Monday, May 20, 2024

How FanDuel adopted a contemporary Amazon Redshift structure to serve important enterprise workloads

This submit is co-written with Sreenivasa Mungala and Matt Grimm from FanDuel.

On this submit, we share how FanDuel moved from a DC2 nodes structure to a contemporary Amazon Redshift structure, which incorporates Redshift provisioned clusters utilizing RA3 situations, Amazon Redshift knowledge sharing, and Amazon Redshift Serverless.

About FanDuel

A part of Flutter Leisure, FanDuel Group is a gaming firm that provides sportsbooks, day by day fantasy sports activities, horse racing, and on-line casinos. The corporate operates sportsbooks in various US and Canadian states. Fanduel first carved out a distinct segment within the US by means of day by day fantasy sports activities, comparable to their hottest fantasy sport: NFL soccer.

As FanDuel’s enterprise footprint grew, so too did the complexity of their analytical wants. An increasing number of of FanDuel’s group of analysts and enterprise customers regarded for complete knowledge options that centralized the information throughout the assorted arms of their enterprise. Their particular person, product-specific, and infrequently on-premises knowledge warehouses quickly grew to become out of date. FanDuel’s knowledge staff solved the issue of making a brand new large knowledge retailer for centralizing the information in a single place, with one model of the reality. On the coronary heart of this new International Information Platform was Amazon Redshift, which quick grew to become the trusted knowledge retailer from which all evaluation was derived. Customers may now assess threat, profitability, and cross-sell alternatives not just for piecemeal divisions or merchandise, but in addition globally for the enterprise as a complete.

FanDuel’s journey on Amazon Redshift

FanDuel’s first Redshift cluster was launched utilizing Dense Compute (DC2) nodes. This was chosen over Dense Storage (DS2) nodes with the intention to benefit from the better compute energy for the complicated queries of their group. As FanDuel grew, so did their knowledge workloads. This meant that there was a relentless problem to scale and overcome rivalry whereas offering the efficiency their consumer group wanted for day-to-day decision-making. FanDuel met this problem initially by repeatedly including nodes and experimenting with workload administration (WLM), however it grew to become abundantly apparent that they wanted to take a extra vital step to fulfill the wants of their customers.

In 2021, FanDuel’s workloads virtually tripled since they first began utilizing Amazon Redshift in 2018, they usually began evaluating Redshift RA3 nodes vs. DC2 nodes to benefit from the storage and compute separation and ship higher efficiency at decrease prices. FanDuel wished to make the transfer primarily to separate storage and compute, and consider knowledge sharing within the hopes of bringing completely different compute to the information to alleviate consumer rivalry on their major cluster. FanDuel determined to launch a brand new RA3 cluster after they had been glad that the efficiency matched that of their current DC2 structure, offering them the power to scale storage and compute independently.

In 2022, FanDuel shifted their focus to utilizing knowledge sharing. Information sharing permits you to share stay knowledge securely throughout Redshift knowledge warehouses for learn and write (in preview) functions. Because of this workloads will be remoted to particular person clusters, permitting for a extra streamlined schema design, WLM configuration, and right-sizing for value optimization. The next diagram illustrates this structure.

To realize a knowledge sharing structure, the plan was to first spin up client clusters for growth and testing environments for his or her knowledge engineers that had been shifting key legacy code to dbt. FanDuel wished their engineers to have entry to manufacturing datasets to check their new fashions and match the outcomes from their legacy SQL-based code units. Additionally they wished to make sure that they’d enough compute to run many roles concurrently. After they noticed the advantages of knowledge sharing, they spun up their first manufacturing client cluster within the spring of 2022 to deal with different analytics use instances. This was sharing a lot of the schemas and their tables from the principle producer cluster.

Advantages of shifting to an information sharing structure

FanDuel noticed a variety of advantages from the information sharing structure, the place knowledge engineers had entry to actual manufacturing knowledge to check their jobs with out impacting the producer’s efficiency. Since splitting the workloads by means of a knowledge sharing structure, FanDuel has doubled their question concurrency and decreased the question queuing, leading to a greater end-to-end question time. FanDuel acquired optimistic suggestions on the brand new setting and shortly reaped the rewards of elevated engineering velocity and decreased efficiency points in manufacturing after deployments. Their preliminary enterprise into the world of knowledge sharing was positively thought of successful.

Given the profitable rollout of their first client in a knowledge sharing structure, they regarded for alternatives to fulfill different customers’ wants with new focused shoppers. With the help of AWS, FanDuel initiated the event of a complete technique geared toward safeguarding their extract, load, and rework (ELT) jobs. This strategy concerned implementing workload isolation and allocating devoted clusters for these workloads, designated because the producer cluster inside the knowledge sharing structure. Concurrently, they deliberate emigrate all different actions onto a number of client clusters, other than the present cluster utilized by their knowledge engineering staff.

They spun up a second client in the summertime of 2022 with the hopes of shifting a few of their extra resource-intensive analytical processes off the principle cluster. With a purpose to empower their analysts over time, they’d allowed a sample by which customers apart from knowledge engineers may create and share their very own objects.

Because the calendar flipped from 2022 to 2023, a number of developments modified the panorama of structure at FanDuel. For one, FanDuel launched their preliminary event-based streaming work for his or her sportsbook knowledge, which allowed them to micro-batch knowledge into Amazon Redshift at a a lot decrease latency than their earlier legacy batch strategy. This allowed them to generate C-Suite income reviews at a a lot earlier SLA, which was an enormous win for the information staff, as a result of this was by no means achieved earlier than the Tremendous Bowl.

