Workday Prism Analytics enables data discovery and interactive Business Intelligence analysis for Workday customers. Workday is a “pure SaaS” company, providing a suite of Financial and HCM (Human Capital Management) apps to about 2000 companies around the world, including more than 30% from Fortune-500 list. There are significant business and technical challenges to support millions of concurrent users and hundreds of millions daily transactions. Using memory-centric graph-based architecture allowed to overcome most of these problems.
As Workday grew, data transactions from existing and new customers generated vast amounts of valuable and highly sensitive data. The next big challenge was to provide in-app analytics platform, which for the multiple types of accumulated data, and also would allow using blend in external datasets. Workday users wanted it to be super-fast, but also intuitive and easy-to-use both for the financial and HR analysts and for regular, less technical users. Existing backend technologies were not a good fit, so we turned to Apache Spark.
In this presentation, we will share the lessons we learned when building highly scalable multi-tenant analytics service for transactional data. We will start with the big picture and business requirements. Then describe the architecture with batch and interactive modules for data preparation, publishing, and query engine, noting the relevant Spark technologies. Then we will dive into the internals of Prism’s Query Engine, focusing on Spark SQL, DataFrames and Catalyst compiler features used. We will describe the issues we encountered while compiling and executing complex pipelines and queries, and how we use caching, sampling, and query compilation techniques to support interactive user experience.
Finally, we will share the future challenges for 2018 and beyond.
Navigating the Workday Analytics and Reporting EcosystemWorkday, Inc.
Learn how to maximize the reporting and analytics capability in Workday for finance and HR teams. This slide deck dives into the functionality of Workday Prism Analytics and Workday People Analytics.
Lightning-fast Analytics for Workday transactional dataPavel Hardak
Workday Prism Analytics enables data discovery and interactive Business Intelligence analysis for Workday customers. Workday is a "pure SaaS" company, providing a suite of Financial and HCM (Human Capital Management) apps to about 2000 companies around the world, including more than 30% from Fortune-500 list. There are significant business and technical challenges to support millions of concurrent users and hundreds of millions of daily transactions. Using memory-centric graph-based architecture allowed to overcome most of these problems. As Workday grew, data transactions from existing and new customers generated vast amounts of valuable and highly sensitive data. The next big challenge was to provide in-app analytics platform, which for the multiple types of accumulated data, and also would allow using blend-in external datasets. Workday users wanted it to be super-fast, but also intuitive and easy-to-use both for the financial and HR analysts and for regular, less technical users. Existing backend technologies were not a good fit, so we turned to Apache Spark. In this presentation, we will share the lessons we learned when building highly scalable multi-tenant analytics service for transactional data. We will start with the big picture and business requirements. Then describe the architecture with batch and interactive modules for data preparation, publishing, and query engine, noting the relevant Spark technologies. Then we will dive into the internals of Prism's Query Engine, focusing on Spark SQL, DataFrames and Catalyst compiler features used. We will describe the issues we encountered while compiling and executing complex pipelines and queries, and how we use caching, sampling, and query compilation techniques to support the interactive user experience. Finally, we will share the future challenges for 2018 and beyond.
Building End-to-End Delta Pipelines on GCPDatabricks
Delta has been powering many production pipelines at scale in the Data and AI space since it has been introduced for the past few years.
Built on open standards, Delta provides data reliability, enhances storage and query performance to support big data use cases (both batch and streaming), fast interactive queries for BI and enabling machine learning. Delta has matured over the past couple of years in both AWS and AZURE and has become the de-facto standard for organizations building their Data and AI pipelines.
In today’s talk, we will explore building end-to-end pipelines on the Google Cloud Platform (GCP). Through presentation, code examples and notebooks, we will build the Delta Pipeline from ingest to consumption using our Delta Bronze-Silver-Gold architecture pattern and show examples of Consuming the delta files using the Big Query Connector.
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Getting Started with Databricks SQL AnalyticsDatabricks
It has long been said that business intelligence needs a relational warehouse, but that view is changing. With the Lakehouse architecture being shouted from the rooftops, Databricks have released SQL Analytics, an alternative workspace for SQL-savvy users to interact with an analytics-tuned cluster. But how does it work? Where do you start? What does a typical Data Analyst’s user journey look like with the tool?
This session will introduce the new workspace and walk through the various key features – how you set up a SQL Endpoint, the query workspace, creating rich dashboards and connecting up BI tools such as Microsoft Power BI.
If you’re truly trying to create a Lakehouse experience that satisfies your SQL-loving Data Analysts, this is a tool you’ll need to be familiar with and include in your design patterns, and this session will set you on the right path.
Modernizing the Analytics and Data Science Lifecycle for the Scalable Enterpr...Data Con LA
Data Con LA 2020
Description
It’s no secret that the roots of Data Science date back to the 1960’s and were first mainstreamed in the 1990’s with the emergence of Data Mining. This occurred when commercially affordable computers started offering the horsepower and storage necessary to perform advanced statistics to scale.
However, the words “to scale” have evolved over time. The leap to “Big Data” is only one serial aspect of growth. Beyond the typical 1-off studies that catalyzed the field of Data Mining, Data Science now fulfills enterprise and multi-enterprise use cases spanning much broader and deeper data sets and integrations. For example, AI and Machine Learning frameworks can interoperate with a variety of other systems to drive alerting, feedback loops, predictive frameworks, prescriptive engines, continual learning, and more. The deployment of AI/ML processes themselves often involves integration with contemporary DevOps tools.
Now segue to SEAL – the Scalable Enterprise Analytic Lifecycle. In this presentation, you’ll learn how to cover the major bases of a modern Data Science projects – and Citizen Data Science as well – from conception, learning, and evaluation through integration, implementation, monitoring, and continual improvement. And as the name implies, your deployments will be performant and scale as expected in today’s environments.
Speaker
Jeff Bertman, CTO, Dfuse Technologies
Navigating the Workday Analytics and Reporting EcosystemWorkday, Inc.
Learn how to maximize the reporting and analytics capability in Workday for finance and HR teams. This slide deck dives into the functionality of Workday Prism Analytics and Workday People Analytics.
