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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesCRN’s 2025 Big Data 100 data-management and integration category is an editorial selection, not a ranked contest. Its accessible list names 35 companies spanning data movement, transformation, catalogs, governance, quality, master data management, streaming, orchestration, unstructured-data processing and AI infrastructure. The companies are not interchangeable: the right shortlist depends on whether your bottleneck is moving data, making it trustworthy, governing access, resolving entities or supporting real-time and AI workloads.
CRN’s category reflects a practical reality. Enterprise data now sits across SaaS applications, ERP and CRM systems, databases, file shares, object stores, warehouses, lakes, lakehouses and AI platforms. Buyers increasingly need metadata, lineage, quality controls, policy enforcement and support for structured and unstructured data—not just an ETL job from one database to another.
What CRN’s list represents
CRN frames the category around discovering data across hybrid and multicloud estates; moving operational data into analytical destinations; transforming data for analytics, machine learning and AI; monitoring quality; governing privacy and security; and supporting real-time streaming and change-data capture. See CRN’s category article.
The accessible continuation names 35 companies, even though the category description is sometimes discussed as a larger group. Unless the original slideshow confirms additional entries, this article refers to the 35 names exposed in CRN’s accessible text. CRN supplies no numerical scores, ranking, comparative benchmark or formal selection methodology. “Coolest” means editorial recognition and market visibility, not “best for every workload.”
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CRN also places some vendors near storage, security, observability, analytics and cloud-platform categories. That overlap is useful: modern data programs increasingly combine movement, governance, compute and storage.
Why the category matters to AI and analytics
CRN, citing Statista, says an estimated 149 zettabytes of global data existed in 2024 and forecasts 394 zettabytes by 2028. Those are market-research estimates, not independently verified measurements. The operational implication is clearer than the exact forecast: more data is distributed across more systems, while AI makes stale, inaccessible or unauthorized data more costly.
A useful platform must answer questions such as: Where is sensitive data? Which source is authoritative? Did a schema change break a pipeline? Can an analyst trace a metric to its origin? Can a model use documents without exposing restricted content? The companies below address different parts of that chain.
Rank #2
Companies grouped by the problem they solve
Data movement, replication and connectivity
| Company | Primary role | Best-fit question |
|---|---|---|
| Airbyte | Open-source and commercial data movement | Do we need extensible connectors, self-management or embedded movement? |
| CData Software | Connectivity, live access, replication, ETL/ELT and B2B/EDI | How will we connect hundreds of applications and systems? |
| Fivetran | Managed replication into warehouses, lakes and databases | Can we deploy reliable SaaS and operational-system ingestion quickly? |
| Matillion | Cloud pipelines, ELT, low-code transformation and orchestration | Do developers and analysts need visual pipeline construction? |
| Nexla | Integration, ETL/ELT, streaming, CDC and RAG-pipeline tooling | Can one data-product layer serve batch, streaming and AI pipelines? |
| Precisely | Integration within a broader data-integrity suite | Can movement be combined with quality, enrichment and governance? |
| Striim | Real-time integration, streaming SQL, CDC and replication | What must arrive continuously rather than in scheduled batches? |
| Syncari | Synchronization, cleansing, merging and activation | How do we keep business applications operationally consistent? |
Airbyte offers open-source, cloud, self-managed enterprise and embedded editions. CRN reported more than 300 connectors at publication time. CData says its products support more than 300 data sources and applications, while CRN reported more than 700 Fivetran connectors at publication time; counts can change. Connector quantity is not proof of support for deletes, schema evolution, nested data, backfills or API-rate limits.
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- dbt Labs provides SQL-based transformation, testing, documentation and workflow management inside cloud data warehouses. It is primarily a transformation and development layer, not a source-system ingestion service. See dbt.
- Coalesce offers visual data development, especially for Snowflake workloads, and expanded toward catalog capabilities after acquiring CastorDoc. See Coalesce.
- Astera Software combines extraction, integration, warehousing, transformation, workflow orchestration and scheduling. See Astera.
- Matillion overlaps this group through no-code and low-code pipeline development.
- DataPelago focuses on accelerated analytics and AI processing across CPU, GPU, TPU and FPGA environments. Any “one to two orders of magnitude faster” statement is a company claim, not an independent benchmark.
In practice, dbt, Fivetran and Airbyte are often complementary: one moves data, another transforms it, and a warehouse or lakehouse stores the result.
Catalogs, metadata and governance
Alation, Ataccama, Atlan, Collibra and Actian help organizations discover, describe and govern data. Alation emphasizes catalog context, lineage and governance; Atlan emphasizes collaborative metadata; Collibra spans governance, catalog, lineage, privacy, quality and observability; Ataccama combines catalog, quality, observability, lineage and MDM; Actian positions data intelligence alongside catalog, governance, quality and metadata management.
A catalog helps people find and understand assets. It does not automatically move or cleanse them. Policy enforcement usually depends on integrations with warehouses, lakes, identity systems and query engines. Catalog value also depends on assigned owners, current definitions and trustworthy lineage.
Quality, observability, privacy and security
- Anomalo monitors data quality with anomaly detection, validation, lineage and root-cause analysis.
