This article explores the innovative approach of using Apache Druid to monitor itself, leveraging Kafka and Grafana for real-time metrics and alerting. It provides a step-by-step guide to setting up a monitoring pipeline without relying on external tools like Prometheus, making it more accessible for developers already familiar with Kafka. By integrating Druid, Kafka, and Grafana, the tutorial demonstrates how to efficiently manage and visualize Druid’s performance, ensuring a robust and streamlined data infrastructure.

Integrate Apache Druid with Cube.js to implement fine-grained access control (FGAC), enabling secure, multi-tenant data management. This article provides a step-by-step guide to setting up row-level security in Cube.js by rewriting queries to filter data based on user roles, compensating for Druid's lack of built-in FGAC.

Learn how to monitor Apache Druid in Grafana with detailed examples of visualization dashboards and key metrics including query success rates, cache hit rates, and query completion times. Gain insights into system health, performance, and efficiency for optimizing configurations and ensuring smooth operations in distributed systems.

Learn how to integrate Grafana with Apache Druid for effective data visualization and analysis. This comprehensive guide covers setting up Druid, installing Grafana, configuring the integration, creating sample dashboards, and ensuring secure data access with role-based access control (RBAC).

The article outlines the implementation of Basic Role-Based Access Control (RBAC) in Apache Druid. Through a step-by-step tutorial, it guides users in setting up RBAC alongside basic authentication, ensuring controlled access to resources based on assigned roles. Users are walked through configuring authentication, creating roles, assigning permissions, and testing the RBAC setup. By establishing these security measures, users can effectively manage resource access within their Apache Druid clusters, ensuring data integrity and confidentiality in production environments while complying with security standards.

Sage+Archer \ Vistar Media, a prominent advertising company, faced data processing challenges with Hadoop and Apache Spark, resulting in delayed online bidding auction results and maintenance issues. To enhance their data pipeline, they partnered with Deep.BI, a big data company experienced in open-source technologies. Deep.BI replaced Hadoop and Spark with Apache Flink and Druid, enabling real-time data processing and streamlining their infrastructure — the implementation allowed for easy code changes, improved data enrichment, and enhanced real-time analytics. The transition significantly boosted speed, efficiency, and cost-effectiveness, giving Sage+Archer \ Vistar Media a competitive edge in the advertising industry.

Learn how to efficiently access data from Druid Segments in PySpark using the Spark Druid Segment Reader. This tutorial covers steps for downloading the solution, configuring it in Spark Context, and reading data from Druid Segments as Spark Data Frames. Ensure you have the right PySpark version, and remember that the connector reads dimensions, excluding metrics. Once set up correctly, you can leverage Data Frames for your data analysis needs. If you encounter any issues or require technical assistance, the article provides guidance on reaching out for support.

Explore the synergy between Apache Druid and data science with our Spark Druid Segment Reader. Learn how to effortlessly extract and analyze Druid data for AI and machine learning, paving the way for better-informed decisions in the world of big data. Discover the open-source solution to bridge the gap, unlock the full potential of your data, and delve into historical data reprocessing, ad hoc analysis, and streamlined workflows for data scientists. Join the journey to seamless integration and data-driven insights.

The blog post explores the capabilities of Apache Druid's SQL queries, discussing its architecture, interfaces, and support for different SQL workloads. It covers the advantages of Apache Druid for OLTP and OLAP tasks, its SQL data types, and its operators.

6 recommandations pour augmenter les revenus lecteurs à long terme pendant la pandémie de COVID-19

Publishers have seen a surge in readership during COVID-19, but turning this traffic into long-term revenue requires strategic engagement. Data shows that users follow a gradual path to subscription, making it crucial to nurture engagement before pushing conversion offers. By targeting engaged users, refining registration strategies, and leveraging personalized outreach, publishers can build lasting reader relationships and drive sustainable growth.

Deep.BI helped SME.sk boost subscriptions with a hybrid paywall using a Propensity to Subscribe (P2S) algorithm. By dynamically locking content for engaged users, paywall CTR increased by 278% and subscription completion by 50%, all while maintaining ad revenue and engagement.

AI is transforming the quick service restaurant industry with real-time insights that reduce fraud, improve operations, and enhance decision-making. From employee scoring to customer intelligence, AI-driven analytics help businesses optimize performance and boost profitability.

A Dynamic Paywall must adapt to real-time user engagement to maximize conversions. Deep.BI’s real-time scoring detects peak moments when users are most likely to subscribe, capturing impulsive purchases and boosting retention.

Deep.BI’s real-time content analytics helps editorial teams track user engagement, content performance, and traffic sources with visually intuitive dashboards. Key metrics like RFV scores, EPV rankings, and heatmaps provide actionable insights to optimize content strategy, boost engagement, and maximize monetization.

Hunting for relevancy (and revenues) in a data informed world

Deep.BI’s Deep Content Attribution Score (DCAS) helps editorial teams identify which content drives engagement and subscriptions beyond basic metrics. By analyzing Acquisition and Conversion Scores, DCAS enables publishers to optimize content strategy and refine editorial decisions for better audience growth and monetization.

Deep.BI’s Deep Content Attribution Score (DCAS) helps publishers track user journeys leading to subscriptions, revealing which content drives conversions. By analyzing interactions over time, DCAS enables editorial teams to optimize content strategy, allocate resources effectively, and boost engagement.

Many businesses drown in Big Data without gaining real insights. Deep Data filters out noise, focusing on key information for faster, smarter decisions. Most companies benefit more from actionable analytics than sheer data volume.

Learn how Druid Kafka Indexing Service (KIS) enables real-time and historical data ingestion from Kafka with exactly-once guarantees. Discover its data flow, partitioning, and deep storage process in this detailed explanation