Lightning-fast Analytics for Workday transactional dataPavel Hardak
Workday Prism Analytics enables data discovery and interactive Business Intelligence analysis for Workday customers. Workday is a "pure SaaS" company, providing a suite of Financial and HCM (Human Capital Management) apps to about 2000 companies around the world, including more than 30% from Fortune-500 list. There are significant business and technical challenges to support millions of concurrent users and hundreds of millions of daily transactions. Using memory-centric graph-based architecture allowed to overcome most of these problems. As Workday grew, data transactions from existing and new customers generated vast amounts of valuable and highly sensitive data. The next big challenge was to provide in-app analytics platform, which for the multiple types of accumulated data, and also would allow using blend-in external datasets. Workday users wanted it to be super-fast, but also intuitive and easy-to-use both for the financial and HR analysts and for regular, less technical users. Existing backend technologies were not a good fit, so we turned to Apache Spark. In this presentation, we will share the lessons we learned when building highly scalable multi-tenant analytics service for transactional data. We will start with the big picture and business requirements. Then describe the architecture with batch and interactive modules for data preparation, publishing, and query engine, noting the relevant Spark technologies. Then we will dive into the internals of Prism's Query Engine, focusing on Spark SQL, DataFrames and Catalyst compiler features used. We will describe the issues we encountered while compiling and executing complex pipelines and queries, and how we use caching, sampling, and query compilation techniques to support the interactive user experience. Finally, we will share the future challenges for 2018 and beyond.
Building End-to-End Delta Pipelines on GCPDatabricks
Delta has been powering many production pipelines at scale in the Data and AI space since it has been introduced for the past few years.
Built on open standards, Delta provides data reliability, enhances storage and query performance to support big data use cases (both batch and streaming), fast interactive queries for BI and enabling machine learning. Delta has matured over the past couple of years in both AWS and AZURE and has become the de-facto standard for organizations building their Data and AI pipelines.
In today’s talk, we will explore building end-to-end pipelines on the Google Cloud Platform (GCP). Through presentation, code examples and notebooks, we will build the Delta Pipeline from ingest to consumption using our Delta Bronze-Silver-Gold architecture pattern and show examples of Consuming the delta files using the Big Query Connector.
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Getting Started with Databricks SQL AnalyticsDatabricks
It has long been said that business intelligence needs a relational warehouse, but that view is changing. With the Lakehouse architecture being shouted from the rooftops, Databricks have released SQL Analytics, an alternative workspace for SQL-savvy users to interact with an analytics-tuned cluster. But how does it work? Where do you start? What does a typical Data Analyst’s user journey look like with the tool?
This session will introduce the new workspace and walk through the various key features – how you set up a SQL Endpoint, the query workspace, creating rich dashboards and connecting up BI tools such as Microsoft Power BI.
If you’re truly trying to create a Lakehouse experience that satisfies your SQL-loving Data Analysts, this is a tool you’ll need to be familiar with and include in your design patterns, and this session will set you on the right path.
Modernizing the Analytics and Data Science Lifecycle for the Scalable Enterpr...Data Con LA
Data Con LA 2020
Description
It’s no secret that the roots of Data Science date back to the 1960’s and were first mainstreamed in the 1990’s with the emergence of Data Mining. This occurred when commercially affordable computers started offering the horsepower and storage necessary to perform advanced statistics to scale.
However, the words “to scale” have evolved over time. The leap to “Big Data” is only one serial aspect of growth. Beyond the typical 1-off studies that catalyzed the field of Data Mining, Data Science now fulfills enterprise and multi-enterprise use cases spanning much broader and deeper data sets and integrations. For example, AI and Machine Learning frameworks can interoperate with a variety of other systems to drive alerting, feedback loops, predictive frameworks, prescriptive engines, continual learning, and more. The deployment of AI/ML processes themselves often involves integration with contemporary DevOps tools.
Now segue to SEAL – the Scalable Enterprise Analytic Lifecycle. In this presentation, you’ll learn how to cover the major bases of a modern Data Science projects – and Citizen Data Science as well – from conception, learning, and evaluation through integration, implementation, monitoring, and continual improvement. And as the name implies, your deployments will be performant and scale as expected in today’s environments.
Speaker
Jeff Bertman, CTO, Dfuse Technologies
Data Architecture Best Practices for Advanced AnalyticsDATAVERSITY
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
Building a Federated Data Directory Platform for Public HealthDatabricks
Healthcare directories underpin most healthcare systems around the world and is often a core component that enables initiatives like ‘Care Coordination’.
Getting started with with SharePoint SyntexDrew Madelung
SharePoint Syntex brings advanced content services solutions into your existing SharePoint environment but is it something that will help you? In this session we will go through what SharePoint Syntex is, how it works, and why it could be an important part of your enterprise in Microsoft 365.
Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
Data Lakes are meant to support many of the same analytics capabilities of Data Warehouses while overcoming some of the core problems. Yet Data Lakes have a distinctly different technology base. This webinar will provide an overview of the standard architecture components of Data Lakes.
This will include:
The Lab and the factory
The base environment for batch analytics
Critical governance components
Additional components necessary for real-time analytics and ingesting streaming data
How to Utilize MLflow and Kubernetes to Build an Enterprise ML PlatformDatabricks
In large enterprises, large solutions are sometimes required to tackle even the smallest tasks and ML is no different. At Comcast we are building a comprehensive, configuration based, continuously integrated and deployed platform for data pipeline transformations, model development and deployment. This is accomplished using a range of tools and frameworks such as Databricks, MLflow, Apache Spark and others. With a Databricks environment used by hundreds of researchers and petabytes of data, scale is critical to Comcast, so making it all work together in a frictionless experience is a high priority. The platform consists of a number of components: an abstraction for data pipelines and transformation to allow our data scientists the freedom to combine the most appropriate algorithms from different frameworks , experiment tracking, project and model packaging using MLflow and model serving via the Kubeflow environment on Kubernetes. The architecture, progress and current state of the platform will be discussed as well as the challenges we had to overcome to make this platform work at Comcast scale. As a machine learning practitioner, you will gain knowledge in: an example of data pipeline abstraction; ways to package and track your ML project and experiments at scale; and how Comcast uses Kubeflow on Kubernetes to bring everything together.
Unable to attend Oracle OpenWorld to learn about the latest developments in Oracle EPM Cloud? It can be difficult to keep abreast of all the changes in this evolving landscape, but we’ve got you covered.
In our webinar, Perficient’s Oracle EPM leadership explored the current cloud offerings and what’s around the corner. Whether you are in IT or finance, your colleagues are driving digital transformation by including cloud in their performance management strategy.
Discussion covered:
-In-depth review of the Oracle EPM Cloud suite
-How the products can be integrated
-How SaaS products compare to on-premises editions
-Benefits of cloud strategies
Data Warehousing with Spark Streaming at ZalandoDatabricks
Zalandos AI-driven products and distributed landscape of analytical data marts cannot wait for long-running, hard-to-recover, monolithic batch jobs taking all night to calculate already outdated data. Modern data integration pipelines need to deliver fast and easy to consume data sets in high quality. Based on Spark Streaming and Delta, the central data warehousing team was able to deliver widely-used master data as S3 or Kafka streams and snapshots at the same time.
The talk will cover challenges in our fashion data platform and a detailed architectural deep dive about separation of integration from enrichment, providing streams as well as snapshots and feeding the data to distributed data marts. Finally, lessons learned and best practices about Delta’s MERGE command, Scala API vs Spark SQL and schema evolution give more insights and guidance for similar use cases.
