Qlik VS Rivery

Qlik is a legacy enterprise vendor known for BI and dashboarding. Its Qlik Replicate product (formerly Attunity) enables database replication using full load and log-based CDC, primarily into data warehouses like Snowflake and Synapse.

While mature in legacy environments, Qlik lacks support for streaming-first architectures, modern SaaS APIs, and developer-friendly workflows.

• CDC support: Mature log-based replication from enterprise databases.
• Strong in SAP/Mainframe: One of few vendors with support for complex legacy systems.

• Legacy-first architecture: No native support for streaming, APIs, or lakehouse targets.
• High complexity: Requires separate tools (e.g. Qlik Compose) for transforms, orchestration, or monitoring.
• Limited extensibility: Closed ecosystem. No SDK or community for custom connectors.
• Not built for the cloud: Self-managed option is brittle. SaaS version is fragmented.
• Opaque pricing: Requires contract negotiations. Difficult to evaluate TCO up front.

Pricing is enterprise-only, opaque, and often varies by reseller. Customers pay per core or task for Qlik Replicate, and additional fees for Qlik Compose and Qlik Cloud. Expect significant licensing and infrastructure overhead for full deployments.

Rivery was founded in 2019. Since then it has grown to 100 people and 350+ customers. It’s a multi-tenant public cloud SaaS ELT platform. It has some ETL features, including inline Python transforms and reverse ETL. It supports workflows and can also load multiple destinations.

But Rivery is also similar to batch ELT. There are a few cases where Rivery is real-time at the source, such as with CDC, which is its own implementation. But even in that case it ends up being batch because it extracts to files and uses Kafka for file streaming to destinations which are then loaded in minimum intervals of 60, 15, and 5 minutes for the starter, professional, and enterprise plans.

If you’re looking for some ETL features and are OK with a public cloud-only option, Rivery is an option. It is less expensive than many ETL vendors, and also less expensive than Fivetran. But its pricing is medium-high for an ELT vendor.

Rivery's future offerings, policies, and pricing may be uncertain as they undergo an acquisition with Boomi.

• Modern data pipelines: Rivery is the one other modern data pipeline platform in this comparison along with Estuary.
• Transforms: You have an option of running Python (ETL) or SQL (ELT). You do need to make sure you use destination-specific SQL.
• Orchestration: Rivery lets you build workflows graphically.
• Reverse ETL: Rivery also supports reverse ETL.
• Load options: Rivery supports soft deletes (append only) and several update-in-place options including switch-merge (to merge updates from an existing table and switch), delete-merge (to delete older versions of rows), and a regular merge.
• Costs: Rivery is lower cost compared to other ETL vendors and Fivetran, though it is still higher than several ELT vendors.

• Batch only: While Rivery does extract from its CDC sources in real-time, which is the best approach, it does not support messaging sources or destinations, and only loads destinations in minimum intervals of 60 (Starter), 15 (Professional), or 5 (Enterprise) minutes.
• Data warehouse focus: While Rivery supports Postgres, Azure SQL, email, cloud storage, and a few other non data warehouse destinations, Rivery’s focus is data warehousing. It doesn’t support the other use cases as well.
• Public SaaS: Rivery is public cloud only. There is no private cloud or self-hosted option.
• Limited schema evolution: Rivery had good schema evolution support for its database sources. But the vast majority of its connectors are API-based, and those do not have good schema evolution support.

Rivery charges per credit, which is $0.75 for Starter, $1.25 for Professional, and negotiated for Enterprise. You pay 1 credit per 100MB of moved data from databases, and 1 credit per API call. There is no charge for connectors. If you have low data volumes this will work well. But by the time you’re moving 20GB per month it starts to get more expensive than some others.

Estuary is the right-time data platform that replaces fragmented data stacks with one dependable system for data movement. Teams use it to move data from databases, SaaS apps, files, and streams into warehouses, lakes, operational stores, and AI systems at the cadence they choose: sub-second streaming, near real-time, or scheduled batch. Founded in 2019, Estuary is built on Gazette, an open-source streaming broker developed by the same founding team that lets Estuary mix CDC, streaming, and batch in a single catalog with exactly-once delivery, deterministic recovery, and targeted backfills.

Unlike traditional ELT tools that focus on batch loads, Estuary stores every event in collections that can be reused for multiple destinations. Captured changes are written once to durable storage and fanned out to any number of targets without reloading the source, which reduces load on primary systems and makes replay easy when schemas change. Estuary runs as a multi-tenant cloud service, private data plane, or BYOC, and ships with 200+ fully-managed native connectors plus support for open-source Airbyte, Meltano, and Stitch connectors.

For AI-native workflows, Estuary ships Agent Skills that work with Claude Code, Cursor, OpenAI Codex, GitHub Copilot, and Gemini CLI, letting developers create captures, materialize into Snowflake, BigQuery, Redshift, or Databricks, and troubleshoot pipelines through natural-language prompts. A separate MCP server handles docs-aware Q&A inside the same assistants.

Customers include Glossier, which cut data costs by 50%; Xometry, which reduced integration costs by 60% with private deployment; Headset, which cut Snowflake ingestion costs by 40% after replacing Airbyte; and Prodege, which built Apache Iceberg pipelines.

• Right-time pipelines from millisecond streaming to scheduled batch. Choose cadence per pipeline so cost and freshness match each workload. Most ELT tools default to 15-minute or hourly intervals.
• One platform for CDC, batch, and streaming. Replaces the typical 3-4 tool stack of Debezium plus Kafka plus Airbyte plus dbt with a single system, reducing tool sprawl and operational overhead.
• Dependable replication built on Gazette. Exactly-once delivery, deterministic recovery, and targeted backfills keep pipelines stable through schema changes and source failures.
• Efficient log-based CDC with collection reuse. Captures inserts, updates, and deletes once, then fans out to any number of destinations without re-reading the source database, reducing load on production systems.
• Predictable usage-based pricing. $0.50 per GB moved plus $100 per connector instance per month for the first 6 instances, then $50 per instance for additional ones. No MAR-based surprises and no per-row charges.
• Agent-native developer experience. Open-source Agent Skills let Claude Code, Cursor, OpenAI Codex, GitHub Copilot, and Gemini CLI build and operate Estuary pipelines from natural language, with an MCP server for docs-aware Q&A in the same tools.

• No graphical transformation UI. Estuary focuses on SQL and TypeScript transformations alongside dbt integration. Teams that need point-and-click visual ETL like Matillion or Informatica PowerCenter will find this a gap, though dbt covers most warehouse-side needs.
• On-premises connectivity is narrower than legacy ETL vendors. For mainframe, SAP ECC on-premises, or other proprietary on-premises systems, vendors like Informatica or Talend may have broader native coverage. Verify legacy on-premises coverage during evaluation.
• Smaller market presence than category incumbents. Fivetran, Informatica (now Salesforce), and Talend (now Qlik) have larger enterprise customer bases and longer procurement track records. Estuary fits teams able to evaluate on technical merit, but buyers requiring a Gartner Magic Quadrant leader may need to factor this in.

Estuary uses a straightforward usage-based pricing model. Data movement is charged at $0.50 per GB sourced or delivered. Connector instances are $100 per month for the first 6 instances, then $50 per month for each additional instance. A Developer tier is free indefinitely up to 10 GB per month and 2 concurrent connector instances, and Cloud-tier customers can request a 30-day free trial.

Use the Estuary pricing calculator to model your specific workload. For larger deployments, Enterprise plans add volume-based discounts, SOC 2 and HIPAA compliance reports, SSO, custom SLA terms, private deployments, and dedicated support.