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

•

1 gefällt mir•377 views

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.

Daten & Analysen

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

Weitere ähnliche Inhalte

Was ist angesagt?

Building a data platform doesn’t have to be like entering a portal to Stranger Things. Join us in one hour for Tableau in the Cloud: A Netflix Original where Albert Wong, Netflix’s analytics expert, will show you how to simplify your data stack to deliver self-service analytics at scale. Albert will discuss the details of connecting to big data, finding datasets, and discovering critical insights from visualizations. He will also share how Netflix is developing and growing their analytics ecosystem with Tableau, and how they prioritize sustaining their data culture of freedom and responsibility.

2016 Tableau in the Cloud - A Netflix Original (AWS Re:invent)

Albert Wong

For Roularta, a news & media publishing company, it is of a great importance to understand reader behavior and what content attract, engage and convert readers. At Roularta, we have built an AI-driven article quality scoring solution on using Spark for parallelized compute, Delta for efficient data lake use, BERT for NLP and MLflow for model management. The article quality score solution is an NLP-based ML model which gives for every article published – a calculated and forecasted article quality score based on 3 dimensions (conversion, traffic and engagement).

Building an ML Tool to predict Article Quality Scores using Delta & MLFlow

Databricks

In the race to invent multi-million dollar business opportunities with exclusive insights, data scientists and engineers are hampered by a multitude of challenges just to make one use case a reality – the need to ingest data from multiple sources, apply real-time analytics, build machine learning algorithms, and intermix different data processing models, all while navigating around their legacy data infrastructure that is just not up to the task. This need has created the demand for Virtual Analytics, where the complexities of disparate data and technology silos have been abstracted away, coupled with a powerful range of analytics and processing horsepower, all in one unified data platform. This talk describes how Databricks is powering this revolutionary new trend with Apache Spark.

Virtualizing Analytics with Apache Spark: Keynote by Arsalan Tavakoli

Spark Summit

Netflix Big Data Paris 2017

Jason Flittner

Mastering Your Customer Data on Apache Spark by Elliott Cordo

Spark Summit

NYC Cassandra March 13- lighting talk

Sanjay Sharma

Zalandos AI-driven products and distributed landscape of analytical data marts cannot wait for long-running, hard-to-recover, monolithic batch jobs taking all night to calculate already outdated data. Modern data integration pipelines need to deliver fast and easy to consume data sets in high quality. Based on Spark Streaming and Delta, the central data warehousing team was able to deliver widely-used master data as S3 or Kafka streams and snapshots at the same time. The talk will cover challenges in our fashion data platform and a detailed architectural deep dive about separation of integration from enrichment, providing streams as well as snapshots and feeding the data to distributed data marts. Finally, lessons learned and best practices about Delta’s MERGE command, Scala API vs Spark SQL and schema evolution give more insights and guidance for similar use cases.

Data Warehousing with Spark Streaming at Zalando

Databricks

Machine learning in the enterprise is rarely delivered by a single team. In order to enable Machine Learning across an organisation you need to target a variety of different skills, processes, technologies, and maturities. To do this is incredibly hard and requires a composite of different techniques to deliver a single platform which empowers all users to build and deploy machine learning models. In this session we discuss how Azure & Databricks enables a Data Science as a Service platform. We look at how a DSaaS platform is empowering users of all abilities to build models, deploy models and enabling organisations to realise and return on investment earlier.

Building a Data Science as a Service Platform in Azure with Databricks

Databricks

Big data bi-mature-oanyc summit

Open Analytics

How did eBay move their ETL computation from conventional RDBMS environment over to Spark? What did it take to go from a strategic vision to a viable solution? This paper will take you through a journey which lead to an implementation of a 1000+ node Spark Cluster running 10,000+ ETL jobs daily, all done in a span of less than 6 months, by a team with limited Spark experience. We will share the vision, technical architecture, critical Management decisions, Challenges and Road ahead. This will be a unique opportunity to look into this awesome Spark success story at eBay!

Moving eBay’s Data Warehouse Over to Apache Spark – Spark as Core ETL Platfor...

Databricks

Big data knolx

Knoldus Inc.

