Data Ingestion Tools Buyer’s Guide [2024]
Swamped with data from different sources and unsure how to handle it? This guide offers expert tips on choosing the right ingestion vendor and implementing best practices.
Being a data geek, I routinely skim through data subreddits, fishing for the common hurdles within the modern data stack. Unsurprisingly, I found many Redditors needing help choosing the right ingestion tool. It cannot be downplayed, as a wrong choice could impact business decisions, a situation best avoided.
In a similar quandary? You’re in luck. This guide is your treasure trove to select the data ingestion vendor for your specific needs, use cases, and the tools in your stack.
We'll explore the benefits, "build vs. buy" decision, key factors, top vendors, and how 5X streamlines the process, ensuring you focus on your business priorities.
Real-life use case
An online retailer wants to build its own data platform. It requires a tool to collect and clean the data from various sources:
- Social media data: Gather Facebook, Twitter, and Instagram data.
- Online shopping data: Capture product and customer details.
- Subscriptions: Track payment and subscription status for premium services.
The data ingestion tool collects information from these sources and integrates it for comprehensive customer profiling, enabling personalized deals and efficient billing. It harmonizes the incoming data for a holistic understanding of customer behavior.
Core benefits of using data ingestion tools
Data ingestion includes a wide range of tasks aimed at getting data ready for analysis. By using ingestion vendors, your business can:
Simplify data collection: As per a Matillion & IDG Research survey, organizations, on average, utilize 400 data sources. Additionally, 20% of surveyed companies had over 1000 data sources integrated into their BI software. Consolidating data from these sources can be time-consuming and can lead to compatibility issues when merging information from different platforms.
Data ingestion tools come to the rescue. They make data collection smoother, bridge the gap caused by compatibility issues, and even include features to reduce data errors. Businesses can use them to easily move data around and make sure it's clean and accurate, even if they don’t have tech experts.
Enhance data protection: Data ingestion tools help secure sensitive data through data encryption, access controls, and audit functionalities. They enable organizations to execute robust data governance practices, ensuring compliance with data regulations.
Scale effortlessly: Data ingestion tools are built to handle increasing data volumes and sources. As businesses grow and add new data sources, these tools can easily adjust to manage the higher data load, maintaining the efficiency and effectiveness of data integration processes.
Choosing data ingestion: build or buy?
When it comes to handling data, you have two options: build your own data ingestion tool or purchase an ingestion vendor. Each choice has its own advantages and disadvantages.
Building a data ingestion tool
Pros
Control and ownership: Building your ingestion tool gives you complete authority over your infrastructure. You decide what data to collect, how to collect it, and where to store it.
Flexibility: With a custom pipeline, you can adapt and adjust it as your needs evolve. You're not confined by pre-existing solutions and can make changes whenever necessary.
Security: You can implement security measures tailored to your organization's standards, ensuring data privacy, encryption, and protection of sensitive information.
Cons
Time and effort: Building a native ingestion tool can be time-consuming, resource-intensive, and complex.
Dependency and limited support: Relying on a few key individuals for tool development may pose risks in case of turnover or unavailability, impacting ongoing development, maintenance, and support.
API changes risk: Manual errors may occur, especially in complex pipelines, or if data source APIs change, risking data integrity.
Adaptation complexity: Implementing changes as technology evolves or business requirements shift can be complex and time-consuming.
Buying a pre-built ingestion tool
Pros
Automated data handling: Pre-built ingestion tools automate the extraction, transformation, and loading of data from diverse sources. This saves time, minimizes errors, and enhances efficiency.
Monitoring made easy: These tools provide insights into data pipeline status, making it simple to identify and fix issues, ensuring smooth and accurate data flow.
Comprehensive integration: Data ingestion tools can handle data from various sources like databases, cloud services, apps, and files. This consolidates data for easier analysis and reporting.
Scalability: Ingestion tools handle large data volumes with minimal delay, adapting as your data sources and volumes grow.
Data security: In-built security features ensure data encryption during transfer and storage, access controls, and compliance with regulations, keeping your data safe.
