Systems | Development | Analytics | API | Testing

September 2022

The Data Challenge Nobody's Talking About: An Interview from CDAO UK

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.

Demo: Unravel Data - Allocating Costs with Precision Using the Enhanced Chargeback Report

DataOps teams need to understand where costs are going. But the reports provided by cloud vendors aren’t very granular - and they only get the reports after excess costs have been racked up. Unravel allows DataOps teams to understand where costs are going at a detailed level: by user, by service, by department. This information is captured and available as soon as a cluster is detected – allowing DataOps teams to take action and optimize in real time.

Demo: Unravel Data - Automated Budget Tracking to Prevent Overruns

DataOps teams need to be able to set budgets at a specific scope - and know if your various teams or departments are tracking to those budgets. But today, most DataOps teams only know that the budget was overrun after it’s too late. With Unravel, establishing and tracking budgets to prevent overruns is easy.

Demo: Unravel Data - Keep DataOps Budgets On Track (Automatically)

Demo: Unravel Data - Keep DataOps Budgets On Track (Automatically) DataOps teams are paying tremendous amounts of money for cloud instances that were spun up and then forgotten. For larger enterprises, this can equate to millions of dollars in waste. But even for smaller teams, this type of inefficiency is not acceptable. With Unravel, your DataOps team can set a budget/cost threshold (or a time duration), to ensure that you keep your budgets on track. Receive alerts as soon as an instance hits your predefined threshold - instead of discovering it in your monthly bills.

Demo: Unravel Data - A Unified View for Data App Performance Details

Today, DataOps teams have to correlate data from far too many point tools. DataOps observability is far too cumbersome; the manual effort to optimize data apps takes time that DataOps teams simply don’t have. With Unravel’s AI-enabled platform, all of this disparate data is pulled together into a unified view of data app performance; every detail in a single view. View configurations, logs, and errors… all in one place.

Demo: Unravel Data - Tuning Data App Performance Automatically

Optimizing data apps shouldn’t be trial and error. This takes nights and weekends away from DataOps teams - and it’s incredibly inefficient. Unravel provides an “expert in a box” feature, driven by AI, that provides DataOps teams with tangible insights and recommendations to optimize data apps. Need to fix a bottleneck to meet an SLA? Trying to improve the overall efficiency of data pipelines? Unravel makes this easy with specific, automated recommendations (all the way down to the code-level) to tune your data apps for better performance.

Demo: Unravel Data - Optimizing Cloud Costs at the Cluster Level

Most DataOps teams have a huge opportunity when it comes to optimizing their cloud costs. Today, the standard for success of many developers is ensuring that their jobs are running at all costs. The efficiency of those jobs isn’t the top priority. With Unravel, DataOps teams can optimize cloud costs by rightsizing their clusters. Unravel makes it easy to identify clusters that are consuming a large percentage of resources, and drill down to see automatic recommendations to improve the efficiency of those clusters.

Demo: Unravel Data - Map Your Workloads to the Cloud (and Calculate Costs)

When a data team is migrating applications to the cloud, they’ll need to anticipate how many resources those apps will consume. This can often take a DataOps teams into unfamiliar territory since on-prem applications are assessed very differently from a utilization standpoint. This information is critical to inform the cloud architecture - and to anticipate the total cost of ownership for the cloud migration.

Unravel: DataOps Observability Designed for Data Teams

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. Designed specifically for data teams, Unravel gives you the observability, AI, and automation to help you understand, optimize and govern your data estate—for performance, cost, and quality.

Demo: Unravel Data - Preparing for Cloud Migration with Automated Cluster Discovery

One of the first steps of any cloud migration is creating an inventory of the applications and services that are currently being used. Today, that involves a lot of manual interviews with people from across the business to understand the needs behind each cluster. This process, as you can imagine, is incredibly prone to errors and miscommunications that can negatively impact migration planning efforts.

Demo: Unravel Data - How to Avoid Tuning & Replatforming Delays

How can your DataOps team anticipate bottlenecks that might occur during a cloud migration? One of the most common issues is version incompatibilities. On prem environments tend to run older instances of applications (vs. newer cloud environments) - which means that your team will need to consider any incompatible code before migrating.

Demo: Unravel Data - Automated Troubleshooting for Job Failures

For DataOps teams, job failures are common. But finding the issue is (traditionally) where things get even worse. It can take hours or days to troubleshoot a job failure. Unravel Data provides a single view where DataOps teams can locate exactly where–and why–a job failed, along with precise recommendations to troubleshoot the error. DataOps teams are now able to both diagnose and troubleshoot job failures in minutes instead of days or weeks.

Demo: Unravel Data - Data Pipeline Optimization (The Easy Way)

Data pipelines fail all the time for a variety of reasons; service downtime, data volume fluctuations, etc. Diagnosing these failures manually is very difficult and time consuming. Unravel Data allows DataOps teams to troubleshoot pipeline failures automatically – showing exactly where and why a pipeline failed, and precise recommendations to remedy the issues. Using Unravel, DataOps teams can now diagnose and fix data pipeline failures in a fraction of the time.

Demo: Unravel Data - Code-Level Insights for DataOps Teams

To ensure that jobs are running optimally, DataOps teams need to look at the detailed code. But DataOps teams don’t have the right tools to easily examine problematic code - or a simple path to optimizing it. With Unravel Data, DataOps teams can quickly troubleshoot applications that are throwing errors - all the way down to a specific line of problematic code. All in a single view.

DataOps Observability Designed for Data Teams

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.

DataOps Observability: The Missing Link for Data Teams

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.

Expert Panel: Challenges with Modern Data Pipelines

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.