Accelerate Data Science Initiatives: Databricks & Privacera
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Accelerating Data Science Initiatives with Databricks’ Rapid SQL Analytics and Privacera’s Centralized Data Access Governance. Databricks’ SQL Analytics helps data teams consolidate and simplify their data architectures. With SQL Analytics, data teams can perform BI and SQL workloads on the same multi-cloud lakehouse architecture enabling data scientists to perform advanced analytics on unstructured and large-scale data. This session will explore how Privacera’s advanced security, privacy, and governance capabilities seamlessly integrate with Databricks’ unified SQL Analytics approach to provide single pane visibility of data analytics from a centralized location. Attendees will learn how to: Rapidly access data to run high-fidelity analytics Implement a fully secure solution that ensures productivity, while controlling data access at fine-grained levels (row, column, and file) Easily enable consistent access policies across all systems and applications Support true data transparency across enterprises Comply with stringent industry and privacy regulations like GDPR, LGPD, HIPAA, CCPA, PCI DSS, RTBF, and more with rich auditing and reporting
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Classic event, incident, problem and change management are ITSM practices that are getting integrated with DevOps/SRE and ML through a competency known as AIOps. Large streams of data generated through logs, metrics and traces are organized and computed using machine learning algorithms to extract insights on the anomalies of system behavior that could be impacting end-users and business transactions. Businesses cannot afford to see their end-users impacted by those anomalies and therefore would want to proactively predict the likelihood of systems regressing and take corrective action long before any material impact.
In this talk, we show the use of simple linear regression and multivariate linear regression techniques to predict the likelihood of system behavior resulting in one or two sigma of standard deviation. We show how to use FOSS tools to predict them using various decision trees that are integrated to high performing streaming platforms like Apache Flink, Apache Beam, Prometheus and Grafana which makes it a lot easier to visualize the various alerts and triage their way back to performing root cause analysis. These high performing systems are also backed by KAFKA for its streaming and distributed computing capabilities by partitioning the data for various staged analysis some of which can be done in parallel and concurrently based on the use cases. We present a fully integrated architecture that helps you realize a commercial AIOps capability without having to license expensive software products. The above open architecture allows you to implement various ML algorithms as needed and its agnostic to programming languages and tools.
The talk will combine various techniques with demos and is focused to practicing engineers and developers who are familiar with ML.
Introduction SQL Analytics on Lakehouse Architecture
This document provides an introduction and overview of SQL Analytics on Lakehouse Architecture. It discusses the instructor Doug Bateman's background and experience. The course goals are outlined as describing key features of a data Lakehouse, explaining how Delta Lake enables a Lakehouse architecture, and defining features of the Databricks SQL Analytics user interface. The course agenda is then presented, covering topics on Lakehouse Architecture, Delta Lake, and a Databricks SQL Analytics demo. Background is also provided on Lakehouse architecture, how it combines the benefits of data warehouses and data lakes, and its key features.
Migrating Your Data Platform At a High Growth Startup
Migrating their data platform from AWS EMR and notebooks to Databricks, Abnormal Security conducted a two-week proof-of-concept that was successful. They are now migrating jobs ranked by cost to Databricks' configuration framework over the first quarter to reduce costs by 50% while gaining improved usability, operational overhead, and ability to scale. The migration of their platform to a single environment using Databricks will allow them to build their first data lakehouse and gain additional future use cases as the company grows rapidly.
Feature store Overview St. Louis Big Data IDEA Meetup aug 2020
Slides from the August 2020 St. Louis Big Data IDEA Meetup discussing feature stores Adam Doyle presenting.
Semantic Image Logging Using Approximate Statistics & MLflow
As organizations launch complex multi-modal models into human-facing applications, data governance becomes both increasingly important, and difficult. Specifically, monitoring the underlying ML models for accuracy and reliability becomes a critical component of any data governance system. When complex data, such as image, text and video, is involved, monitoring model performance is particularly problematic given the lack of semantic information. In industries such as health care and automotive, fail-safes are needed for compliant performance and safety but access to validation data is in short supply, or in some cases, completely absent. However, to date, there have been no widely accessible approaches for monitoring semantic information in a performant manner.
In this talk, we will provide an overview of approximate statistical methods, how they can be used for monitoring, along with debugging data pipelines for detecting concept drift and out-of-distribution data in semantic-full data, such as images. We will walk through an open source library, whylogs, which combines Apache Spark and novel approaches to semantic data sketching. We will conclude with practical examples equipping ML practitioners with monitoring tools for computer vision, and semantic-full models.
