How Nielsen Marketing Cloud Uses Druid for Audience and Marketing Performance Analysis
Nov 21, 2019
Itai Yaffe
Nielsen Marketing Cloud provides a way to profile the various audiences that marketers and publishers would like to target on digital media, activate via various ad networks, and then gain insights on that activation after the fact.
How do we do that? First, we collect device-level data (device can be a mobile phone, a laptop, a smart TV, etc.) from online and offline sources, and enrich it using various machine learning (ML) algorithms. This process assigns each device a set of attributes, such as gender, age, interests, demographics – you name it.
For more details and Q & A with the Nielsen team, register for the webinar happening December 11th, 10 AM Pacific
After the data is stored in our systems, our customers can use our SaaS offering to query it in various ways. The most common type of questions is sizing the audience, and this is basically a COUNT DISTINCT question. For instance, determining how many unique devices fulfill a certain criteria or a certain set of attributes that our customers can choose in our dashboards, for a given date range.
We provide them with the ability to input a Boolean formula. They can add as many attributes as they want. The number of combinations is endless. The date range is not predefined, and we have to provide the number of unique devices that fulfill that criteria.
That’s a tough question to answer, especially in real time.
If you’re talking about, say, targeting or campaign analysis, usually we’re interested in a trend. Most of the time, we’re not interested in what the customer or the user browsing the web has done at a specific second in time, but rather, what was the result over time of being exposed to and clicking on an ad.
Historically, we used Elasticsearch to store the raw data (i.e all the events we are collecting). At query time, we had to scan all the data and provide the number of unique devices. As the scale and volume grew – we’re talking about over 10 billion devices today – Elasticsearch was no longer the answer, and we needed to find out a better way to provide those answers.
Just to give you an idea of how this pipeline used to work back then (about four years ago):
Every day, we took a sample of our data (only about 250 gigabytes per day out of the entire daily data), and ingested that sample into Elasticsearch. It took us about 10 hours (!) per day. Now, during that time, the response time of the queries went through the roof. Some queries even timed out, so it wasn’t really scalable.
Also there was the cost. If you want to handle more queries, you had to scale your Elasticsearch cluster, but that wasn’t enough because of all sorts of limitations with the way we modeled our data and with Elasticsearch itself.
Once we understood that Elasticsearch could no longer address the demand, we set out to find a better solution. Some of them were based on other capabilities in Elasticsearch, and some of them were completely outside of Elasticsearch. After some research and some help from fellow Israeli company AppsFlyer, we started pursuing Druid as an alternative. The proof of concept results were really good for us — in terms of scalability, concurrent queries, performance, and cost — so we just went with Druid and never looked back.
We’ve had Druid in production for over three and a half years now. We have several clusters, and one of them has over 20 nodes. The historical nodes are based one of AWS’s largest instances, i3.8xlarge (which as 32 cores and 244GB RAM each, and uses NVMe SSD). We ingest dozens of terabytes per day, though after the Druid rollup pre-aggregation process occurs – we aggregate using daily granularity – we end up with only 40 terabytes of aggregated data for a year’s worth of data!
I think it’s also worth mentioning that we’re using a unique ability Druid provides, which is ThetaSketch. That’s a large part of our solution. Without it, we wouldn’t be able to provide that COUNT DISTINCT capability, because we want to count how many unique devices are relevant for this, say, campaign, not just how many hits a certain webpage receives or something like that.
ThetaSketch lets us perform a very quick approximation of COUNT DISTINCT. It’s an amazing capability that allows customers to build a Boolean formula combining various attributes, which is translated into numerous unions and intersections between those attributes.
For example, how many devices are used by females that are interested in technology? That’s an intersection question. And ThetaSketch helps us to answer it in real time rather than having to try to calculate all of the results beforehand. We have over 80,000 attributes in the system, though not all of them are translated into a dimension in Druid. It really depends on the use case.
We have our web application, our SaaS offering, which the customer can build the Boolean formula. And then this is being sent to our proprietary component that translates that formula into queries via the Druid REST API. A complex Boolean formula from a single user may generate hundreds of Druid queries behind the scenes.
We have a few layers in the system. The first layer is the frontend layer (a.k.a. our serving layer) that receives data from both online and offline sources. An online event can be when someone browses a website, and there’s a redirect to our serving layer. Offline data, on the other hand, comes from files we get from various data partners and alike. Our very fast serving layer processes those events, runs different machine learning algorithms to enrich the data, and then stores it in our data layer. From there we use the Hadoop-based ingestion to ingest the data into Druid.
I think that one of the interesting things in our journey with Druid is that we started off with one use case, which is the COUNT DISTINCT use case that I mentioned earlier, and later on we transformed more use cases into Druid, replacing other tools. Elasticsearch was used for part of one category of use cases, and another category of use cases was served by a distributed MySQL engine. In both cases they were not scalable enough.
For the near future, one of the interesting things we currently are trying to handle is to intersect two attributes with a totally different orders of magnitude. Say you want to know all the US located devices (a large dataset) that are interested in Porsche (a small dataset), for example. When using ThetaSketch, this kind of use case may result in a very high error rate. So we’re researching how we can mitigate this type of queries using machine learning based tools in addition to ThetaSketch.
If you want to hear more or you have questions, sign up for our webinar happening December 11th, 10 AM Pacific
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.