Scaling Self Service Analytics with Databricks and Apache Spark with Amelia Chu and Dan Morris
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The document discusses how Viacom scaled self-service analytics using Databricks and Apache Spark. It provides examples of recent sightings of key roles including data scientist, data engineer, and business analyst interacting with the single analytics platform. The platform allows for parameterized dashboards, a UDF library, and self-service capabilities to empower various roles. Feedback is welcomed via email or Twitter to the authors.
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TIAD : Automating the modern datacenter
Keynote from Mitchell Hashimoto, Founder of HashiCorp and creator of Vagrant, Packer, Serf, Terraform, Atlas & Consul
Advanced Analytics and Recommendations with Apache Spark - Spark Maryland/DC ...
Title
Real-time, Advanced Analytics and Recommendations using Machine Learning, Graph Processing, Natural Language Processing, and Approximations with Apache Spark, Stanford CoreNLP, and Twitter Algebird
BONUS: Netflix Recommendations: Then and Now
Agenda
Intro
Live, Interactive Recommendations Demo
Spark ML, GraphX, Streaming, Kafka, Cassandra, Docker
Types of Similarity
Euclidean vs. Non-Euclidean Similarity
User-to-User Similarity
Content-based, Item-to-Item Similarity (Amazon)
Collaborative-based, User-to-Item Similarity (Netflix)
Graph-based, Item-to-Item Similarity Pathway (Spotify)
Similarity Approximations at Scale
Twitter Algebird
MinHash and Bucketing
Locality Sensitive Hashing (LSH)
BONUS: Netflix Recommendations: From Ratings to Real-Time
DVD-Ratings-based $1M Netflix Prize (2009)
Streaming-based "Trending Now" (2016)
Wrap Up
Q & A
Bio
Chris Fregly is a Principal Data Solutions Engineer for the newly-formed IBM Spark Technology Center, an Apache Spark Contributor, and a Netflix Open Source Committer.
Chris is also the founder of the global Advanced Apache Spark Meetup and author of the upcoming book, Advanced Spark @ advancedspark.com.
Previously, Chris was a Data Solutions Engineer at Databricks and a Streaming Data Engineer at Netflix.
Related Links
https://github.com/fluxcapacitor/pipeline/wiki
http://cdn.oreillystatic.com/en/assets/1/event/105/Algebra%20for%20Scalable%20Analytics%20Presentation.pdf
http://static.echonest.com/BoilTheFrog/
http://www.netflixprize.com/assets/GrandPrize2009_BPC_BellKor.pdf
http://blog.echen.me/2011/10/24/winning-the-netflix-prize-a-summary/
http://www.cc.gatech.edu/~zha/CSE8801/CF/kdd-fp074-koren.pdf
5th Athens Big Data Meetup - PipelineIO Workshop - Real-Time Training and Dep...
Title: Real-Time Training and Deploying Spark ML Recommendations With Kafka and NetflixOSS
Speaker: Chris Fregly (https://linkedin.com/in/cfregly/)
Date: Monday, October 17, 2016
Event: https://meetup.com/Athens-Big-Data/events/234546355/
Apache Spark Structured Streaming for Machine Learning - StrataConf 2016
Streaming machine learning is being integrated in Spark 2.1+, but you don’t need to wait. Holden Karau and Seth Hendrickson demonstrate how to do streaming machine learning using Spark’s new Structured Streaming and walk you through creating your own streaming model. By the end of this session, you’ll have a better understanding of Spark’s Structured Streaming API as well as how machine learning works in Spark.
Chicago Spark Meetup 03 01 2016 - Spark and Recommendations
Chicago Spark Meetup 03 01 2016 - Spark and Recommendations
Advanced Apache Spark Meetup Project Tungsten Nov 12 2015
Advanced Apache Spark Meetup Project Tungsten Nov 12 2015
Datacamp @ Transparency Camp 2010
Datacamp - Data management and publishing web application
http://github.com/Stiivi/datacamp
OSDC 2015: Mitchell Hashimoto | Automating the Modern Datacenter, Development...