- BigID focuses on discovery and classification, data-security posture management, privacy, governance, lifecycle management and mapping.
- Immuta provides discovery, usage monitoring and cross-platform access-policy creation and enforcement.
- Informatica includes quality and observability in a broad data-management cloud.
- Precisely, Ataccama and Collibra combine quality capabilities with wider integrity, governance or MDM suites.
Ask vendors whether an “AI-powered” feature detects an issue, diagnoses it, suggests a fix or applies remediation automatically. Confirm human approval, auditability, evidence and rollback; those are materially different capabilities.
The Tool Desk
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Reltio provides cloud MDM, entity resolution, quality, governance, integration and multidomain unification. Tamr applies machine learning to mastering, enrichment, customer and healthcare 360, supplier data and entity resolution. Precisely, Ataccama and Informatica include MDM in broader suites, while Syncari unifies and cleanses records across business systems.
MDM is not ordinary integration. Integration synchronizes data; MDM establishes governed, deduplicated records for entities such as customers, products, suppliers or providers. Require domain-specific precision and recall testing, survivorship rules and named data owners.
Streaming and real-time data
Confluent provides streaming infrastructure based on Apache Kafka, connectors, governance and cloud and on-premises options. Striim emphasizes streaming integration, streaming SQL, CDC and replication. CData, Nexla and Actian also address real-time movement; CRN associates Apache Kafka with NetApp’s wider data-management offerings.
Distinguish scheduled batch, micro-batch, CDC, event streaming, streaming transformations, querying data in motion and persisting streams into open table formats. CDC is not automatically real time: source-log latency, queues, destination batching, duplicates, ordering and delete handling all affect observed freshness.
Best Value
Orchestration, unstructured data and AI infrastructure
- Astronomer offers managed orchestration and observability built on Apache Airflow. Airflow is open source; Astro is commercial.
- Unstructured converts documents and other complex content into structured data for analytics and generative-AI pipelines. Extraction quality varies with tables, scans, handwriting, layouts and languages.
- Aparavi focuses on discovery, classification and optimization of unstructured data. Claims that 80% of enterprise data is unstructured require attribution rather than universal treatment.
- Alluxio orchestrates distributed data access between compute and storage.
- Datadobi specializes in unstructured-data mobility across hybrid and multicloud storage.
- Vast Data combines storage, database, compute, cataloging, enrichment and security for AI workloads.
- Weka provides a high-performance AI data platform and infrastructure.
- NetApp contributes data mobility and management through BlueXP and related products.
- Domino Data Lab is an enterprise AI and MLOps platform for development, collaboration, reproducibility and governance.
Concise vendor guide
| Vendor | Likely buyer | Main qualification |
|---|---|---|
| Actian/HCL Software | Teams wanting hybrid integration, intelligence, quality and analytics | Its capabilities span multiple products, not one SKU |
| Airbyte | Engineering-led organizations needing extensibility or self-hosting | Self-management can shift connector and operations work to the buyer |
| Alation | Organizations formalizing discovery and stewardship | It is not primarily an ingestion engine |
| Alluxio | Data-intensive teams bringing compute closer to distributed data | Infrastructure layer rather than classic ETL |
| Anomalo | Teams needing automated quality monitoring | Detection does not guarantee remediation |
| Aparavi | Enterprises classifying and optimizing files and content | Validate handling of sensitive and complex documents |
| Astera Software | Organizations combining extraction, integration and workflows | Check connector depth and deployment requirements |
| Astronomer | Airflow users wanting managed operations | Commercial value is around managed Airflow, not ownership of Airflow itself |
| Ataccama | Enterprises seeking quality, governance and MDM breadth | Broad suites may require substantial implementation |
| Atlan | Modern data teams prioritizing collaborative metadata | Adoption determines catalog value |
| BigID | Privacy and sensitive-data discovery programs | More security/privacy-led than pipeline-led |
| CData | Enterprises needing broad application connectivity | Distinguish drivers, Sync, Virtuality and Arc products |
| Coalesce | Snowflake-centric development teams | Assess fit if your estate is heterogeneous |
| Collibra | Large governance and lineage programs | Requires stewardship ownership |
| Confluent | Event-driven and real-time architectures | Kafka skills and operating cost matter |
| Datadobi | Hybrid and multicloud file-mobility projects | Not a general-purpose transformation platform |
| DataPelago | Teams needing hardware-aware acceleration | Require independent performance evidence for major claims |
| dbt Labs | Analytics engineers working in cloud warehouses | Requires SQL, testing and deployment discipline |
| Denodo | Organizations querying across sources without copying everything | Virtualization does not remove latency, outages or access limits |
| Domino Data Lab | Enterprises governing model development and MLOps | More AI-platform-oriented than general ETL |
| Fivetran | Teams seeking managed ingestion and fast deployment | Consumption costs rise with backfills and frequent syncs |
| Hitachi Vantara/Pentaho | Established enterprise and hybrid environments | Portfolio can be modular and complex |
| Immuta | Cross-platform data-access governance | Needs compatible platforms and identity integration |
| Informatica | Buyers seeking broad enterprise data management | Licensing and implementation complexity require scrutiny |
| Matillion | Teams wanting low-code cloud pipelines | Credit-based consumption needs workload modeling |
| NetApp | Infrastructure teams focused on mobility and storage integration | Not a direct substitute for dbt or a catalog |
| Nexla | Teams building governed data products and RAG pipelines | Translate “data fabric” into concrete workflows |
| Precisely | Organizations combining quality, enrichment, MDM and integration | Broad portfolios may require separate modules |
| Reltio | Customer, product or supplier 360 programs | Requires authoritative-data ownership |
| Striim | CDC and real-time delivery projects | Streaming adds operational complexity |
| Syncari | Businesses synchronizing operational applications | Best fit is cross-application consistency and activation |
| Tamr | AI-assisted entity resolution and mastering | Test matching quality on domain-specific records |
| Unstructured | Teams preparing documents for analytics or LLMs | Output quality depends on formats and extraction rules |
| Vast Data | Infrastructure-scale AI deployments | Major infrastructure purchase, not lightweight SaaS |
| Weka | High-performance AI pipelines and inference | Requires infrastructure-scale use cases |
How to choose a shortlist
Start with the bottleneck
- Choose ingestion or replication products when source-to-destination movement is the issue.
