10 Data Ingestion Platforms to Consider in 2025

Every day, businesses generate a mountain of data, most of which is left under-used or even unused. In fact, nearly 90% of enterprise data is left to languish, unstructured and buried. We commonly referred to it as “dark data,” and it’s valuable intelligence literally sitting in the shadows, waiting to fuel smarter decisions—and AI—if only we could tap into it and transform it effectively.

Enter data ingestion platforms: The critical pipelines that bridge your raw data, unstructured data and the information you can use. As companies increase their AI initiatives and real-time analytics, having a reliable and scalable strategy to process (ingest) their data is no longer just a technical nice‑to‑have; it’s a strategic necessity. Industry foresight supports this: By 2032, the global data ingestion tool market is projected to grow nearly 390 percent, climbing from approximately $1.3 billion in 2023 to $4.9 billion.

Whether you’re a business leader seeking up-to-date insights, a data analyst navigating an increasingly complex stack, or a budding AI enthusiast, understanding your data ingestion options is essential for uncovering the value hidden in your data and avoiding being left behind.

What is a data ingestion platform?

A data ingestion platform is a system or service for organizations to collect data from multiple sources and move it to a destination like a data warehouse, data lake, or analytics platform. These tools support either batch ingestion (scheduled data transfers) or real-time ingestion (continuous data streaming).

Data ingestion platforms serve as a foundational layer in the modern data stack, acting as the bridge between data creation and data activation.

Benefits of using a data ingestion platform

The right data ingestion platform does more than just move data. It creates a streamlined, scalable, and reliable pipeline that:

• Speeds up insights by reducing the time between data generation and data access.
• Improves data quality through transformation and error handling.
• Brings together different sources into a centralized analytics environment.
• Supports real-time analytics for operational and AI-driven use cases.
• Makes compliance and governance easier with auditable workflows and access controls.

For teams building data dashboards, using machine learning models, or sharing insights across the organization, data ingestion is a crucial first step.

Key features to look for in a data ingestion platform

Not all ingestion platforms are built the same. As you evaluate tools, consider the following features:

• Real-time and batch ingestion capabilities
• Support for diverse connectors, like databases, APIs, cloud apps, and flat files
• Built-in data transformation or ETL/ELT functionality
• Monitoring and observability tools
• Data quality enforcement
• Scalability for handling large volumes of data
• Security and compliance with standards like SOC 2, HIPAA, and GDPR

Depending on your team size and data maturity, you might also want tools that support no-code/low-code workflows or custom scripting for more control.

10 best data ingestion platforms in 2025

1. Domo

Domo is a modern cloud-native platform that streamlines the entire data pipeline, from ingestion to visualization. It offers thousands of prebuilt connectors and tools to help users move, transform, and quickly put their data to use.

With Domo, users can ingest cloud-based and on-premises data into a centralized hub. Magic ETL provides no-code transformations, while automated workflows ensure continuous updates. For example, a retailer can integrate POS, e-commerce, and inventory data to monitor supply chain activity in real time.

The platform is designed for both technical users and business professionals, providing flexibility without sacrificing control. Governance features ensure data integrity, while built-in AI and machine learning help uncover trends and trigger alerts.

With built-in data transformation, governance, and AI/ML capabilities, Domo consolidates ingestion, preparation, and analytics in one unified platform. The platform is designed for both technical and non-technical users, making it ideal for organizations looking to democratize data. 

2. Apache Kafka

Apache Kafka is a distributed streaming platform designed for high-throughput, real-time data pipelines. Originally developed by LinkedIn and now open-source under the Apache Software Foundation, Kafka is optimized for handling large-scale, event-driven data flows. It’s a go-to choice for enterprises that need to collect, store, process, and re-route massive amounts of data with near-zero latency.

Kafka operates on a publish-subscribe model. Data producers write messages to topics, and consumers read them asynchronously, allowing high parallelization and durability. Kafka brokers store data for configurable retention periods, enabling both stream and batch processing.

In practice, a fintech company might use Kafka to process millions of financial transactions per second, flagging anomalies in real-time for fraud detection. It connects easily with tools like Apache Flink, Spark, and Elasticsearch for downstream analytics and enrichment.

Kafka’s strengths include scalability, reliability, and fault tolerance. It can replicate data across clusters and ensure continuity even in the event of node failures. With Kafka Connect and Kafka Streams, developers can build reliable data ingestion and processing pipelines natively within the ecosystem.

However, Kafka comes with a learning curve. Deploying and maintaining a Kafka cluster requires a solid understanding of distributed systems. It’s often overkill for smaller teams or basic ETL jobs.

For organizations with the engineering resources to manage it, Kafka is a powerful solution for streaming ingestion—particularly when paired with real-time processing frameworks and a need for high reliability and scale.

