Real-Time Database for
IoT Monitoring and Analytics

Predictive Maintenance: Perform proactive analysis on sensor data from manufacturing equipment to anticipate machine failures or maintenance needs. Monitor variables like temperature, vibration, and wear for timely interventions, reducing downtime and operational costs.

Production Optimization: Continuously monitor production parameters such as temperature, pressure, and vibration to identify inefficiencies and bottlenecks. Real-time analytics enable immediate adjustments, improving throughput, product quality, and minimizing waste.

Quality Control: Utilize real-time analytics and machine vision technologies for instant defect detection and maintaining consistent product reliability. Analyze data from IoT sensors and visual inspection systems to uphold stringent quality standards.

Inventory Management: Navigate inventory complexities with real-time analytics powered by RFID tags and sensors. Gain precise insights into inventory levels, preventing stockouts, and streamlining the ordering process. Optimize your manufacturing processes with practical and data-driven solutions.

A Fortune 100 multinational manufacturer, specializing in safety, building, and aerospace technologies, has leveraged its smart asset management platform to enhance the safety, security, and productivity of physical assets across various sites. Facing challenges with its previous database’s inability to scale for the massive volume of IoT sensor data—ranging from temperatures to humidity levels, with up to 11,000 KPIs and 7.9 million hourly data points per KPI—the company adopted Imply Manager to deploy Druid clusters on Microsoft Azure. This upgrade significantly improved data reading, updating, deduplication, and increased transaction and query handling capabilities, while also introducing an extension for efficiently sampling large datasets. Now, the platform not only supports real-time monitoring and anomaly alerts but also facilitates predictive maintenance scheduling, thereby optimizing operational efficiency and reducing costs.

Predictive Maintenance: Continuously monitor vehicle health through sensors and telematics to predict maintenance needs, preventing unexpected breakdowns. This proactive approach ensures timely servicing, reduces the risk of disruptions, and enhances overall operational reliability.

Route Optimization: Analyze traffic data and vehicle performance metrics to empower logistics companies in dynamically optimizing delivery routes. This results in faster delivery times, reduced fuel consumption, and minimized vehicle wear and tear, fostering a more efficient delivery process, lowering operational costs, and ensuring enhanced customer satisfaction through timely deliveries.

Real-time Tracking: Track vehicles and goods in real-time for unparalleled visibility into the supply chain. Make informed decisions, improve asset utilization, and enhance customer communication with accurate, up-to-the-minute information on delivery status.

Fleet Management: Analyze driving patterns and performance metrics to optimize fuel consumption and promote safer driving behaviors. Monitor factors like speed, idling time, and braking patterns for targeted feedback and training, resulting in reduced fuel costs, fewer accidents, and lower insurance premiums.

Warehouse Automation: Leverage robotics and sensor-equipped machinery in warehouse operations to significantly improve efficiency and accuracy in inventory management. Automated systems streamline operations, reduce errors, enhance order fulfillment speeds, and free up human resources for more complex tasks, leading to a scalable and flexible logistics infrastructure.

An analytics provider for public transit, serving over 100 cities including Miami, Oakland, and Philadelphia, faced scalability and cost challenges with PostgreSQL while managing data from 25,000 vehicles generating JSON data. The high annual cost of over $200,000, limited scalability, and frequent outages of PostgreSQL hindered the company’s growth and ability to showcase its platform to potential clients. By switching to Druid, the company significantly reduced its operational costs to around $30,000 and enhanced its database architecture’s scalability and reliability. This transition not only supported growth by enabling the service of up to 500 customers but also improved internal stakeholders’ access to insights and prepared the company for future enhancements, including moving from batch to real-time data streaming with Amazon Kinesis.

Smart Grid Management: Leverage analytics to analyze energy consumption patterns and weather data for optimized electricity production, efficient power distribution, and a stable energy supply.

Renewable Energy Integration: Analytics ensures a seamless balance of variable energy outputs from renewables, facilitating a smooth transition to sustainable energy without compromising reliability.

Demand Forecasting: Explore how analytics, fueled by historical data and weather forecasts, enables accurate predictions of future energy demand. This leads to informed pricing strategies and optimized operations.

Asset Management: Continuous monitoring of energy infrastructure through analytics extends asset lifespan, ensures uninterrupted service, and avoids costly repairs or replacements.

Distributed Energy Management: Discover how analytics optimizes energy sharing in microgrids, allowing stakeholders to make informed decisions on resource allocation, improving microgrid efficiency, and fostering energy independence within communities.