Physical, virtual, containers. Public cloud, private cloud, hybrid cloud. IaaS, PaaS, SaaS. These are the choices that we're faced with when architecting a datacenter of today. And the choice is not one or the other; it is often a combination of many of these. How do we remain in control of our datacenters? How do we deploy and configure software, manage change across disparate systems, and enforce policy/security? How do we do this in a way that operations engineers and developers alike can rejoice in the processes and workflow?
In this talk, I will discuss the problems faced by the modern datacenter, and how a set of open source tools including Vagrant, Packer, Consul, and Terraform can be used to tame the rising complexity curve and provide solutions for these problems.
USF Seminar Series: Apache Spark, Machine Learning, Recommendations Feb 05 2016
Title:
Real-time, Advanced Analytics and Recommendations using Machine Learning, Natural Language Processing, Graph Processing, and Approximations with Apache Spark, Stanford CoreNLP, and Twitter Algebird
Agenda
Intro
Live, Interactive Recommendations Demo
Spark ML, GraphX, Streaming, Kafka, Cassandra, Docker
Types of Similarity
Euclidean vs. Non-Euclidean Similarity
User-to-User Similarity
Content-based, Item-to-Item Similarity (Amazon)
Collaborative-based, User-to-Item Similarity (Netflix)
Graph-based, Item-to-Item Similarity Pathway (Spotify)
Similarity Approximations at Scale
Twitter Algebird
MinHash and Bucketing
Locality Sensitive Hashing (LSH)
Netflix Recommendations: From Ratings to Real-Time
DVD-Ratings-based $1M Netflix Prize (2009)
Streaming-based "Trending Now" (2016)
Wrap Up
Q & A
*Bio*
Chris Fregly is a Principal Data Solutions Engineer for the newly-formed IBM Spark Technology Center, an Apache Spark Contributor, and a Netflix Open Source Committer. Chris is also the founder of the global Advanced Apache Spark Meetup and author of the upcoming book, Advanced Spark @ advancedspark.com. Previously, Chris was a Data Solutions Engineer at Databricks and a Streaming Data Engineer at Netflix.
*Related Links*
https://github.com/fluxcapacitor/pipeline/wiki
http://cdn.oreillystatic.com/en/assets/1/event/105/Algebra%20for%20Scalable%20Analytics%20Presentation.pdf
http://static.echonest.com/BoilTheFrog/
http://www.netflixprize.com/assets/GrandPrize2009_BPC_BellKor.pdf
http://blog.echen.me/2011/10/24/winning-the-netflix-prize-a-summary/
http://www.cc.gatech.edu/~zha/CSE8801/CF/kdd-fp074-koren.pdf
A Journey into Databricks' Pipelines: Journey and Lessons Learned
With components like Spark SQL, MLlib, and Streaming, Spark is a unified engine for building data applications. In this talk, we will take a look at how we use Spark on our own Databricks platform throughout our data pipeline for use cases such as ETL, data warehousing, and real time analysis. We will demonstrate how these applications empower engineering and data analytics. We will also share some lessons learned from building our data pipeline around security and operations. This talk will include examples on how to use Structured Streaming (a.k.a Streaming DataFrames) for online analysis, SparkR for offline analysis, and how we connect multiple sources to achieve a Just-In-Time Data Warehouse.
Spark, Similarity, Approximations, NLP, Recommendations - Boulder Denver Spar...
Spark, Similarity, Approximations, NLP, Recommendations @ Boulder Denver Spark Meetup Feb 24 2016
Similar to Scaling Self Service Analytics with Databricks and Apache Spark with Amelia Chu and Dan Morris (20)
New developments in open source ecosystem spark3.0 koalas delta lake
New developments in open source ecosystem spark3.0 koalas delta lake
Koalas: Making an Easy Transition from Pandas to Apache Spark
Koalas: Making an Easy Transition from Pandas to Apache Spark
Design for X: Exploring Product Design with Apache Spark and GraphLab
Design for X: Exploring Product Design with Apache Spark and GraphLab
DataFusion-and-Arrow_Supercharge-Your-Data-Analytical-Tool-with-a-Rusty-Query...
