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Accelerate data modernization initiatives with Talend Change Data Capture

In times of economic uncertainty, businesses need to get the most value from their data, while minimizing pressure on their systems and databases. But with the exponential growth of the variety and volume of data, extracting business value out of that data is only getting increasingly difficult. The result is data transfer latency, data loss, high cost of managing such data, data sources, leading to an inability to use data to make high-ROI business decisions.

Yellowfin 9.8 Release Highlights

Introduced in 9.7 as the simplest way to ask questions of your data, Yellowfin 9.8 delivers exciting updates to Guided NLQ, and additional improvements to our report builder. The latest release makes Guided NLQ even more powerful and simpler to use, with new question types (Cross-tab), more intuitive questions with field synonyms and default date periods, and protection against long-running queries - in addition to faster report building.

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.