Modularized ETL Writing with Apache Spark
Apache Spark has been an integral part of Stitch Fix’s compute infrastructure. Over the past five years, it has become our de facto standard for most ETL and heavy data processing needs and expanded our capabilities in the Data Warehouse.
Since all our writes to the Data Warehouse are through Apache Spark, we took advantage of that to add more modules that supplement ETL writing. Config driven and purposeful, these modules perform tasks onto a Spark Dataframe meant for a destination Hive table.
These are organized as a sequence of transformations on the Apache Spark dataframe prior to being written to the table.These include a process of journalizing. It is a process which helps maintain a non-duplicated historical record of mutable data associated with different parts of our business.
Data quality, another such module, is enabled on the fly using Apache Spark. Using Apache Spark we calculate metrics and have an adjacent service to help run quality tests for a table on the incoming data.
And finally, we cleanse data based on provided configurations, validate and write data into the warehouse. We have an internal versioning strategy in the Data Warehouse that allows us to know the difference between new and old data for a table.
Having these modules at the time of writing data allows cleaning, validation and testing of data prior to entering the Data Warehouse thus relieving us, programmatically, of most of the data problems. This talk focuses on ETL writing in Stitch Fix and describes these modules that help our Data Scientists on a daily basis.
Delivering Insights from 20M+ Smart Homes with 500M+ Devices
We started out processing big data using AWS S3, EMR clusters, and Athena to serve Analytics data extracts to Tableau BI.
However as our data and teams sizes increased, Avro schemas from source data evolved, and we attempted to serve analytics data through Web apps, we hit a number of limitations in the AWS EMR, Glue/Athena approach.
This is a story of how we scaled out our data processing and boosted team productivity to meet our current demand for insights from 20M+ Smart Homes and 500M+ devices across the globe, from numerous internal business teams and our 150+ CSP partners.
We will describe lessons learnt and best practices established as we enabled our teams with DataBricks autoscaling Job clusters and Notebooks and migrated our Avro/Parquet data to use MetaStore, SQL Endpoints and SQLA Console, while charting the path to the Delta lake…
Leveraging Apache Spark to Develop AI-Enabled Products and Services at Bosch
The Bosch Center for Artificial Intelligence provides AI services to Bosch’s business units and manufacturing plants. We strive to generate value for our customers by deploying machine learning in their products, services, and processes across different domains such Manufacturing, Engineering, Supply Chain Management, as well as Intelligent Services.
Scaling and Modernizing Data Platform with Databricks
This document summarizes Atlassian's adoption of Databricks to manage their growing data pipelines and platforms. It discusses the challenges they faced with their previous architecture around development time, collaboration, and costs. With Databricks, Atlassian was able to build scalable data pipelines using notebooks and connectors, orchestrate workflows with Airflow, and provide self-service analytics and machine learning to teams while reducing infrastructure costs and data engineering dependencies. The key benefits included reduced development time by 30%, decreased infrastructure costs by 60%, and increased adoption of Databricks and self-service across teams.
Building Sessionization Pipeline at Scale with Databricks Delta
"Comcast has made a concerted effort to transform itself from a cable/ISP company to a technology company. Data-driven decision making is at the heart of this transformation, and we use data to understand how customers interact with our products, and we see data as the most truthful representation of the voice of our customer. My team, Product Analytics & behavior science (PABS) team plays the role as interpreter, transforming data into consumable insights.
The X1 entertainment operating system, is one of the largest video streaming platforms in the world, and our customers consume more than a billion hours of content a week on X1. Our team consumes X1 telemetry at a rate of more than 25TBs of data per day and uses this data to inform our product teams members about the performance of and engagement with the platform. We also use this data to research customer behaviors to help better inform our product team members about areas of opportunity in our products, which range from fixing bugs to creating new features.
To power these insights, we need to have a reliable real-time data pipelines to deliver these insights, and we need our data scientists and data engineers to be able to quickly and efficiently be able to develop and commit new code to ensure we can measure new features the product teams are developing. To do this in an environment at this scale, we have been using Databricks, and Databricks delta to gain operational efficiencies, optimization and cost savings.
