I was chatting with Sanjeev Mohan, Principal and Founder of SanjMo Consulting and former Research Vice President at Gartner, about how the emergence of DataOps is changing people’s idea of what “data observability” means. Not in any semantic sense or a definitional war of words, but in terms of what data teams need to stay on top of an increasingly complex modern data stack.
Chief Data & Analytics Officer UK (CDAO UK) is the United Kingdom’s premier event for senior data and analytics executives. The three-day event, with more than 200 attendees and 50+ industry-leading speakers, was packed with case studies, thought leadership, and practical advice around data culture, data quality and governance, building a data workforce, data strategy, metadata management, AI/MLOps, self-service strategies, and more.
Today every company is a data company. And even with all the great new data systems and technologies, it’s people—data teams—who unlock the power of data to drive business value. But today’s data teams are getting bogged down. They’re struggling to keep pace with the increased volume, velocity, variety, complexity—and cost—of the modern data stack. That’s where Unravel DataOps observability comes in.
As organizations invest ever more heavily in modernizing their data stacks, data teams—the people who actually deliver the value of data to the business—are finding it increasingly difficult to manage the performance, cost, and quality of these complex systems. Data teams today find themselves in much the same boat as software teams were 10+ years ago. Software teams have dug themselves out the hole with DevOps best practices and tools—chief among them full-stack observability.
Modern data pipelines have become more business-critical than ever. Every company today is a data company, looking to leverage data analytics as a competitive advantage. But the complexity of the modern data stack imposes some significant challenges that are hindering organizations from realizing their goals and realizing the value of data.
The Eckerson Group recently presented a CDO TechVent that explored data observability, “Data Observability: Managing Data Quality and Pipelines for the Cloud Era.” Hosted by Wayne Eckerson, president of Eckerson Group, Dr.
Summary: Sometimes the insight you’re shown isn’t the one you were expecting. Unravel DataOps observability provides the right, and actionable, insights to unlock the full value and potential of your Spark application. One of the key features of Unravel is our automated insights. This is the feature where Unravel analyzes the finished Spark job and then presents its findings to the user. Sometimes those findings can be layered and not exactly what you expect.
Apache Kafka is an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. Administrators, developers, and data engineers who use Kafka clusters struggle to understand what is happening in their Kafka implementations.
Poorly written queries and rouge queries can create a nightmare for data teams when it comes to fixing and preventing performance issues, and as a result, costs can quickly spiral out of control. Whether you want to move your on-premises data to Google BigQuery or make the most of your Google BigQuery investments, Unravel can help businesses that struggle to find the optimal balance of performance and cost of Google BigQuery.
Whether they know it or not, every company has become a data company. Data is no longer just a transactional byproduct, but a transformative enabler of business decision-making. In just a few years, modern data analytics has gone from being a science project to becoming the backbone of business operations to generate insights, fuel innovation, improve customer satisfaction, and drive revenue growth. But none of that can happen if data applications and pipelines aren’t running well.
