5 Best Data Pipeline Tools (2024 Guide)
5 Best Data Pipeline Tools (2024 Guide)
It's a real challenge for businesses to manage customer info scattered across diverse systems. Take for example a company where the sales team uses Salesforce and the marketing team employs HubSpot. Both teams possess valuable data, but it's like each team holds a different map to the same treasure. Without integrating these maps, the treasure — a comprehensive understanding of the customer — cannot be fully discovered.
Manually gathering and combining data from scattered sources is a recipe for disaster. It's time-consuming, error-prone and can't scale with a growing business. For instance, imagine a company where the customer service department logs interactions in a CRM system while the finance team uses an ERP system to track transactions. Without an effective way to integrate these systems, relevant information about customer behavior and financial interactions remains siloed. The disconnect leads to missed opportunities and poor decisions.
Data pipeline tools act as the puzzle master, effortlessly making sure all the pieces are in the same box. They automatically extract raw data from different sources, regardless of format, and seamlessly centralize it into a unified repository. Think of it as having all your puzzle pieces neatly arranged and ready to be assembled, with the ability to easily add more pieces as your puzzle expands.
With this complete picture in place, businesses can see everything more clearly. They're able to analyze customer interactions, spot trends, and make well-informed decisions that drive growth. Armed with this greater clarity, businesses can make the most of their data.
This guide reviews several types of data pipelines, highlighting the unique features and benefits each one offers.
How data pipelines operate
Data pipelines consist of three main components: the data source, the destination and data transformation, all of which are integral to data extraction, transformation and loading for EL, ETL and ELT pipelines. ELT extracts data from the source, loads into the target system and transforms it into the required format within the data warehouse or database. The table defines and gives examples of the three ELT components:
Label
Definition
Example
Data Source
Internal databases or external platforms from which you extract data.
PostgreSQL (internal database), Qualtrics (external data source), Shopify (cloud application)
Destination
Locations where data lives after extraction, also known as data lakes or data warehouses.
Snowflake, Redshift, Google BigQuery
Data Transformation
The process of structuring and normalizing data for interpretation and analysis.
Platforms: dbt, Azure Data Factory, AWS Glue, Google BigQuery
Examples: Data deduplication, standardization, adding or copying data, summarizing data
Several organizations also use ETL, which stands for Extract, Transform, Load, to extract data from various sources, transform it into a desired format and load it into a target data warehouse. In contrast, ELT is best suited for cloud-based data ecosystems where resources are infinitely scalable, allowing data to be stored as-is and transformed on-the-fly for downstream use cases. Ultimately, ETL is a remnant of the finite nature of on-premise hardware. Very few companies can scale up/down infrastructure like a public cloud hyperscaler.
While both ETL and ELT offer benefits for various use cases, most businesses today adopt the ELT architecture as part of the modern data stack due to its flexibility, scalability and cost-effectiveness.
Types of data pipelines
Based on the frequency of data transfer and whether they are deployed on-premise or in the cloud, we can categorize data pipelines into three types:
- PLEASE FILL THIS IN
Let's explore each type.
Real-time vs. batch data pipelines
The differences between these two pipeline types are significant. Real-time data pipelines typically replicate data from source to destination within minutes. They are effective in scenarios such as fleet management, where immediate data access is critical. Conversely, batch-processing data pipelines suit contexts where there is no urgency in data analysis. A breakdown listing the advantages and disadvantages of each:
Type
Advantages
Disadvantages
Real-time
Captures and transfers data instantly
Requires sophisticated infrastructure and skills
Ideal for time-sensitive operations
Potentially higher costs
Batch Data
Processes data in cost-effective intervals
Data is not processed in real-time
Suitable for less time-sensitive tasks
Delays in data availability can occur
Balancing time sensitivity with cost and infrastructure demands can help you select the most suitable option.
Open source vs. commercial data pipeline tools
Companies sometimes opt for open source data pipelines due to flexibility, customization potential and often lower upfront costs. These benefits allow businesses to tailor the technology to their unique requirements and avoid vendor lock-in. The trade-offs include slower, community-based support, lack of documentation and a need for deeper technical expertise, which typically results in higher long-term TCO.
On the other hand, a commercial data pipeline could be better due to its robust support and reliability, producing a more controlled environment.