What you need to know about Generative AI and Data Management?Denodo
Watch full webinar here: https://buff.ly/3UXy0A2
It should be no surprise that Generative AI will have a profound impact to data management in years to come. Much like other areas of the technology sector, the opportunities presented by GenAI will accelerate our efforts around all aspects of data management, including self-service, automation, data governance and security. On the other hand, it is also becoming clearer that to unleash the true potential of AI assistants powered by GenAI, we need novel implementation strategies and a reimagined data architecture. This presents an exhilarating yet challenging future, demanding innovative thinking and methodologies in data management.
Join us on this webinar to learn about:
- The opportunities and challenges presented by GenAI today.
- Exploiting GenAI to democratize data management.
- How to augment GenAI applications with corporate data and knowledge.
- How to get started.
Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
Democratizing Data Quality Through a Centralized PlatformDatabricks
Bad data leads to bad decisions and broken customer experiences. Organizations depend on complete and accurate data to power their business, maintain efficiency, and uphold customer trust. With thousands of datasets and pipelines running, how do we ensure that all data meets quality standards, and that expectations are clear between producers and consumers? Investing in shared, flexible components and practices for monitoring data health is crucial for a complex data organization to rapidly and effectively scale.
At Zillow, we built a centralized platform to meet our data quality needs across stakeholders. The platform is accessible to engineers, scientists, and analysts, and seamlessly integrates with existing data pipelines and data discovery tools. In this presentation, we will provide an overview of our platform’s capabilities, including:
Giving producers and consumers the ability to define and view data quality expectations using a self-service onboarding portal
Performing data quality validations using libraries built to work with spark
Dynamically generating pipelines that can be abstracted away from users
Flagging data that doesn’t meet quality standards at the earliest stage and giving producers the opportunity to resolve issues before use by downstream consumers
Exposing data quality metrics alongside each dataset to provide producers and consumers with a comprehensive picture of health over time
Cloudera - The Modern Platform for AnalyticsCloudera, Inc.
This presentation provides an overview of Cloudera and how a modern platform for Machine Learning and Analytics better enables a data-driven enterprise.
Effective Data Lakes: Challenges and Design Patterns (ANT316) - AWS re:Invent...Amazon Web Services
Data lakes are emerging as the most common architecture built in data-driven organizations today. A data lake enables you to store unstructured, semi-structured, or fully-structured raw data as well as processed data for different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning. Well-designed data lakes ensure that organizations get the most business value from their data assets. In this session, you learn about the common challenges and patterns for designing an effective data lake on the AWS Cloud, with wisdom distilled from various customer implementations. We walk through patterns to solve data lake challenges, like real-time ingestion, choosing a partitioning strategy, file compaction techniques, database replication to your data lake, handling mutable data, machine learning integration, security patterns, and more.
AWS re:Invent 2016: How to Build a Big Data Analytics Data Lake (LFS303)Amazon Web Services
For discovery-phase research, life sciences companies have to support infrastructure that processes millions to billions of transactions. The advent of a data lake to accomplish such a task is showing itself to be a stable and productive data platform pattern to meet the goal. We discuss how to build a data lake on AWS, using services and techniques such as AWS CloudFormation, Amazon EC2, Amazon S3, IAM, and AWS Lambda. We also review a reference architecture from Amgen that uses a data lake to aid in their Life Science Research.
Building Modern Data Platform with Microsoft AzureDmitry Anoshin
This presentation will cover Cloud history and Microsoft Azure Data Analytics capabilities. Moreover, it has a real-world example of DW modernization. Finally, we will check the alternative solution on Azure using Snowflake and Matillion ETL.
Amazon QuickSight is a fast, cloud-powered business intelligence (BI) service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. In this session, we demonstrate how you can point Amazon QuickSight to AWS data stores, flat files, or other third-party data sources and begin visualizing your data in minutes. We also introduce SPICE - a new Super-fast, Parallel, In-memory, Calculation Engine in Amazon QuickSight, which performs advanced calculations and render visualizations rapidly without requiring any additional infrastructure, SQL programming, or dimensional modeling, so you can seamlessly scale to hundreds of thousands of users and petabytes of data. Lastly, you will see how Amazon QuickSight provides you with smart visualizations and graphs that are optimized for your different data types, to ensure the most suitable and appropriate visualization to conduct your analysis, and how to share these visualization stories using the built-in collaboration tools.
Presented by: Matthew McClean, AWS Partner Solutions Architect, Amazon Web Services
Making Data Timelier and More Reliable with Lakehouse TechnologyMatei Zaharia
Enterprise data architectures usually contain many systems—data lakes, message queues, and data warehouses—that data must pass through before it can be analyzed. Each transfer step between systems adds a delay and a potential source of errors. What if we could remove all these steps? In recent years, cloud storage and new open source systems have enabled a radically new architecture: the lakehouse, an ACID transactional layer over cloud storage that can provide streaming, management features, indexing, and high-performance access similar to a data warehouse. Thousands of organizations including the largest Internet companies are now using lakehouses to replace separate data lake, warehouse and streaming systems and deliver high-quality data faster internally. I’ll discuss the key trends and recent advances in this area based on Delta Lake, the most widely used open source lakehouse platform, which was developed at Databricks.
ADV Slides: When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
Data Architecture Best Practices for Advanced AnalyticsDATAVERSITY
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
Building a Federated Data Directory Platform for Public HealthDatabricks
Healthcare directories underpin most healthcare systems around the world and is often a core component that enables initiatives like ‘Care Coordination’.
Getting started with with SharePoint SyntexDrew Madelung
SharePoint Syntex brings advanced content services solutions into your existing SharePoint environment but is it something that will help you? In this session we will go through what SharePoint Syntex is, how it works, and why it could be an important part of your enterprise in Microsoft 365.
Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
Data Lakes are meant to support many of the same analytics capabilities of Data Warehouses while overcoming some of the core problems. Yet Data Lakes have a distinctly different technology base. This webinar will provide an overview of the standard architecture components of Data Lakes.
This will include:
The Lab and the factory
The base environment for batch analytics
Critical governance components
Additional components necessary for real-time analytics and ingesting streaming data
How to Utilize MLflow and Kubernetes to Build an Enterprise ML PlatformDatabricks
In large enterprises, large solutions are sometimes required to tackle even the smallest tasks and ML is no different. At Comcast we are building a comprehensive, configuration based, continuously integrated and deployed platform for data pipeline transformations, model development and deployment. This is accomplished using a range of tools and frameworks such as Databricks, MLflow, Apache Spark and others. With a Databricks environment used by hundreds of researchers and petabytes of data, scale is critical to Comcast, so making it all work together in a frictionless experience is a high priority. The platform consists of a number of components: an abstraction for data pipelines and transformation to allow our data scientists the freedom to combine the most appropriate algorithms from different frameworks , experiment tracking, project and model packaging using MLflow and model serving via the Kubeflow environment on Kubernetes. The architecture, progress and current state of the platform will be discussed as well as the challenges we had to overcome to make this platform work at Comcast scale. As a machine learning practitioner, you will gain knowledge in: an example of data pipeline abstraction; ways to package and track your ML project and experiments at scale; and how Comcast uses Kubeflow on Kubernetes to bring everything together.