Constant innovation: Frequent product updates help you enhance your data capabilities, stay agile, and strengthen security and compliance.
Support: Pre-built ingestion tools offer customer support, promptly addressing issues and minimizing disruptions.
Cons
Limited customization: Pre-built ingestion tools may not cater to highly specialized data integration needs.
Cost: Purchasing and maintaining these tools can be costly, particularly for smaller businesses.
Vendor lock-In: Committing to a specific tool can limit future flexibility, as switching tools can be challenging.
Sync frequency: Some tools may have limitations in syncing data frequently, which can be an issue for real-time data needs.
Key considerations for selecting the right data ingestion tool
What's your budget? And which pricing structure suits you?
Begin by evaluating your budget and the pricing structure of the tool. Different data ingestion tools offer varying pricing models, such as per active rows, connectors, or runs. Examine the data sources you have and estimate the volume before selecting the ingestion tool.
Does the tool have an existing connector for your data sources?
Check if the tool provides existing connectors for your data sources. Visit the vendor's website to verify if they support connectors for your specific data types. If not, check whether the tool offers custom connectors if needed.
Do you need incremental or full updates?
Incremental updates refer to adding or changing specific parts of your data without starting from scratch. They are quick and efficient for small changes.
Full updates refer to replacing all your data, whether it has changed or not. They are useful when you want a complete refresh.
So, before selecting a tool, consider your data update requirements. Some tools excel at handling incremental updates, while others are better suited for full updates. Choose a tool that aligns with the type of updates your business requires.
What is the reliability of the connector, and does the tool have data recovery capabilities in case of failures?
Look for a highly reliable tool that handles large data volumes without failures and ensures accurate data recovery in case of any issues. If you can, try it out in a trial. Also, inquire about its long-term support.
Do you have security and compliance requirements?
Verify that the tool offers robust security features, including encryption, authentication, and authorization. Ensure it complies with relevant data protection regulations to safeguard sensitive data during ingestion.
What is the minimum sync frequency of the tool?
Determine how frequently you need data updates. Match the tool's sync frequency options with your specific business requirements. Whether you require updates every minute or find 24 hours sufficient, the tool should cater to your needs.
How robust are the error handling and alerting capabilities of the tool?
Look for tools equipped with effective error handling mechanisms. They should log errors and provide alerts or notifications when problems arise. Monitoring features are vital for swift problem identification and resolution.
What is the quality of the tool's community and customer support, and how does the vendor's reputation in the industry stack up?
Assess the tool's community and customer support resources. A strong support system can be invaluable for troubleshooting and seeking assistance when necessary. Additionally, consider the vendor's reputation in delivering reliable solutions.
Data ingestion tools comparison matrix
Comparison
Fivetran
Airbyte
Stitch
Hevo
Based on Monthly Active Rows (MAR) rather than just rows, the cost varies depending on how frequently your records are updated within a month.
(for 10 GB of data)
Because Aribyte charges based on storage as well, the cost varies depending on your data size.
You have access to just one destination and ten data sources; additional data sources require an upgrade to a higher plan.
1 minute for the enterprise plan
Fivetran
Founded in
Headquartered at
Cloud Compatibility
custom cloud-based
Pricing Plans
Monthly Active Rows (MAR)
Our Recommendation
Use Fivetran if ...
You prioritize data security, governance, compliance, and scalability.
Your data gets updated frequently because Fivetran charges based on active rows instead of just rows (unlike other ingestion tools).
Your data source schema changes frequently (Fivetran handles the schema mapping).
You want detailed visibility into your usage for each connector.
Pros
Supports a wide range of sources and destinations.
Offers an easy-to-use interface for creating and maintaining pipelines.
Provides a highly scalable platform.
Offers excellent customer support and SLA.
Features comprehensive documentation for each data source connector.
Includes column blocking and hashing for GDPR compliance.
Cons
Not as customizable as some other platforms.
Doesn't support CRON and allows only one sync schedule for all tables in a data source.