3. Apache NiFi

Apache NiFi is a powerful, flow-based data ingestion tool built for automating the movement and transformation of data between systems. Originally developed by the NSA and now maintained by the Apache Software Foundation, NiFi is particularly well-suited for use cases where traceability, governance, and complex routing are important.

NiFi offers a drag-and-drop interface for building data pipelines and supports over 300 processors for tasks like filtering, enriching, transforming, encrypting, and routing data. It allows users to create detailed, visually defined data flows without writing custom code.

One of NiFi’s standout features is its built-in data provenance, which tracks the lifecycle of every piece of data moving through a pipeline. This makes it highly valuable for industries like healthcare, government, and finance, where auditability and compliance are essential.

For instance, a public sector organization might use NiFi to collect data from IoT sensors, anonymize and encrypt it, and transmit it to secure storage for further analysis. With support for back-pressure, load balancing, and prioritization, NiFi ensures data flows remain reliable even under heavy loads.

While it’s not as scalable for extreme real-time scenarios as Kafka, NiFi excels in flexibility, extensibility, and ease of use. Its REST API support and NiFi Registry enable integration into CI/CD workflows, further boosting enterprise adoption.

Organizations seeking a secure, traceable, and configurable platform for managing complex ingestion workflows will find NiFi to be a strong contender.

4. Talend

Talend offers a strong suite of data integration tools for ingestion, transformation, and quality management. Its flagship product, Talend Data Fabric, is designed to handle ingestion across on-prem, cloud, and hybrid environments, offering both batch and real-time support.

The platform includes Talend Studio for designing ETL jobs, Talend Pipeline Designer for fast cloud-native development, and Talend Data Preparation for data cleansing. It also integrates with major cloud providers like AWS, Azure, and Google Cloud.

Talend’s key differentiator is its strong focus on data health. Built-in tools enable profiling, deduplication, enrichment, and governance. Users can monitor ingestion pipelines in real time and track lineage to ensure traceability.

Consider a healthcare provider ingesting data from EMRs, patient scheduling systems, and third-party APIs. Talend can help unify this data, apply validation rules, and ensure sensitive information is anonymized before analysis.

For businesses needing open-source flexibility, Talend Open Studio remains a cost-effective option with solid community support. For enterprise deployments, the subscription-based Data Fabric delivers enterprise-grade scalability and advanced monitoring.

However, Talend’s licensing can be complex, and the learning curve for building sophisticated data flows is steep without training.

Talend is ideal for organizations with diverse data sources, stringent quality requirements, and a need for extensive control over the ingestion and transformation lifecycle.

5. Informatica

Informatica is a market leader in enterprise-grade data integration and ingestion. Its Intelligent Data Management Cloud (IDMC) is a comprehensive suite that supports ingestion from virtually any source—on-prem, cloud, or multi-cloud—and includes transformation, metadata management, data quality, and governance features.

With more than 200 prebuilt connectors, Informatica simplifies access to SaaS apps, legacy databases, streaming platforms, and cloud storage. The platform supports both batch and real-time ingestion, making it highly adaptable to a wide range of data environments.

Informatica’s CLAIRE engine uses AI and machine learning to automate metadata discovery, impact analysis, and data classification. This makes it easier for teams to set up ingestion workflows that are secure, auditable, and compliant with data privacy laws.

A large enterprise might use Informatica to ingest and consolidate data from hundreds of internal systems and external partners, transforming it in transit and enriching it with master data before loading it into a centralized warehouse like Snowflake or Azure Synapse.

The platform offers strong capabilities for data cataloging, lineage tracking, and policy enforcement, making it a favorite among regulated industries like banking and pharmaceuticals.

While Informatica offers unmatched breadth and power, it’s best suited for large organizations with dedicated IT and data engineering resources. The interface can be complex, and licensing costs may be prohibitive for smaller teams.

For global enterprises with complex data estates and compliance needs, Informatica is a proven, scalable, and future-ready ingestion platform.

6. Fivetran

Fivetran specializes in automated, fully managed data ingestion for analytics pipelines. It offers hundreds of prebuilt connectors that allow users to sync data from popular SaaS tools, databases, and files into cloud data warehouses like Snowflake, BigQuery, and Redshift.

What makes Fivetran unique is its “set it and forget it” model. After a connector is configured, Fivetran automatically handles schema changes, updates, and incremental loads—drastically reducing the need for manual maintenance.

A marketing team, for instance, could use Fivetran to continuously pull campaign data from platforms like Facebook Ads, HubSpot, and Google Analytics into a centralized dashboard. The setup takes minutes, and the data stays current without ongoing oversight.

Fivetran supports transformations via dbt integration, allowing users to apply business logic post-ingestion. However, it focuses on ELT rather than full ETL, so complex pre-ingestion transformations may require additional tooling.

Its built-in alerting, monitoring, and usage analytics help data teams maintain performance and control costs. Fivetran’s security posture includes SOC 2 Type II, GDPR, and HIPAA compliance.