Unable to attend Oracle OpenWorld to learn about the latest developments in Oracle EPM Cloud? It can be difficult to keep abreast of all the changes in this evolving landscape, but we’ve got you covered.
In our webinar, Perficient’s Oracle EPM leadership explored the current cloud offerings and what’s around the corner. Whether you are in IT or finance, your colleagues are driving digital transformation by including cloud in their performance management strategy.
Discussion covered:
-In-depth review of the Oracle EPM Cloud suite
-How the products can be integrated
-How SaaS products compare to on-premises editions
-Benefits of cloud strategies
Data Warehousing with Spark Streaming at ZalandoDatabricks
Zalandos AI-driven products and distributed landscape of analytical data marts cannot wait for long-running, hard-to-recover, monolithic batch jobs taking all night to calculate already outdated data. Modern data integration pipelines need to deliver fast and easy to consume data sets in high quality. Based on Spark Streaming and Delta, the central data warehousing team was able to deliver widely-used master data as S3 or Kafka streams and snapshots at the same time.
The talk will cover challenges in our fashion data platform and a detailed architectural deep dive about separation of integration from enrichment, providing streams as well as snapshots and feeding the data to distributed data marts. Finally, lessons learned and best practices about Delta’s MERGE command, Scala API vs Spark SQL and schema evolution give more insights and guidance for similar use cases.
What you need to know about Generative AI and Data Management?Denodo
Watch full webinar here: https://buff.ly/3UXy0A2
It should be no surprise that Generative AI will have a profound impact to data management in years to come. Much like other areas of the technology sector, the opportunities presented by GenAI will accelerate our efforts around all aspects of data management, including self-service, automation, data governance and security. On the other hand, it is also becoming clearer that to unleash the true potential of AI assistants powered by GenAI, we need novel implementation strategies and a reimagined data architecture. This presents an exhilarating yet challenging future, demanding innovative thinking and methodologies in data management.
Join us on this webinar to learn about:
- The opportunities and challenges presented by GenAI today.
- Exploiting GenAI to democratize data management.
- How to augment GenAI applications with corporate data and knowledge.
- How to get started.
Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
Democratizing Data Quality Through a Centralized PlatformDatabricks
Bad data leads to bad decisions and broken customer experiences. Organizations depend on complete and accurate data to power their business, maintain efficiency, and uphold customer trust. With thousands of datasets and pipelines running, how do we ensure that all data meets quality standards, and that expectations are clear between producers and consumers? Investing in shared, flexible components and practices for monitoring data health is crucial for a complex data organization to rapidly and effectively scale.
At Zillow, we built a centralized platform to meet our data quality needs across stakeholders. The platform is accessible to engineers, scientists, and analysts, and seamlessly integrates with existing data pipelines and data discovery tools. In this presentation, we will provide an overview of our platform’s capabilities, including:
Giving producers and consumers the ability to define and view data quality expectations using a self-service onboarding portal
Performing data quality validations using libraries built to work with spark
Dynamically generating pipelines that can be abstracted away from users
Flagging data that doesn’t meet quality standards at the earliest stage and giving producers the opportunity to resolve issues before use by downstream consumers
Exposing data quality metrics alongside each dataset to provide producers and consumers with a comprehensive picture of health over time
Cloudera - The Modern Platform for AnalyticsCloudera, Inc.
This presentation provides an overview of Cloudera and how a modern platform for Machine Learning and Analytics better enables a data-driven enterprise.
Effective Data Lakes: Challenges and Design Patterns (ANT316) - AWS re:Invent...Amazon Web Services
Data lakes are emerging as the most common architecture built in data-driven organizations today. A data lake enables you to store unstructured, semi-structured, or fully-structured raw data as well as processed data for different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning. Well-designed data lakes ensure that organizations get the most business value from their data assets. In this session, you learn about the common challenges and patterns for designing an effective data lake on the AWS Cloud, with wisdom distilled from various customer implementations. We walk through patterns to solve data lake challenges, like real-time ingestion, choosing a partitioning strategy, file compaction techniques, database replication to your data lake, handling mutable data, machine learning integration, security patterns, and more.
AWS re:Invent 2016: How to Build a Big Data Analytics Data Lake (LFS303)Amazon Web Services
For discovery-phase research, life sciences companies have to support infrastructure that processes millions to billions of transactions. The advent of a data lake to accomplish such a task is showing itself to be a stable and productive data platform pattern to meet the goal. We discuss how to build a data lake on AWS, using services and techniques such as AWS CloudFormation, Amazon EC2, Amazon S3, IAM, and AWS Lambda. We also review a reference architecture from Amgen that uses a data lake to aid in their Life Science Research.
Building Modern Data Platform with Microsoft AzureDmitry Anoshin
This presentation will cover Cloud history and Microsoft Azure Data Analytics capabilities. Moreover, it has a real-world example of DW modernization. Finally, we will check the alternative solution on Azure using Snowflake and Matillion ETL.
Amazon QuickSight is a fast, cloud-powered business intelligence (BI) service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. In this session, we demonstrate how you can point Amazon QuickSight to AWS data stores, flat files, or other third-party data sources and begin visualizing your data in minutes. We also introduce SPICE - a new Super-fast, Parallel, In-memory, Calculation Engine in Amazon QuickSight, which performs advanced calculations and render visualizations rapidly without requiring any additional infrastructure, SQL programming, or dimensional modeling, so you can seamlessly scale to hundreds of thousands of users and petabytes of data. Lastly, you will see how Amazon QuickSight provides you with smart visualizations and graphs that are optimized for your different data types, to ensure the most suitable and appropriate visualization to conduct your analysis, and how to share these visualization stories using the built-in collaboration tools.
Presented by: Matthew McClean, AWS Partner Solutions Architect, Amazon Web Services
Making Data Timelier and More Reliable with Lakehouse TechnologyMatei Zaharia
Enterprise data architectures usually contain many systems—data lakes, message queues, and data warehouses—that data must pass through before it can be analyzed. Each transfer step between systems adds a delay and a potential source of errors. What if we could remove all these steps? In recent years, cloud storage and new open source systems have enabled a radically new architecture: the lakehouse, an ACID transactional layer over cloud storage that can provide streaming, management features, indexing, and high-performance access similar to a data warehouse. Thousands of organizations including the largest Internet companies are now using lakehouses to replace separate data lake, warehouse and streaming systems and deliver high-quality data faster internally. I’ll discuss the key trends and recent advances in this area based on Delta Lake, the most widely used open source lakehouse platform, which was developed at Databricks.