Technical knowledge is required for creating custom connectors; it doesn't provide no-code options.
The pricing can be expensive and challenging to predict due to the pricing curve and the monthly active rows pricing structure.
Fivetran dashboard
The Fivetran dashboard serves as the web-based control center for managing your Fivetran account. The main features include: connectors, transformations, destinations, and alerts.
Fivetran offers three sync modes:
Historical/Initial sync: Extracts and processes all historical data from selected source tables for free.
Incremental sync (Default): Extracts and processes only modified or added data, known as incremental changes, on a set schedule
Re-sync: Used to rerun a historical sync to address data integrity errors
Capture deletes: Fivetran efficiently handles data deletion by capturing it whenever possible, enabling analysis on data that may no longer exist in your source. When source data is deleted, Fivetran soft-deletes it in the destination, adding an extra '_fivetran_deleted' column with a 'TRUE' value for deleted rows. The method for capturing deletes varies by connector type.
Column blocking and hashing for PII data: To protect sensitive data like Personally Identifiable Information (PII), Fivetran offers column blocking, allowing exclusion of specific tables or columns from replication to your destination. Additionally, column hashing anonymizes sensitive data in the destination while preserving its analytical value. Note that column blocking and hashing are available for select connectors.
Data Pipeline
Create and manage pipeline: Each account can have multiple destinations and you can specify the data sources and destinations you want to sync. You can check the status of past syncs, view logs, select schema to be synced, and change configurations and sync frequency for each connector.
Function connectors: You can create a serverless ELT data pipeline for unsupported data sources or private APIs using Function connectors. When paired with your custom function, you only need to write the data extraction code, and Fivetran handles the data loading and transformation into your destination
Transformation
Fivetran offers two transformation options which are “Quickstart Data Models” and “Transformations for dbt Core”. These are free in Fivetran and don't count towards your Fivetran costs. However, these transformations run on your warehouse's compute resources, so it's crucial to ensure your warehouse is properly sized for smooth execution.
Quickstart data models: Fivetran provides dbt Core-compatible data models for popular connectors, transforming your destination data into analytics-ready datasets. You can use these pre-built models without creating your own dbt project. Fivetran sets up the dbt project and transformations for you, running them according to your chosen schedule.
Transformations for dbt Core: Fivetran seamlessly integrates with dbt Core for transformations, compatible with projects from dbt Cloud or dbt Core. You can choose between 'Scheduled in Fivetran' or 'Scheduled in Code' based on whether you prefer the schedule from Fivetran's dashboard or your dbt project.
Alerts and notification: Alerts are automatic messages generated within the Fivetran dashboard to inform you of issues in your Fivetran account, such as broken connectors or incomplete syncs, along with guidance on resolving them. Errors indicate issues preventing data syncing, while Warnings suggest problems that may require attention but won't halt data synchronization.
Airbyte
Founded in
Headquartered at
Pricing Plans
- Free Connector Program tier
- Growth tier: $2.50 per credit
- Enterprise tier: Custom pricing
Our Recommendation
Use Airbyte if:
You are looking for an open source tool.
Your data volume is relatively small (in terms of TB) and need a cost-effective option.
You prefer dark mode for the UI.
Pros
Open-source option available.
Easy to build custom connectors.
Provides out-of-the-box API access, eliminating the need to pay for it beyond a certain pricing plan.
Cons
Setup can be complex.
Transformations can only be performed after data is loaded into your data warehouse; it lacks pre-load transformation capabilities.
Offers fewer features compared to other platforms.
Airbyte Dashboard
You can explore the user interface in the demo instance to experience it firsthand. The main elements include connectors, sources, and destinations. Additionally, there's a "builder" feature for creating connectors with ease. Please note that Airbyte Cloud has specific limitations, such as a maximum of 20 connectors per workspace; you can find more details about these limitations here.
Sync Mode
Airbyte offers four sync modes:
1. Full Refresh | Overwrite: Syncs all records from the source and replaces data in the destination by overwriting it.
2. Full Refresh | Append: Syncs all records from the source and adds them to the destination without deleting any data.
3. Incremental Sync | Append: Syncs new records from the source and appends them to the destination without deleting any data.