Some of the features from delta that we took advantage of to achieve the desired levels of efficiencies, optimization and cost savings are:
· Distributed writes to s3 (essentially eliminating 500 errors)
· s3 log with fast reads and ACID transactions (massive increases in s3 scans/reads, and enabling consistent views of the bucket/table)
· Vacuum
· Pptimize (which has allowed us to reduce a 640 node job to 40, and massively increase efficiencies of our clusters as well as our DS/DE’s)"
Running cost effective big data workloads with Azure Synapse and ADLS (MS Ign...
Presentation by James Baker and myself on Running cost effective big data workloads with Azure Synapse and Azure Datalake Storage (ADLS) at Microsoft Ignite 2020. Covers Modern Data warehouse architecture supported by Azure Synapse, integration benefits with ADLS and some features that reduce cost such as Query Acceleration, integration of Spark and SQL processing with integrated meta data and .NET For Apache Spark support.
Intro to databricks delta lake
Delta Lake is an open-source storage layer that brings ACID
transactions to Apache Spark™ and big data workloads.
Intro to Delta Lake
Delta Lake brings reliability, performance, and security to data lakes. It provides ACID transactions, schema enforcement, and unified handling of batch and streaming data to make data lakes more reliable. Delta Lake also features lightning fast query performance through its optimized Delta Engine. It enables security and compliance at scale through access controls and versioning of data. Delta Lake further offers an open approach and avoids vendor lock-in by using open formats like Parquet that can integrate with various ecosystems.
Databricks Delta Lake and Its Benefits
Delta Lake, an open-source innovations which brings new capabilities for transactions, version control and indexing your data lakes. We uncover how Delta Lake benefits and why it matters to you. Through this session, we showcase some of its benefits and how they can improve your modern data engineering pipelines. Delta lake provides snapshot isolation which helps concurrent read/write operations and enables efficient insert, update, deletes, and rollback capabilities. It allows background file optimization through compaction and z-order partitioning achieving better performance improvements. In this presentation, we will learn the Delta Lake benefits and how it solves common data lake challenges, and most importantly new Delta Time Travel capability.
Automated Metadata Management in Data Lake – A CI/CD Driven Approach
We as data engineers are aware of trade off’s between development speed, metadata governance and schema evolution (or restriction) in rapidly evolving organization. Our day to day activities involve adding/removing/updating tables, protecting PII Information, curating and exposing data to our consumers. While our data lake keeps growing exponentially, there is equal increase in our downstream consumers. Struggle is to maintain balance between quickly promoting metadata changes with robust validation for downstream systems stability. In relational world DDL, DML changes can be managed through numerous options available for every kind of database from the vendor or 3rd party. As engineers we developed a tool which uses centralized git managed repository of data schemas in yml structure with ci/cd capabilities which maintains stability of our data lake and downstream systems.
In this presentation Northwestern Mutual Engineers, will discuss how they designed and developed new end-to-end ci/cd driven metadata management tool to make introduction of new tables/views, managing access requests etc in a more robust, maintainable and scalable way, all with only checking in yml files. This tool can be used by people who have no or minimal knowledge of spark.
Key focus will be:
Need for metadata management tool in a data lake
Architecture and Design of the tool
Maintaining information on databases/tables/views like schema, owner, PII, description etc in simple to understand yml structure
Live demo of creating a new table with CI/CD promotion to production
Data Engineering Roles
Slides from the December 2021 St. Louis Big Data IDEA meeting. Adam Doyle discussed different microroles that Data Engineers may be embodying.
ETL Made Easy with Azure Data Factory and Azure Databricks
This document summarizes Mark Kromer's presentation on using Azure Data Factory and Azure Databricks for ETL. It discusses using ADF for nightly data loads, slowly changing dimensions, and loading star schemas into data warehouses. It also covers using ADF for data science scenarios with data lakes. The presentation describes ADF mapping data flows for code-free data transformations at scale in the cloud without needing expertise in Spark, Scala, Python or Java. It highlights how mapping data flows allow users to focus on business logic and data transformations through an expression language and provides debugging and monitoring of data flows.
