Qlik Open Lakehouse

Name: Qlik Open Lakehouse
Brand: Qlik Technologies

Leader

Do Data Differently

Est 1993|Valuation $2.9B|Raised $13.7B

LinkedIn 307K|Website|Web traffic 1.0M|FTEs 4,586|HQ King of Prussia, Pennsylvania, US

MarketsData Warehouses & Lakehouses Data Integration (ETL/ELT)

01Competitive Landscape

Narrative analysis

Where Qlik Open Lakehouse appears differentiated: - Vendor materials emphasize operational tasks associated with Iceberg table management (schema evolution, compaction, CDC merges, SCD handling) and position the product as automating these tasks. - The offering is framed around Iceberg on object storage with multi-engine access and catalog integrations. Where competitors have relative strengths: - Databricks is commonly selected where unified engineering, ML, and governance workflows are primary requirements. - Snowflake may be preferred when an organization seeks a warehouse-centric governance and operational model, including Snowflake’s managed capabilities. - Starburst and Trino-based strategies are often selected for federated queries across diverse data sources and Trino-centric architectures. - Fivetran and Airbyte are alternatives when the primary requirement is robust connectors and straightforward ingestion without adopting a broader managed lakehouse optimizer. Sources: Official vendor documentation; competitor product and pricing pages; industry commentary and community discussion.

	Qlik Open Lakehouse	Databricks Lakehouse	Dremio Cloud
Starting Price	Not publicly itemized for Open Lakehouse; bundled into Qlik Talend Cloud tiers — published tiered pricing available from the vendor	Published usage-based pricing based on compute and cloud infrastructure consumption; pricing varies with workload and cloud provider	Published consumption-based pricing available from the vendor
Best For	Organizations seeking managed Iceberg ingestion and table maintenance integrated with Qlik Talend Cloud, primarily on AWS with multi-engine query access	Organizations standardizing on Databricks for engineering, ML, and governance workflows	Teams focused on a managed query/semantic layer over data in object storage
Key Strength	Managed ingestion into Iceberg, automated table maintenance tasks, and integrations with catalogs and connectors	Broad platform for engineering and ML workloads, deep Spark-native capabilities	Consumption-based pricing for query acceleration and semantic layer functionality
Key Weakness	Limited public pricing detail; initial public materials emphasize a single cloud provider; independent user reviews specifically for Open Lakehouse are limited	Cost management and platform commitment can be challenging; requires platform skills	Focused on query layer and semantic acceleration and not a full ingestion/CDC stack

01Competitive Landscape

Narrative analysis

Where Qlik Open Lakehouse appears differentiated:

Vendor materials emphasize operational tasks associated with Iceberg table management (schema evolution, compaction, CDC merges, SCD handling) and position the product as automating these tasks.
The offering is framed around Iceberg on object storage with multi-engine access and catalog integrations.

Where competitors have relative strengths:

Databricks is commonly selected where unified engineering, ML, and governance workflows are primary requirements.
Snowflake may be preferred when an organization seeks a warehouse-centric governance and operational model, including Snowflake’s managed capabilities.
Starburst and Trino-based strategies are often selected for federated queries across diverse data sources and Trino-centric architectures.
Fivetran and Airbyte are alternatives when the primary requirement is robust connectors and straightforward ingestion without adopting a broader managed lakehouse optimizer.

Sources: Official vendor documentation; competitor product and pricing pages; industry commentary and community discussion.

	Qlik Open Lakehouse	Databricks Lakehouse	Dremio Cloud
Starting Price	Not publicly itemized for Open Lakehouse; bundled into Qlik Talend Cloud tiers — published tiered pricing available from the vendor	Published usage-based pricing based on compute and cloud infrastructure consumption; pricing varies with workload and cloud provider	Published consumption-based pricing available from the vendor
Best For	Organizations seeking managed Iceberg ingestion and table maintenance integrated with Qlik Talend Cloud, primarily on AWS with multi-engine query access	Organizations standardizing on Databricks for engineering, ML, and governance workflows	Teams focused on a managed query/semantic layer over data in object storage
Key Strength	Managed ingestion into Iceberg, automated table maintenance tasks, and integrations with catalogs and connectors	Broad platform for engineering and ML workloads, deep Spark-native capabilities	Consumption-based pricing for query acceleration and semantic layer functionality
Key Weakness	Limited public pricing detail; initial public materials emphasize a single cloud provider; independent user reviews specifically for Open Lakehouse are limited	Cost management and platform commitment can be challenging; requires platform skills	Focused on query layer and semantic acceleration and not a full ingestion/CDC stack