DataFusion-and-Arrow_Supercharge-Your-Data-Analytical-Tool-with-a-Rusty-Query...
Advanced Analytics and Recommendations with Apache Spark - Spark Maryland/DC ...
Advanced Analytics and Recommendations with Apache Spark - Spark Maryland/DC ...
5th Athens Big Data Meetup - PipelineIO Workshop - Real-Time Training and Dep...
5th Athens Big Data Meetup - PipelineIO Workshop - Real-Time Training and Dep...
Apache Spark Structured Streaming for Machine Learning - StrataConf 2016
Apache Spark Structured Streaming for Machine Learning - StrataConf 2016
Chicago Spark Meetup 03 01 2016 - Spark and Recommendations
Chicago Spark Meetup 03 01 2016 - Spark and Recommendations
Advanced Apache Spark Meetup Project Tungsten Nov 12 2015
Advanced Apache Spark Meetup Project Tungsten Nov 12 2015
OSDC 2015: Mitchell Hashimoto | Automating the Modern Datacenter, Development...
OSDC 2015: Mitchell Hashimoto | Automating the Modern Datacenter, Development...
DataFusion and Arrow_ Supercharge Your Data Analytical Tool with a Rusty Quer...
DataFusion and Arrow_ Supercharge Your Data Analytical Tool with a Rusty Quer...
USF Seminar Series: Apache Spark, Machine Learning, Recommendations Feb 05 2016
USF Seminar Series: Apache Spark, Machine Learning, Recommendations Feb 05 2016
A Journey into Databricks' Pipelines: Journey and Lessons Learned
A Journey into Databricks' Pipelines: Journey and Lessons Learned
Spark, Similarity, Approximations, NLP, Recommendations - Boulder Denver Spar...
Spark, Similarity, Approximations, NLP, Recommendations - Boulder Denver Spar...
More from 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.
Data Lakehouse Symposium | Day 4
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 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.
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
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.
Processing Large Datasets for ADAS Applications using Apache Spark
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 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!
More from Databricks (20)
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
Processing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache Spark
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本公司还可以按照客户原版印刷制作且能够达到客户理想的要求。有需要办理证件的客户请联系我们在线客服中心微信：95270640 或咨询在线已转到了尽头他的城市生活也将划上一个不很圆满的句号了值得庆幸的是山娃早记下了他们的学校和联系方式说也奇怪在山娃离城的头一天父亲居然请假陪山娃耍了一天那一天父亲陪着山娃辗转长隆水上乐园疯了一整天水上漂流高空冲浪看大马戏大凡里面有的父亲都带着他去疯一把山娃算了算这一次足足花了老爸元够他挣上半个月的山娃很不解一向节俭的父亲啥时变得如此阔绰大方大把大把掏钱时居然连眉头也不皱一下车票早买好了直达卧铺车得经子
The affect of service quality and online reviews on customer loyalty in the E...
Journal of Management and Creative Business.published book./
Malana- Gimlet Market Analysis (Portfolio 2)
A market analysys on Spotify's parent Podcast company Gimlet.