What's hot (20)
Databricks: A Tool That Empowers You To Do More With Data
Databricks: A Tool That Empowers You To Do More With Data
Effective AIOps with Open Source Software in a Week
Effective AIOps with Open Source Software in a Week
Introduction SQL Analytics on Lakehouse Architecture
Introduction SQL Analytics on Lakehouse Architecture
Migrating Your Data Platform At a High Growth Startup
Migrating Your Data Platform At a High Growth Startup
Feature store Overview St. Louis Big Data IDEA Meetup aug 2020
Feature store Overview St. Louis Big Data IDEA Meetup aug 2020
Semantic Image Logging Using Approximate Statistics & MLflow
Semantic Image Logging Using Approximate Statistics & MLflow
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Delivering Insights from 20M+ Smart Homes with 500M+ Devices
Leveraging Apache Spark to Develop AI-Enabled Products and Services at Bosch
Leveraging Apache Spark to Develop AI-Enabled Products and Services at Bosch
Scaling and Modernizing Data Platform with Databricks
Scaling and Modernizing Data Platform with Databricks
Building Sessionization Pipeline at Scale with Databricks Delta
Building Sessionization Pipeline at Scale with Databricks Delta
Running cost effective big data workloads with Azure Synapse and ADLS (MS Ign...
Running cost effective big data workloads with Azure Synapse and ADLS (MS Ign...
Automated Metadata Management in Data Lake – A CI/CD Driven Approach
Automated Metadata Management in Data Lake – A CI/CD Driven Approach
ETL Made Easy with Azure Data Factory and Azure Databricks
ETL Made Easy with Azure Data Factory and Azure Databricks
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High-Performance Analytics in the Cloud with Apache Impala
With more and more data being generated and stored in the cloud, you need a modern data platform that can extend to any environment so you can derive value from all your data. Cloudera Enterprise is the leading enterprise Hadoop platform for cloud deployments. It’s the easiest way to manage and secure Hadoop data across any cloud environment and includes component-level support for cloud-native object stores. This makes the platform uniquely suited to handle transient jobs like ETL and BI analytics, as well as persistent workloads like stream processing and advanced analytics.
With the recent release of Cloudera 5.8, Apache Impala (incubating) has added support for Amazon S3, enabling business analysts to get instant insights from all data through high-performance exploratory analytics and BI.
3 Things to learn:
Join David Tishgart, Director of Product Marketing, and James Curtis, Senior Analyst Data Platforms & Analytics at 451 Research, as they discuss:
* Best practices for analytic workloads in the cloud
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Demystifying Data Warehouse as a Service (DWaaS)
This is from the talk I gave at the 30th Anniversary NoCOUG meeting in San Jose, CA.
We all know that data warehouses and best practices for them are changing dramatically today. As organizations build new data warehouses and modernize established ones, they are turning to Data Warehousing as a Service (DWaaS) in hopes of taking advantage of the performance, concurrency, simplicity, and lower cost of a SaaS solution or simply to reduce their data center footprint (and the maintenance that goes with that).
But what is a DWaaS really? How is it different from traditional on-premises data warehousing?
In this talk I will:
• Demystify DWaaS by defining it and its goals
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Historic Opportunities: Discover the Power of Ignition's Historian
This document provides an overview of Ignition's tag historian capabilities. It discusses Ignition's technology stack for serving as an historian, including storing tag data in SQL databases. The document addresses common historian questions around scaling, data access, visualization and benchmarks. It highlights that Ignition can handle millions of tags across various SQL databases and supports features like dashboards, reports, streaming data to other systems and remote data collection.
2022 Trends in Enterprise Analytics
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and Data Architecture. William will kick off the fourth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
Architect’s Open-Source Guide for a Data Mesh Architecture
Data Mesh is an innovative concept addressing many data challenges from an architectural, cultural, and organizational perspective. But is the world ready to implement Data Mesh?
In this session, we will review the importance of core Data Mesh principles, what they can offer, and when it is a good idea to try a Data Mesh architecture. We will discuss common challenges with implementation of Data Mesh systems and focus on the role of open-source projects for it. Projects like Apache Spark can play a key part in standardized infrastructure platform implementation of Data Mesh. We will examine the landscape of useful data engineering open-source projects to utilize in several areas of a Data Mesh system in practice, along with an architectural example. We will touch on what work (culture, tools, mindset) needs to be done to ensure Data Mesh is more accessible for engineers in the industry.
The audience will leave with a good understanding of the benefits of Data Mesh architecture, common challenges, and the role of Apache Spark and other open-source projects for its implementation in real systems.
This session is targeted for architects, decision-makers, data-engineers, and system designers.
Optimized Data Management with Cloudera 5.7: Understanding data value with Cl...
Across all industries, organizations are embracing the promise of Apache Hadoop to store and analyze data of all types, at larger volumes than ever before possible. But to tap into the true value of this data, organizations need to manage this data and its subsequent metadata to understand its context, see how it’s changing, and take actions on it.
