How Apache Spark Changed the Way We Hire People with Tomasz Magdanski

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As big data technology matures, you’d think there would be more talent available to hire. Although the number of people interested and engaged in the big data world has dramatically increased, job demand is far ahead. Companies like Google or Facebook have access to the best talent — thousands of engineers with PhDs from the best schools, which is why they are able to innovate. How can a company close the skills gap while innovating and creating product advantage? This talk highlights how the right technology can allow you to compete without having an army of PhDs at your disposal. At iPass, we’ve created an environment where our engineers can be empowered to create value without getting bogged down by big data and Ops challenges. As a result, we have been able to more easily recruit internal engineers to our big data team, leveraging their current expertise, while bringing them up to speed on big data projects much faster. Join this talk to learn how you can do the same for your organization.

Données & analyses

Tomasz Magdanski, iPass
HOW APACHE SPARK CHANGED
THE WAY WE HIRE PEOPLE
#EntSAIS17

What if the war for talent ended and
your company lost?
2#EntSAIS17
• War for talent
– Late ’90s warning from McKinsey about talent
shortage
– Urged companies to prioritize strategies around
recruiting, retaining and developing key employees
• One percent problem

Hiring is tough
3#EntSAIS17
Source: edureka

And its going to get worse
4#EntSAIS17
Source: Hour of Code

Since war for talent started we
have made a full circle
5#EntSAIS17
• Apart from hiring skilled engineers companies look inside
to fill in the gap
• Create path to grow within your organization
• But wait a minute ?
Didn't we just say there is a big skills gap ?

Goals
• Scalable platform
• Cost effective
• No data loss
• Code portability
• Easy R&D
• Extendable
• Support many languages
• Support batch, stream
• ML enabled
• Collaborative
7#EntSAIS17

Who we were looking for ?
• MapReduce
• Hadoop / HDFS
• Hive / Pig
• Storm
• Caching
• Avro / Parquet
• Distributed Computing
• Manage Clusters and Infrastructure
• Integrate tools
• Data Warehousing and Modeling
8#EntSAIS17

Who we were looking for ?
• CAP Theorem
• Data Transformation
• Data Collection
• SQL
• Cassandra / Hbase / MongoDB / mysql
• Kafka
• AWS
• Scala / Java / Python
• Understanding data structures and algorithms
• Visualization and Data Analysis
• Team player
• SPECIFIC INDUSTRY KNOWLEDGE
9#EntSAIS17

Spark changed what we are
looking for
10#EntSAIS17

Spark
• Simple
• Easy to learn
• High abstraction API
• Build in connectors to major data sources
• Supports Batch, Stream
• Highly optimized and extendible
• ML library to run at scale
• Spark provides transactional writes and exact once semantic
11#EntSAIS17

Spark and Databricks
• A single platform that unifies data engineering and data science
• Automated cluster management / zero-management infrastructure
• Intuitive notebooks supporting multiple programming languages
• Makes collaboration easy
• Blends Data Engineering and Data Science workloads
• APIs to integrate with other tools
12#EntSAIS17

Spark and Databricks
• Integrated meta store
• Integrated Managed and unmanaged tables
• Workspace API
• Engineering and Customer Support including Solution Architects
• Easy dashboards
• DbUtils
• SBT tools for easy deployment
13#EntSAIS17

Who we are looking for now?
• Experience in programming using APIs
• Understanding data structures and algorithms
• Scala / Java / Python / R
• Visualization and Data Analysis
• Team player
• SPECIFIC INDUSTRY KNOWLEDGE
14#EntSAIS17

Business needs
• Wi-Fi connectivity patterns
• Our business and customers
• Existing system architecture
• Skip lengthy onboarding process
• One stack to learn
15#EntSAIS17

How did that change the way we hire ?
• We have internally hired:
– QA engineer
– App developers
– Ex developer / product manager
– Backend engineer
• Externally hired:
– One very experienced senior Data Engineer
– Few collage grads – Junior data engineers
16#EntSAIS17

Summary
• Thanks to Databricks we didn’t have to build a
platform
• Hired mixed of internal and external candidates
• Focus on business needs
• Created 6 data products
• New seven digit revenue stream for our company
• Continue to innovate
17#EntSAIS17

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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.