FanDuel launched a brand new inside KPI known as Question Effectivity, a measure to seize the period of time customers spent ready for his or her queries to run. Because the workload began rising exponentially, FanDuel additionally observed a rise on this KPI, particularly for threat and buying and selling workloads.

Working with AWS Enterprise Assist and the Amazon Redshift service staff, FanDuel quickly realized that the chance and buying and selling use case was an ideal alternative to maneuver it to Amazon Redshift Serverless. Redshift Serverless presents scalability throughout dimensions such a knowledge quantity modifications, concurrent customers and question complexity, enabling you to robotically scale compute up or right down to handle demanding and unpredictable workloads. As a result of billing is just accrued whereas queries are run, it additionally implies that you now not have to cowl prices for compute you’re not using. Redshift Serverless additionally manages workload administration (WLM) completely, permitting you to focus solely on the question monitoring guidelines (QMRs) you need and utilization limits, additional limiting the necessity so that you can handle your knowledge warehouses. This adoption additionally complimented knowledge sharing, the place Redshift Serverless endpoints can learn and write (in preview) from provisioned clusters throughout peak hours, providing versatile compute scalability and workload isolation and avoiding the influence on different mission-critical workloads. Seeing the advantages of what Redshift Serverless presents for his or her threat and buying and selling workloads, in addition they moved a few of their different workloads like enterprise intelligence (BI) dashboards and threat and buying and selling (RT) to a Redshift Serverless setting.

Advantages of introducing Redshift Serverless in a knowledge sharing structure

By a mixture of knowledge sharing and a serverless structure, FanDuel may elastically scale their most important workloads on demand. Redshift Serverless Automated WLM allowed customers to get began with out the necessity to configure WLM. With the clever and automatic scaling capabilities of Redshift Serverless, FanDuel may concentrate on their enterprise targets with out worrying concerning the knowledge warehouse capability. This structure alleviated the constraints of a single predefined Redshift provisioned cluster and decreased the necessity for FanDuel to handle knowledge warehouse capability and any WLM configuration.

When it comes to value, Redshift Serverless enabled FanDuel to elegantly deal with essentially the most demanding workloads with a pay-as-you-go mannequin, paying solely when the information warehouse is in use, together with full separation of compute and storage.

Having now launched workload isolation and Redshift Serverless, FanDuel is ready to obtain a extra granular understanding of every staff’s compute necessities with out the noise of ELT and contending workloads all in the identical setting. This allowed complete analytics workloads to be carried out on shoppers with vastly minimized rivalry whereas additionally being serviced with essentially the most cost-efficient configuration potential.

The next diagram illustrates the up to date structure.


FanDuel’s re-architecting efforts for workload isolation with threat and buying and selling (RT) workloads utilizing Redshift knowledge sharing and Redshift Serverless resulted in essentially the most important enterprise SLAs ending thrice sooner, together with a rise in common question effectivity of 55% for general workloads. These SLA enhancements have resulted into an general saving of tenfold in enterprise value, they usually have been capable of ship enterprise insights to different verticals comparable to product, industrial, and advertising a lot sooner.


By harnessing the facility of Redshift provisioned clusters and serverless endpoints with knowledge sharing, FanDuel has been capable of higher scale and run analytical workloads with out having to handle any knowledge warehouse infrastructure. FanDuel is trying ahead to future Amazon partnerships and is worked up to embark on a journey of latest innovation with Redshift Serverless and continued enhancements comparable to machine studying optimization and auto scaling.

In the event you’re new to Amazon Redshift, you may discover demos, different buyer tales, and the newest options at Amazon Redshift. In the event you’re already utilizing Amazon Redshift, attain out to your AWS account staff for help, and be taught extra about what’s new with Amazon Redshift.

Concerning the authors

Sreenivasa Munagala is a Principal Information Architect at FanDuel Group. He defines their Amazon Redshift optimization technique and works with the information analytics staff to offer options to their key enterprise issues.

Matt Grimm is a Principal Information Architect at FanDuel Group, shifting the corporate to an event-based, data-driven structure utilizing the combination of each streaming and batch knowledge, whereas additionally supporting their Machine Studying Platform and growth groups.

Luke Shearer is a Cloud Assist Engineer at Amazon Internet Providers for the Information Perception Analytics profile, the place he’s engaged with AWS prospects daily and is all the time working to establish the most effective resolution for every buyer.

Dhaval Shah is Senior Buyer Success Engineer at AWS and focuses on bringing essentially the most complicated and demanding knowledge analytics workloads to Amazon Redshift. He has extra then 20 years of experiences in several databases and knowledge warehousing applied sciences. He’s keen about environment friendly and scalable knowledge analytics cloud options that drive enterprise worth for purchasers.

Ranjan Burman is an Sr. Analytics Specialist Options Architect at AWS. He focuses on Amazon Redshift and helps prospects construct scalable analytical options. He has greater than 17 years of expertise in several database and knowledge warehousing applied sciences. He’s keen about automating and fixing buyer issues with cloud options.

Sidhanth Muralidhar is a Principal Technical Account Supervisor at AWS. He works with giant enterprise prospects who run their workloads on AWS. He’s keen about working with prospects and serving to them architect workloads for value, reliability, efficiency, and operational excellence at scale of their cloud journey. He has a eager curiosity in knowledge analytics as properly.

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