Cloudera Navigator is the only integrated data management and governance for Hadoop and is designed to do exactly this. With Cloudera 5.7, we have further expanded the capabilities in Cloudera Navigator to make it even easier to understand your data and maintain metadata consistency as it moves through Hadoop.
What are the basic key concepts before learning Azure Data Engineer.docx
Before learning Azure Data Engineering, grasp fundamental concepts like data engineering, cloud computing, and Azure services. Challenges include service complexity, data integration, scalability, and security, addressed with services like Azure HDInsight for simplified big data processing, enabling innovation and maximizing data value.
Modern Data Security with MySQL
Security landscape has been a constantly and rapidly changing scenario in the last decades. Threats have evolved from targeting services' availability to targeting data and data integrity. Therefore, now more than ever, data protection becomes critical and needs an in-depth approach which starts from the databes. Learn more about what MySQL has to offer to help you put in place security best practices to start protecting your data straight from the database!
Cloud and Analytics - From Platforms to an Ecosystem
Zurich North America is one of the largest providers of insurance solutions and services in the world with customers representing a wide range of industries from agriculture to construction and more than 90 percent of the Fortune 500.
Cloud and Analytics -- 2020 sparksummit
This document discusses Zurich Insurance Group's use of cloud analytics platforms and technologies. It outlines how Zurich leverages multiple data sources and tools for data exploration, integration, modeling and deployment. Key elements of their ecosystem include a data lake on Azure, various analytics tools, containerization, and DevOps processes to automate deployments and upgrades. The goal is to accelerate insights, improve agility and reduce costs through this cloud-based analytics environment.
Get Started with Cloudera’s Cyber Solution
Cloudera empowers cybersecurity innovators to proactively secure the enterprise by accelerating threat detection, investigation, and response through machine learning and complete enterprise visibility. Cloudera’s cybersecurity solution, based on Apache Spot, enables anomaly detection, behavior analytics, and comprehensive access across all enterprise data using an open, scalable platform. But what’s the easiest way to get started?
Join Cloudera, StreamSets, and Arcadia Data as we show you first hand how we have made it easier to get your first use case up and running. During this session you will learn:
Signs you need Cloudera’s cybersecurity solution
How StreamSets can help increase enterprise visibility
Providing your security analyst the right context at the right time with modern visualizations
3 things to learn:
Signs you need Cloudera’s cybersecurity solution
How StreamSets can help increase enterprise visibility
Providing your security analyst the right context at the right time with modern visualizations
Leveraging cloud database connectors to automate analytics in alteryx
This webinar discusses leveraging cloud database connectors to automate analytics in Alteryx. It introduces Grazitti Interactive, an Alteryx partner that develops connectors. The webinar covers the growth of cloud databases, how Alteryx helps analyze database data, challenges integrating databases into Alteryx, an overview of Alteryx connectors, and how Grazitti created a custom Azure Analysis Services connector for a customer.
Introducing DocumentDB
DocumentDB is a powerful NoSQL solution. It provides elastic scale, high performance, global distribution, a flexible data model, and is fully managed. If you are looking for a scaled OLTP solution that is too much for SQL Server to handle (i.e. millions of transactions per second) and/or will be using JSON documents, DocumentDB is the answer.
[2016 데이터 그랜드 컨퍼런스] 5 1(보안,품질). 웨어밸리 data security challenges and its solutio...
비용절감 및 클라우드를 통한 각종 효율성을 위해 기존의 인프라에서 구축된 데이터베이스들이 클라우드 환경으로 이관되거나 신규 구축될 때, 데이터베이스 보안 구축도 기존 고객이 직접 운영하는 인프라가 아닌 클라우드 서비스 사업자의 인프라로 이관되며 이에 따른 인프라 환경의 변화와 침해 사고 시 발생할 수 있는 책임소재의 분쟁, 법규의 변화 등 고민할 부분이 많아지게 된다. 따라서, 클라우드 환경에서의 데이터베이스 보안 구축은 클라우드로 데이터를 이관하기 전, 이관하는 단계 그리고 이관 이후에 데이터를 어떻게 보호할 것인가를 다양한 기술적인 측면으로 살펴볼 필요가 있다.
ADV Slides: Building and Growing Organizational Analytics with Data Lakes
Data lakes are providing immense value to organizations embracing data science.