ADV Slides: When and How Data Lakes Fit into a Modern Data ArchitectureDATAVERSITY
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
Develop a Custom Data Solution Architecture with NorthBayAmazon Web Services
Organizations that have vast amounts of data in legacy applications often experience difficulties delivering that data to business unit end-users. Register to learn how Eliza Corporation and Scholastic overcame this challenge by leveraging a Data Lake solution from NorthBay on AWS to optimize data analytics and provide greater visibility. AWS and NorthBay will give you an in-depth overview of how you can use a Data Lake in conjunction with your existing on-premises or cloud-based Data Warehouse. NorthBay helps organizations scale their ETL and data warehousing workloads using Amazon EMR and Amazon Redshift. Join us to learn: • Best practices for using a Data Lake in conjunction with your existing data warehouse • The key aspects of introducing agile and scrum methodologies into an enterprise • The most impactful cost-savings levers that are addressed via a cloud data warehouse migration
Who should attend: Heads of Analytics, Heads of BI, Analytics Managers, BI Teams, Senior Analysts
Best Practices for Building and Deploying Data Pipelines in Apache SparkDatabricks
Many data pipelines share common characteristics and are often built in similar but bespoke ways, even within a single organisation. In this talk, we will outline the key considerations which need to be applied when building data pipelines, such as performance, idempotency, reproducibility, and tackling the small file problem. We’ll work towards describing a common Data Engineering toolkit which separates these concerns from business logic code, allowing non-Data-Engineers (e.g. Business Analysts and Data Scientists) to define data pipelines without worrying about the nitty-gritty production considerations.
We’ll then introduce an implementation of such a toolkit in the form of Waimak, our open-source library for Apache Spark (https://github.com/CoxAutomotiveDataSolutions/waimak), which has massively shortened our route from prototype to production. Finally, we’ll define new approaches and best practices about what we believe is the most overlooked aspect of Data Engineering: deploying data pipelines.
Managing data analytics in a hybrid cloudKaran Singh
We’ll talk about the changes in the industry that customers are faced with and how Red Hat Hyperconverged Infrastructure can address those challenges . Our customers are struggling not only to manage the growth of big data (structured and unstructured), but also to reap timely business insights from their data using their existing data infrastructure like monolithic Hadoop clusters. This often leads to alternative approaches that often lead to disappointing results.
Today, data lakes are widely used and have become extremely affordable as data volumes have grown. However, they are only meant for storage and by themselves provide no direct value. With up to 80% of data stored in the data lake today, how do you unlock the value of the data lake? The value lies in the compute engine that runs on top of a data lake.
Join us for this webinar where Ahana co-founder and Chief Product Officer Dipti Borkar will discuss how to unlock the value of your data lake with the emerging Open Data Lake analytics architecture.
Dipti will cover:
-Open Data Lake analytics - what it is and what use cases it supports
-Why companies are moving to an open data lake analytics approach
-Why the open source data lake query engine Presto is critical to this approach
Enabling Key Business Advantage from Big Data through Advanced Ingest Process...StampedeCon
At StampedeCon 2014, Ronald Indeck (VelociData), "Enabling Key Business Advantage from Big Data through Advanced Ingest Processing."
All too often we see critical data dumped into a “Data Lake” causing the data waters to stagnate and become a “Data Swamp”. We have found that many data transformation, quality, and security processes can be addressed a priori on ingest to enhance goodness and improve accessibility to the data. Data can still be stored in raw form if desired but this processing on ingest can unlock operational effectiveness and competitive advantage by integrating fresh and historical data and enable the full potential of the data. We will discuss the underpinnings of stream processing engines, review several relevant business use cases, and discuss future applications.
The Data World Distilled
Understanding how the data world works in the Big Data era
I created this slide deck as a learning tool for new employees, I figured I would post it in case it can help others understand the data space.
This slide deck covers:
- Big Data
- Data Warehouses
- ETL/Data Integration
- Business Intelligence and Analytics
- Data Quality
- Data Testing
- Data Governance
It provides a brief description along with key vendors in the space.
Transform your DBMS to drive engagement innovation with Big DataAshnikbiz
Erik Baardse and Ajit Gadge from EDB Postgres presented on how to transform your DBMS in order to drive digital business. How Postgres enables you to support a wider range of workloads with your relational database which opens the Big Data doors. They also cover EnterpriseDB’s Strategy around Big Data which focuses on 3 areas and finally last but not the last how to find money in IT with Big Data and digital transformation
OPEN'17_4_Postgres: The Centerpiece for Modernising IT InfrastructuresKangaroot
Postgres is the leading open source database management system that is being developed by a very active community for more than 15 years. Gaby Schilders is Sales Engineer at EnterpriseDB, supplier of the EDB Postgres data platform.
Gaby Schilders, Sales Engineer at EnterpriseDB, will be explaining why companies take open source as the centerpiece for modernising their IT infrastructure, thus increasing their scalability and taking full advantage today's technologies offer them.
Powering a Startup with Apache Spark with Kevin KimSpark Summit
In Between (A mobile App for couples, downloaded 20M in Global), from daily batch for extracting metrics, analysis and dashboard. Spark is widely used by engineers and data analysts in Between, thanks to the performance and expendability of Spark, data operating has become extremely efficient. Entire team including Biz Dev, Global Operation, Designers are enjoying data results so Spark is empowering entire company for data driven operation and thinking. Kevin, Co-founder and Data Team leader of Between will be presenting how things are going in Between. Listeners will know how small and agile team is living with data (how we build organization, culture and technical base) after this presentation.
QuerySurge Slide Deck for Big Data Testing WebinarRTTS
This is a slide deck from QuerySurge's Big Data Testing webinar.
Learn why Testing is pivotal to the success of your Big Data Strategy .
Learn more at www.querysurge.com
The growing variety of new data sources is pushing organizations to look for streamlined ways to manage complexities and get the most out of their data-related investments. The companies that do this correctly are realizing the power of big data for business expansion and growth.
Learn why testing your enterprise's data is pivotal for success with big data, Hadoop and NoSQL. Learn how to increase your testing speed, boost your testing coverage (up to 100%), and improve the level of quality within your data warehouse - all with one ETL testing tool.
