Lumi Lounge Recap: 3 Takeaways from Splunk .conf26
The Imply Lumi Lounge sat directly across the street from Splunk .conf26 and reached venue capacity on both days. Digital signs around the Colorado Convention Center pointed attendees our way with a simple...
Learn MoreLumi Loglake is now generally available in Imply Lumi
Search unstructured logs in place in your data lake
Learn MoreAgents don’t query like your detections do
A security team of four has run a major SIEM since February. Nobody on the team writes SPL. Nobody opens the console or looks at the dashboards. Every question goes through an AI agent, which writes the query,...
Learn MoreWhy You Shouldn’t Have to Delete Your VPC Flow Logs
When a security incident happens, investigators almost always start with the same questions: Which systems communicated? Where did the traffic originate? What changed before the incident? Was data exfiltrated?...
Learn MoreSplunk Smartstore vs Lumi Loglake: Two Very Different Ways to Search Logs in Object Storage
One copies data back before it can be searched. The other queries it where it lives. Lumi Loglake lets Splunk teams query logs directly in object storage, including AWS S3, Delta Lake, Apache Iceberg, using...
Learn MoreSupercharging Schema-On-Read: Logs in Object Storage Don’t Need a Data Catalog
Machine data architectures are rapidly changing. As telemetry volumes continue to grow and as costs rise, organizations are increasingly moving logs and other machine data into object stores such as AWS S3....
Learn MoreImply Lumi Loglake vs Splunk Federated Search for S3
Teams are increasingly moving log data into AWS S3 to reduce costs and extend retention. Both Lumi Loglake and Splunk Federated Search to S3 help you query data in AWS S3 to lower costs, however the two technologies...
Learn MoreA First Look at Lumi Loglake: Query Logs Where They Live
TL;DR: Imply Lumi Loglake is a lakehouse (separated compute/storage) architecture for unstructured logs that reduces costs from 40% up to orders of magnitude on your hardware/AWS/Azure bill used to run your...
Learn MoreImply Lumi Major Release Preview: Continuing the Journey Towards Decoupled Observability/SIEM
We are getting ready to introduce the next major expansion of Imply Lumi and the observability warehouse. When we introduced the industry’s first observability warehouse, the goal was clear: decouple the...
Learn MoreQuery Lumi from Grafana: Now in Private Preview
Imply Lumi's Grafana Loki integration is now in Private Preview. The same logs you've loaded into Lumi for Splunk are now queryable natively in Grafana using LogQL with no second pipeline, no duplicate storage,...
Learn MoreBTG Pactual + Imply Lumi: More Data. Longer Retention. Lower Cost. Without Replacing Splunk.
BTG Pactual, a global financial institution, ran into a familiar problem. As their Splunk environment scaled, so did the pressure: But the underlying system hadn’t changed. Detection and investigation were...
Learn MoreImply Lumi Product Preview: Removing the Cost–Performance Tradeoff in Observability
If you caught our recent product update, you’ve already seen the pace of development on Imply Lumi has been relentless. Last quarter, we delivered major performance and usability improvements to data...
Learn MoreImply Lumi product update: what’s new
Since releasing Imply Lumi in September 2025 as a decoupled data layer for observability, the Imply R&D team has been hard at work to make it easier and more economical to retain, query, and analyze observability...
Learn MoreThe Most-Read Imply Blogs of 2025 (and what they signal for 2026)
Before we take on 2026, let’s rewind. 2025 was the year observability teams stopped asking, “How do we reduce data?” and started asking the real question: “How do we build an architecture that can keep...
Learn MoreThe Breaking Point for Observability Leaders
Observability is at a crossroads For years, observability has promised to give teams the visibility they need to keep digital services resilient. But as data volumes explode, many leaders are realizing the...
Learn MoreHow to Efficiently Scale Splunk with Imply Lumi
The Observability Warehouse that helps you keep more data, move faster, and spend less without changing how you work Observability Is Hitting Its Limits Splunk has long been the system of record for observability...
