What Problem do we Solve?

Building a scalable, reliable, secure data pipeline that delivers meaningful insights from multiple data sources to a diverse group of data consumers is hard. Most enterprises can’t keep up with the demand for new data dashboards.

Technical Challenges

Diverse Data Sources

It is a common strategy to move data from multiple sources (like S3 and HDFS) into a common repository for analysis (like Snowflake, or an enterprise data lake for example). However, just being able to do so reliably, at scale is the first technical hurdle. What often goes unexamined in this process is the role of data quality metrics and monitoring to ensure that the data that’s being transferred is actually useful to downstream users and processes.

Mashing and combining data together is HARD

Data stored and managed by one application is rarely designed with the goal of sharing it with other applications. Bridging the data and semantic disconnect between repositories, implementing quality and filtering metrics, and clearly describing what is in the combined repository (so that hopefully it can be used by other business users and downstream processes) is a huge challenge

Too many tools with custom code

Data integration becomes a software integration project. Going from raw data to value requires integrating multiple tools which requires specializeded developers. By the time, the projects finish, it is already too late!


With a fragmented and complex data environment, data teams struggle to maintain the integrations between different tools. Collaboration between data producers and data consumers is lacking and everytime things go wrong, precious resources from various teams need to come together to detect and resolve issues.

Data Lineage

Can’t tell how, when, who, and where data sets were generated or whether they are still valid? This makes it difficult to trust the quality of the derived data assets and impossible for other downstream users and processes to leverage them.

Data Quality

When you are brining data from different sources, external vendors and data marketplaces, there is no control over the quality of data being brought in. Without predictable data quality and means to fix issues detected, it is impossible to reliably use the data downstream.


Dataworkz is a no-code data pipeline technology that unifies data discovery, transformation, correlation, lineage, with collaborative experience and predictable data quality.


Use cases

Dataworkz solves data fragmentation, sprawl, and complexity by bringing together valuable insights from disparate sources

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