Intuit Analytics Cloud 101

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The document discusses Intuit's vision to transform customers' lives by unleashing the power of data. It describes Intuit's Analytics Cloud (IAC), which provides a data platform and foundational services to derive value from data. The IAC allows for real-time and batch data ingestion from various sources and provides services like business lookups, unified customer profiles, and personalization. An example use case of using tax data to personalize the tax preparation experience is also mentioned. The document outlines Intuit's journey to building the IAC, including initially lifting existing systems to the cloud and now focusing on real-time streaming capabilities. Key practices for planning, deploying and managing the IAC are also listed.

Tecnología

Intuit Confidential and Proprietary 1
Tilmann Bruckhaus
Group Engineering Manager
Intuit Data Engineering and Analytics (IDEA)
Intuit Analytics Cloud 101

Intuit Confidential and Proprietary 2
Tilmann Bruckhaus
Group Engineering Manager Intuit
tilmann_bruckhaus@intuit.com
Follow me on twitter: @tillirix

Intuit’s big vision for data
Transform the lives of our customers by unleashing the
power of data
Customer
Inspired

Era of Windows Era of Web Era of the CloudEra of DOS
Spanning three decades of data
Compliant
data
Mobile First
1980s 1990s 2000s
• Employees: 150
• Customers: 1.3M customers
• Revenue: $33M
• Employees: 4,500
• Customers: 5.6M
• Revenue: $1.04B
• Employees: 7,700
• Customers: 37M
• Revenue: $4.2B
20162010
Regulatory data Transactional data Batch data Real time data Complex, secure data

Use Case 1: Tax Personalization & Ease of Use

Key Practices for Planning, Deploying and Managing
❏ Data security
❏ Data governance
❏ Managed metadata
❏ Committed schemas
❏ Auto Deploy
❏ Monitored Operations

The Big Data Lake Journey at Intuit
On Premise
First Generation IAC
Batch BatchCloud
Lift & Shift

˝
The Big Data Lake Journey at Intuit
Real-Time IAC in the Cloud
On Premise
First Generation IAC
Batch BatchCloud
Lift & Shift
Cloud Streaming

˝
The Big Data Lake Journey at Intuit
Real-Time IAC in the Cloud
Cloud Challenges
❏ Service Security
❏ Whitelisting
❏ Restacking
❏ Auto Deploy
❏ Service Monitoring
On Premise
First Generation IAC
Batch BatchCloud
Lift & Shift
Cloud Streaming

Questions?
Questions?
We’re Hiring!
data@intuit.com

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Dataflow with Apache NiFi

DataWorks Summit/Hadoop Summit

Many Organizations are currently processing various types of data and in different formats. Most often this data will be in free form, As the consumers of this data growing it’s imperative that this free-flowing data needs to adhere to a schema. It will help data consumers to have an expectation of about the type of data they are getting and also they will be able to avoid immediate impact if the upstream source changes its format. Having a uniform schema representation also gives the Data Pipeline a really easy way to integrate and support various systems that use different data formats. SchemaRegistry is a central repository for storing, evolving schemas. It provides an API & tooling to help developers and users to register a schema and consume that schema without having any impact if the schema changed. Users can tag different schemas and versions, register for notifications of schema changes with versions etc. In this talk, we will go through the need for a schema registry and schema evolution and showcase the integration with Apache NiFi, Apache Kafka, Apache Storm.

Schema Registry - Set you Data Free

DataWorks Summit/Hadoop Summit

There is increasing need for large-scale recommendation systems. Typical solutions rely on periodically retrained batch algorithms, but for massive amounts of data, training a new model could take hours. This is a problem when the model needs to be more up-to-date. For example, when recommending TV programs while they are being transmitted the model should take into consideration users who watch a program at that time. The promise of online recommendation systems is fast adaptation to changes, but methods of online machine learning from streams is commonly believed to be more restricted and hence less accurate than batch trained models. Combining batch and online learning could lead to a quickly adapting recommendation system with increased accuracy. However, designing a scalable data system for uniting batch and online recommendation algorithms is a challenging task. In this talk we present our experiences in creating such a recommendation engine with Apache Flink and Apache Spark.