This information is geared towards:
- Big Data & Data Warehouse Architects,
- ETL Developers
- ETL Testers, Big Data Testers
- Data Analysts
- Operations teams
- Business Intelligence (BI) Architects
- Data Management Officers & Directors
You will learn how to:
- Improve your Data Quality
- Accelerate your data testing cycles
- Reduce your costs & risks
- Provide a huge ROI (as high as 1,300%)
Webinar: “ditch Oracle NOW”: Best Practices for Migrating to MongoDBMongoDB
This webinar will guide you through the best practices for migrating off of a relational database. Whether you are migrating an existing application, or considering using MongoDB in place of your traditional relational database for a new project, this webinar will get you to production faster, with less effort, cost and risk.
AWS re:Invent 2016: Migrating Your Data Warehouse to Amazon Redshift (DAT202)Amazon Web Services
Amazon Redshift is a fast, simple, cost-effective data warehousing solution, and in this session, we look at the tools and techniques you can use to migrate your existing data warehouse to Amazon Redshift. We will then present a case study on Scholastic’s migration to Amazon Redshift. Scholastic, a large 100-year-old publishing company, was running their business with older, on-premise, data warehousing and analytics solutions, which could not keep up with business needs and were expensive. Scholastic also needed to include new capabilities like streaming data and real time analytics. Scholastic migrated to Amazon Redshift, and achieved agility and faster time to insight while dramatically reducing costs. In this session, Scholastic will discuss how they achieved this, including options considered, technical architecture implemented, results, and lessons learned.
Data Lakehouse Symposium | Day 1 | Part 1Databricks
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
Data Lakehouse Symposium | Day 1 | Part 2Databricks
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
The world of data architecture began with applications. Next came data warehouses. Then text was organized into a data warehouse.
Then one day the world discovered a whole new kind of data that was being generated by organizations. The world found that machines generated data that could be transformed into valuable insights. This was the origin of what is today called the data lakehouse. The evolution of data architecture continues today.
Come listen to industry experts describe this transformation of ordinary data into a data architecture that is invaluable to business. Simply put, organizations that take data architecture seriously are going to be at the forefront of business tomorrow.
This is an educational event.
Several of the authors of the book Building the Data Lakehouse will be presenting at this symposium.
5 Critical Steps to Clean Your Data Swamp When Migrating Off of HadoopDatabricks
In this session, learn how to quickly supplement your on-premises Hadoop environment with a simple, open, and collaborative cloud architecture that enables you to generate greater value with scaled application of analytics and AI on all your data. You will also learn five critical steps for a successful migration to the Databricks Lakehouse Platform along with the resources available to help you begin to re-skill your data teams.
Learn to Use Databricks for Data ScienceDatabricks
Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.
Why APM Is Not the Same As ML MonitoringDatabricks
Application performance monitoring (APM) has become the cornerstone of software engineering allowing engineering teams to quickly identify and remedy production issues. However, as the world moves to intelligent software applications that are built using machine learning, traditional APM quickly becomes insufficient to identify and remedy production issues encountered in these modern software applications.
As a lead software engineer at NewRelic, my team built high-performance monitoring systems including Insights, Mobile, and SixthSense. As I transitioned to building ML Monitoring software, I found the architectural principles and design choices underlying APM to not be a good fit for this brand new world. In fact, blindly following APM designs led us down paths that would have been better left unexplored.
In this talk, I draw upon my (and my team’s) experience building an ML Monitoring system from the ground up and deploying it on customer workloads running large-scale ML training with Spark as well as real-time inference systems. I will highlight how the key principles and architectural choices of APM don’t apply to ML monitoring. You’ll learn why, understand what ML Monitoring can successfully borrow from APM, and hear what is required to build a scalable, robust ML Monitoring architecture.
The Function, the Context, and the Data—Enabling ML Ops at Stitch FixDatabricks
Autonomy and ownership are core to working at Stitch Fix, particularly on the Algorithms team. We enable data scientists to deploy and operate their models independently, with minimal need for handoffs or gatekeeping. By writing a simple function and calling out to an intuitive API, data scientists can harness a suite of platform-provided tooling meant to make ML operations easy. In this talk, we will dive into the abstractions the Data Platform team has built to enable this. We will go over the interface data scientists use to specify a model and what that hooks into, including online deployment, batch execution on Spark, and metrics tracking and visualization.
Stage Level Scheduling Improving Big Data and AI IntegrationDatabricks
In this talk, I will dive into the stage level scheduling feature added to Apache Spark 3.1. Stage level scheduling extends upon Project Hydrogen by improving big data ETL and AI integration and also enables multiple other use cases. It is beneficial any time the user wants to change container resources between stages in a single Apache Spark application, whether those resources are CPU, Memory or GPUs. One of the most popular use cases is enabling end-to-end scalable Deep Learning and AI to efficiently use GPU resources. In this type of use case, users read from a distributed file system, do data manipulation and filtering to get the data into a format that the Deep Learning algorithm needs for training or inference and then sends the data into a Deep Learning algorithm. Using stage level scheduling combined with accelerator aware scheduling enables users to seamlessly go from ETL to Deep Learning running on the GPU by adjusting the container requirements for different stages in Spark within the same application. This makes writing these applications easier and can help with hardware utilization and costs.
There are other ETL use cases where users want to change CPU and memory resources between stages, for instance there is data skew or perhaps the data size is much larger in certain stages of the application. In this talk, I will go over the feature details, cluster requirements, the API and use cases. I will demo how the stage level scheduling API can be used by Horovod to seamlessly go from data preparation to training using the Tensorflow Keras API using GPUs.
The talk will also touch on other new Apache Spark 3.1 functionality, such as pluggable caching, which can be used to enable faster dataframe access when operating from GPUs.
Simplify Data Conversion from Spark to TensorFlow and PyTorchDatabricks
In this talk, I would like to introduce an open-source tool built by our team that simplifies the data conversion from Apache Spark to deep learning frameworks.
Imagine you have a large dataset, say 20 GBs, and you want to use it to train a TensorFlow model. Before feeding the data to the model, you need to clean and preprocess your data using Spark. Now you have your dataset in a Spark DataFrame. When it comes to the training part, you may have the problem: How can I convert my Spark DataFrame to some format recognized by my TensorFlow model?
The existing data conversion process can be tedious. For example, to convert an Apache Spark DataFrame to a TensorFlow Dataset file format, you need to either save the Apache Spark DataFrame on a distributed filesystem in parquet format and load the converted data with third-party tools such as Petastorm, or save it directly in TFRecord files with spark-tensorflow-connector and load it back using TFRecordDataset. Both approaches take more than 20 lines of code to manage the intermediate data files, rely on different parsing syntax, and require extra attention for handling vector columns in the Spark DataFrames. In short, all these engineering frictions greatly reduced the data scientists’ productivity.
The Databricks Machine Learning team contributed a new Spark Dataset Converter API to Petastorm to simplify these tedious data conversion process steps. With the new API, it takes a few lines of code to convert a Spark DataFrame to a TensorFlow Dataset or a PyTorch DataLoader with default parameters.
