We call them modern analytics applications: apps that serve real-time analytics to hundreds or thousands of concurrent users on streaming (as well as batch) data. They’re the apps developed at Netflix, Twitter, Confluent, Salesforce, and 1000s of others that play a key role in their businesses.
So what do these apps actually do? And why don’t data warehouses or the other 300+ databases out there fit the bill? Let’s look at what devs are trying to build in these apps and why they turn to Druid. These include:
Operational visibility at scale
Customer-facing analytics
Rapid-drill down exploration
Real-time decisioning
Operational visibility at scale
Devs building apps for operational visibility (e.g. observability, product analytics, digital operations, IoT, and fraud detection) are tasked with closing the gap between events created and time-to-insight.
Now technology-wise, at the surface there are several databases that can analyze events in real-time. These include stream processors like Apache Flink or ksqlDB, time-series databases like InfluxDB or TimeScaleDB or even key-value stores like Redis. But all of these technologies have the same Achilles’ heel: they can’t do OLAP-style queries well at any meaningful scale.
Stream Processors
Time-Series DBs
Key-Value Stores
Real-time alerts
X
X
X
Basic SELECT queries
X
X
X
Complex OLAP queries
–
–
–
Sub-second response at scale
–
–
–
Let’s say the app calls for analyzing time and non-time-based dimensions over the past month (assume ingestion was 1 million events per second, that’d be 2.6+ trillion events). Any database that supports stream ingestion can query the current status (setting aside the challenge to handle ingestion at this scale) and to a degree simple aggregation of numbers and counters. But if the app requires aggregation across all of these events, GROUP BY on non-time-based attributes, or high concurrency, then the wrong database = spinning wheel of death as people sit and sit waiting for results.
That’s why devs turn to Apache Druid. Salesforce engineers built an analytics app using Druid to monitor their product experience. The app enables engineers, product owners, and customer service reps to query any combination of dimensions, filters, and aggregations on real-time logs for performance analysis, trend analysis, and troubleshooting – query results returning in seconds with billions to trillions of streaming events ingested every day.
Customer-facing analytics
Atlassian, Forescout, and Twitter are seemingly unrelated but they have one thing in common. They see analytics for more than internal decision-making. They’re giving their customers insights as part of a value-added service or a core product offering entirely.
When building analytics for external users, a whole different set of tech challenges need to be addressed. Concurrency is a clear top factor. Not only should the database support current and future user growth with ease, but concurrency has to be addressed economically or the infrastructure cost will go through the roof.
Another key factor is sub-second query response for an interactive experience. External users are typically paying customers and they don’t want to wait for queries to process. Instead of fighting PostgreSQL or Hive or any other analytics database to meet the performance reqs at scale, Druid’s the easy answer.
Confluent, for example, built Confluent Health+ (their cloud-based monitoring service) with Druid. They originally built their app with a different database, and in their words, “as the volume of data grew, our legacy pipeline struggled to keep up with our data ingestion and query loads”. And now with Druid, they’re able to deliver that great user experience every dev wants to build – and do it with ease.
Rapid drill-down exploration
A report simply reports (e.g. what is the top selling product, what is the average customer’s age). But the real gem is understanding why something happened to either solve a problem in the present, or anticipate it happening again in the future.
Answering ‘why’ requires slicing and dicing data to explore and find root causes, at lightning speed. So when a person asks a question, they get the answer in less than a second. This is easy when the data set is small. But when dealing with data from cloud services, clicks, and IoT/telemetry, the underlying database has to correlate a few to hundreds of dimensions across highly cardinal data with billions to trillions of rows. That’s not so easy.
With data at this scale, full table scans take too long and the typical query-shaping techniques used to speed up performance have expensive tradeoffs.
Performance Technique
Tradeoff
Analyze recent data only
Misses the complete story
Precompute all the queries
Expensive and inflexible
Analyze roll-up aggregations
Can’t drill down into the data
But with the right database it’s easy to build an interactive data experience without any tradeoffs. For example, developers at HUMAN built an analytics app for internet bot detection using Apache Druid with Imply. Prior to Imply, their cloud data warehouse was too slow to keep up with their need to drill into massive web traffic quickly. Now, the app gives their data scientists the ability to instantly aggregate and filter across trillions of digits events, so they can classify anomalies and train ML models to detect real-time malicious activity.
Finding the needle in the haystack is hard enough. But with Apache Druid it’s easy to investigate behavior, diagnose problems, and enable an interactive data experience for any amount of data.