Building a Large-Scale, Adaptive Recommendation Engine with Apache Flink and ...

DataWorks Summit/Hadoop Summit

DeepLearning is not just a hype - it outperforms state-of-the-art ML algorithms. One by one. In this talk we will show how DeepLearning can be used for detecting anomalies on IoT sensor data streams at high speed using DeepLearning4J on top of different BigData engines like ApacheSpark and ApacheFlink. Key in this talk is the absence of any large training corpus since we are using unsupervised machine learning - a domain current DL research threats step-motherly. As we can see in this demo LSTM networks can learn very complex system behavior - in this case data coming from a physical model simulating bearing vibration data. Once draw back of DeepLearning is that normally a very large labaled training data set is required. This is particularly interesting since we can show how unsupervised machine learning can be used in conjunction with DeepLearning - no labeled data set is necessary. We are able to detect anomalies and predict braking bearings with 10 fold confidence. All examples and all code will be made publicly available and open sources. Only open source components are used.

Real-Time Anomaly Detection using LSTM Auto-Encoders with Deep Learning4J on ...

DataWorks Summit/Hadoop Summit

QE automation for large systems is a great step forward in increasing system reliability. In the big-data world, multiple components have to come together to provide end-users with business outcomes. This means, that QE Automations scenarios need to be detailed around actual use cases, cross-cutting components. The system tests potentially generate large amounts of data on a recurring basis, verifying which is a tedious job. Given the multiple levels of indirection, the false positives of actual defects are higher, and are generally wasteful. At Hortonworks, we’ve designed and implemented Automated Log Analysis System - Mool, using Statistical Data Science and ML. Currently the work in progress has a batch data pipeline with a following ensemble ML pipeline which feeds into the recommendation engine. The system identifies the root cause of test failures, by correlating the failing test cases, with current and historical error records, to identify root cause of errors across multiple components. The system works in unsupervised mode with no perfect model/stable builds/source-code version to refer to. In addition the system provides limited recommendations to file/open past tickets and compares run-profiles with past runs.

Mool - Automated Log Analysis using Data Science and ML

DataWorks Summit/Hadoop Summit

Improving business performance is never easy! The Natixis Pack is like Rugby. Working together is key to scrum success. Our data journey would undoubtedly have been so much more difficult if we had not made the move together. This session is the story of how ‘The Natixis Pack’ has driven change in its current IT architecture so that legacy systems can leverage some of the many components in Hortonworks Data Platform in order to improve the performance of business applications. During this session, you will hear: • How and why the business and IT requirements originated • How we leverage the platform to fulfill security and production requirements • How we organize a community to: o Guard all the players, no one gets left on the ground! o Us the platform appropriately (Not every problem is eligible for Big Data and standard databases are not dead) • What are the most usable, the most interesting and the most promising technologies in the Apache Hadoop community We will finish the story of a successful rugby team with insight into the special skills needed from each player to win the match! DETAILS This session is part business, part technical. We will talk about infrastructure, security and project management as well as the industrial usage of Hive, HBase, Kafka, and Spark within an industrial Corporate and Investment Bank environment, framed by regulatory constraints.

How Hadoop Makes the Natixis Pack More Efficient

DataWorks Summit/Hadoop Summit

HBase hast established itself as the backend for many operational and interactive use-cases, powering well-known services that support millions of users and thousands of concurrent requests. In terms of features HBase has come a long way, overing advanced options such as multi-level caching on- and off-heap, pluggable request handling, fast recovery options such as region replicas, table snapshots for data governance, tuneable write-ahead logging and so on. This talk is based on the research for the an upcoming second release of the speakers HBase book, correlated with the practical experience in medium to large HBase projects around the world. You will learn how to plan for HBase, starting with the selection of the matching use-cases, to determining the number of servers needed, leading into performance tuning options. There is no reason to be afraid of using HBase, but knowing its basic premises and technical choices will make using it much more successful. You will also learn about many of the new features of HBase up to version 1.3, and where they are applicable.