Investigate & Recover / StarCompliance.io / Crypto_Crimes
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一比一原版(ArtEZ毕业证)ArtEZ艺术学院毕业证成绩单
ArtEZ毕业证【微信95270640】☀《ArtEZ艺术学院毕业证购买》Q微信95270640《ArtEZ毕业证模板办理》文凭、本科、硕士、研究生学历都可以做,《文凭ArtEZ毕业证书原版制作ArtEZ成绩单》《仿制ArtEZ毕业证成绩单ArtEZ艺术学院学位证书pdf电子图》毕业证
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多年留学服务公司,拥有海外样板无数能完美1:1还原海外各国大学degreeDiplomaTranscripts等毕业材料。海外大学毕业材料都有哪些工艺呢？工艺难度主要由：烫金.钢印.底纹.水印.防伪光标.热敏防伪等等组成。而且我们每天都在更新海外文凭的样板以求所有同学都能享受到完美的品质服务。
国外毕业证学位证成绩单办理方法：
1客户提供办理ArtEZ艺术学院ArtEZ艺术学院毕业证假文凭信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
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3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
— — — — 我们是挂科和未毕业同学们的福音我们是实体公司精益求精的工艺！ — — — -
一真实留信认证的作用(私企外企荣誉的见证):
1：该专业认证可证明留学生真实留学身份同时对留学生所学专业等级给予评定。
2：国家专业人才认证中心颁发入库证书这个入网证书并且可以归档到地方。
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4：个人职称评审加20分个人信誉贷款加10分。
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做(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书学费发票原版一模一样
原版定制【Q微信:741003700】《(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书》【Q微信:741003700】成绩单 、雅思、外壳、留信学历认证永久存档查询，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。
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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【Q微信741003700】文凭即可
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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
【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。
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
Object oriented system analysis and design
一比一原版(BU毕业证)波士顿大学毕业证成绩单
BU毕业证【微信95270640】购买（波士顿大学毕业证成绩单硕士学历）Q微信95270640代办BU学历认证留信网伪造波士顿大学学位证书精仿波士顿大学本科/硕士文凭证书补办波士顿大学 diplomaoffer,Transcript购买波士顿大学毕业证成绩单购买BU假毕业证学位证书购买伪造波士顿大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
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如果你回国在学历认证方面有以下难题请联系我们我们将竭诚为你解决认证瓶颈
1所有材料真实但资料不全无法提供完全齐整的原件。【如：成绩单丶毕业证丶回国证明等材料中有遗失的。】
2获得真实的国外最终学历学位但国外本科学历就读经历存在问题或缺陷。【如：国外本科是教育部不承认的或者是联合办学项目教育部没有备案的或者外本科没有正常毕业的。】
3学分转移联合办学等情况复杂不知道怎么整理材料的。时间紧迫自己不清楚递交流程的。
如果你是以上情况之一请联系我们我们将在第一时间内给你免费咨询相关信息。我们将帮助你整理认证所需的各种材料.帮你解决国外学历认证难题。
国外波士顿大学波士顿大学毕业证offer办理方法：
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2开始安排制作波士顿大学毕业证成绩单电子图；
3波士顿大学毕业证成绩单电子版做好以后发送给您确认；
4波士顿大学毕业证成绩单电子版您确认信息无误之后安排制作成品；
5波士顿大学成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。二条巴掌般大的裤衩衩走出泳池山娃感觉透身粘粘乎乎散发着药水味有点痒山娃顿时留恋起家乡的小河潺潺活水清凉无比日子就这样孤寂而快乐地过着寂寞之余山娃最神往最开心就是晚上无论多晚多累父亲总要携山娃出去兜风逛夜市流光溢彩人潮涌动的都市夜生活总让山娃目不暇接惊叹不已父亲老问山娃想买什么想吃什么山娃知道父亲赚钱很辛苦除了书籍和文具山娃啥也不要能牵着父亲的手满城闲逛他已心满意足了父亲连挑了三套童装叫山娃试穿山伸
Empowering Data Analytics Ecosystem.pptx
Show drafts
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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.
Adjusting primitives for graph : SHORT REPORT / NOTES
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).
