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.

We like to keep things light at Unravel. In a recent event, we hosted a group of industry experts for a night of laughs and drinks as we discussed cloud migration and heard from our friends at Don’t Tell Comedy. Unravel VP of Solutions Engineering Chris Santiago and AWS Sr. Worldwide Business Development Manager for Analytics Kiran Guduguntla moderated a discussion with data professionals from Black Knight, TJX Companies, AT&T Systems, Georgia Pacific, and IBM, among others.

The term DataOps (like its older cousin, DevOps) means different things to different people. But one thing that everyone agrees on is its objective: accelerate the delivery of data-driven insights to the business quickly and reliably.