Type
Advantages
Disadvantages
Open source
Lower cost
Requires technical and coding knowledge
Easy to modify and adapt
No dedicated support
Commercial
Out-of-the-box features, easy to deploy
Higher costs
Little to no maintenance required
Less flexibility compared to open source options
Choosing between open source and commercial tools often hinges on your project’s complexity and whether you need to tailor solutions to your specific needs.
Cloud-based vs. self-hosted data pipeline tools
Cloud-based data pipelines are highly scalable and easily integrate into existing cloud services. This option also offers the advantage of reduced hardware maintenance costs. In contrast, a self-hosted data pipeline provides greater control over data security and compliance.
Type
Advantages
Disadvantages
Cloud-based
Easy integration between systems
Potential security concerns with data
No need for physical infrastructure
Less control over the hardware
Self-hosted
Data remains in-house, offering better security
Requires investment in physical infrastructure
Full control over the data pipeline
Requires continuous maintenance and updates
Selecting the right data pipeline architecture — cloud-based or self-hosted — involves anticipating future scalability and integration with emerging technologies. This strategic foresight can safeguard companies against the need for costly future overhauls to accommodate growth.
Top six data pipeline tools
Our guide covers the six best data pipeline tools, starting with our own solution, Fivetran.
1. Fivetran
Fivetran offers cloud-based ELT data pipelines as well as self-hosted options that centralize data from over 600 sources into your warehouse in minutes. This zero-maintenance solution features pre-built data connectors for various clouds, databases, applications and destinations.
Fivetran offers several features to enhance data management capabilities. Here's a closer look at what makes Fivetran effective:
- Connector reliability: All connectors offer 99.9% uptime and adapt to vendor changes like schema updates or API modifications, ensuring the freshest data for analytics.
- Fault tolerance: Our data pipelines are built for fault tolerance, automatically recovering from disruptions and maintaining data normalization for immediate analysis.
- Metadata API: Enhances data governance by offering detailed visibility into data provenance, access and changes.
- Supported data destinations: Fivetran supports a range of data destinations, including Snowflake, Databricks, Google Cloud, Azure, Amazon RedShift and Amazon S3. This compatibility makes it an excellent marketing, sales, support, finance and operations tool.
Users value Fivetran for its ability to lessen the burden of maintaining and monitoring data connections. This efficiency saves developers significant time they would otherwise spend on integration setup and testing.
Learn more about our pricing plans, including our free tier.
Case study: Fivetran and Westwing
Westwing, an e-commerce retailer specializing in home products, enhanced its marketing ROI by integrating Fivetran into its data architecture. The automation provided by Fivetran saves Westwing 40 hours of engineering time each week, allowing the team to focus on its strategic initiatives.
This shift to automation also allows the company to scale more effectively. The result is a more agile, future-proof infrastructure that can keep pace with the company's growth.
The key advantages Westwing experiences with Fivetran include:
- Increased ROI from paid marketing campaigns through better analysis.
- Significant time savings in engineering, focusing resources on strategic growth.
- Enhanced customer insights, understanding interactions more clearly.
- Faster scalability without complex manual builds.
- Future-proofed data architecture to support ongoing expansion.
These benefits demonstrate how Fivetran can be a valuable asset for organizations seeking to streamline their data pipeline architecture while limiting resource expansion.
2. AWS Data Pipeline
AWS Data Pipeline enables businesses to automate data transfers between AWS compute and storage services and between AWS and on-premises data sources. This web service allows you to create complex data processing workflows that you can schedule to run at specified intervals.
Businesses can leverage AWS Data Pipeline to process large volumes of data by automating data movement and using diverse data-driven workflows.
Key features of AWS Data Pipeline include:
- Drag-and-drop console: Allows you to manage pipelines with an intuitive interface.
- Scalable design: Handle millions of files with the same ease as processing a single file.
- Predefined logic and conditions: Use built-in preconditions, eliminating the need for custom code.
- Pipeline templates: Templates for complex tasks, such as running periodic SQL queries, without starting from scratch.
- Fault-tolerant workflows: Create sophisticated data processing workflows that are robust and fault-tolerant.
- Automated error handling: Activities automatically retry on failure, with notifications sent via Amazon Simple Notification Service (Amazon SNS).
AWS Data Pipeline pricing is determined by the level of usage.
3. Hevo Data
Hevo Data is a fully managed data pipeline platform that automates and simplifies data integration and transformation processes. It allows you to move data from multiple sources to your cloud data warehouse or data lake in real time without coding.