一比一原版(UVic毕业证)维多利亚大学毕业证成绩单
UVic毕业证【微信95270640】（维多利亚大学毕业证成绩单本科学历）Q微信95270640(补办UVic学位文凭证书)维多利亚大学留信网学历认证怎么办理维多利亚大学毕业证成绩单精仿本科学位证书硕士文凭证书认证Seneca College diplomaoffer,Transcript办理硕士学位证书造假维多利亚大学假文凭学位证书制作UVic本科毕业证书硕士学位证书精仿维多利亚大学学历认证成绩单修改制作，办理真实认证、留信认证、使馆公证、购买成绩单，购买假文凭，购买假学位证，制造假国外大学文凭、毕业公证、毕业证明书、录取通知书、Offer、在读证明、雅思托福成绩单、假文凭、假毕业证、请假条、国际驾照、网上存档可查！
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— — — 留学归国服务中心 — — -
【主营项目】
一.维多利亚大学毕业证成绩单使馆认证教育部认证成绩单等！
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四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
国外毕业证学位证成绩单办理流程：
1客户提供维多利亚大学维多利亚大学毕业证文凭证书办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
专业服务请勿犹豫联系我！本公司是留学创业和海归创业者们的桥梁。一次办理终生受用一步到位高效服务。详情请在线咨询办理,欢迎有诚意办理的客户咨询!洽谈。
招聘代理：本公司诚聘英国加拿大澳洲新西兰美国法国德国新加坡各地代理人员如果你有业余时间有兴趣就请联系我们咨询顾问：+微信:95270640刀劈开抑或用拳头砸开每人抱起一大块就啃啃得满嘴满脸猴屁股般的红艳大家一个劲地指着对方吃吃地笑瓜裂得古怪奇形怪状却丝毫不影响瓜味甜丝丝的满嘴生津遍地都是瓜横七竖八的活像掷满了一地的大石块摘走二三只爷爷是断然发现不了的即便发现爷爷也不恼反而教山娃辨认孰熟孰嫩孰甜孰淡名义上是护瓜往往在瓜棚里坐上一刻饱吃一顿后山娃就领着阿黑漫山遍野地跑阿黑是一条黑色的大猎狗挺机灵的是山娃多年的忠实伙伴平时山娃上学阿黑也静
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我们从事工作十余年的有着丰富经验的业务顾问，熟悉海外各国大学的学制及教育体系，并且以挂科生解决毕业材料不全问题为基础，为客户量身定制1对1方案，未能毕业的回国留学生成功搭建回国顺利发展所需的桥梁。我们一直努力以高品质的教育为起点，以诚信、专业、高效、创新作为一切的行动宗旨，始终把“诚信为主、质量为本、客户第一”作为我们全部工作的出发点和归宿点。同时为海内外留学生提供大学毕业证购买、补办成绩单及各类分数修改等服务；归国认证方面，提供《留信网入库》申请、《国外学历学位认证》申请以及真实学籍办理等服务，帮助众多莘莘学子实现了一个又一个梦想。
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1所有材料真实但资料不全无法提供完全齐整的原件。【如：成绩单丶毕业证丶回国证明等材料中有遗失的。】
2获得真实的国外最终学历学位但国外本科学历就读经历存在问题或缺陷。【如：国外本科是教育部不承认的或者是联合办学项目教育部没有备案的或者外本科没有正常毕业的。】
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6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。父亲太伟大了居然能单匹马地跑到这么远这么大这么美的地方赚大钱高楼大厦鳞次栉比大街小巷人潮涌动山娃一路张望一路惊叹他发现城里的桥居然层层叠叠扭来扭去桥下没水却有着水一般的车水马龙山娃惊诧于城里的公交车那么大那么美不用买票乖乖地掷下二枚硬币空调享受还能坐着看电视呢屡经辗转山娃终于跟着父亲到家了山娃没想到父亲城里的家会如此寒碜更没料到父亲的城里竟有如此简陋的鬼地方父亲的家在高楼最底屋最下面很矮很黑是子
Predicting Product Ad Campaign Performance: A Data Analysis Project Presentation
Predicting Product Ad Campaign Performance: A Data Analysis Project PresentationBoston Institute of Analytics
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/一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
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6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
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- 2. ==== WANTED Cash Reward if Found Recent Sightings 9:00 AM As a Data Scientist 11:00 AM As a Data Engineer 1:00 PM As a Business Analyst 3:00 PM As a DevOps Engineer
- 9. DATA ENGINEER DATA SCIENTISTNON-TECHNICAL SINGLE PLATFORM
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