4. Incremental Sync | Append + Deduped: Syncs new records from the source, adds them to the destination, and provides a deduplicated view reflecting the source stream's state.
High-volume Data Replication with Change Data Capture (CDC) and SSH Tunnels: Airbyte supports high-volume data replication using Change Data Capture (CDC) methods, efficiently capturing incremental changes in source data. Additionally, SSH tunnels are available for secure connections, ensuring reliable and encrypted data transfer between sources and destinations.
Data Pipeline
Create and manage pipeline: Each account supports multiple destinations, and you can select the data sources and destinations you wish to sync. You can also review the status of past syncs, access sync history, choose schemas, and configure sync methods for each connector.
Manage Schema Change
For each connection, you can define how Airbyte should manage changes in the source's schema. You can review and address non-breaking schema changes, as well as resolve any breaking schema changes.
Transformation
dbt cloud models: In Airbyte Cloud, you can create and run dbt transformations as part of the sync process using the dbt Cloud integration. After data extraction is complete, a dbt job is triggered to perform the transformation. You have the flexibility to run multiple transformations for each connection.
Alerts and Notifications
Airbyte offers an easy method to send webhook alerts when schema changes occur. Once configured, you can receive alerts and notifications via email or webhook
Airbyte Open Source
You can deploy the Airbyte open-source version on a VM or Kubernetes cluster, benefiting from over 300 off-the-shelf connectors and a vibrant community with over 10,000 GitHub stars.
Stitch
Founded in
(acquired by Talend in
November 2018)
Headquartered at
Pricing Plans
- Monthly and annual subscriptions
- 14-day free trial
- Pricing based on replicated rows and destinations.
Our Recommendation
Use Stitch if:
You can use their existing supported integrations (data sources) and have access to their Open Source Framework, Singer.
You have just one data warehouse and fewer than 10 data sources, making Stitch a cost-effective solution.
You don't require complex data transformations within the tool.
Your data is located in the US or Europe
Pros
Cost-effective.
Advanced scheduling options for precise start times and specific pipeline hours.
Integration with the Singer protocol for open-source development.
Volume-based pricing for newly added or edited rows.
Cons
Limited to North America and Europe regions only.
Singer connectors can break without warning and aren't maintained by Stitch.
Some integrations may be partially or fully incompatible with certain destinations.
Limited functionality compared to other platforms, and the UI could be improved.
Stitch Dashboard
The Stitch dashboard is a web-based interface for monitoring and managing your integrations. It offers real-time updates, ensuring you always have the latest information about your integrations. The dashboard includes features like integration and destination lists, status overviews, the latest sync information, and notifications.
Sync Mode
Stitch provides 3 different sync modes:
1. Log-based Incremental Replication: Stitch identifies record modifications (inserts, updates, deletes) through a database's binary log files
2. Key-based Incremental Replication: Stitch detects new and updated data using a column known as a Replication Key
3. Full Table Replication: Replicates all rows in a table, including new, updated, and existing data in each replication job
Smart cache refreshes: Stitch includes custom columns in your data for tracking the recency and frequency of new records.
Customization: You can select specific tables and columns for your pipeline, reducing load time and storage costs. You can also set precise initiation times for data extraction and specify hours for whitelisted activities using advanced scheduling options.
Data Pipeline
Create and Manage Pipeline: The number of destinations and integrations depends on your Stitch plan. In the Stitch Dashboard, you can access detailed insights for each integration, including status, sync information, table and row counts, logs, metrics, notifications, scheduling, and error handling options.
Stitch Import API
The Stitch Import API is a method-oriented RESTful API. It enables you to send data from a source (even those without existing Stitch integrations) to Stitch. With the Import API, you can push data, monitor its status, and validate push requests and batches without storing them permanently in Stitch.
Transformation
Transformation is not available in Stitch.