Learn MoreThe State of Log Management 2025
Logs are exploding. Costs are climbing. Performance is stalling. If you manage logs, you’re in the hot seat Every app, every integration, every security risk—it all generates more data. And when something...
Learn MoreThe next evolution in observability: How architecture is following in BI’s footsteps
Modern observability systems are hitting the same wall business intelligence did a decade ago. As data volumes explode, the traditional model — where a single product handles ingestion, storage, compute,...
Learn MoreFrom Cribl Stream to Imply Lumi in minutes
After a few energizing days at CriblCon 2025, one message stood out everywhere I looked: teams don’t want to do less with their data — they want to do more. Cribl showed how to unlock that freedom at the...
Learn MoreCriblCon 2025 Recap: 3 Takeaways from the Front Lines of Observability
I spent a few days in National Harbor, just outside Washington, D.C., for CriblCon 2025—and it was impossible not to feel the energy in the room. From mainstage keynotes to hallway conversations, one message...
Learn MoreQuickstart: Add Lumi to Splunk as a Drop-In Data Layer
As the volume of log data continues to rise, even great observability solutions force tough trade-offs: rising license fees, limited retention windows, and slow searches caused by archived data. That’s why...
Learn MoreSplunk expands federation, Imply Lumi extends it to observability
Splunk’s Snowflake partnership is the latest sign of federation’s rise. The pattern is clear: stop duplicating data, query it where it sits. That model works for business data, and with Imply Lumi it now...
Learn MoreSplunk .conf25 Recap: Imply Lumi’s Big Debut in the Partnerverse
At .conf25, Splunk set the tone: digital resilience in the AI era. From AI-powered SOCs to agentic observability, the message was clear, resilience is about visibility, speed, and smarter response. But resilience...
Learn MoreIntroducing Imply Lumi: The Industry’s First Observability Warehouse
Every organization I talk to wants the same three things: keep more data, search it faster, and spend less doing it. The problem isn’t ambition — it’s architecture. Today’s observability platforms weren’t...
Learn More10 Years of Imply: From Apache Druid to What’s Next in Real-Time Analytics
We’ve officially hit double digits! Ten years ago, a few Druid-obsessed engineers asked a radical question: What if analytics didn’t have to be slow, stale, or stuck in dashboards? Back then, we were just...
Learn MoreReal-Time Observability Without Operational Overhead: What’s Next?
Observability is meant to provide clarity, speed, and confidence in modern systems. Yet for many organizations, it has become a source of complexity, cost, and operational drag. Managing pipelines, tuning...
Learn MoreIt’s Time to Rethink Observability: The Event-Driven Future
Observability has evolved. Forward-looking teams are already moving beyond static dashboards and fragmented telemetry—treating all observability data as events and unlocking real-time insights across their...
Learn More5 Reasons to Use Imply Polaris over Apache Druid for Real-Time Analytics
Introduction Real-time analytics is a game-changer for businesses that need to make fast, data-driven decisions. Whether you’re analyzing user activity, monitoring applications and infrastructure, detecting...
Learn MoreIntroducing Apache Druid® 32.0
We are excited to announce the release of Apache Druid 32.0. This release contains over 341 commits from 52 contributors. It’s exciting to see a 30% increase in our contributors! Druid 32.0 is a significant...
Learn More2024 Product Innovation Recap
We’ve made a lot of progress over the past decade. As we reflect upon the past year, we’re proud to share a summary of the top 2024 product updates across both Druid and Imply. 2024 was a banner year,...
Learn MoreDruid Summit Lakehouse Panel: A Deep Dive into Data Lakehouses and Apache Druid
At the inaugural in-person Druid Summit this past October, industry leaders gathered to explore the future of data, streaming analytics, and more. In these panels industry experts answered questions about streaming...
Learn MoreRecap: Druid Summit 2024 – A Vibrant Community Shaping the Future of Data Analytics
In today’s fast-paced world, organizations rely on real-time analytics to make critical decisions. With millions of events streaming in per second, having an intuitive, high-speed data exploration tool to...
