Analytics

Data Fabric Implementation: 6 Best Practices for IT Leaders

Trying to integrate data without knowing your starting point is like taking a road trip without a map—you’re bound to get lost. To navigate the challenges of data integration, IT leaders must first evaluate their current data setup. This means taking stock of all your data sources, understanding their quality, and identifying integration points. It’s like conducting a thorough inspection before renovating a house; you must know what you’re working with.

hDs Chapter 5 - Mastering the Data Journey: Quality, Governance, and Lineage for Informed Decision-Making

In the digital age, data is the lifeblood of organizations, driving strategies, innovation, and decisions. However, harnessing its power requires more than just collecting the data. It demands meticulous management of data quality, governance, and lineage. These pillars form the backbone of informed decision-making, enabling organizations to transform raw data into actionable insights. According to Gartner, poor data quality costs organizations an average of $12.9 million every year.

AWS and Confluent: Meeting the Requirements of Real-Time Operations

As government agencies work to improve both customer experience and operational efficiency, two tools have become critical: cloud services and data. Confluent and Amazon Web Services (AWS) have collaborated to make the move to and management of cloud easier while also enabling data streaming for real-time insights and action. We’ll be at the AWS Public Sector Summit in Washington, DC on June 26-27 to talk about and demo how our solutions work together.

What is API Monitoring? Best Practices to Track API Performance and Metrics

API downtime can cost businesses an average of $140,000 to $540,000 per hour. Maintaining reliable and high-performing APIs has become critical for any digital business’s success, with much at stake. This scenario is where API monitoring steps in. An important part of API management, monitoring API metrics allows organizations to detect issues rapidly and optimize their API performance.

Data Lineage: A Complete Guide

Data lineage is an important concept in data governance. It outlines the path data takes from its source to its destination. Understanding data lineage helps increase transparency and decision-making for organizations reliant on data. This complete guide examines data lineage and its significance for teams. It also covers the difference between data lineage and other important data governance terms and common data lineage techniques.

Snowflake: Automate tuning for data cloud speed and scale

40% of companies surveyed will increase their AI investment because of advances in GenAI (McKinsey). And 80% plan to maintain or increase their investment in data quality/observability (dbt). With this in mind, Unravel is hosting a live event to help you leverage data observability to achieve speed and scale with Snowflake. Join Unravel Data for this event about automating tuning with AI-powered data performance management for Snowflake with Eric Chu, Unravel Data VP of Product, and Clinton Ford, Unravel Data VP of Product Marketing.

Addressing the Elephant in the Room - Welcome to Today's Cloudera

Hadoop. The first time that I really became familiar with this term was at Hadoop World in New York City some ten or so years ago. There were thousands of attendees at the event – lining up for book signings and meetings with recruiters to fill the endless job openings for developers experienced with MapReduce and managing Big Data. This was the gold rush of the 21st century, except the gold was data.