Driving DataOps Culture with LinkedIn DataHub

Your data is not changing slowly, so why should your metadata?

LinkedIn DataHub was open-sourced to enable other organizations to harness the power of metadata and unleash excellent DataOps practices. Doing DataOps well requires bringing together multiple disciplines of data science, data analytics, and data engineering into a cohesive unit. However, this is complicated, because there are a wide variety of data tools that are in use by these different tribes. Shirshanka, who founded and architected DataHub at LinkedIn, will describe its journey in enabling DataOps use-cases on top of the metadata platform. He will also showcase the latest integrations and features in the tool and share the roadmap for the project.


Shirshanka Das, Co-Founder & CTO @ Acryl Data

My mission is to make engineers productive with data, ethically. I am a technical lead in the Data team at LinkedIn. I like solving large scale challenges in distributed data systems. I've built several data infrastructure projects at LinkedIn, some of which are open source: Apache Helix, Espresso and Databus.

I'm involved in the following projects aimed at simplifying the big data management space: Apache Gobblin (incubating), LinkedIn DataHub, Apache Pinot (incubating) and Dali.

What is the cost to attend and watch the virtual sessions?

Data Team Summit is always free and open for all to attend.

What is Data Teams Summit?

Data Team Summit is the official DataOps peer-to-peer community.

It's a time each year for everyone, from DataOps, CloudOps, AIOps, MLOps, to other technology professionals, to gather virtually to share the latest trends and best practices for running, managing, and monitoring data pipelines and data-intensive analytics workloads.

Sessions include talks by DataOps professionals at leading organizations, detailing how they’re establishing data predictability, increasing reliability, and reducing costs.

New to DataOps?

DataOps is a holistic approach to the creation, deployment, monitoring, management, and optimization of data-driven applications. It describes the culture and rules of engagement that allow data teams to deliver and maintain high-quality, on-time data products, often powered by AI and machine learning, in an agile and cost-effective way.

DataOps defines how data teams work and also affects data consumers and those whose work causes new data to be created and used within the organization. Their work enables the entire organization to access data efficiently for data-driven decision-making and for the creation and delivery of data-driven applications.

Organizations with well-developed DataOps strategies, governance, and processes can expedite the delivery of data-driven workflows and results faster and better than others.

Who comes to Data Teams Summit?

DataOps professionals and experts including data administrators, data architects, data engineers, data analysts, AI/ML professionals, and data technology leadership.

Join us for sessions on:

  • Data teams & best practices
  • Data pipelines & applications
  • DataOps observability
  • Data quality & data governance
  • Operations observability
  • MLOps
  • Data modernization & architecture
  • Biz/FinOps observability

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