Hevo Data supports a range of data sources, including databases, SaaS applications and cloud storage platforms. Its intuitive interface and automated processes ensure your data is up-to-date and ready for analysis, making it an excellent choice for businesses seeking efficient data management solutions.
Key features of Hevo Data include:
- Real-time data streaming: Provides continuous, real-time data flow for immediate analysis.
- Automated ETL: Manages extraction, transformation and loading (ETL) processes
- Multiple integrations: Supports over 100 data sources, including popular databases and SaaS applications.
- No coding required: User-friendly interface allows users to manage data pipelines without code.
- Data transformation: Offers powerful data transformation capabilities to prepare data for analysis.
- Scalability and reliability: Ensures high performance and reliability, with automatic scaling to handle large data volumes.
Read a comparison of Fivetran and Hevo Data.
4. Stitch Data
Stitch is a user-friendly data pipeline tool that connects and replicates data from various sources to your cloud data warehouse without requiring any coding. It supports a wide range of databases, including MySQL, and integrates with SaaS applications like Salesforce and Zendesk. This feature helps you streamline data workflows and keep your data up-to-date effortlessly.
With Stitch, you can set the replication frequency to keep your data up-to-date and ready for analysis whenever you need it. Stitch also offers extensive integrations and data transformation features, making it easy to prepare your data for analytical tasks.
Key features of Stitch Data include:
- Security compliance: SOC 2 Type II, PCI, GDPR and HIPAA compliant, providing enhanced security and regulatory adherence for your data.
- Range of integrations: Offers connections to over 140+ SaaS applications, making it easy to centralize data.
- Cloud destination support: Supports cloud destinations like Microsoft Azure Synapse Analytics, Snowflake, Amazon Redshift and Google BigQuery.
- User-friendly interface: Simple-to-use interface that allows team members to get started quickly.
- Reliability and redundancy: Handles critical workloads and includes multiple redundant safeguards to keep data safe during outages.
- Continuous updates: Pipelines automatically update, reducing the need for ongoing maintenance.
Pricing begins at $100 per month.
5. Apache NiFi
Apache NiFi is a powerful data integration tool that automates the flow of data between systems. It offers a flexible and scalable way to manage and transfer data to achieve seamless data integration and transformation without the need for coding. Apache NiFi allows you to design complex data flows with an easy-to-use web interface. It also offers real-time data ingestion, routing and transformation capabilities.
Key features of Apache NiFi include:
- User-friendly interface: Intuitive web-based UI for designing, managing and monitoring data flows.
- Scalability: Can scale from a single node to a cluster of nodes to handle large data volumes.
- Data provenance: Detailed tracking and visualization of data as it flows through the system.
- Security: Fine-grained access control and data encryption to maintain data integrity and security.
- Integration: Supports a wide range of data sources and destinations, including databases, cloud services and messaging systems.
- Extensibility: Custom processors and connectors to meet specific data flow requirements.
- Reliability: Built-in fault tolerance and guaranteed delivery to ensure reliable data processing.
Apache NiFi is open source, making it free and available for use.
Optimize your data workflows with Fivetran
Modern businesses may find it challenging to manage diverse data streams from various cloud applications. On average, companies introduce 6.2 new apps every 30 days, indicating that some older apps are being phased out as new ones are introduced.
The right data pipeline can simplify this complexity and turn raw data into valuable insights. Selecting the right data pipeline tool can help you use your data effectively.
Each data pipeline tool has its own unique features, limitations, pricing options and specific use cases. Finding a tool that aligns with your business requirements means you'll need to consider the following aspects:
- Ease of Use: Does the tool offer out-of-the-box functionality, or does it require ongoing maintenance?
- Data Syncing: Is real-time data synchronization a necessity for your operations?
- Compatibility: What data sources can the tool integrate with?
- Performance: How efficiently does the tool transfer data from source to destination without errors?
- Support: What do customer reviews indicate about the quality of support?
- Cost: What are the pricing options available?
Fivetran is a cloud-based platform that streamlines the building, deployment, management, monitoring and analysis of data pipelines. Our platform enables users to set up and operate data pipelines swiftly, often within minutes. To explore our features, we invite you to sign up for a 14-day free trial.
Articles associés
Commencer gratuitement
Rejoignez les milliers d’entreprises qui utilisent Fivetran pour centraliser et transformer leur data.