Real-time decisioning
Lastly, when people think of analytics, what comes to mind is a synthesized data set presented via a UI like a chart, graph, report, dashboard etc where a person comprehends it and then takes action. But sometimes the user just wants to know what the data means and what they should do about it – or the decision needs to be made so quickly that people themselves become a bottleneck. Some decisions are too fast for humans.
Think Google Maps and how it optimizes routing based on real-time and historical traffic flow and patterns – or real-time ad placement within milliseconds of a new visitor. There’s no data viz to interpret. Those use automated decisions derived by inference and that’s a sweet spot for Druid.
A key reason Netflix constantly delivers a great streaming experience is through an optimized, automated content delivery network. Netflix engineers have built an analytics application with Druid that ingests 2 million events per second packed with filters, aggregations, and group-by queries that infers anomalies within their infrastructure, endpoint activity, and content flow across a data set of over 1.5 trillion rows – all to optimize content delivery.
Devs choose Druid for powering inference in apps – including diagnostics, recommendations, and automated decisions – when the optimal answer requires instant query response of high cardinality, highly dimensional event data at scale. The speed and data freshness of a Druid query result feeds into rules engines and ML frameworks for real-time decisioning. If the app is for updating merchandise pricing once a day – Druid might be overkill. But if the app needs real-time decisioning – then Druid can’t be beat.
Conclusion
The database market is crowded, but the best ones are purpose-built for specific use cases. In the case of Apache Druid, it’s the best choice for use cases that need an interactive experience querying lots of real-time and historical data for lots of users. If this sounds like something you’re trying to build, you should read this white paper on Druid Architecture & Concepts and definitely try a free trial of Imply Polaris, the cloud database built for Druid.
Introducing 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...
Migrate 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...
How 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.
Migrate 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...
Apache Kafka, Flink, and Druid: Open Source Essentials for Real-Time 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...
Visualizing 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...
Bringing 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
When 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
How 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...
Three 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
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...
Unleashing 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...
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...
Automate 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',...
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...
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...
Introducing 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.
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...
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.
What’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...
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...
How 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...
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...
Learn how to achieve sub-second responses with Apache Druid
Learn how to achieve sub-second responses with Apache Druid. This article is an in-depth look at how Druid resolves queries and describes data modeling techniques that improve performance.
Apache Druid uses load rules to manage the ageing of segments from one historical tier to another and finally to purge old segments from the cluster. In this article, we’ll show what happens when you make...
Real-Time Analytics: Building Blocks and Architecture
This blog identifies the key technical considerations for real-time analytics. It answers what is the right data architecture and why. It spotlights the technologies used at Confluent, Reddit, Target and 1000s...
What’s new in Imply Polaris – Our Real-Time Analytics DBaaS
This blog explains some of the new features, functionality and connectivity added to Imply Polaris over the last two months. We've expanded ingestion capabilities, simplified operations and increased reliability...
Wow, that was easy – Up and running with Apache Druid
The objective of this blog is to provide a step-by-step guide on setting up Druid locally, including the use of SQL ingestion for importing data and executing analytical queries.
Tales at Scale Podcast Kicks off with the Apache Druid Origin Story
Tales at Scale cracks open the world of analytics projects and shares stories from developers and engineers who are building analytics applications or working within the real-time data space. One of the key...
Real-time Analytics Database uses partitioning and pruning to achieve its legendary performance
Apache Druid uses partitioning (splitting data) and pruning (selecting subset of data) to achieve its legendary performance. Learn how to use the CLUSTERED BY clause during ingestion for performance and high...
Easily embed analytics into your own apps with Imply’s DBaaS
This blog explains how developers can leverage Imply Polaris to embed robust visualization options directly into their own applications without them having to build a UI. This is super important because consuming...
Building an Event Analytics Pipeline with Confluent Cloud and Imply’s real time DBaaS, Polaris
Learn how to set up a pipeline that generates a simulated clickstream event stream and sends it to Confluent Cloud, processes the raw clickstream data using managed ksqlDB in Confluent Cloud, delivers the processed...
We are excited to announce the availability of Imply Polaris in Europe, specifically in AWS eu-central-1 region based in Frankfurt. Since its launch in March 2022, Imply Polaris, the fully managed Database-as-a-Service...
Should You Build or Buy Security Analytics for SecOps?
When should you build—or buy—a security analytics platform for your environment? Here are some common considerations—and how Apache Druid is the ideal foundation for any in-house security solution.
Combating financial fraud and money laundering at scale with Apache Druid
Learn how Apache Druid enables financial services firms and FinTech companies to get immediate insights from petabytes-plus data volumes for anti-fraud and anti-money laundering compliance.