HBase in Practice

DataWorks Summit/Hadoop Summit

There has been an explosion of data digitising our physical world – from cameras, environmental sensors and embedded devices, right down to the phones in our pockets. Which means that, now, companies have new ways to transform their businesses – both operationally, and through their products and services – by leveraging this data and applying fresh analytical techniques to make sense of it. But are they ready? The answer is “no” in most cases. In this session, we’ll be discussing the challenges facing companies trying to embrace the Analytics of Things, and how Teradata has helped customers work through and turn those challenges to their advantage.

The Challenge of Driving Business Value from the Analytics of Things (AOT)

DataWorks Summit/Hadoop Summit

In this talk, we will present a new distribution of Hadoop, Hops, that can scale the Hadoop Filesystem (HDFS) by 16X, from 70K ops/s to 1.2 million ops/s on Spotiy's industrial Hadoop workload. Hops is an open-source distribution of Apache Hadoop that supports distributed metadata for HSFS (HopsFS) and the ResourceManager in Apache YARN. HopsFS is the first production-grade distributed hierarchical filesystem to store its metadata normalized in an in-memory, shared nothing database. For YARN, we will discuss optimizations that enable 2X throughput increases for the Capacity scheduler, enabling scalability to clusters with >20K nodes. We will discuss the journey of how we reached this milestone, discussing some of the challenges involved in efficiently and safely mapping hierarchical filesystem metadata state and operations onto a shared-nothing, in-memory database. We will also discuss the key database features needed for extreme scaling, such as multi-partition transactions, partition-pruned index scans, distribution-aware transactions, and the streaming changelog API. Hops (www.hops.io) is Apache-licensed open-source and supports a pluggable database backend for distributed metadata, although it currently only support MySQL Cluster as a backend. Hops opens up the potential for new directions for Hadoop when metadata is available for tinkering in a mature relational database.

Breaking the 1 Million OPS/SEC Barrier in HOPS Hadoop

DataWorks Summit/Hadoop Summit

In high-risk manufacturing industries, regulatory bodies stipulate continuous monitoring and documentation of critical product attributes and process parameters. On the other hand, sensor data coming from production processes can be used to gain deeper insights into optimization potentials. By establishing a central production data lake based on Hadoop and using Talend Data Fabric as a basis for a unified architecture, the German pharmaceutical company HERMES Arzneimittel was able to cater to compliance requirements as well as unlock new business opportunities, enabling use cases like predictive maintenance, predictive quality assurance or open world analytics. Learn how the Talend Data Fabric enabled HERMES Arzneimittel to become data-driven and transform Big Data projects from challenging, hard to maintain hand-coding jobs to repeatable, future-proof integration designs. Talend Data Fabric combines Talend products into a common set of powerful, easy-to-use tools for any integration style: real-time or batch, big data or master data management, on-premises or in the cloud.

From Regulatory Process Verification to Predictive Maintenance and Beyond wit...

DataWorks Summit/Hadoop Summit

While you could be tempted assuming data is already safe in a single Hadoop cluster, in practice you have to plan for more. Questions like: "What happens if the entire datacenter fails?, or "How do I recover into a consistent state of data, so that applications can continue to run?" are not a all trivial to answer for Hadoop. Did you know that HDFS snapshots are handling open files not as immutable? Or that HBase snapshots are executed asynchronously across servers and therefore cannot guarantee atomicity for cross region updates (which includes tables)? There is no unified and coherent data backup strategy, nor is there tooling available for many of the included components to build such a strategy. The Hadoop distributions largely avoid this topic as most customers are still in the "single use-case" or PoC phase, where data governance as far as backup and disaster recovery (BDR) is concerned are not (yet) important. This talk first is introducing you to the overarching issue and difficulties of backup and data safety, looking at each of the many components in Hadoop, including HDFS, HBase, YARN, Oozie, the management components and so on, to finally show you a viable approach using built-in tools. You will also learn not to take this topic lightheartedly and what is needed to implement and guarantee a continuous operation of Hadoop cluster based solutions.

