Data Con LA 2022 - Moving Data at Scale to AWS
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George Mansoor, Chief Information Systems Officer at California State University Overview of the CSU Data Architecture on moving on-prem ERP data to the AWS Cloud at scale using Delphix for Data Replication/Virtualization and AWS Data Migration Service (DMS) for data extracts
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- Atlas Data Federation to seamlessly query, transform, and aggregate data from one or more MongoDB Atlas databases, Atlas Data Lake and AWS S3 buckets
- Queryable Encryption lets you run expressive queries on fully randomized encrypted data to meet the most stringent security requirements
- Relational Migrator which analyzes your existing relational schemas and helps you design a new MongoDB schema.
- And more!
Data Con LA 2022 - Real world consumer segmentation
Data Con LA 2022 - Real world consumer segmentationData Con LA Jaysen Gillespie, Head of Analytics and Data Science at RTB House
1. Shopkick has over 30M downloads, but the userbase is very heterogeneous. Anecdotal evidence indicated a wide variety of users for whom the app holds long-term appeal.
2. Marketing and other teams challenged Analytics to get beyond basic summary statistics and develop a holistic segmentation of the userbase.
3. Shopkick's data science team used SQL and python to gather data, clean data, and then perform a data-driven segmentation using a k-means algorithm.
4. Interpreting the results is more work -- and more fun -- than running the algo itself. We'll discuss how we transform from ""segment 1"", ""segment 2"", etc. to something that non-analytics users (Marketing, Operations, etc.) could actually benefit from.
5. So what? How did team across Shopkick change their approach given what Analytics had discovered.
Data Con LA 2022 - Modernizing Analytics & AI for today's needs: Intuit Turbo...
Data Con LA 2022 - Modernizing Analytics & AI for today's needs: Intuit Turbo...Data Con LA Ravi Pillala, Chief Data Architect & Distinguished Engineer at Intuit
TurboTax is one of the well known consumer software brand which at its peak serves 385K+ concurrent users. In this session, We start with looking at how user behavioral data & tax domain events are captured in real time using the event bus and analyzed to drive real time personalization with various TurboTax data pipelines. We will also look at solutions performing analytics which make use of these events, with the help of Kafka, Apache Flink, Apache Beam, Spark, Amazon S3, Amazon EMR, Redshift, Athena and Amazon lambda functions. Finally, we look at how SageMaker is used to create the TurboTax model to predict if a customer is at risk or needs help.
Data Con LA 2022 - Collaborative Data Exploration using Conversational AI
Data Con LA 2022 - Collaborative Data Exploration using Conversational AIData Con LA Anand Ranganathan, Chief AI Officer at Unscrambl
Conversational AI is getting more and more widely used for customer support and employee support use-cases. In this session, I'm going to talk about how it can be extended for data analysis and data science use-cases ... i.e., how users can interact with a bot to ask analytical questions on data in relational databases.
This allows users to explore complex datasets using a combination of text and voice questions, in natural language, and then get back results in a combination of natural language and visualizations. Furthermore, it allows collaborative exploration of data by a group of users in a channel in platforms like Microsoft Teams, Slack or Google Chat.
For example, a group of users in a channel can ask questions to a bot in plain English like ""How many cases of Covid were there in the last 2 months by state and gender"" or ""Why did the number of deaths from Covid increase in May 2022"", and jointly look at the results that come back. This facilitates data awareness, data-driven collaboration and joint decision making among teams in enterprises and outside.
In this talk, I'll describe how we can bring together various features including natural-language understanding, NL-to-SQL translation, dialog management, data story-telling, semantic modeling of data and augmented analytics to facilitate collaborate exploration of data using conversational AI.
Data Con LA 2022 - Why Database Modernization Makes Your Data Decisions More ...
