool will support transformations, cleaning, normalization, aggregation, enrichment, and other operations to prepare the data for analysis or storage.
A data pipeline solution should scale to handle varying workloads and data volumes, both across spatial (depending on the organization’s geographic footprint) and temporal dimensions—for example, seasonal spikes in data workloads and traffic. The tool should seamlessly scale up or down based on demand to ensure optimal performance without interruptions or delays.
Data pipelines supporting complex environments and mission-critical data workflows should provide several redundant layers of fault-tolerance and infrastructure resilience and reliability. These mechanisms may entail features for error handling, retrying failed tasks, data recovery, and validating data integrity throughout the pipeline.
When evaluating data pipeline tools, data professionals should give careful consideration to features that enable visibility into the data pipeline’s performance, health, and throughput status. These should include monitoring tools and dashboards that allow operators to track key metrics, detect issues, and optimize performance. Additionally, features for managing workflow orchestration, scheduling, and resource allocation contribute to efficient operation and maintenance of the pipeline.
In evaluating data pipeline tools, we took into consideration the following key domains with a heavier emphasis on core features, enterprise features, and vendor profile:
Sub-criteria in this area include extract, load, transform (ELT) and extract, transform, load (ETL) tools, dashboards and visualizations, data workflow management features, and data collaboration and analytics tools.
Enterprise feature sub-criteria include multi-language and region availability, hybrid options, data privacy and compliance controls, data estate management tools, and the frequency of feature enhancements.
In evaluating the vendors’ profile, we took into consideration the breadth of their solution suite, business type, the size and quality of their customer bases, length of time in business, and reputational aspects like publicized data breaches and privacy incidents.
In terms of support, sub-criteria elements include the existence of live chat support, availability of live phone support, email support, as well self-service support options like documentation and knowledge bases. The price and availability of premium support was also taken into consideration.
Pricing sub-criteria includes whether or not a free trial or free tier is available, the overall cost, pricing tier diversity, cost of add-ons and options, as well as cost-effective upgrade paths and discounts, if any.
Integrations were evaluated based on the availability of a full-realized API, ecosystem and developer resources, the extent of the solution’s plugins and add-on library, and the latter’s usability.
Consider factors such as scalability, flexibility, ease of use, integration capabilities, performance, reliability, and cost. Determine whether the solution can handle your current data volume and processing needs while also scaling to accommodate future growth.
Assess the solution’s compatibility with your existing infrastructure and data sources, as well as its ability to support different data formats and types.
The decision between a cloud-based or on-premises data pipeline solution depends on various factors, including your organization’s IT strategy, budget, security requirements, and scalability needs. Cloud-based solutions offer scalability, flexibility, and ease of deployment, while on-premises solutions provide greater control over data governance and compliance.
When it comes to data pipeline tools, the importance of proper automation and monitoring cannot be overstated. Select a solution that provides robust automation features—such as workflow orchestration, scheduling, and error handling—to streamline the pipeline development and operation process. Additionally, ensure that the solution offers comprehensive monitoring and alerting capabilities to track pipeline performance, detect anomalies, and troubleshoot issues in real-time. Ultimately, the proper selection of data pipeline tooling hinges on your specific data requirements and strategic objectives.
Data pipelines are just one component of an overarching enterprise data management program. Read about the best practices for effective data management and our predictions for the future of data management to learn what you need to know about these efforts.
Leon Yen is a staff writer for Datamation. He has been reporting on technology for over a decade and has written for CNET and BigThink. Before that, he was the co-founder and CEO of a cybersecurity startup, where he led the development of an industry-first cyber risk management platform. He has an MBA from the University of North Carolina, Charlotte and a BS in Information Systems from the University of San Francisco.
Datamation is the leading industry resource for B2B data professionals and technology buyers. Datamation's focus is on providing insight into the latest trends and innovation in AI, data security, big data, and more, along with in-depth product recommendations and comparisons. More than 1.7M users gain insight and guidance from Datamation every year.
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