Top 10 Data Pipeline Tools for 2023
In today’s data-driven world, extracting data from various resources helps organizations stay ahead of the competition by using data insights to streamline their operations and decision-making. However, managing data from multiple sources can be overwhelming and time-consuming, so moving data to a single location is easier.
This is where data pipeline tools come in, which help organizations automate and streamline data collection, processing, and analysis. In this article, we will provide a comprehensive overview of the types and benefits of data pipeline tools and the top ten choices.
What are data pipeline tools?
Data pipeline tools are software applications that facilitate data movement from one system to another, typically from sources to target systems for further processing or analysis. They automate and streamline the process of collecting, transforming, and moving data, making it easier for businesses to extract insights from their data efficiently.
Data pipeline tools help organizations handle large volumes of data from different sources, ensuring the data is accurate, consistent, and ready for analysis.
Types of data pipeline tools
There are several types of data pipeline tools that organizations can use to manage their data effectively.
- ● Extraction, transformation, and loading (ETL): Extract data from various sources, transform it into a suitable format, and load it into a target system for analysis.
- ● Change data capture (CDC): Capture changes made to a database and transfer those changes to a target system, ensuring that the data in the target system is always up-to-date.
- ● Data integration tools: Integrate data from different sources into a single system, assuring the data is accurate, consistent, and readily available for analysis.
- ● Data wrangling tools: Clean, transform, and prepare data for analysis so it is ready for use in data-driven decision-making.
- ● Extraction, loading, and transformation (ELT): As a variation of ETL, ELT means the data from the source is loaded into the data warehouse or data lake before the transformation is performed.
● Data streaming: Continuous flow of data as it’s generated for real-time analytics and data processing, e.g. Kafka, UpSolver, and Azure Stream Analytics.
Benefits of data pipeline tools
Data pipeline tools offer numerous benefits for organizations.
- 1. Improved efficiency: By automating and streamlining the data management process, data pipeline tools can significantly improve efficiency, reducing the time and effort required to collect, process, and analyze data.
- 2. Enhanced data quality: Data pipeline tools help ensure data quality by cleaning and validating data, eliminating errors and inconsistencies, and ensuring data accuracy.
- 3. Better decision-making: You'll get real-time access to accurate and reliable data to make more informed decisions based on data-driven insights.
- 4. Increased scalability: Data pipeline tools can handle large volumes of data (i.e. Big Data) from multiple sources, making it easier for organizations to scale their data management processes as their business grows and foster better collaboration.
Key features to look for in a data pipeline tool
Understanding the key features to look for in a data pipeline tool can help your organization make an informed decision that aligns with your data management needs.
- ● Data integration capabilities: Look for a tool that supports integration with various data sources, including databases, cloud platforms, and APIs.
- ● Data transformation capabilities: The tool should be capable of transforming data into a format suitable for analysis and handling data cleansing, aggregation, and enrichment.
- ● Data security: Robust security features are a must to protect sensitive data (i.e. PII), such as encryption, access controls, and data masking.
● Scalability: The tool should be able to handle large volumes of data and the ability to scale as the organization grows. - ● Real-time data processing: Look for a tool that can process data in real-time, enabling organizations to make informed decisions quickly.
● User-friendly interface: The tool must be simple to use, with a user-friendly interface that allows users to easily configure and manage data pipelines.
Top 10 Data Pipeline Tools
Before we dive into the top ten tools, in order to maximize data management across your pipeline you must have data cataloging and data lineage solutions that support the transformations. This is where Aggua comes in. With automated lineage and catalog capabilities, you’ll get a holistic view of your data pipelines and a new way to save on your cloud data costs.
1. Stitch Data
Stitch Data is a cloud-based data integration platform that enables businesses to transfer their data from various sources into a central data warehouse. Stitch Data provides pre-built connectors to extract data from various data sources like Snowflake, Google BigQuery, Amazon Redshift, and Microsoft Azure Synapse Analytics.
Main features:
- ● Cloud-based data integration platform.
- ● Connects to over 100 data sources.
● ETL (Extract, Transform, Load) capabilities. - ● Real-time data syncing.
Best for: Small to midsize businesses that require a simple and affordable data integration solution.
Price: Stitch Data offers a 14-day free trial. After that, pricing starts at $100 per month, with a range of plans available depending on the number of data rows and connectors needed.
2. Airflow
Airflow is an open-source platform that lets you schedule, monitor, and manage data workflows. It’s highly flexible and customizable, making it great for complex data pipelines. Airflow supports multiple third-party services, including amongst others: Google Cloud Platform (GCP), Amazon Web Services, and Microsoft Azure.
Main features:
- ● Programmatically author, schedule, and monitor workflows.
● Extensible architecture with a rich UI.
● Supports a wide range of integrations and operators. - ● Robust set of tools for monitoring and troubleshooting workflows.
Best for: Data engineers, data scientists, and DevOps teams who need to orchestrate complex data pipelines.
Price: Airflow is free and open-source software.
3. Databricks
Databricks (by Apache Spark) is a unified data analytics platform designed to help data science teams and organizations manage their entire data analytics workflow in a collaborative workspace. As well as the transformation tool, the Databricks platform offers many more tools with different capabilities.
Main features:
- ● Unified data analytics platform.
- ● Scalable processing power and storage.
- ● Automated machine learning and model management.