Data Con LA 2022 - Why Database Modernization Makes Your Data Decisions More ...Data Con LA Anil Inamdar, VP & Head of Data Solutions at Instaclustr
The most modernized enterprises utilize polyglot architecture, applying the best-suited database technologies to each of their organization's particular use cases. To successfully implement such an architecture, though, you need a thorough knowledge of the expansive NoSQL data technologies now available.
Attendees of this Data Con LA presentation will come away with:
-- A solid understanding of the decision-making process that should go into vetting NoSQL technologies and how to plan out their data modernization initiatives and migrations.
-- They will learn the types of functionality that best match the strengths of NoSQL key-value stores, graph databases, columnar databases, document-type databases, time-series databases, and more.
-- Attendees will also understand how to navigate database technology licensing concerns, and to recognize the types of vendors they'll encounter across the NoSQL ecosystem. This includes sniffing out open-core vendors that may advertise as “open source,"" but are driven by a business model that hinges on achieving proprietary lock-in.
-- Attendees will also learn to determine if vendors offer open-code solutions that apply restrictive licensing, or if they support true open source technologies like Hadoop, Cassandra, Kafka, OpenSearch, Redis, Spark, and many more that offer total portability and true freedom of use.
Data Con LA 2022 - Intro to Data Science
Data Con LA 2022 - Intro to Data ScienceData Con LA Zia Khan, Computer Systems Analyst and Data Scientist at LearningFuze
Data Science tutorial is designed for people who are new to Data Science. This is a beginner level session so no prior coding or technical knowledge is required. Just bring your laptop with WiFi capability. The session starts with a review of what is data science, the amount of data we generate and how companies are using that data to get insight. We will pick a business use case, define the data science process, followed by hands-on lab using python and Jupyter notebook. During the hands-on portion we will work with pandas, numpy, matplotlib and sklearn modules and use a machine learning algorithm to approach the business use case.
Data Con LA 2022 - How are NFTs and DeFi Changing Entertainment
Data Con LA 2022 - How are NFTs and DeFi Changing EntertainmentData Con LA Mariana Danilovic, Managing Director at Infiom, LLC
We will address:
(1) Community creation and engagement using tokens and NFTs
(2) Organization of DAO structures and ways to incentivize Web3 communities
(3) DeFi business models applied to Web3 ventures
(4) Why Metaverse matters for new entertainment and community engagement models.
Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...
Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...Data Con LA Curtis ODell, Global Director Data Integrity at Tricentis
Join me to learn about a new end-to-end data testing approach designed for modern data pipelines that fills dangerous gaps left by traditional data management tools—one designed to handle structured and unstructured data from any source. You'll hear how you can use unique automation technology to reach up to 90 percent test coverage rates and deliver trustworthy analytical and operational data at scale. Several real world use cases from major banks/finance, insurance, health analytics, and Snowflake examples will be presented.
Key Learning Objective
1. Data journeys are complex and you have to ensure integrity of the data end to end across this journey from source to end reporting for compliance
2. Data Management tools do not test data, they profile and monitor at best, and leave serious gaps in your data testing coverage
3. Automation with integration to DevOps and DataOps' CI/CD processes are key to solving this.
4. How this approach has impact in your vertical
Data Con LA 2022-Perfect Viral Ad prediction of Superbowl 2022 using Tease, T...
Data Con LA 2022-Perfect Viral Ad prediction of Superbowl 2022 using Tease, T...Data Con LA 1. The document discusses methods for predicting and engineering viral Super Bowl ads, including a panel-based analysis of video content characteristics and a deep learning model measuring social media effects.
2. It provides examples of ads from Super Bowl 2022 that scored well using these methods, such as BMW and Budweiser ads, and compares predicted viral rankings to actual results.
3. The document also demonstrates how to systematically test, tweak, and target an ad campaign like Bajaj Pulsar's to increase virality through modifications to title, thumbnail, tags and content based on audience feedback.
Data Con LA 2022- Embedding medical journeys with machine learning to improve...