In this webinar, William will discuss the value of having broad, detailed, and seemingly obscure data available in cloud storage for purposes of expanding Data Science in the organization.
24 HOP edición Español -Diferentes técnicas de administración de logins y usu...
This document discusses different techniques for managing logins and users in SQL Server. It begins by covering historical methods like Windows and SQL Server logins and database users and roles. It then discusses security best practices, built-in server and database roles, configuring permissions, and configuration options. The document also introduces the concepts of contained databases and partially contained databases, which allow for database user authentication and isolation from the SQL Server instance. It provides examples of enabling and creating contained databases and contained database users.
Techcello hp-arch workshop
This document discusses building secure multi-tenant applications on .NET for cloud environments. It covers topics like tenant data isolation, role-based access control, securing data transmission and storage, addressing common web application vulnerabilities, and implementing security auditing. The speakers are introduced and their backgrounds are provided. Contact details and additional resources are listed at the end.
Building multi tenant highly secured applications on .net for any cloud - dem...
This document discusses building secure multi-tenant applications on .NET for cloud environments. It covers topics like tenant data isolation, role-based access control, securing data transmission and storage, addressing common security risks, and implementing auditing. The presentation provides an overview of the Techcello framework for developing multi-tenant SaaS applications and managing the full lifecycle. It then dives deeper into specific security practices and considerations for multi-tenant applications.
Modern MySQL Monitoring and Dashboards.
MyDBOPS Team has presented on Oracle MySQL user Camp ( 29-07-2016 ). This presentation is about Grafana and Prometheus for MySQL alerting and Dashboard setup.
IoT-Enabled Predictive Maintenance
IoT is reshaping the manufacturing and industrial processes, effectively changing the paradigm from one of repair and replace to more of predict and prevent. Using data streaming from connected equipment and machinery, organizations can now monitor the health of their assets and effectively predict when and how an asset might fail. However, without the right data management strategy and tools, investments in IoT can yield limited results. Join Cloudera and Tata Consultancy Services (TCS) for a joint webinar to learn more about how organizations are using advanced analytics and machine learning to drive IoT enabled predictive maintenance.
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High-Performance Analytics in the Cloud with Apache Impala
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Cloud and Analytics - From Platforms to an Ecosystem
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DW Migration Webinar-March 2022.pptx
The document discusses migrating a data warehouse to the Databricks Lakehouse Platform. It outlines why legacy data warehouses are struggling, how the Databricks Platform addresses these issues, and key considerations for modern analytics and data warehousing. The document then provides an overview of the migration methodology, approach, strategies, and key takeaways for moving to a lakehouse on Databricks.
Data Lakehouse Symposium | Day 1 | Part 1
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 2
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 2
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.
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5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
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.
Democratizing Data Quality Through a Centralized Platform
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.
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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
Learn to Use Databricks for Data Science
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 Monitoring
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 Fix
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 Integration
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 PyTorch
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 Kubernetes
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 Pipelines
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 Aggregations
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.
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
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 Spark
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 Queries
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.
Massive Data Processing in Adobe Using Delta Lake
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 Detection
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.