In the talk, I will use an example to show how to use the Spark Dataset Converter to train a Tensorflow model and how simple it is to go from single-node training to distributed training on Databricks.
Scaling your Data Pipelines with Apache Spark on KubernetesDatabricks
There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal.
In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.
Scaling and Unifying SciKit Learn and Apache Spark PipelinesDatabricks
Pipelines have become ubiquitous, as the need for stringing multiple functions to compose applications has gained adoption and popularity. Common pipeline abstractions such as “fit” and “transform” are even shared across divergent platforms such as Python Scikit-Learn and Apache Spark.
Scaling pipelines at the level of simple functions is desirable for many AI applications, however is not directly supported by Ray’s parallelism primitives. In this talk, Raghu will describe a pipeline abstraction that takes advantage of Ray’s compute model to efficiently scale arbitrarily complex pipeline workflows. He will demonstrate how this abstraction cleanly unifies pipeline workflows across multiple platforms such as Scikit-Learn and Spark, and achieves nearly optimal scale-out parallelism on pipelined computations.
Attendees will learn how pipelined workflows can be mapped to Ray’s compute model and how they can both unify and accelerate their pipelines with Ray.
Sawtooth Windows for Feature AggregationsDatabricks
In this talk about zipline, we will introduce a new type of windowing construct called a sawtooth window. We will describe various properties about sawtooth windows that we utilize to achieve online-offline consistency, while still maintaining high-throughput, low-read latency and tunable write latency for serving machine learning features.We will also talk about a simple deployment strategy for correcting feature drift – due operations that are not “abelian groups”, that operate over change data.
We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark.
Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue
· Why?
o Custom queries on top a table; We load the data once and query N times
· Why not Structured Streaming
· Working Solution using Redis
Niche 2 : Distributed Counters
· Problems with Spark Accumulators
· Utilize Redis Hashes as distributed counters
· Precautions for retries and speculative execution
· Pipelining to improve performance
Re-imagine Data Monitoring with whylogs and SparkDatabricks
In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data.
In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.
Raven: End-to-end Optimization of ML Prediction QueriesDatabricks
Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components.
We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure.
This allows us to introduce optimization rules that
(i) reduce unnecessary computations by passing information between the data processing and ML operators
(ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and
(iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator.
We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.
Processing Large Datasets for ADAS Applications using Apache SparkDatabricks
Semantic segmentation is the classification of every pixel in an image/video. The segmentation partitions a digital image into multiple objects to simplify/change the representation of the image into something that is more meaningful and easier to analyze [1][2]. The technique has a wide variety of applications ranging from perception in autonomous driving scenarios to cancer cell segmentation for medical diagnosis.
Exponential growth in the datasets that require such segmentation is driven by improvements in the accuracy and quality of the sensors generating the data extending to 3D point cloud data. This growth is further compounded by exponential advances in cloud technologies enabling the storage and compute available for such applications. The need for semantically segmented datasets is a key requirement to improve the accuracy of inference engines that are built upon them.
Streamlining the accuracy and efficiency of these systems directly affects the value of the business outcome for organizations that are developing such functionalities as a part of their AI strategy.
This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.
Massive Data Processing in Adobe Using Delta LakeDatabricks
At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences.
What are we storing?
Multi Source – Multi Channel Problem
Data Representation and Nested Schema Evolution
Performance Trade Offs with Various formats
Go over anti-patterns used
(String FTW)
Data Manipulation using UDFs
Writer Worries and How to Wipe them Away
Staging Tables FTW
Datalake Replication Lag Tracking
Performance Time!
Machine Learning CI/CD for Email Attack DetectionDatabricks
Detecting advanced email attacks at scale is a challenging ML problem, particularly due to the rarity of attacks, adversarial nature of the problem, and scale of data. In order to move quickly and adapt to the newest threat we needed to build a Continuous Integration / Continuous Delivery pipeline for the entire ML detection stack. Our goal is to enable detection engineers and data scientists to make changes to any part of the stack including joined datasets for hydration, feature extraction code, detection logic, and develop/train ML models.
In this talk, we discuss why we decided to build this pipeline, how it is used to accelerate development and ensure quality, and dive into the nitty-gritty details of building such a system on top of an Apache Spark + Databricks stack.
Jeeves Grows Up: An AI Chatbot for Performance and QualityDatabricks
Sarah: CEO-Finance-Report pipeline seems to be slow today. Why
Jeeves: SparkSQL query dbt_fin_model in CEO-Finance-Report is running 53% slower on 2/28/2021. Data skew issue detected. Issue has not been seen in last 90 days.
Jeeves: Adding 5 more nodes to cluster recommended for CEO-Finance-Report to finish in its 99th percentile time of 5.2 hours.
Who is Jeeves? An experienced Spark developer? A seasoned administrator? No, Jeeves is a chatbot created to simplify data operations management for enterprise Spark clusters. This chatbot is powered by advanced AI algorithms and an intuitive conversational interface that together provide answers to get users in and out of problems quickly. Instead of being stuck to screens displaying logs and metrics, users can now have a more refreshing experience via a two-way conversation with their own personal Spark expert.
We presented Jeeves at Spark Summit 2019. In the two years since, Jeeves has grown up a lot. Jeeves can now learn continuously as telemetry information streams in from more and more applications, especially SQL queries. Jeeves now “knows” about data pipelines that have many components. Jeeves can also answer questions about data quality in addition to performance, cost, failures, and SLAs. For example:
Tom: I am not seeing any data for today in my Campaign Metrics Dashboard.
Jeeves: 3/5 validations failed on the cmp_kpis table on 2/28/2021. Run of pipeline cmp_incremental_daily failed on 2/28/2021.
This talk will give an overview of the newer capabilities of the chatbot, and how it now fits in a modern data stack with the emergence of new data roles like analytics engineers and machine learning engineers. You will learn how to build chatbots that tackle your complex data operations challenges.
Intuitive & Scalable Hyperparameter Tuning with Apache Spark + FugueDatabricks
Hyperparameter tuning is critical in model development. And its general form: parameter tuning with an objective function is also widely used in industry. On the other hand, Apache Spark can handle massive parallelism, and Apache Spark ML is a solid machine learning solution.
But we have not seen a general and intuitive distributed parameter tuning solution based on Apache Spark, why?
Not every tuning problem is on Apache Spark ML models. How can Apache Spark handle general models?
Not every tuning problem is a parallelizable grid or random search. Bayesian optimization is sequential, how can Apache Spark help in this case?
Not every tuning problem is single epoch, deep learning is not. How to fit algos such as hyperband and ASHA into Apache Spark?
Not every tuning problem is a machine learning problem, for example simulation + tuning is also common. How to generalize?