- Choose dbt, Coalesce or Matillion when warehouse-native transformation and development are the priority.
- Choose catalog and governance platforms when discovery, ownership, lineage or policy is the constraint.
- Choose Anomalo, Ataccama, Informatica, Precisely or similar products when quality and observability need formal controls.
- Choose Reltio, Tamr, Precisely, Ataccama, Informatica or Syncari when the problem is authoritative entity data.
- Choose Confluent or Striim when latency and event flow justify streaming operations.
- Choose Unstructured, Astronomer, Domino, Alluxio, Vast Data or Weka for document, orchestration, AI or infrastructure-specific needs.
Check architecture and portability
Evaluate SaaS, self-managed, on-premises, hybrid and multicloud options; support for Snowflake, Databricks, BigQuery, Fabric, Redshift, PostgreSQL, Kafka, Iceberg, Delta Lake and major SaaS systems; pushdown versus vendor-managed compute; APIs, SDKs, CLI and infrastructure-as-code; open formats; and network, storage and egress costs.
Verify governance and security
Ask about row-, column-, object- and purpose-based controls; SSO, SCIM, RBAC, ABAC and customer-managed keys; residency; audit logs; sensitive-data classification; and whether lineage is inferred, imported or captured natively. Match controls to the buyer’s geography and industry obligations.
Model the complete cost
Compare rows processed or changed, compute hours or credits, connectors, users, storage, API calls, throughput, environments, support, services, marketplace fees and minimum commitments. A headline starting price is not a total-cost model.
Pricing information observed August 16, 2026 included usage signals for Fivetran, Matillion, Airbyte and Actian. Fivetran advertises a free introductory plan, Standard, Enterprise and Business Critical options, consumption pricing, 14-day trials and advertised annual-contract discounts of up to 22% at its pricing page. Matillion lists Developer, Teams and Scale editions with credit-based consumption at its pricing page. Airbyte’s product and pricing pages are airbyte.com and airbyte.com/pricing; CRN reported a shift toward capacity-based pricing in February 2025. Actian lists quote-based Data Intelligence buying at its buying page and displays Actian Units priced per hour, with storage separate, for its Analytics AI Platform at that product page. Enterprise platforms such as Collibra, Alation, Informatica, Immuta, BigID, Reltio, Tamr and Ataccama generally require a sales quote.
Proof-of-concept checks that prevent expensive surprises
- Load representative volumes, nested records and historical backfills.
- Change source schemas, rename fields and delete records; verify destination behavior.
- Measure end-to-end latency separately for batch, CDC and streaming paths.
- Stop a source and destination, then test retries, deduplication, ordering and recovery.
- Trace a metric or model input through lineage and confirm coverage across every tool.
- Apply row, column and purpose-based policies and inspect audit history.
- Test document extraction on scans, tables, multilingual files and changing templates.
- Run realistic development, test and production workloads to model credits, compute, storage and network charges.
- Export metadata, configurations and data to test migration and exit options.
Representative combinations
- Warehouse analytics: Fivetran, Airbyte or CData for ingestion; dbt, Coalesce or Matillion for transformation; Alation, Atlan or Collibra for catalog and governance.
- Real-time operations: Confluent or Striim for events and CDC, with governance and quality controls layered around the stream.
- Customer 360: Reltio or Tamr, potentially alongside Precisely, Ataccama, Informatica or Syncari for integration and enrichment.
- Document-to-LLM: Unstructured for extraction, Astronomer for workflow scheduling, and policy controls from the organization’s security and governance stack.
These are architectural patterns, not endorsements. A proof of concept should test the buyer’s exact systems and policies.
The Bottom Line
CRN’s 2025 selection is best used as a map of the data-management market, not a league table. Select vendors by the specific failure you need to fix—movement, transformation, trust, governance, entity resolution, latency or unstructured-data processing—and validate cost, recovery, policy enforcement and portability before committing.
Quick Recap
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