A leading utility management company that offers an AI-enabled SaaS platform for power plants, retailers, utilities, and grid operators transitioned its data architecture to improve service for over 45 million global customers. Initially reliant on a complex process involving Amazon S3, Athena tables, Apache Spark, and MongoDB for data analysis and visualization, the company struggled with scalability and latency as its data intake surged from 40 to 70 million smart meters. By switching to Druid, removing Apache Spark, and embracing real-time data ingestion, the company not only streamlined operations but also enhanced its ability to offer precise energy demand predictions and interactive, customizable analytics through a Pivot-based interface. This strategic overhaul significantly reduced operational costs by $4 million, increased forecast accuracy by 40%, and boosted customer lifetime value by $3,000, demonstrating the transformative impact of optimized data architecture on utility management and customer service.

Network Optimization: In an era of heightened connectivity expectations, telecom operators analyze network traffic data to fine-tune networks. This enhances call quality, improves data speeds, and future-proofs against growing bandwidth demands, ultimately boosting customer satisfaction.

Fraud Detection: Analytics helps telecom companies mitigate financial risks by monitoring real-time phone call patterns and data usage. This proactive approach allows operators to detect anomalies indicative of fraudulent behavior, ensuring both revenue protection and customer trust.

Customer Experience Optimization: Understanding customer behavior is paramount in the competitive telecom sector. Analytics facilitates customer segmentation, anticipates needs, and tailors communications, leading to an improved customer experience, higher engagement, and revenue growth.

Predictive Maintenance: Ensuring reliable network equipment is vital for uninterrupted service delivery. Predictive analytics helps monitor infrastructure health, predicting potential failures and allowing for proactive maintenance. This minimizes network downtime and maintains high service quality standards.

Spectrum Management: With the challenges of 5G and a surge in connected devices, efficient spectrum use is critical. Analytics plays a pivotal role in spectrum management, allowing operators to analyze usage patterns and optimize resource allocation. This ensures enhanced network capacity, improved coverage, and the delivery of faster, more reliable services.

A global leader in telecommunications, facing the need to manage vast amounts of data from its network operations, turned to advanced analytics solutions to enhance its operational efficiency and customer service. The adoption of a scalable analytics platform enabled the company to handle real-time data analysis, leading to improved network performance, reduced operational costs, and a superior customer experience. This strategic move not only bolstered the company’s competitive edge but also paved the way for innovations in service delivery and customer engagement.

Energy Management: Relies on analytics to optimize consumption, identify inefficiencies, make real-time adjustments, and significantly reduce energy waste for cost savings and environmental sustainability.

Occupancy Management: Utilizes analytics for real-time adjustments based on occupancy data, fostering energy conservation, and enhancing occupant comfort.

Predictive Maintenance: Employs continuous monitoring and analytics to anticipate potential equipment failures, minimize breakdown risks, and extend the lifespan of building systems.

Indoor Air Quality Monitoring: Ensures effective management of air quality for occupant well-being.

Security and Access Control: Identifies unusual activities, efficiently manages access, and ensures overall safety.

Thing-it, a German-based leader in smart office building management software, faced scalability and performance challenges with MySQL as their data architecture backbone.

Initially, Thing-it utilized a batch data ingestion process through Amazon SNS and Lambda, culminating in MySQL for storage and querying, which hindered the speed and consistency of data aggregations critical for their interactive dashboards.

To address these limitations and support their rapid customer expansion, Thing-it transitioned to a streaming data architecture using Amazon Kinesis and Druid, significantly enhancing their data handling capabilities to 2 billion rows per month.

This shift not only halved their database expenses but also dramatically improved query performance, with the slowest queries being executed in 300 milliseconds and a near-100% success rate.

$4 million cost reduction

40% improvement in forecast accuracy

$3,000 increase in customer lifetime value

Provide a smoother user experience with faster data retrieval, particularly crucial for dashboards and real-time monitoring.

Increase productivity and efficiency for engineers and analysts by reducing waiting times for data queries.

Enable more complex and resource-intensive analyses, leading to deeper insights into vehicle performance, customer behavior, and operational efficiency.

Freed up developers, allowing them to focus on more strategic and high-value tasks.

Accelerated query responses, thereby enhancing the overall user experience, enabling faster decision-making, and improving operational efficiency.

Scalable and cost-effective database delivers the flexibility and agility needed to effectively support evolving business needs and customer demands.

Real-time Data Ingestion from over 100,000 buses, trains, and ferries swiftly and efficiently, facilitating the generation of real-time predictions for riders.

Fast Query Performance enables Swiftly to deliver highly interactive dashboards and customer-facing visualizations.

Flexible Data Exploration enables users to explore data efficiently, leading to faster data analysis, and contributing to increased on-time performance and improved service.