Backup and Disaster Recovery in Hadoop

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The action of the next cyber saga takes place in the mystical lands of the Asia-Pacific region, where the main characters began their digital activities in the middle of 2021 and qualitatively strengthened it in 2022. Corporate espionage, document theft, audio recordings, and data leaks from messaging platforms were all a matter of one day for Dark Pink. Their geographical focus may have started in the Asia-Pacific region, but their ambitions knew no bounds, targeting a European government ministry in a bold move to expand their portfolio. Their victim profile was as diverse as a UN meeting, targeting military organizations, government agencies, and even a religious organization. Because discrimination is not a fashionable agenda. In the world of cybercrime, they serve as a reminder that sometimes the most serious threats come in the most unassuming packages with a pink bow.

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Overkill Security

Dubai, known for its towering skyscrapers, luxurious lifestyle, and relentless pursuit of innovation, often finds itself in the global spotlight. However, amidst the glitz and glamour, the emirate faces its own set of challenges, including the occasional threat of flooding. In recent years, Dubai has experienced sporadic but significant floods, disrupting normalcy and posing unique challenges to its infrastructure. Among the critical nodes in this bustling metropolis is the Dubai International Airport, a vital hub connecting the world. This article delves into the intersection of Dubai flood events and the resilience demonstrated by the Dubai International Airport in the face of such challenges.

Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf

Orbitshub

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In the thrilling conclusion to 2023, ransomware groups had a banner year, really outdoing themselves in the "make everyone's life miserable" department. LockBit 3.0 took gold in the hacking olympics, followed by the plucky upstarts Clop and ALPHV/BlackCat. Apparently, 48% of organizations were feeling left out and decided to get in on the cyber attack action. Business services won the "most likely to get digitally mugged" award, with education and retail nipping at their heels. Hackers expanded their repertoire beyond boring old encryption to the much more exciting world of extortion. The US, UK and Canada took top honors in the "countries most likely to pay up" category. Bitcoins were the currency of choice for discerning hackers, because who doesn't love untraceable money?

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Keynote 2: APIs in 2030: The Risk of Technological Sleepwalk Paolo Malinverno, Growth Advisor - The Business of Technology Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...

apidays

Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows. We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases. This video focuses on the deployment of external web forms using Jotform for Bonterra Impact Management. This solution can be customized to your organization’s needs and deployed to support the common use cases below: - Intake and consent - Assessments - Surveys - Applications - Program registration Interested in deploying web form automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.

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Jeffrey Haguewood

Following the popularity of “Cloud Revolution: Exploring the New Wave of Serverless Spatial Data,” we’re thrilled to announce this much-anticipated encore webinar. In this sequel, we’ll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR. Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios. Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects. Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you’re building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME

Safe Software

Effective data discovery is crucial for maintaining compliance and mitigating risks in today's rapidly evolving privacy landscape. However, traditional manual approaches often struggle to keep pace with the growing volume and complexity of data. Join us for an insightful webinar where industry leaders from TrustArc and Privya will share their expertise on leveraging AI-powered solutions to revolutionize data discovery. You'll learn how to: - Effortlessly maintain a comprehensive, up-to-date data inventory - Harness code scanning insights to gain complete visibility into data flows leveraging the advantages of code scanning over DB scanning - Simplify compliance by leveraging Privya's integration with TrustArc - Implement proven strategies to mitigate third-party risks Our panel of experts will discuss real-world case studies and share practical strategies for overcoming common data discovery challenges. They'll also explore the latest trends and innovations in AI-driven data management, and how these technologies can help organizations stay ahead of the curve in an ever-changing privacy landscape.

TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery

TrustArc

AXA XL - Insurer Innovation Award Americas 2024

The Digital Insurer

Scaling API-first – The story of a global engineering organization Ian Reasor, Senior Computer Scientist - Adobe Radu Cotescu, Senior Computer Scientist - Adobe Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/

Apidays New York 2024 - Scaling API-first by Ian Reasor and Radu Cotescu, Adobe

apidays

Webinar Recording: https://www.panagenda.com/webinars/why-teams-call-analytics-is-critical-to-your-entire-business Nothing is as frustrating and noticeable as being in an important call and being unable to see or hear the other person. Not surprising then, that issues with Teams calls are among the most common problems users call their helpdesk for. Having in depth insight into everything relevant going on at the user’s device, local network, ISP and Microsoft itself during the call is crucial for good Microsoft Teams Call quality support. To ensure a quick and adequate solution and to ensure your users get the most out of their Microsoft 365. But did you know that ‘bad calls’ are also an excellent indicator of other problems arising? Precisely because it is so noticeable!? Like the canary in the mine, bad calls can be early indicators of problems. Problems that might otherwise not have been noticed for a while but can have a big impact on productivity and satisfaction. Join this session by Christoph Adler to learn how true Microsoft Teams call quality analytics helped other organizations troubleshoot bad calls and identify and fix problems that impacted Teams calls or the use of Microsoft365 in general. See what it can do to keep your users happy and productive! In this session we will cover - Why CQD data alone is not enough to troubleshoot call problems - The importance of attributing call problems to the right call participant - What call quality analytics can do to help you quickly find, fix-, and prevent problems - Why having retrospective detailed insights matters - Real life examples of how others have used Microsoft Teams call quality monitoring to problem shoot problems with their ISP, network, device health and more.

Why Teams call analytics are critical to your entire business

panagenda

Three things you will take away from the session: • How to run an effective tenant-to-tenant migration • Best practices for before, during, and after migration • Tips for using migration as a springboard to prepare for Copilot in Microsoft 365 Main ideas: Migration Overview: The presentation covers the current reality of cross-tenant migrations, the triggers, phases, best practices, and benefits of a successful tenant migration Considerations: When considering a migration, it is important to consider the migration scope, performance, customization, flexibility, user-friendly interface, automation, monitoring, support, training, scalability, data integrity, data security, cost, and licensing structure Next Wave: The next wave of change includes the launch of Copilot, which requires businesses to be prepared for upcoming changes related to Copilot and the cloud, and to consolidate data and tighten governance ShareGate: ShareGate can help with pre-migration analysis, configurable migration tool, and automated, end-user driven collaborative governance

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff

sammart93

Angeliki Cooney has spent over twenty years at the forefront of the life sciences industry, working out of Wynantskill, NY. She is highly regarded for her dedication to advancing the development and accessibility of innovative treatments for chronic diseases, rare disorders, and cancer. Her professional journey has centered on strategic consulting for biopharmaceutical companies, facilitating digital transformation, enhancing omnichannel engagement, and refining strategic commercial practices. Angeliki's innovative contributions include pioneering several software-as-a-service (SaaS) products for the life sciences sector, earning her three patents. As the Senior Vice President of Life Sciences at Avenga, Angeliki orchestrated the firm's strategic entry into the U.S. market. Avenga, a renowned digital engineering and consulting firm, partners with significant entities in the pharmaceutical and biotechnology fields. Her leadership was instrumental in expanding Avenga's client base and establishing its presence in the competitive U.S. market.

Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...

Angeliki Cooney

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WSO2

Dubai, often portrayed as a shimmering oasis in the desert, faces its own set of challenges, including the occasional threat of flooding. Despite its reputation for opulence and modernity, the emirate is not immune to the forces of nature. In recent years, Dubai has experienced sporadic but significant floods, testing the resilience of its infrastructure and communities. Among the critical lifelines in this bustling metropolis is the Dubai International Airport, a bustling hub that connects the city to the world. This article explores the intersection of Dubai flood events and the resilience demonstrated by the Dubai International Airport in the face of such challenges.

Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...

Orbitshub

Intuit Analytics Cloud 101

1. Intuit Confidential and Proprietary 1 Tilmann Bruckhaus Group Engineering Manager Intuit Data Engineering and Analytics (IDEA) Intuit Analytics Cloud 101

2. Intuit Confidential and Proprietary 2 Tilmann Bruckhaus Group Engineering Manager Intuit tilmann_bruckhaus@intuit.com Follow me on twitter: @tillirix

4. Intuit’s big vision for data Transform the lives of our customers by unleashing the power of data Customer Inspired

5. Era of Windows Era of Web Era of the CloudEra of DOS Spanning three decades of data Compliant data Mobile First 1980s 1990s 2000s • Employees: 150 • Customers: 1.3M customers • Revenue: $33M • Employees: 4,500 • Customers: 5.6M • Revenue: $1.04B • Employees: 7,700 • Customers: 37M • Revenue: $4.2B 20162010 Regulatory data Transactional data Batch data Real time data Complex, secure data

6. The Intuit Analytics Cloud (IAC) Data-Driven Users Data-Driven Products CG SBG Generic Data products derive value from IAC IntuitAnalyticsCloud Real Time Ingest Batch Ingest IAC = Data + Infrastructure + Foundational Services Business Lookup Unified Profile Personalization A/B Testing … Real Time Data Layer Intermediate Data Source Data Online Accountant

7. Use Case 1: Tax Personalization & Ease of Use

8. Key Practices for Planning, Deploying and Managing ❏ Data security ❏ Data governance ❏ Managed metadata ❏ Committed schemas ❏ Auto Deploy ❏ Monitored Operations

9. Key Practices for Planning, Deploying and Managing ❏ Data security ❏ Data governance ❏ Managed metadata ❏ Committed schemas ❏ Auto Deploy ❏ Monitored Operations

10. Key Practices for Planning, Deploying and Managing ❏ Data security ❏ Data governance ❏ Managed metadata ❏ Committed schemas ❏ Auto Deploy ❏ Monitored Operations

11. Key Practices for Planning, Deploying and Managing ❏ Data security ❏ Data governance ❏ Managed metadata ❏ Committed schemas ❏ Auto Deploy ❏ Monitored Operations

12. Key Practices for Planning, Deploying and Managing ❏ Data security ❏ Data governance ❏ Managed metadata ❏ Committed schemas ❏ Auto Deploy ❏ Monitored Operations

13. Key Practices for Planning, Deploying and Managing ❏ Data security ❏ Data governance ❏ Managed metadata ❏ Committed schemas ❏ Auto Deploy ❏ Monitored Operations

14. The Big Data Lake Journey at Intuit On Premise First Generation IAC Batch BatchCloud Lift & Shift

15. ˝ The Big Data Lake Journey at Intuit Real-Time IAC in the Cloud On Premise First Generation IAC Batch BatchCloud Lift & Shift Cloud Streaming

16. ˝ The Big Data Lake Journey at Intuit Real-Time IAC in the Cloud Cloud Challenges ❏ Service Security ❏ Whitelisting ❏ Restacking ❏ Auto Deploy ❏ Service Monitoring On Premise First Generation IAC Batch BatchCloud Lift & Shift Cloud Streaming

17. ˝ The Big Data Lake Journey at Intuit Real-Time IAC in the Cloud Cloud Challenges ❏ Service Security ❏ Whitelisting ❏ Restacking ❏ Auto Deploy ❏ Service Monitoring Streaming Challenges ❏ Stateful processes ❏ Performance ❏ Elastic scaling ❏ Replay ❏ Event ordering On Premise First Generation IAC Batch BatchCloud Lift & Shift Cloud Streaming

18. IAC Real-Time Ingest Architecture

19. IAC Architecture in the cloud

20. Event -Ingest Architecture

21. File -Ingest Architecture

22. Questions? Questions? We’re Hiring! data@intuit.com