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5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop
Democratizing Data Quality Through a Centralized Platform
Democratizing Data Quality Through a Centralized Platform
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix
Stage Level Scheduling Improving Big Data and AI Integration
Stage Level Scheduling Improving Big Data and AI Integration
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Simplify Data Conversion from Spark to TensorFlow and PyTorch
Scaling your Data Pipelines with Apache Spark on Kubernetes
Scaling your Data Pipelines with Apache Spark on Kubernetes
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Scaling and Unifying SciKit Learn and Apache Spark Pipelines
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink
Raven: End-to-end Optimization of ML Prediction Queries
Raven: End-to-end Optimization of ML Prediction Queries
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Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
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The Building Blocks of QuestDB, a Time Series Database
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness in Kansas City, MO
To: Tom Tresser
From: Roger Warren
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This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
Global Situational Awareness of A.I. and where its headed
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
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State of Artificial intelligence Report 2023
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
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查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃的头说还好醒得及时要不家早被人掏空了到时连电视也没得看啰不过父亲还是夸山娃能临危不乱随机应变有胆有谋山娃笑笑说那都是书上学的看童话和小说时多
一比一原版(Harvard毕业证书)哈佛大学毕业证如何办理
毕业原版【微信:41543339】【(Harvard毕业证书)哈佛大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（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（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(Chester毕业证书)切斯特大学毕业证如何办理
毕业原版【微信:41543339】【(Chester毕业证书)切斯特大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（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（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理
毕业原版【微信:41543339】【(爱大毕业证书)爱丁堡大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（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（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理
毕业原版【微信:41543339】【(Coventry毕业证书)考文垂大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（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（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
End-to-end pipeline agility - Berlin Buzzwords 2024
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
毕业原版【微信:176555708】【(GWU,GW毕业证书)乔治·华盛顿大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（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（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Analysis insight about a Flyball dog competition team's performance
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
一比一原版(Glasgow毕业证书)格拉斯哥大学毕业证如何办理
毕业原版【微信:41543339】【(Glasgow毕业证书)格拉斯哥大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（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（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
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二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
原版一模一样【微信：741003700 】【(uts毕业证书)悉尼科技大学毕业证学历证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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Accelerate Data Science Initiatives: Databricks & Privacera
- 1. Accelerating Data Science Initiatives with Databricks’ SQL Analytics and Privacera’s Centralized Data Access Governance Don Bosco Durai Co-Founder and CTO, Privacera
- 2. Agenda ▪ Backgrounds ▪ SQL Analytics Overview ▪ Security Challenges ▪ Privacera + SQL Analytics Integration Overview ▪ Demo ▪ Key Benefits
- 3. About the Speaker ▪ Co-founder & CTO, Privacera ▪ Founding member of Apache Ranger ▪ Strong security partner with Databricks
- 4. Databricks Lakehouse - Persona Driven Approach ● Batch process v/s Real-time ● SQL Analytics simplifies usage of the Lakehouse for Business users ● Data Scientist and Architects can continue using ML Workflow Spark Clusters ● Customizable security policies for different use cases
- 5. Security and Compliance Challenges Need consistent policies for the underlying dataset Persona driven policies Executives, Data Analyst, Data Scientist, Data Wranglers, etc. Managing access permissions for different Personas Can everyone run grant/revoke SQL commands? What IAM role should be attached to the cluster? Who can create a new cluster?
- 6. Security v/s Privacy Policies ▪ Preventing unauthorized usage of systems ▪ Ensuring users don’t see the incorrect information ▪ Creating boundaries to enforce right action of the system • “Data privacy may be defined as the authorized, fair, and legitimate processing of personal information” • Consent rights • Do not share Privacy Security
- 8. Centralized Auditing and Reporting ● Centralize auditing ● Monitoring data access by classification ● Track usage by Purpose ● Generate attestation reports
- 9. Databricks Native Support for Security Credential Passthrough SQL Grant/Revoke Cluster Policies IAM Roles for Cluster
- 10. Integration Overview ● Privacera extends Databrick’s native security ● Centrally manage policies for all Databricks workloads ● Consistent policies between Databricks SQL Analytics and Python/SQL Workspaces ● Centralized collection of Audit Records ● Built on top of Apache Ranger ○ High Scalable ○ Support for multiple services and Clouds
- 11. Privacera - Traditional Databricks Workspaces Support Fine Grained Access Control SQL - Database, Table, Column SQL - Dynamic Row Level Filtering SQL - Dynamic Column Masking S3/ADLS file level access control Attribute Based Access Policies Role Based Access Policies Tag Based Policies S3/ADLS file level access control Attribute Based Access Policies Role Based Access Policies Tag Based Policies • Scala Cluster • Python/SQL Cluster
- 12. Databricks/Privacera - Ranger Plugin Architecture
- 13. SQL Analytics - Databricks + Privacera Seamless integration No Privacera deployment No plugins and no init scripts Privacera’s Policy Sync integration design Security features identical to Databricks SQL Cluster
- 14. Privacera + SQL Analytics Key Benefits Privacera’s data access governance platform seamlessly integrates with SQL Analytics to provide: ● Fine-grained access control at row- and column- levels with dynamic masking ● Single-pane visibility of data across multiple cloud services in SQL Analytics ● Precise data usage with real-time monitoring, logging, and audit trails ● Compliance with industry and privacy regulations like GDPR, CCPA, HIPAA, LGPD
- 15. Demo
- 16. Thank you!
- 17. Contact Me SOCIAL LinkedIn: https://www.linkedin.com/in/donboscodurai/ Sign up for a free 30-day trial of PrivaceraCloud at privacera.com/try-privaceracloud