In this talk, we are going to show how using Fugue-Tune and Apache Spark together can eliminate these painpoints
Fugue-Tune like Fugue, is a “super framework” – an absraction layer unifying existing solutions such as Hyperopt and Optuna
It firstly models the general tuning problems, independent from machine learning
It is designed for both small and large scale problems. It can always fully parallelize the distributable part of a tuning problem
It works for both classical and deep learning models. With Fugue, running hyperband and ASHA becomes possible on Apache Spark.
In the demo, you will see how to do any type of tuning in a consistent, intuitive, scalable and minimal way. And you will see a live demo of the amazing performance.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
The affect of service quality and online reviews on customer loyalty in the E...
Lightning-Fast Analytics for Workday Transactional Data with Pavel Hardak and Ned Borisov
1. Pavel Hardak, Dir Product (Workday)
Ned Borisov (Ph.D), Sr Eng Mgr (Workday)
Lightning-Fast Analytics for
Workday Transactional Data
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2. Agenda
• Workday (Pavel H)
– Introduction to Workday
– Business challenges
– Platform for Transactional Apps
• Prism Analytics (Ned B)
– High Level Architecture
– Functional Modules
– Problems encountered
• Wrap-up (Pavel H)
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3. Workday
• Pure SaaS company (founded in 2005)
• Enterprise cloud apps – HCM and Finances
– Named as “Leader” in Gartner Magic Quadrants
• 2200+ customers, 175+ of Fortune 500
– Revenue: $2.1B, 36% YoY
• 8600+ employees worldwide
– #7 in FORTUNE "100 Best Companies to Work For”
– Pleasanton (HQ), San Mateo, San Francisco
– Boulder (CO), Dublin (Ireland), Victoria (BC), …
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7. Enterprise SaaS Challenges
• Concurrency
– From small to huge companies - every ‘worker’ is Workday user
• Reliability
– All users add and change data, generating many transactions
• Security
– Customers trust us with very confidential and private information
• Scalability
– Import several years from the previous system(s) and keep growing
• Speed
– Everybody wants fast response time J
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9. Object
Data Model
One Source for Data | One Security Model | One Experience | One Community
One Platform
Object Data Model
MetadataExtensibleDurable
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10. Reporting and
Analytics
One Source for Data | One Security Model | One Experience | One Community
One Platform
Reporting and Analytics
Dashboards CollaborationDistribution
11. But we want more…
• Import 3rd party data from external sources
– Unknown schema, need validations and cleansing
• Blend external data with Workday data
– Self Service Data Preparation
– Publish custom report sources
– Leverage the same security paradigms
• Data Discovery and Reporting
– Visualize, slice and dice by any dimension
– Perform faster than ever before
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13. Just add some …
• Water (?)
• Coffee (?)
• Energy drink (?)
• Apache Spark (!)
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14. Why Apache Spark
• Wanted to standardize on ONE data processing
technology which keeps evolving
• Needed extensibility to handle diverse use cases
• Scalability for on-disk views and in-memory
processing
• SQL processing is a HUGE plus
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15. High Level Prism Architecture
Report Queries Web UI Requests
Data Prep:
Interactive Transforms
HDFS
Workday Data
External Data
Samples
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Prism Server
16. Data Preparation
• A dataset may import
other datasets to
transform them (think
SQL View)
• Transforms include:
Filter, Join, Union,
Group By, etc.
• Example data are shown
to help verify the
transformation
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17. High Level Prism Architecture
Report Queries Web UI Requests
Data Prep:
Interactive Transforms
Lens Build:
Batch Transforms
HDFS
Workday Data
External Data
Samples
Data
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Prism Server
19. High Level Prism Architecture
Report Queries Web UI Requests
Query Engine:
Interactive BI Queries
Data Prep:
Interactive Transforms
Lens Build:
Batch Transforms
HDFS
Workday Data
External Data
Samples
Lens
Data
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Prism Server
20. Query Engine
• Analyst-driven Analysis
• Drag & drop chart creation
• Analyst defined computed fields
• Quick measurement aggregates
• Execution
• Query Engine executes the queries
• Interactive response is required
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21. High Level Prism Architecture
Report Queries Web UI Requests
Query Engine:
Interactive BI Queries
Data Prep:
Interactive Transforms
Lens Build:
Batch Transforms
HDFS
Workday Data
External Data
Samples
Lens
Data
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Prism Server
22. Spark in Prism Architecture
Prism Analytics launches and maintains lifecycle of three types
of Spark Applications
• Data Prep: a single (smaller) always-on Spark Application
– executes dataset transformations over small samples of data
• Lens Build: on-demand batch Application
– one per Lens Build process
– executes dataset transformations over full datasets
• Query Engine: a single (larger) always-on Application
– executes reporting queries over Lens data
– caches columns of Lenses in memory
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26. Row-Level Security
• Implemented as a dimension predicate. For example:
• In-List for supervisory_org could be very large
• More than one In-List
• Complex list values (e.g. nested conjunctions)
SELECT employee, SUM(quantity)
FROM Employee_Stock_Grants
WHERE supervisory_org IN (org1, org33, org_508)
GROUP BY employee;
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27. Scenario Details
• Customer Use Case
– Predicates with 10+ In-Lists
– Values between 6K and 12K
– Additional mix of conjunctions and disjunctions
• The Same Query
With Security = 100X Without Security
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28. Analysis
• Finding 1
– Parsing, planning and optimizing was taking ~27 seconds
– We did it 4 times
• Finding 2
– Major cause is the number of times the Catalyst
expressions (In and InSet) and their arguments were
being traversed and copied during plan analysis and
optimization.
– Minor cause is the amount of time spent in serializing
Scala’s TrieSet when shipping the plan to executors
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29. Solution
• Custom InSet-Like expressions (case classes)
– Hide the large literals sets through a curried-argument
– Resulted in queries going from 27 sec to 4 sec.
• Further Optimizations
– Our InSet-Like expression did not materialize the target
in-sets until after the plan was de-serialized on the
executors
– Resulted in improvement from 4 sec to 2 sec.
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30. Future Plans
• Better query latency for big datasets
• Deeper integration with reports and apps
• Integration with Kubernetes and AWS
• Improved scalability and concurrency
• Achieve ‘Zero DownTime’
…and much more I can not share here J
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31. Questions?
• IF ( you are looking for …
Great work culture &&
Technology challenges &&
Lots of fun and perks )
• THEN
Come to work with us!!!
workday.com/jobs
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32. More Info
• Building a modern data discovery and BI platform using
Apache Spark and Catalyst by Kevin Beyer
• Data Preparation in Workday Prism Analytics: Solving
Complex Problems the Workday Way by Jianneng Li
• Exploring Workday’s Architecture by James Pasley
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