Learn MorePivot by Imply: A High-Speed Data Exploration UI for Druid
In today’s fast-paced world, organizations rely on real-time analytics to make critical decisions. With millions of events streaming in per second, having an intuitive, high-speed data exploration tool to...
Learn MoreIntroducing Apache Druid® 31.0
We are excited to announce the release of Apache Druid 31.0. This release contains over 525 commits from 45 contributors.
Learn MoreAn Overview to Data Tiering in Imply and Apache Druid
Learn all about tiering for Imply and Apache Druid—when it makes sense, how it works, and its opportunities and limitations.
Learn MoreLast Call—and Know Before You Go—For Druid Summit 2024
Druid Summit 2024 is almost here! Learn what to expect—so you can block off your schedule and make the most of this event.
Learn MoreThe Top Five Articles from the Imply Developer Center (Fall 2024 edition)
Build, troubleshoot, and learn—with the top five articles, lessons, and tutorials from Imply’s Developer Center for Fall 2024.
Learn MoreFrom Code to Connections: Druid Summit 2024 is Live & In-Person This October
This October, head to Redwood City, California, for the first ever in-person Druid Summit. Meet new people, reconnect with previous acquaintances, and learn from the best and brightest minds in the Apache Druid...
Learn MoreFederated Queries, Separation of Compute and Storage, and Real-Time Analytics — All from Imply Polaris
Imply Polaris provides a powerful solution for real-time analytics by combining federated queries, separation of compute and storage, and fast, interactive queries. It allows organizations to maintain always-on...
Learn MoreReal-time analytics architecture with Imply Polaris on Microsoft Azure
This article provides an architectural overview of how Imply Polaris integrates with Microsoft Azure services to power real-time analytics applications.
Learn MoreStreamlining Time Series Analysis with Imply Polaris
We are excited to share the latest enhancements in Imply Polaris, introducing time series analysis to revolutionize your analytics capabilities across vast amounts of data in real time.
Learn MoreUsing Upserts in Imply Polaris
Transform your data management with upserts in Imply Polaris! Ensure data consistency and supercharge efficiency by seamlessly combining insert and update operations into one powerful action. Discover how Polaris’s...
Learn MoreMake Imply Polaris the New Home for your Rockset Data
Rockset is deprecating its service—so try Imply Polaris, built on Apache Druid, a popular database designed for real-time Kafka and Kinesis streaming, and highly concurrent, subsecond queries on petabytes...
Learn MoreAnnouncing Imply Polaris on Microsoft Azure: Elevating Real-Time Analytics for Developers
We are excited to announce that Imply Polaris, our Database-as-a-Service (DBaaS) solution built from Apache Druid, is now available on Microsoft Azure. Azure customers worldwide can now take advantage of a...
Learn MoreCommunity Spotlight: Using Netflix’s Spectator Histogram and Kong’s DDSketch in Apache Druid for Advanced Statistical Analysis
In Apache Druid, sketches can be built from raw data at ingestion time or at query time. Apache Druid 29.0.0 included two community extensions that enhance data accuracy at the extremes of statistical distributions...
Learn MoreIntroducing Apache Druid® 30.0
We are excited to announce the release of Apache Druid 30.0. This release contains over 409 commits from 50 contributors. Druid 30 continues the investment across the following three key pillars: Ecosystem...
Learn MoreWhy I Joined Imply
After reviewing the high-level technical overview video of Apache Druid and learning about how the world's leading companies use Apache Druid, I immediately saw the immense potential in the product. Data is...
Learn MoreAWS IoT Core Explained: Features, Benefits, and Use Cases
Dive into AWS IoT Core with our fast-track guide. Discover its key features, benefits, real-world applications, and simple steps to kickstart your IoT projects.
Learn MoreTuning into Success: Streaming Music Done the Imply Way
Learn how a top streaming music service uses Imply to provide visibility into audience engagement and other key metrics.