- ● Integration with popular data sources and tools.
Best for: Data science teams and organizations looking for a unified platform to manage their data analytics workflow.
Price: Databricks offers a flexible pricing model based on usage, with options for hourly or annual billing.
4. Rivery
Rivery is a cloud-based ELT / CDC platform that allows businesses to consolidate and transform data from various sources into a single, unified data store. It is designed to simplify data integration and automation.
Main features:
- ● Code-free data integration and transformation.
- ● Automatic data pipeline orchestration.
- ● Pre-built connectors for popular data sources.
- ● Data quality monitoring and management.
Best for: Businesses of any size looking for an easy-to-use, scalable data integration solution.
Price: From $0.75 per RPU credit.
5. Airbyte
Airbyte is an open-source data integration platform that enables businesses to replicate data from various sources to their data warehouses in a simplified manner. It offers a user-friendly interface to manage data replication, monitoring, and troubleshooting.
Main features:
- ● Supports a wide range of data sources, including databases, APIs, and files.
- ● Provides pre-built connectors for popular data sources like Salesforce, HubSpot, Shopify, etc.
- ● Provides a robust scheduler to automate data replication tasks.
- ● Enables users to create custom transformations with code or visual tools.
Best for: Small to mid-sized businesses looking for a cost-effective, easy-to-use data integration platform.
Price: Airbyte is free and open-source, with no limit on data volume or the number of connectors used.
6. Fivetran
Fivetran is a cloud-based data integration platform that enables businesses to centralize their data sources and automate data pipeline workflows. It supports more than 300 connectors allowing users to easily connect to popular data warehouses or data lakes like Amazon Redshift, Google BigQuery, Snowflake, and others.
Main features:
- ● Provides a centralized dashboard to monitor pipeline status.
● Automated data pipeline creation and maintenance.
● Cloud-native architecture for scalability and reliability.
● Transformation options to prepare data for analysis.
Best for: Enterprise companies looking for a reliable and scalable data integration and pipeline management solution.
Price: Fivetran offers a usage-based pricing model, with prices varying based on the number of data connectors and the volume of data transferred. There’s also a free plan to start off.
7. AWS Data Pipeline
AWS Data Pipeline is a web service provided by Amazon Web Services that allows the orchestration and automation of data-driven workflows. Furthermore, it lets you easily define complex data processing tasks to move and transform data between different AWS compute and storage services.
Main features:
- ● Supports a wide variety of data sources and destinations.
● Provides a flexible scheduling system to automate data movement and processing.
● Offers customizable templates to simplify pipeline creation and management.
● Supports error handling and retry mechanisms for more reliable data processing.
Best for: Organizations that need to move and process large volumes of data between different AWS services.
Price: There’s a free tier, and AWS Data Pipeline offers a pay-as-you-go pricing model with no upfront costs or minimum fees. Users only pay for the resources they use, such as EC2 instances and S3 storage. You can use the AWS Pricing Calculator to work out the fees.
8. Snowpark
Snowpark is a data engineering solution built by and for Snowflake. It allows developers to write Snowflake-native transformations using popular programming languages like Python, Scala, and Java. The main goal of Snowpark is to extend the functionality of Snowflake by executing complex computations and data manipulations closer to data resources, improving performance and efficiency.
Main features:
- ● Provides a familiar development experience for developers with its code-first approach.
● Enables efficient processing of large datasets by leveraging Snowflake’s compute and storage resources.
● Offers a high degree of flexibility and scalability to accommodate complex data engineering requirements.
Best for: Snowpark is ideal for data engineers and developers who want to build Snowflake-native data transformations.
Price: By inquiry (30-day free trial available). See our latest blog for tips on how to reduce your Snowflake costs.
9. Google Dataflow
Google Dataflow is a fully-managed service provided by Google Cloud Platform (GCP). It is mainly used for developing and executing data processing patterns like ETL, batch computation, and continuous computation.
Main features:
- ● Scalable, fully managed, and serverless.
● Supports batch and stream data processing.
● Supports multiple languages and data sources.
● Offers built-in monitoring and troubleshooting capabilities.
Best for: Large-scale data processing, ETL, and real-time data processing workflows.
Price: Pay only for what you use. Pricing is based on a number of factors, including the number of CPU hours, storage, and networking usage. New customers get $300 in free credits to spend on Dataflow.
10. Azure Data Factory
Azure Data Factory is a cloud-based data integration service offered by Microsoft Azure. It allows you to create, schedule, and manage data pipelines for ingesting, transforming, and moving data between various sources.
Main features:
- ● Data integration from various sources.
● Scalable data processing and transformation.
● Code-free or code-based workflows.
● Monitoring and logging of data pipelines.
Best for: Enterprises running on Azure that are looking to automate data integration and transformation workflows with a scalable, cloud-based solution.
Price: Azure Data Factory offers pay-as-you-go pricing with no upfront costs. Prices vary based on the region and data transfer rates.
Gain a Holistic View of Your Data Pipeline
Combining any great data pipeline tool with a data management platform like Aggua is essential to truly gain a holistic view of your data pipeline. Aggua's data lineage feature empowers you to understand your data tech stack effortlessly from source to target in minutes. With this comprehensive visibility, you get a comparative advantage and can make more accurate cost decisions, enabling holistic, data-centric decision-making.