This is a what's new to Imply in Dec 2022. We’ve added two new features to Imply Polaris to make it easier for your end users to take advantage of real-time insights.
Imply Pivot delivers the final mile for modern analytics applications
This blog is focused on how Imply Pivot delivers the final mile for building an anlaytics app. It showcases two customer examples - Twitch and ironsource.
For decades, analytics has been defined by the standard reporting and BI workflow, supported by the data warehouse. Now, 1000s of companies are realizing an expansion of analytics beyond reporting, which requires...
Apache Druid is at the heart of Imply. We’re an open source business, and that’s why we’re committed to making Druid the best open source database for modern analytics applications
When it comes to modern data analytics applications, speed is of the utmost importance. In this blog we discuss two approximation algorithms which can be used to greatly enhance speed with only a slight reduction...
The next chapter for Imply Polaris: celebrating 250+ accounts, continued innovation
Today we announced the next iteration of Imply Polaris, the fully managed Database-as-a-Service that helps you build modern analytics applications faster, cheaper, and with less effort. Since its launch in...
Elasticity is important, but beware the database that can only save you money when your application is not in use. The best solution will have excellent price-performance under all conditions.
Druid 0.23 – Features And Capabilities For Advanced Scenarios
Many of Druid’s improvements focus on building a solid foundation, including making the system more stable, easier to use, faster to scale, and better integrated with the rest of the data ecosystem. But for...
Apache Druid 0.23.0 contains over 450 updates, including new features, major performance enhancements, bug fixes, and major documentation improvements.
Imply Polaris is a fully managed database-as-a-service for building realtime analytics applications. John is the tech lead for the Polaris UI, known internally as the Unified App. It began with a profound question:...
There is a new category within data analytics emerging which is not centered in the world of reports and dashboards (the purview of data analysts and data scientists), but instead centered in the world of applications...
We are in the early stages of a stream revolution, as developers build modern transactional and analytic applications that use real-time data continuously delivered.
Developers and architects must look beyond query performance to understand the operational realities of growing and managing a high performance database and if it will consume their valuable time.
Building high performance logging analytics with Polaris and Logstash
When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...
Horizontal scaling is the key to performance at scale, which is why every database claims this. You should investigate, though, to see how much effort it takes, especially compared to Apache Druid.
When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...
Building Analytics for External Users is a Whole Different Animal
Analytics aren’t just for internal stakeholders anymore. If you’re building an analytics application for customers, then you’re probably wondering…what’s the right database backend?
After over 30 years of working with data analytics, we’ve been witness (and sometimes participant) to three major shifts in how we find insights from data - and now we’re looking at the fourth.
Every year industry pundits predict data and analytics becoming more valuable the following year. But this doesn’t take a crystal ball to predict. There’s instead something much more interesting happening...
Today, I'm prepared to share our progress on this effort and some of our plans for the future. But before diving further into that, let's take a closer look at how Druid's core query engine executes queries,...
Product Update: SSO, Cluster level authorization, OAuth 2.0 and more security features
When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...
When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...
Druid Nails Cost Efficiency Challenge Against ClickHouse & Rockset
To make a long story short, we were pleased to confirm that Druid is 2 times faster than ClickHouse and 8 times faster than Rockset with fewer hardware resources!.
Unveiling Project Shapeshift Nov. 9th at Druid Summit 2021
There is a new category within data analytics emerging which is not centered in the world of reports and dashboards (the purview of data analysts and data scientists), but instead centered in the world of applications...
How we made long-running queries work in Apache Druid
When you think of querying with Apache Druid, you probably imagine queries over massive data sets that run in less than a second. This blog is about some of the things we did as a team to discover the user...
Uneven traffic flow in streaming pipelines is a common problem. Providing the right level of resources to keep up with spikes in demand is a requirement in order to deliver timely analytics.
Community Discoveries: multi-value dimensions in Apache Druid
Hellmar Becker is an Imply solutions engineer based in Germany, where he has been delving into the nooks-and-crannies of multi-valued dimension support in Druid. In this interview, Hellmar explains why...
Community Spotlight: Apache Pulsar and Apache Druid get close…
The community team at Imply spoke with an Apache Pulsar community member, Giannis Polyzos, about how collaboration between open source communities generates great things, and more specifically, about how...
Meet the team: Abhishek Agarwal, engineering lead in India
Abhishek is Imply’s first engineer in India. We spoke to him about setting up our operations in Bangalore and asked what kind of local talent the company is looking for.