Notifications extensibility
Stitch provides in-app and email notifications for various alert types: Critical, Warning, and Delay. Additionally, you can integrate with external monitoring systems by forwarding Stitch notifications to services like Datadog, PagerDuty, and Slack.
Hevo
Founded in
Headquartered at
Pricing Plans
- One-month free trial
- Monthly and annual plans based on events
- Custom pricing for business plans
Our Recommendation
Use Hevo if:
You need a vendor that can load data in near real-time, with intervals as short as every 5 minutes.
You require support for ETL & ELT, along with support for perform Python-based transformations.
Pros
Cost-effective solution.
Supports both ETL and ELT, allowing data transformation before loading into the destination.
Enables near real-time data loading.
Features a comprehensive user interface tailored for technical users.
Cons
Fewer connectors compared to competitors.
May not be very user-friendly for non-technical users.
Hevo Dashboard
Hevo's web-based dashboard provides an overview of your selected pipeline. It displays information about events (rows loaded/updated) at each stage, from ingestion to load. You can also view object-level status and a graph showing events loaded in previous syncs, with convenient access to errors and options for object-level resynchronization. Additionally, a region selector at the top allows you to manage your workspaces across different regions.
Multi-region Support
Hevo enables users to manage a single account across all Hevo regions, offering up to five workspaces. Each workspace can be linked to different regions, and customers can easily switch between regions directly from the Hevo user interface.
Sync mode
Hevo provides three types of sync modes:
1. Incremental: This mode gathers new or modified data that arises after creating the pipeline
2. Historical: This mode imports existing data from your source when you initiate the pipeline, allowing you to capture historical records
3. Refresher: Specifically designed for advertising and analytics sources, this mode conducts periodic data refreshes to prevent data loss and capture attribution-related updates
Data Pipeline
Create and Manage Pipeline: With Hevo, each account can have multiple destinations. You have the flexibility to choose the data sources and destinations you want to sync. Additionally, you can monitor the status of previous syncs, access logs, select schemas for synchronization, and customize configurations and sync frequencies for each connector.
Hevo offers built-in data transformation features within the data pipeline. You can prepare data in various ways before sending it to the destination. Two transformation options are available:
Python-based transformation script: Modify ingested events using Python code before loading them into the destination. You can add, modify, or remove fields, and even join fields for specific events. Hevo provides three classes for data transformation: Event, TimeUtils, and Utils.
Drag and Drop transformation: This new feature offers a no-code option for creating transformations, simplifying the process of building data transformations.
Transformation
After your data arrives in the data warehouse, Hevo's transformation feature allows you to convert the source data into a format suitable for analytics. You can utilize the "Model" tab to execute transformations using SQL or dbt Core. Additionally, the "Workflow" feature lets you create a Directed Acyclic Graph (DAG) for managing your transformation processes efficiently.
SQL & dbt Models
You can utilize SQL queries or Hevo's hosted dbt Core to create your data models. There are two types of models available: Full Models, which recreate the table with every run, and Incremental Models, which enable you to export only the changed data to the output table after the primary key is defined.
Workflows
In Directed Acyclic Graphs (DAGs), you can define the dependencies between SQL and dbt™ Models, combine the data transformed by these Models with or without data load conditions, and load the transformed data into the Destination tables as per your Workflow setup.
Activity log - CloudWatch Sync
Amazon CloudWatch Logs is a monitoring and management service provided by Amazon Web Services (AWS). You can push the activity logs corresponding to actions, status updates, and failures for any Hevo assets, such as Pipelines, Models, and Workflows, to your CloudWatch Logs account.
Alerts and Notifications
Hevo sends out alerts for any changes that occur in any of your Pipelines, Models, Workflows, Destinations, and Activations. Hevo also sends out a periodic status update on the above entities, through various mediums such as emails, slack, Microsoft Teams etc.
Pipeline Prioritization
Hevo's data ingestion involves executing tasks based on Source settings and Pipeline priority. if you want data from a Pipeline urgently or want to analyze it in near real-time, Hevo provides you with the feature of prioritizing the Pipeline. Prioritization ensures that Hevo replicates your business-critical data first while ensuring resource availability for other Pipelines.
