Data lineage and observability with Marquez
Data is increasingly becoming core to many products. Whether to improve recommendations for users, getting insights on how they use the product or using machine learning to improve the experience. This creates a critical need for understanding how data is flowing through our systems. Data pipelines must be auditable, reliable and run on time.
Tracking lineage and metadata is the underlying foundation that enables many use cases related to data.
It provides understanding of dependencies between many teams consuming and producing data and how constant changes impact them. It enables governance and compliance and generally helps you keep you data running.
Marquez is an open source project part of the LF AI which instruments data pipelines to collect lineage and metadata and enable those use cases. It provides context by making visible dependencies across organisations and technologies and enables lineage governance and discovery.
Julien Le Dem
CTO, Co-Founder, Datakin
He co-created Apache Parquet and is involved in several open source projects including Marquez (LF AI), Apache Pig, Apache Arrow, Apache Iceberg and a few others. Previously, he was a senior principal at Wework; principal architect at Dremio; tech lead for Twitter’s data processing tools, where he also obtained a two-character Twitter handle (@J_); and a principal engineer and tech lead working on content platforms at Yahoo, where he received his Hadoop initiation. His French accent makes his talks particularly attractive.