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Databricks

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 your Data Pipelines with Apache Spark on Kubernetes

Databricks

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.

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Databricks

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.

Sawtooth Windows for Feature Aggregations

Databricks

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

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Databricks

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.

Re-imagine Data Monitoring with whylogs and Spark

Databricks

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.

Raven: End-to-end Optimization of ML Prediction Queries

Databricks

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.

Processing Large Datasets for ADAS Applications using Apache Spark

Databricks

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!

Massive Data Processing in Adobe Using Delta Lake

Databricks

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Stage Level Scheduling Improving Big Data and AI Integration

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Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Sawtooth Windows for Feature Aggregations

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Re-imagine Data Monitoring with whylogs and Spark

Raven: End-to-end Optimization of ML Prediction Queries

Processing Large Datasets for ADAS Applications using Apache Spark

Massive Data Processing in Adobe Using Delta Lake

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Digital Advertising Lecture for Advanced Digital & Social Media Strategy at U...

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怎样办理伦敦大学毕业证（UoL毕业证书）成绩单学校原版复制

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怎样办理纽约州立大学宾汉姆顿分校毕业证（SUNY-Bin毕业证书）成绩单学校原版复制

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In my capstone project, I investigated the impact of COVID-19 on education. Using data analysis and statistical methods, I explored various aspects such as enrollment trends, access to resources, and socioeconomic disparities. I found a significant association between children missing classes and a lack of internet connection at home, as well as between household financial situations and children's enrollment in school. These findings highlight the importance of addressing disparities in internet access, household finances, and geographical location to ensure equal educational opportunities for all students during and beyond the pandemic.

Capstone in Interprofessional Informatic // IMPACT OF COVID 19 ON EDUCATION

LakpaYanziSherpa

As electricity is difficult to store, it is crucial to strictly maintain the balance between production and consumption. The integration of intermittent renewable energies into the production mix has made the management of the balance more complex. However, access to near real-time data and communication with consumers via smart meters suggest demand response. Specifically, sending signals would encourage users to adjust their consumption according to the production of electricity. The algorithms used to select these signals must learn consumer reactions and optimize them while balancing exploration and exploitation. Various sequential or reinforcement learning approaches are being considered.

Sequential and reinforcement learning for demand side management by Margaux B...

Paris Women in Machine Learning and Data Science

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原版定制【微信:176555708】【旧金山城市学院毕业证（CCSF毕业证书）】【微信:176555708】（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。【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：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部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！

怎样办理旧金山城市学院毕业证（CCSF毕业证书）成绩单学校原版复制

vexqp

Yilin Xia (yilinx2@illinois.edu), Shawn Bowers (bowers@gonzaga.edu), Lan Li (lanl2@illinois.edu), and Bertram Ludäscher (ludaesch@illinois.edu) Presented at IDCC-2024 in Edinburg. ABSTRACT. We propose a new approach for modeling and reconciling conflicting data cleaning actions. Such conflicts arise naturally in collaborative data curation settings where multiple experts work independently and then aim to put their efforts together to improve and accelerate data cleaning. The key idea of our approach is to model conflicting updates as a formal argumentation framework (AF). Such argumentation frameworks can be automatically analyzed and solved by translating them to a logic program PAF whose declarative semantics yield a transparent solution with many desirable properties, e.g., uncontroversial updates are accepted, unjustified ones are rejected, and the remaining ambiguities are exposed and presented to users for further analysis. After motivating the problem, we introduce our approach and illustrate it with a detailed running example introducing both well-founded and stable semantics to help understand the AF solutions. We have begun to develop open source tools and Jupyter notebooks that demonstrate the practicality of our approach. In future work we plan to develop a toolkit for conflict resolution that can be used in conjunction with OpenRefine, a popular interactive data cleaning tool.