Puzzled about the right ingestion tool choice? Count on 5X for free advice and easy setup.
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How 5X streamlines data ingestion
Assessing needs for best-fit vendor recommendations
We understand your business & use cases you want to implement. We then assess your data sources, data stack, and security & compliance needs. Based on these, we recommend a tool that fits your budget.
Creating proof of concepts with your real data
We help build your data pipelines using your actual data sources. This allows you to directly compare tools based on your real contextual use cases, aiding in the decision-making process. We can also help build custom connectors if your data source is not supported by any existing connectors.
Ensuring best practice
5X Black service (i.e. our consultancy) can help you setup your data pipelines with best practice and run validations, ensuring data quality and integrity.
Streamlined negotiations and contract handling
5X takes care of all the negotiations, paperwork, and contract management on your behalf. We engage with ingestion vendors to secure the best contract, eliminating the need for you to navigate complex sales conversations.
Seamless integration with the rest of your data stack
We offer easy integration of your selected ingestion vendor with other tools using a simple 1-click process. When you onboard data vendors like data warehouses to the 5X platform, the new ingestion vendor smoothly configures with your data warehouse via APIs, eliminating manual work and maintenance so you can focus on analytics.
Centralized billing, user management, and insights
Through the 5X platform, all vendors provisioned under 5X are consolidated into a single monthly bill. This simplifies financial management by eliminating the need to handle multiple invoices. Additionally, 5X platform allows you to manage user access, monitor usage, centralize, and manage your data with 5X’s trusted data ingestion solutions, so your data team can focus on insights, not infrastructure.
Tool implementation best practices
Setting up the tool
1. Configure connectors: Start by setting up connectors for your data sources, using clear naming conventions. This helps you easily identify each connector.
2. Define replication and sync: Specify data replication methods and data sync frequencies to align with your requirements. Also, name your destination schemas logically and consistently.
3. Document everything: Thoroughly document your configurations, schedules, and mappings with descriptive names. This makes maintenance and troubleshooting easier.
Maximizing efficiency & performance
1. Use built-in tools: Make the most of the tool's dashboards and alerts to quickly spot issues.
2. Review and optimize: Regularly check and improve your data pipelines as your data system grows.
3. Stay updated: Keep up with the tool's latest updates and features. Vendors often enhance their tools, so staying informed helps you work more efficiently.
Security & compliance
1. Control access: Use access controls and authentication methods like role-based access and multi-factor authentication to limit access to authorized users.
2. Protect data: Implement security measures like data hashing and masking for sensitive information, such as personal data.
3. Audit and monitor: Enable audit trails and logs, and regularly review them for unusual activities or security incidents.
Future trends in data ingestion tools
Real-time and streaming data: The demand for real-time insights continues to surge. Data ingestion pipelines are evolving to prioritize the acquisition and handling of streaming data from a variety of sources, including IoT devices, social media streams, sensor networks, and more.
Cloud-native solutions: Cloud-based data ingestion solutions continue to be crucial, capitalizing on the scalability, flexibility, and cost-efficiency of cloud platforms. The adoption of serverless computing and managed services simplifies the development and management of data pipelines.
Integration of AI and ML: Data ingestion tools are increasingly incorporating artificial intelligence and machine learning techniques. This integration empowers automated tasks such as data transformation, quality assessment, anomaly detection, and even predictive analysis directly at the ingestion stage.
Conclusion
Ingestion is the first step in your data pipeline, enabling efficient data collection for analysis and decision-making. Consider factors like scalability, ease of use, pricing structure, support, and documentation to make an informed purchase decision. Ensure the tool accommodates various data types and sources aligned with your business needs. Acknowledge any limitations or downsides honestly.
Once you've chosen the tool, make sure to set it up and configure it smoothly. Use the built-in tools to identify and address any issues promptly. Most importantly, restrict access to authorized users to prevent security incidents.
Remember to align your choice with your company's goals so that the tool supports your current needs and promotes a data-driven culture.
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