Learn MoreImply Polaris is now on Microsoft Azure
We are thrilled to announce that Imply Polaris is now available on Microsoft Azure! Now, organizations can power their real-time analytics applications with Druid-powered Polaris clusters hosted in Microsoft...
Learn MoreA Builder’s Guide to Security Analytics
When should you build, and when should you buy a security analytics platform? Read on about the challenges, use cases, and opportunities of doing so—and what database you’ll need.
Learn MoreHow to Monitor Your IoT Environment in Real Time
As IoT environments become more complex, so too does data grow in volume, variety, and velocity. Learn why, when, and how to monitor your IoT environment.
Learn MoreHow GameAnalytics Provides Flexible Data Exploration with Imply
Learn how GameAnalytics, the leading analytics provider for the gaming industry, provides insights on over 100,000 games, 1.75 billion players, and 24 billion monthly sessions.
Learn MoreSmart Devices, Intelligent Insights: How Rivian and Thing-it use Apache Druid for IoT Analytics
Learn how engineers and architects from electric vehicle manufacturer Rivian and smart asset management platform Thing-it use Apache Druid for their IoT analytics environments.
Learn MoreWhat’s new in Imply Polaris – January 2024
At Imply, we're excited to share the latest enhancements in Imply Polaris, our real-time analytics Database-as-a-Service (DBaaS) powered by Apache Druid®. Our commitment to refining your experience with Polaris...
Learn MoreIntroducing Apache Druid 29.0
Apache Druid® is an open-source distributed database designed for real-time analytics at scale. We are excited to announce the release of Apache Druid 29.0. This release contains over 350 commits & 67 contributors.
Learn MoreApache Druid vs. ClickHouse
If your project needs a real-time analytics database that provides subsecond performance at scale you should consider both Apache Druid and ClickHouse. Find out how to make an informed choice.
Learn MoreEnhancing Data Security with Role-Based Access Control in Druid and Imply
Managing user access to relevant data is a crucial aspect of any data platform. In a typical Role Based Access Control (RBAC) setup, users are assigned roles that determine their access to relevant data. We...
Learn MoreComparing Data Formats for Analytics: Parquet, Iceberg, and Druid Segments
In this blog, I will give you a detailed overview of each choice. We will cover key features, benefits, defining characteristics, and provide a table comparing the file formats. Dive in and explore the characteristics...
Learn MoreScheduling batch ingestion with Apache Airflow
This guide is your map to navigating the confluence of Airflow and Druid for smooth batch ingestion. We'll get you started by showing you how to setup Airflow and the Druid Provider and use it to ingest some...
Learn MoreA Buyer’s Guide to OLAP Tools
How do OLAP databases work—and which one is right for you? Read this blog post to learn more about which OLAP solutions are best for different use cases.
Learn MoreWhat is IoT Analytics?
Because it deals with fast-moving, real-time data, IoT analytics is uniquely challenging. Learn how to overcome these challenges and how to extract (and act on) valuable insights from IoT data.
Learn MoreOLTP and OLAP Databases: How They Differ and Where to Use Them
Learn about the differences between analytical and transactional databases—their strengths and weaknesses, what they’re used for, and which option to choose for your own use case.
Learn MoreQuery from deep storage: Introducing a new performance tier in Apache Druid
Now, Druid offers a simpler, cost-effective solution with its new feature, Query from Deep Storage. This feature enables you to query Druid’s deep storage layer directly without having to preload all of your...
Learn MoreHow KakaoBank Uses Imply for Financial Analysis
As a mobile-first digital platform, KakaoBank accumulates a substantial amount of data. Therefore, analysts need a solution that can effectively analyze and pre-process large quantities of data, visualize the...
Learn MoreJoins, Multi-Stage Queries, and More: Relive the Excitement of Druid Summit 2023
Druid Summit kicked off its fourth year as a global gathering of minds passionate about real-time analytics and the power of Apache Druid. This year’s event revealed a common theme: the growing significance...
