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.

Technologie

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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Time Difference: How Tomorrow's Companies Will Outpace Today's

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Keeping Up with the ELK Stack: Elasticsearch, Kibana, Beats, and Logstash

Plus de DataWorks Summit/Hadoop Summit

Running Apache Spark & Apache Zeppelin in Production

DataWorks Summit/Hadoop Summit

State of Security: Apache Spark & Apache Zeppelin

DataWorks Summit/Hadoop Summit

Unleashing the Power of Apache Atlas with Apache Ranger

DataWorks Summit/Hadoop Summit

Enabling Digital Diagnostics with a Data Science Platform

DataWorks Summit/Hadoop Summit

Revolutionize Text Mining with Spark and Zeppelin

DataWorks Summit/Hadoop Summit

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DataWorks Summit/Hadoop Summit

Hadoop Crash Course

DataWorks Summit/Hadoop Summit

Data Science Crash Course

DataWorks Summit/Hadoop Summit

Apache Spark Crash Course

DataWorks Summit/Hadoop Summit

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

DataWorks Summit/Hadoop Summit

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State of Security: Apache Spark & Apache Zeppelin

Unleashing the Power of Apache Atlas with Apache Ranger

Enabling Digital Diagnostics with a Data Science Platform

Revolutionize Text Mining with Spark and Zeppelin

Double Your Hadoop Performance with Hortonworks SmartSense

Hadoop Crash Course

Data Science Crash Course

Apache Spark Crash Course

Dataflow with Apache NiFi

Schema Registry - Set you Data Free

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

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

Mool - Automated Log Analysis using Data Science and ML

How Hadoop Makes the Natixis Pack More Efficient

HBase in Practice

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

Breaking the 1 Million OPS/SEC Barrier in HOPS Hadoop

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

Backup and Disaster Recovery in Hadoop

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This presentations targets students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many developer tools, platforms & APIs? This comprehensive yet still high-level overview outlines the most impactful tools for where to run your code, store & analyze your data. It will also inspire you as to what's possible. This talk is 50 minutes in length.

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In this session, we will delve into strategic approaches for optimizing knowledge management within Microsoft 365, amidst the evolving landscape of Copilot. From leveraging automatic metadata classification and permission governance with SharePoint Premium, to unlocking Viva Engage for the cultivation of knowledge and communities, you will gain actionable insights to bolster your organization's knowledge-sharing initiatives. In this session, we will also explore how to facilitate solutions to enable your employees to find answers and expertise within Microsoft 365. You will leave equipped with practical techniques and a deeper understanding of how there is more to effective knowledge management than just enabling Copilot, but building actual solutions to prepare the knowledge that Copilot and your employees can use.

Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...

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With more memory available, system performance of three Dell devices increased, which can translate to a better user experience Conclusion When your system has plenty of RAM to meet your needs, you can efficiently access the applications and data you need to finish projects and to-do lists without sacrificing time and focus. Our test results show that with more memory available, three Dell PCs delivered better performance and took less time to complete the Procyon Office Productivity benchmark. These advantages translate to users being able to complete workflows more quickly and multitask more easily. Whether you need the mobility of the Latitude 5440, the creative capabilities of the Precision 3470, or the high performance of the OptiPlex Tower Plus 7010, configuring your system with more RAM can help keep processes running smoothly, enabling you to do more without compromising performance.

Boost PC performance: How more available memory can improve productivity

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MINDCTI Revenue Release Quarter One 2024

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Discord is a free app offering voice, video, and text chat functionalities, primarily catering to the gaming community. It serves as a hub for users to create and join servers tailored to their interests. Discord’s ecosystem comprises servers, each functioning as a distinct online community with its own channels dedicated to specific topics or activities. Users can engage in text-based discussions, voice calls, or video chats within these channels. Understanding Discord Servers Discord servers are virtual spaces where users congregate to interact, share content, and build communities. Servers may revolve around gaming, hobbies, interests, or fandoms, providing a platform for like-minded individuals to connect. Communication Features Discord offers a range of communication tools, including text channels for messaging, voice channels for real-time audio conversations, and video channels for face-to-face interactions. These features facilitate seamless communication and collaboration. What Does NSFW Mean? The acronym NSFW stands for “Not Safe For Work,” indicating content that may be inappropriate for professional or public settings. NSFW Content NSFW content encompasses material that is sexually explicit, violent, or otherwise graphic in nature. It often includes nudity, profanity, or depictions of sensitive topics.

Understanding Discord NSFW Servers A Guide for Responsible Users.pdf

UK Journal

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

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The value of a flexible API Management solution for Open Banking Steve Melan, Manager for IT Innovation and Architecture - State's and Saving's Bank of Luxembourg 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/

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Increase engagement and revenue with Muvi Live Paywall! In this presentation, we will explore the five key benefits of using Muvi Live Paywall to monetize your live streams. You'll learn how Muvi Live Paywall can help you: Monetize your live content easily: Set up pay-per-view access to your live streams and start generating revenue from your content. Increase audience engagement: Provide exclusive, premium content behind the paywall to keep your viewers engaged. Gain valuable viewer insights: Track viewer data and analytics to better understand your audience and tailor your content accordingly. Reduce content piracy: Muvi Live Paywall's security features help protect your content from unauthorized distribution. Streamline your workflow: The all-in-one platform simplifies the process of managing and monetizing your live streams. With Muvi Live Paywall, you can take control of your live stream monetization and create a sustainable business model for your content. Learn more about Muvi Live Paywall and start generating revenue from your live streams today!

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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