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Analytics

Top 8 Legacy Modernization Tools for 2024

According to Statista, the market for legacy modernization tools might reach $36.86 billion by 2027. This growth signifies an increasing demand for modernization in organizations worldwide. However, the journey towards modernization isn’t always successful. In fact, nearly three out of four organizations fail to modernize legacy systems effectively. The best legacy modernization tools can help your organization overcome these challenges.

Present & Share Performance With Your Company and Clients | Databox 101 | Chapter 3.5

While dashboards let you see real-time performance to answer “How am I doing right now?”, Reports take it a step further. With Reports, you can provide a more comprehensive view of your historical performance - with all the context - to your teams, clients, or stakeholders. In this video, you’ll learn: Bookmark this tab AND share the series with your entire company/team! Databox empowers everyone - leaders, managers, and individual contributors alike.

Databricks: Achieve performance and reliability with purpose-built AI

88% of Databricks users surveyed are turning to AI to improve bug-fixing effectiveness (Databricks). Why? Troubleshooting modern data stacks is typically a toilsome and manual process. The good news – data teams that use DataOps practices and tools will be 10 times more productive (Gartner). With this in mind, Unravel is hosting this live event to demonstrate how AI-enabled observability for Databricks’ Data Intelligence Platform helps you proactively achieve performance and reliability.

Manage Resource Utilization and Allocation with ClearML

Written by Noam Wasersprung, Head of Product at ClearML Last month we released the Resource Allocation & Policy Management Center to help teams visualize their compute infrastructure and understand which users have access to what resources. This new feature makes it easy for administrators to visualize their resource policies for enabling workload prioritization across available resources.

How to Scale RAG and Build More Accurate LLMs

This article was originally published on The New Stack on June 10, 2024. Retrieval augmented generation (RAG) has emerged as a leading pattern to combat hallucinations and other inaccuracies that affect large language model content generation. However, RAG needs the right data architecture around it to scale effectively and efficiently.

Unlocking the Edge: Data Streaming Goes Where You Go with Confluent

While cloud computing adoption continues to accelerate due to its tremendous value, it has also become clear that edge computing is better suited for a variety of use cases. Organizations are realizing the benefits of processing data closer to its source, leading to reduced latency, security and compliance benefits, and more efficient bandwidth utilization as well as supporting scenarios where networking has challenging constraints.

Running Apache Kafka at the Edge Requires Confluent's Enterprise-Grade Data Streaming Platform

Modern edge computing is transforming industries including manufacturing, healthcare, transportation, defense, retail, energy, and much more—pushing data management to far-reaching data sources to enable connected, low latency operations and enhanced decision making. These new use cases shift workloads to the left—requiring real-time data streaming and processing at the edge, right where the data is generated.