Learn MoreAn Introduction to Online Analytical Processing (OLAP)
Online analytical processing (OLAP) analyzes data at scale—and provides actionable insights to organizations. Learn about how OLAP works, what a data cube is, and which OLAP product to use.
Learn MoreReal-Time Data: What it is, Why it Matters, and More
Real-time data travels directly from the source to end users, so that it can be processed and acted on instantly. Learn all about the challenges, benefits, and best practices for real-time data.
Learn MoreDruid vs Pinot: Choosing the best database for Real-Time Analytics
Do you want fast analytics, with subsecond queries, high concurrency, and combination of streams and batch data? If so, you want real-time analytics, and you probably want to consider the two Apache Software...
Learn MoreWhat’s new in Imply Polaris – October and November 2023
At Imply, our commitment to continually improving your experience with Imply Polaris—our real-time analytics Database-as-a-Service (DBaaS) powered by Apache Druid®—is evident in recent developments. Over...
Learn MoreIntroducing Apache Druid 28.0.0
Apache Druid 28.0, an open-source database for real-time analytics, introduces Async queries, UNION ALL support, SQL WINDOW functions, enhanced ingestion features, including multi-Kafka topic support, and...
Learn MoreMigrating Data From S3 To Apache Druid
This blog covers the rationale, advantages, and step-by-step process for data transfer from AWS s3 to Apache Druid for faster real-time analytics and querying.
Learn MoreWhat’s new in Imply Polaris, our real-time analytics DBaaS – September 2023
Every week, we add new features and capabilities to Imply Polaris. Throughout September, we've focused on enhancing your experience as you explore trials, navigate data integration, oversee data management,...
Learn MoreIntroducing incremental encoding for Apache Druid dictionary encoded columns
In this blog post we deep dive on a recent engineering effort: incremental encoding of STRING columns. In preliminary testing, it has shown to be quite promising at significantly reducing the size of segment...
Learn MoreMigrate Analytics Data from MongoDB to Apache Druid
This blog presents a concise guide on migrating data from MongoDB to Druid. It includes Python scripts to extract data from MongoDB, save it as CSV, and then ingest it into Druid. It also touches on maintaining...
Learn MoreHow Druid Facilitates Real-Time Analytics for Mass Transit
Mass transit plays a key role in reimagining life in a warmer, more densely populated world. Learn how Apache Druid helps power data and analytics for mass transit.
Learn MoreMigrate Analytics Data from Snowflake to Apache Druid
This blog outlines the steps needed to migrate data from Snowflake to Apache Druid, a platform designed for high-performance analytical queries. The article covers the migration process, including Python scripts...
Learn MoreApache Kafka, Flink, and Druid: Open Source Essentials for Real-Time Data Applications
Apache Kafka, Flink, and Druid, when used together, create a real-time data architecture that eliminates all these wait states. In this blog post, we’ll explore how the combination of these tools enables...
Learn MoreVisualizing Data in Apache Druid with the Plotly Python Library
In today's data-driven world, making sense of vast datasets can be a daunting task. Visualizing this data can transform complicated patterns into actionable insights. This blog delves into the utilization of...
Learn MoreCombating fraud at Ibotta with Imply
For years, fraud was primarily a game of strategy. Fraudsters sought to disguise their true intentions, and fraud prevention was an art of detection.
Learn MoreBringing Real-Time Data to Solar Power with Apache Druid
In a rapidly warming world, solar power is critical for decarbonization. Learn how Apache Druid empowers a solar equipment manufacturer to provide real-time data to users, from utility plant operators to homeowners
Learn MoreWhen to Build (Versus Buy) an Observability Application
Observability is the key to software reliability. Here’s how to decide whether to build or buy your own solution—and why Apache Druid is a popular database for real-time observability
Learn MoreHow Innowatts Simplifies Utility Management with Apache Druid
Data is a key driver of progress and innovation in all aspects of our society and economy. By bringing digital data to physical hardware, the Internet of Things (IoT) bridges the gap between the online and...