Data Con LA 2022- Embedding medical journeys with machine learning to improve...Data Con LA Jai Bansal, Senior Manager, Data Science at Aetna
This talk describes an internal data product called Member Embeddings that facilitates modeling of member medical journeys with machine learning.
Medical claims are the key data source we use to understand health journeys at Aetna. Claims are the data artifacts that result from our members' interactions with the healthcare system. Claims contain data like the amount the provider billed, the place of service, and provider specialty. The primary medical information in a claim is represented in codes that indicate the diagnoses, procedures, or drugs for which a member was billed. These codes give us a semi-structured view into the medical reason for each claim and so contain rich information about members' health journeys. However, since the codes themselves are categorical and high-dimensional (10K cardinality), it's challenging to extract insight or predictive power directly from the raw codes on a claim.
To transform claim codes into a more useful format for machine learning, we turned to the concept of embeddings. Word embeddings are widely used in natural language processing to provide numeric vector representations of individual words.
We use a similar approach with our claims data. We treat each claim code as a word or token and use embedding algorithms to learn lower-dimensional vector representations that preserve the original high-dimensional semantic meaning.
This process converts the categorical features into dense numeric representations. In our case, we use sequences of anonymized member claim diagnosis, procedure, and drug codes as training data. We tested a variety of algorithms to learn embeddings for each type of claim code.
We found that the trained embeddings showed relationships between codes that were reasonable from the point of view of subject matter experts. In addition, using the embeddings to predict future healthcare-related events outperformed other basic features, making this tool an easy way to improve predictive model performance and save data scientist time.
Data Con LA 2022 - Data Streaming with Kafka
Data Con LA 2022 - Data Streaming with KafkaData Con LA Jie Chen, Manager Advisory, KPMG
Data is the new oil. However, many organizations have fragmented data in siloed line of businesses. In this topic, we will focus on identifying the legacy patterns and their limitations and introducing the new patterns packed by Kafka's core design ideas. The goal is to tirelessly pursue better solutions for organizations to overcome the bottleneck in data pipelines and modernize the digital assets for ready to scale their businesses. In summary, we will walk through three uses cases, recommend Dos and Donts, Take aways for Data Engineers, Data Scientist, Data architect in developing forefront data oriented skills.
Data Con LA 2022 - Building Field-level Lineage from Scratch for Modern Data ...
Data Con LA 2022 - Building Field-level Lineage from Scratch for Modern Data ...Data Con LA Xuanzi Han, Senior Software Engineer at Monte Carlo
For modern data teams, lineage is a critical component of the data pipeline root cause and impact analysis workflow, as well as a means of ensuring that data, models, and other data assets are healthy and reliable. That being said, the complexity of SQL queries can make it challenging to build lineage manually, particularly at the field level. Xuanzi Han, a member of Monte Carlo's data and product teams, tackled this challenge head-on by leveraging some of the most popular tools in the modern data stack, including dbt, Airflow, Snowflake, and ANother Tool for Language Recognition (ANTLR). In this talk, they share how they designed the data model, query parser, and larger database design for field-level lineage, highlighting learnings, wrong turns, and best practices developed along the way.
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Process Mining at Deutsche Bank - Journey
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定制学历(美国Purdue毕业证)普渡大学电子版毕业证
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Automation Platforms and Process Mining - success story
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Zig Websoftware creates process management software for housing associations. Their workflow solution is used by the housing associations to, for instance, manage the process of finding and on-boarding a new tenant once the old tenant has moved out of an apartment.
Paul Kooij shows how they could help their customer WoonFriesland to improve the housing allocation process by analyzing the data from Zig's platform. Every day that a rental property is vacant costs the housing association money.
But why does it take so long to find new tenants? For WoonFriesland this was a black box. Paul explains how he used process mining to uncover hidden opportunities to reduce the vacancy time by 4,000 days within just the first six months.