While Fivetran is excellent for analytics-ready data ingestion, it may be limiting for teams that need deep customization or event-based triggers. It’s best suited for modern data teams looking for rapid deployment and low maintenance.

For organizations that prioritize simplicity, reliability, and time-to-value, Fivetran is one of the fastest ways to move data into the hands of decision-makers.

7. Amazon Kinesis

Amazon Kinesis is AWS’s real-time data ingestion and streaming platform designed to handle high-throughput, low-latency workloads. It includes several components—Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics—that work together to support continuous data collection, processing, and delivery.

Kinesis Data Streams lets you build custom real-time applications for processing data such as log events, user activity, or IoT sensor readings. Kinesis Data Firehose simplifies delivery by automatically loading data to destinations like Amazon S3, Redshift, or Elasticsearch with minimal configuration.

A common use case would be an e-commerce company using Kinesis to ingest clickstream data in real time, enabling responsive personalization and rapid customer insights. Integration with AWS Lambda and CloudWatch allows for automated processing and monitoring without infrastructure management.

Kinesis scales automatically with demand and ensures fault-tolerance through data replication across availability zones. It’s deeply integrated with the AWS ecosystem, making it an attractive option for teams already building on AWS.

However, the learning curve and cost complexity can grow with more advanced use cases. It’s best suited for organizations with technical teams looking to build event-driven, real-time data architectures.

8. Azure Data Factory

Azure Data Factory (ADF) is Microsoft’s fully managed cloud-based ETL and data orchestration service. It allows organizations to create, schedule, and manage data pipelines that move and transform data from a wide range of sources into Azure data services.

ADF supports over 100 connectors, enabling ingestion from SQL Server, Oracle, SAP, Amazon S3, Salesforce, and many more. It enables both batch and near-real-time data movement and supports transformation through Data Flow—a visual interface for complex logic without writing code.

ADF also includes features like parameterization, triggers, and integration runtimes, making it well-suited for enterprise-scale workflows. For example, a financial institution might use ADF to collect data from multiple global offices and process it centrally in Azure Synapse for unified reporting.

The platform integrates natively with Azure services such as Functions, Logic Apps, and Key Vault. However, users unfamiliar with Azure’s ecosystem may face a moderate learning curve.

ADF is ideal for businesses already invested in Microsoft technologies and looking for scalable, secure, and automated ingestion pipelines across hybrid environments.

9. AWS Glue

AWS Glue is a serverless data integration service designed to simplify ingestion, transformation, and preparation workflows across AWS-native data lakes and warehouses. It supports both ETL and ELT paradigms and offers broad compatibility with Amazon S3, Redshift, Athena, and Lake Formation.

Glue includes a visual tool—Glue Studio—that enables users to build, monitor, and troubleshoot ETL jobs without needing deep Spark knowledge. The AWS Glue Data Catalog acts as a centralized metadata repository, improving discoverability and governance across your data ecosystem.

A logistics company might use Glue to ingest real-time shipment tracking data from multiple locations, apply business rules, and prepare it for query analysis in QuickSight or Redshift.

Because it’s serverless, AWS Glue scales automatically with workload demands and minimizes infrastructure management. It supports job scheduling, dependency chaining, and versioning for development teams managing complex pipelines.

That said, some aspects—like performance tuning or custom logic—may require scripting. Glue is best suited for AWS-native teams that want to centralize ingestion, maintain flexibility, and reduce manual overhead.

10. Google Cloud Dataflow

Google Cloud Dataflow is a fully managed service for real-time and batch data processing built on Apache Beam. It allows developers to design unified pipelines using Java or Python, enabling consistent logic across both streaming and historical data.

Dataflow integrates natively with GCP services like Pub/Sub (for streaming ingestion), BigQuery (for analytics), and Vertex AI (for machine learning). This makes it a powerful tool for end-to-end data workflows in GCP-native environments.

A digital media company might use Dataflow to process real-time ad impressions and user engagement data, enriching it with metadata and pushing it to BigQuery dashboards for instant insights.

Dataflow supports autoscaling, dynamic work rebalancing, and advanced features like windowing, stateful computation, and session management. These features make it ideal for event-based architectures and complex use cases.

Although powerful, the Beam programming model introduces a learning curve—especially for teams unfamiliar with functional programming concepts or GCP tools. Still, for those already working in Google Cloud, Dataflow is a reliable, scalable option for intelligent data ingestion.

Don’t let your data go dark

As organizations ramp up their AI and automation initiatives, real-time data access is becoming nonnegotiable. A modern data ingestion platform ensures you’re not just collecting data—you’re activating it.

Whether you’re a growing business or a global enterprise, evaluating your ingestion options in 2025 is a smart investment in your data future.

Curious how Domo handles data ingestion? Start your free trial or connect with a Domo expert to learn more.