Learn MoreThree Ways to Use Apache Druid for Machine Learning Workflows
An excellent addition to any machine learning environment, Apache Druid® can facilitate analytics, streamline monitoring, and add real-time data to operations and training
Learn MoreIntroducing Apache Druid 27.0.0
Apache Druid® is an open-source distributed database designed for real-time analytics at scale. Apache Druid 27.0 contains over 350 commits & 46 contributors. This release's focus is on stability and scaling...
Learn MoreUnleashing Real-Time Analytics in APJ: Introducing Imply Polaris on AWS AP-South-1
Imply, the company founded by the original creators of Apache Druid, has exciting news for developers in India seeking to build real-time analytics applications. Introducing Imply Polaris, a powerful database-as-a-Service...
Learn MoreEmbedding Visualizations using React and Express
In this guide, we will walk you through creating a very simple web app that shows a different embedded chart for each user selected from a drop-down. While this example is simple it highlights the possibilities...
Learn MoreApache Druid: Making 1000+ QPS for Analytics Look Easy
This 2-part blog post explores key technical considerations to support high QPS for analytics and the strengths of Apache Druid
Learn MoreThings to Consider When Scaling Analytics for High QPS
This 2-part blog post explores key technical considerations to support high QPS for analytics and the strengths of Apache Druid
Learn MoreAutomate Streaming Data Ingestion with Kafka and Druid
In this blog post, we explore the integration of Kafka and Druid for data stream management and analysis, emphasizing automatic topic detection and ingestion. We delve into the creation of 'Ingestion Spec',...
Learn MoreSchema Auto-Discovery with Apache Druid
This guide explores configuring Apache Druid to receive Kafka streaming messages. To demonstrate Druid's game-changing automatic schema discovery. Using a real-world scenario where data changes are handled...
Learn MoreWhat’s new in Imply Polaris – Q2 2023
Imply Polaris, our ever-evolving Database-as-a-Service, recently focused on global expansion, enhanced security, and improved data handling and visualization. This fully managed cloud service, based on Apache...
Learn MoreIntroducing hands-on developer tutorials for Apache Druid
The objective of this blog is to introduce the new set of interactive tutorials focused on the Druid API fundamentals. These tutorials are available as Jupyter Notebooks and can be downloaded as a Docker container.
Learn MoreIntroducing Schema Auto-Discovery in Apache Druid
In this blog article I’ll unpack schema auto-discovery, a new feature now available in Druid 26.0, that enables Druid to automatically discover data fields and data types and update tables to match changing...
Learn MoreExploring Unnest in Druid
Druid now has a new function, Unnest. Unnest explodes an array into individual elements. This blog contains design methodology and examples for this new Unnest function both from native and SQL binding perspectives.
Learn MoreWhat’s new in Imply Polaris – Our Real-Time Analytics DBaaS
Every week we add new features and capabilities to Imply Polaris. This month, we’ve expanded security capabilities, added new query functionality, and made it easier to monitor your service with your preferred...
Learn MoreIntroducing Apache Druid 26.0
Apache Druid® 26.0, an open-source distributed database for real-time analytics, has seen significant improvements with 411 new commits, a 40% increase from version 25.0. The expanded contributor base of 60...
Learn MoreACID and Apache Druid
ACID and Druid, an interesting dive into some of the Druid capabilities in the light of ACID compliance
Learn MoreHow to Build a Sentiment Analysis Application with ChatGPT and Druid
Leveraging ChatGPT for sentiment analysis, when combined with Apache Druid, offers results from large data volumes. This integration is easily achievable, revealing valuable insights and trends for businesses...
Learn MoreSnowflake and Apache Druid
In this blog, we will compare Snowflake and Druid. It is important to note that reporting data warehouses and real-time analytics databases are different domains. Choosing the right tool for your specific requirements...
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