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

Bertram Ludäscher

SR-101-01012024-EN.docx Federal Constitution of the Swiss Confederation

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怎样办理伦敦大学毕业证（UoL毕业证书）成绩单学校原版复制

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Capstone in Interprofessional Informatic // IMPACT OF COVID 19 ON EDUCATION

Sequential and reinforcement learning for demand side management by Margaux B...

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怎样办理旧金山城市学院毕业证（CCSF毕业证书）成绩单学校原版复制

Reconciling Conflicting Data Curation Actions: Transparency Through Argument...

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How Apache Spark Changed the Way We Hire People with Tomasz Magdanski

1. Tomasz Magdanski, iPass HOW APACHE SPARK CHANGED THE WAY WE HIRE PEOPLE #EntSAIS17

2. What if the war for talent ended and your company lost? 2#EntSAIS17 • War for talent – Late ’90s warning from McKinsey about talent shortage – Urged companies to prioritize strategies around recruiting, retaining and developing key employees • One percent problem

3. Hiring is tough 3#EntSAIS17 Source: edureka

4. And its going to get worse 4#EntSAIS17 Source: Hour of Code

5. Since war for talent started we have made a full circle 5#EntSAIS17 • Apart from hiring skilled engineers companies look inside to fill in the gap • Create path to grow within your organization • But wait a minute ? Didn't we just say there is a big skills gap ?

6. What are we building ? 6#EntSAIS17

7. Goals • Scalable platform • Cost effective • No data loss • Code portability • Easy R&D • Extendable • Support many languages • Support batch, stream • ML enabled • Collaborative 7#EntSAIS17

8. Who we were looking for ? • MapReduce • Hadoop / HDFS • Hive / Pig • Storm • Caching • Avro / Parquet • Distributed Computing • Manage Clusters and Infrastructure • Integrate tools • Data Warehousing and Modeling 8#EntSAIS17

9. Who we were looking for ? • CAP Theorem • Data Transformation • Data Collection • SQL • Cassandra / Hbase / MongoDB / mysql • Kafka • AWS • Scala / Java / Python • Understanding data structures and algorithms • Visualization and Data Analysis • Team player • SPECIFIC INDUSTRY KNOWLEDGE 9#EntSAIS17

10. Spark changed what we are looking for 10#EntSAIS17

11. Spark • Simple • Easy to learn • High abstraction API • Build in connectors to major data sources • Supports Batch, Stream • Highly optimized and extendible • ML library to run at scale • Spark provides transactional writes and exact once semantic 11#EntSAIS17

12. Spark and Databricks • A single platform that unifies data engineering and data science • Automated cluster management / zero-management infrastructure • Intuitive notebooks supporting multiple programming languages • Makes collaboration easy • Blends Data Engineering and Data Science workloads • APIs to integrate with other tools 12#EntSAIS17

13. Spark and Databricks • Integrated meta store • Integrated Managed and unmanaged tables • Workspace API • Engineering and Customer Support including Solution Architects • Easy dashboards • DbUtils • SBT tools for easy deployment 13#EntSAIS17

14. Who we are looking for now? • Experience in programming using APIs • Understanding data structures and algorithms • Scala / Java / Python / R • Visualization and Data Analysis • Team player • SPECIFIC INDUSTRY KNOWLEDGE 14#EntSAIS17

15. Business needs • Wi-Fi connectivity patterns • Our business and customers • Existing system architecture • Skip lengthy onboarding process • One stack to learn 15#EntSAIS17

16. How did that change the way we hire ? • We have internally hired: – QA engineer – App developers – Ex developer / product manager – Backend engineer • Externally hired: – One very experienced senior Data Engineer – Few collage grads – Junior data engineers 16#EntSAIS17

17. Summary • Thanks to Databricks we didn’t have to build a platform • Hired mixed of internal and external candidates • Focus on business needs • Created 6 data products • New seven digit revenue stream for our company • Continue to innovate 17#EntSAIS17

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