RAG Chatbot using AWS Bedrock and Streamlit Framework
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Mining a Global Trade Process with Data Science - Microsoft
Mining a Global Trade Process with Data Science - MicrosoftProcess mining Evangelist
The third speaker at Process Mining Camp 2018 was Dinesh Das from Microsoft. Dinesh Das is the Data Science manager in Microsoft’s Core Services Engineering and Operations organization.
Machine learning and cognitive solutions give opportunities to reimagine digital processes every day. This goes beyond translating the process mining insights into improvements and into controlling the processes in real-time and being able to act on this with advanced analytics on future scenarios.
Dinesh sees process mining as a silver bullet to achieve this and he shared his learnings and experiences based on the proof of concept on the global trade process. This process from order to delivery is a collaboration between Microsoft and the distribution partners in the supply chain. Data of each transaction was captured and process mining was applied to understand the process and capture the business rules (for example setting the benchmark for the service level agreement). These business rules can then be operationalized as continuous measure fulfillment and create triggers to act using machine learning and AI.
Using the process mining insight, the main variants are translated into Visio process maps for monitoring. The tracking of the performance of this process happens in real-time to see when cases become too late. The next step is to predict in what situations cases are too late and to find alternative routes.
As an example, Dinesh showed how machine learning could be used in this scenario. A TradeChatBot was developed based on machine learning to answer questions about the process. Dinesh showed a demo of the bot that was able to answer questions about the process by chat interactions. For example: “Which cases need to be handled today or require special care as they are expected to be too late?”. In addition to the insights from the monitoring business rules, the bot was also able to answer questions about the expected sequences of particular cases. In order for the bot to answer these questions, the result of the process mining analysis was used as a basis for machine learning.
定制(意大利Rimini毕业证)布鲁诺马代尔纳嘉雷迪米音乐学院学历认证
定制(意大利Rimini毕业证)布鲁诺马代尔纳嘉雷迪米音乐学院学历认证Taqyea 2025年新版意大利毕业证布鲁诺马代尔纳嘉雷迪米音乐学院文凭【q微1954292140】办理布鲁诺马代尔纳嘉雷迪米音乐学院毕业证(Rimini毕业证书)2025年新版毕业证书【q微1954292140】布鲁诺马代尔纳嘉雷迪米音乐学院offer/学位证、留信官方学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作【q微1954292140】Buy Conservatorio di Musica "B.Maderna G.Lettimi" Diploma购买美国毕业证，购买英国毕业证，购买澳洲毕业证，购买加拿大毕业证，以及德国毕业证，购买法国毕业证（q微1954292140）购买荷兰毕业证、购买瑞士毕业证、购买日本毕业证、购买韩国毕业证、购买新西兰毕业证、购买新加坡毕业证、购买西班牙毕业证、购买马来西亚毕业证等。包括了本科毕业证，硕士毕业证。
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Process Mining and Official Statistics - CBS
Process Mining and Official Statistics - CBSProcess mining Evangelist Johan Lammers from Statistics Netherlands has been a business analyst and statistical researcher for almost 30 years. In their business, processes have two faces: You can produce statistics about processes and processes are needed to produce statistics. As a government-funded office, the efficiency and the effectiveness of their processes is important to spend that public money well.
Johan takes us on a journey of how official statistics are made. One way to study dynamics in statistics is to take snapshots of data over time. A special way is the panel survey, where a group of cases is followed over time. He shows how process mining could test certain hypotheses much faster compared to statistical tools like SPSS.
Process Mining at Dimension Data - Jan vermeulen
Process Mining at Dimension Data - Jan vermeulenProcess mining Evangelist Dimension Data has over 30,000 employees in nine operating regions spread over all continents. They provide services from infrastructure sales to IT outsourcing for multinationals. As the Global Process Owner at Dimension Data, Jan Vermeulen is responsible for the standardization of the global IT services processes.
