How WalkMe uses Druid and Imply Cloud to Analyze Clickstreams and User Behavior
Apr 03, 2019
Yotam Spenser, WalkMe
This is a guest post from Yotam Spenser, Head of Data Engineering @ WalkMe
WalkMe is a Digital Adoption Platform (DAP) pioneer that offers a 360-degree solution to leading organizations worldwide. WalkMe helps employees and customers at some of the world’s largest companies engage and adopt digital products, and ensures organizations of all sizes can undergo smooth digital transformations.
WalkMe works as an embedded solution that integrates into any web, mobile, or desktop host application and works seamlessly from within to engage, support, or convert users based on their behavior (combined with predetermined rules).
A key element of any intelligent embedded application is that it must monitor its own effectiveness (ideally, as discreetly as possible). This means any tracking should remain effectively invisible, even as the embedded application undergoes rapid evolution.
A WalkMe step-by-step Walk-Thru in action.
Migrating from ElasticSearch to Druid
The legacy analytics system that was originally used to track core product usage was Elasticsearch (ES), which we initially leveraged as a simple log management system. We primarily used this system for one purpose: to troubleshoot specific problems in our embedded application (as opposed to monitoring broad trends across host applications).
While ES functioned well as a simple query system to troubleshoot errors in the embedded application, our requirements grew more sophisticated as our product matured. Over time, we started monitoring various aspects of the application’s performance and usage (e.g., length of operations on the client, accuracy of operations, etc.), and as the data grew in both volume and complexity, we realized an ES-based stack wasn’t optimal for real-time arithmetic operations over time-series data. Simply put, our queries were primarily ad-hoc analytic queries that grouped and filtered on several dimensions and aggregated several complex metrics. As our queries evolved from simple troubleshooting queries to ones that measured complex engagement stats, they became less and less well-suited to the search-focused architecture of ES.
Once we realized the legacy architecture was not well suited to behavioural analytics, and would not scale with our growth, we began searching for an alternative to transition our classic log search approach to a real-time OLAP one that scales linearly with our traffic. Druid met this criteria, so we started developing over the open source solution, testing specific use cases as well as load management in a cluster.
Productionalizing Druid
We quickly realized through our early evaluation with Druid that complex distributed systems such as Druid require significant devops and fine-tuning in order to achieve optimal performance and resiliency. Early on, we partnered with Imply, whose founders developed Druid, allowing us to focus on our business objectives, which included monitoring and creating analytics applications. Today, we run our entire Druid cluster on Imply.
High performance user analytics with Druid
Druid is now a big part of WalkMe’s internal and external analytics applications. Druid enables us to monitor performance across billions of client devices in real time. We can leverage Druid to compute any arbitrary metrics over any ad-hoc groups of users. We can track business critical measures such as retention and attrition, plus many other forms of engagement and usage metrics. As a result, we can now gain the type of insight we need to optimize and segment our code for different host platforms, applications, and websites, per their specific needs.
Run Powerful User Behavior Queries Interactively
There are some extremely powerful queries we can run with Druid, that would otherwise be impossible, to analyze the behavior of our users. For example, consider the following query:
SELECT ... FROM tbl WHERE userId IN (SELECT userId FROM tbl WHERE ...)
We issue many queries similar to this one where we first select a large group of users based on an ad-hoc set of criteria. We then further select a subset of these users and compute a wide range of metrics. For many traditional data warehouses, this can be a very expensive query. The first group of users may be in the millions, and to materialize and maintain such a list would be very expensive if we wanted the query to return interactively.
In contrast, Druid can leverage Bloom filters to return all results in a single query. This means that we don’t need to materialize the full set of users that match the first set of criteria. This is extremely powerful because it allows us to interactively engage with the data cost effectively, something we couldn’t do with ES. Although Bloom filters are approximate, we’ve found they have a high degree of accuracy and allow us to see all the important trends we need to follow. If we need 100% accuracy on a few key results, Druid also supports exact computations.
How Bloom filters work.
Conclusions
WalkMe is now using Druid as a part of our flagship customer-facing analytics product, WalkMe Insights, as well as internally for all of our monitoring purposes. If you have problems similar to ours around tracking users for digital products, we encourage you to evaluate Druid and Imply.
Other blogs you might find interesting
No records found...
Mar 21, 2024
How 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.
Smart 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.
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...
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.
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.
Enhancing 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...
Comparing 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...
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...
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.
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.
OLTP 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.
Query 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...
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...
Joins, 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...
An 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.
Real-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.
Druid 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...
What’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...
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.
What’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,...
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 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...
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...
We obviously talk a lot about #ApacheDruid on here. But what are folks actually building with Druid? What is a modern analytics application, exactly? Let's find out
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.