Jan shares his journey of establishing process mining as a methodology to improve process performance and compliance, to grow their business, and to increase the value in their operations. These three pillars form the foundation of Dimension Data's business case for process mining.
Jan shows examples from each of the three pillars and shares what he learned on the way. The growth pillar is particularly new and interesting, because Dimension Data was able to compete in a RfP process for a new customer by providing a customized offer after analyzing the customer's data with process mining.
Controlling Financial Processes at a Municipality
Controlling Financial Processes at a MunicipalityProcess mining Evangelist
The fourth speaker at Process Mining Camp 2018 was Wim Kouwenhoven from the City of Amsterdam. Amsterdam is well-known as the capital of the Netherlands and the City of Amsterdam is the municipality defining and governing local policies. Wim is a program manager responsible for improving and controlling the financial function.
A new way of doing things requires a different approach. While introducing process mining they used a five-step approach:
Step 1: Awareness
Introducing process mining is a little bit different in every organization. You need to fit something new to the context, or even create the context. At the City of Amsterdam, the key stakeholders in the financial and process improvement department were invited to join a workshop to learn what process mining is and to discuss what it could do for Amsterdam.
Step 2: Learn
As Wim put it, at the City of Amsterdam they are very good at thinking about something and creating plans, thinking about it a bit more, and then redesigning the plan and talking about it a bit more. So, they deliberately created a very small plan to quickly start experimenting with process mining in small pilot. The scope of the initial project was to analyze the Purchase-to-Pay process for one department covering four teams. As a result, they were able show that they were able to answer five key questions and got appetite for more.
Step 3: Plan
During the learning phase they only planned for the goals and approach of the pilot, without carving the objectives for the whole organization in stone. As the appetite was growing, more stakeholders were involved to plan for a broader adoption of process mining. While there was interest in process mining in the broader organization, they decided to keep focusing on making process mining a success in their financial department.
Step 4: Act
After the planning they started to strengthen the commitment. The director for the financial department took ownership and created time and support for the employees, team leaders, managers and directors. They started to develop the process mining capability by organizing training sessions for the teams and internal audit. After the training, they applied process mining in practice by deepening their analysis of the pilot by looking at e-invoicing, deleted invoices, analyzing the process by supplier, looking at new opportunities for audit, etc. As a result, the lead time for invoices was decreased by 8 days by preventing rework and by making the approval process more efficient. Even more important, they could further strengthen the commitment by convincing the stakeholders of the value.
Step 5: Act again
After convincing the stakeholders of the value you need to consolidate the success by acting again. Therefore, a team of process mining analysts was created to be able to meet the demand and sustain the success. Furthermore, new experiments were started to see how process mining could be used in three audits in 2018.
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Understanding Complex Development Processes
Understanding Complex Development ProcessesProcess mining Evangelist The fifth talk at Process Mining Camp was given by Olga Gazina and Daniel Cathala from Euroclear. As a data analyst at the internal audit department Olga helped Daniel, IT Manager, to make his life at the end of the year a bit easier by using process mining to identify key risks.
She applied process mining to the process from development to release at the Component and Data Management IT division. It looks like a simple process at first, but Daniel explains that it becomes increasingly complex when considering that multiple configurations and versions are developed, tested and released. It becomes even more complex as the projects affecting these releases are running in parallel. And on top of that, each project often impacts multiple versions and releases.
After Olga obtained the data for this process, she quickly realized that she had many candidates for the caseID, timestamp and activity. She had to find a perspective of the process that was on the right level, so that it could be recognized by the process owners. In her talk she takes us through her journey step by step and shows the challenges she encountered in each iteration. In the end, she was able to find the visualization that was hidden in the minds of the business experts.
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Data Con LA 2022 - Moving Data at Scale to AWS
- 1. Getting Data Cloud Ready at Scale Foundational Element in Supporting University and Campus Goals 1 George Mansoor, Chief Information Systems Officer
- 2. Agenda • A Focus On Why We Are Here • Modernizing Strategically • The Path Forward 2
- 3. A focus on Students Graduation Initiative 2025: Launched in 2015, it is an ambitious plan to increase graduation rates, eliminate completion and meet California’s workforce needs. Being strategic: Each campus and the Chancellors office uses this vision as the litmus test when spending scarce strategic initiatives. 3
- 4. Background on our ERP system • PeopleSoft ERP supporting our Finance, Student and HR data since 2001 • 23 Campus Solutions • 23 Human Resources, migrating to Single HR starting 2022 • Single Finance since 2011 • 23 campuses, and eight off-campus centers • Support to the largest public higher education institution - 485,500 students with 56,000 faculty and staff 4
- 5. Challenges of Accessing ERP Data • Students and faculty need improved engagement and expect a stellar and seamless experience • Data from the ERP provides critical information about our students (financial aid, enrollment, graduation requirements, hiring, etc.) Campuses need data from our ERP • Current PeopleSoft ERP is highly customized making integrations costly and time consuming. • ERP is aging and we have grown! • 2001: 370,000 students with 40,000 faculty and staff • Today: 485,000 students with 56,000 faculty and staff • To accommodate ERP gaps we have an assortment of systems – Especially true in the Student Information Systems where student engagement systems have grown. • Limited resources 5
- 6. Overview • Objective: CSU was looking to modernize how we process data and wanted to get our data “cloud ready” and “report ready” to take advantage of new capabilities that the cloud offered. • Problem: CSU data resides in on-prem legacy ERP (PeopleSoft/Oracle) systems
- 7. Cloud Ready Repository (CMS Data Lake) • Repository of raw copies of source system data • Today that is largely CMS data sources with plans to include other sources over time (CSULearn/SumTotal, Person Data Management (PDM)) • Data is stored in Apache Parquet format • Intended to facilitate the use of cloud-based data tools using CMS data • Built to support the CSU Data Lake 7 Our Data Lakes
- 8. Report Ready Repository (CSU Data Lake) • Fairly comprehensive data and reporting solution • Uses source data and transforms data into rich data collections • Data collections targeted towards reporting 8 Our Data Lakes
- 9. © 2017 Unisys Corporation. All rights reserved. 9 ON-PREM PROD AWS CMS Data Lake - Data as a Service CMS Data Lake CSU Data Lake VPC Peering Students Students by Term Students by Class Students by Degree Classes by Section Applications by Applicant
- 10. First Big Problem • Our biggest source of data is our ERP. They are on-prem and will stay there. • 47 production instances • Approx 1TB aggregate of “interesting” data. • Oracle RDBMS • OLTP Optimized How do we get this to the cloud?
- 11. Delphix Data Virtualization Platform Data virtualization decouples the database layer that sits between the storage and application layers in the application stack. Just like a hypervisor sits between the server and the OS to create a virtual server, database virtualization software sits between the database and the OS to abstract/virtualize the data store resources. Because database resources are virtualized, they require a much smaller storage footprint than the source database. Instead of making and moving new blocks of data, virtual data (virtual data copies) use pointers to data blocks, providing high-performance access to data already in place. 11
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- 13. AWS Database Migration Service (DMS) AWS Database Migration Service (AWS DMS) helps you migrate databases to AWS quickly and securely. The source database remains fully operational during the migration, minimizing downtime to applications that rely on the database. The AWS Database Migration Service can migrate your data to and from the most widely used commercial and open-source databases. 13
- 14. © 2017 Unisys Corporation. All rights reserved. 14 ON-PREMISE PRODUCTION CS HR FIN Oracle DB on Ec2 Campus Specific S3 buckets Continues Replication over Secure VPN • Data Gets Copied in to Encrypted S3 buckets • Historical Data is stored in date wise S3 bucket folders AWS CLOUD DMS Instances DMS Tasks S3 Copy CMS Data Lake - Data as a Service
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- 16. Questions 16