The ability to validate credit cards using a mobile device is fruitful for many e-commerce platforms including Uber. Not only does this provide a seamless experience to users, but it also enables the company to verify that a user has physical possession of the credit card. In this talk we will discuss our new application that uses object detection neural networks to scan credit cards. Traditionally, machine learning models are hosted server side, but with challenges including high bandwidth inputs, low network speeds, and a greater focus on user privacy, hosting these models server side is not always feasible. Recent advancements bring up the possibility of deploying these models directly on the mobile devices. We will discuss the challenges we faced in designing the vision model to run on a mobile device. These include reducing the model’s size footprint and optimizing the model to run on various different types of mobile hardware. Speakers Richard Ash, Mobile Software Engineer, Uber Lenny Evans, Data Scientists, Uber

Validating credit cards on mobile using deep learning

DataWorks Summit

Life occurs in real-time, and not surprisingly, more solutions are being built using streaming technologies. Event-based architectures are becoming the norm, and customers are expecting immediate access to their data. This new world offers many exciting opportunities, but also some new challenges. What do you do when your streaming data is not complete? What if it relies on another data source? Does the dependent data exist yet, and does it come from a 3rd party? How do we merge a complete picture of data when data is sourcing from multiple places at the same time? A new norm in the world of distributed services. Join us as we dive deep into the technical details around these scenarios and more. Expect to learn about stream-stream joins, enriching stream data using local or remote data, and ways to anticipate and correct errors within the stream. Leave with a better understanding of managing data dependencies within a Spark Structured Streaming application.

Life is but a Stream

Databricks

Big Data Ecosystem- Impetus Technologies

Impetus Technologies

Cloud expo june 2013: Building a Real Time Analytics Platform on Big Data in ...

Sanjay Sharma

Operationalizing Machine Learning at Scale at Starbucks

Databricks

No sql and sql - open analytics summit

Open Analytics

How did Devon move from a traditional reporting and data warehouse approach to a modern data lake? What did it take to go from a slow and brittle technical landscape to an a flexible, scalable, and agile platform? In the past, Devon addressed data solutions in dozens of ways depending on the user and the requirements. Through a visionary program, driven by Databricks, Devon has begun a transformation of how it consumes data and enables engineers, analysts, and IT developers to deliver data driven solutions along all levels of the data analytics spectrum. We will share the vision, technical architecture, influential decisions, and lessons learned from our journey. Join us to hear the unique Databricks success story at Devon.

Transforming Devon’s Data Pipeline with an Open Source Data Hub—Built on Data...

Databricks

Vulnerabilities such as Spectre and Meltdown continue to plague many production servers, based on Intel CPUs. Our solution involves software-based monitoring of hardware counters and sending that data to Apache Spark clusters for threat detection. We leverage Spark's support for support vector machine (SVM) inference. Our machine learning models are trained off-line by a data scientist within a Jupyter notebook environment. As new models are validated, they can be easily deployed to the Spark cluster from the notebook. We have standardized model export and import using the ONNX machine learning open file format. In our presentation, we will demo the full pipeline, from model training to deployment. We will discuss the various challenges when deploying ML-based cyber-threat detection at scale using Apache Spark. For example, we found that gaps in detection can occur when Spark models are updated. We will describe a novel data ingestion architecture, based on Apache Kafka, that we developed to deal with this issue.

Scaling ML-Based Threat Detection For Production Cyber Attacks

Databricks

More data is being created than ever before and the rate at which it is being created is not slowing. This means that new techniques are being developed and used to tackle big data challenges. At Nielsen, big data means opportunities for new products like digital measurement, but also exposed a skills gap for dealing with big data. This session will cover Nielsen’s successful implementation of Spark and Databricks which has allowed Nielsen to scale its products and its Data Scientists’ skillsets.

Scaling Your Skillset with Your Data with Jarrett Garcia (Nielsen)

Spark Summit

Was ist angesagt? (20)

2016 Tableau in the Cloud - A Netflix Original (AWS Re:invent)

Building an ML Tool to predict Article Quality Scores using Delta & MLFlow

Virtualizing Analytics with Apache Spark: Keynote by Arsalan Tavakoli

Netflix Big Data Paris 2017

Mastering Your Customer Data on Apache Spark by Elliott Cordo

NYC Cassandra March 13- lighting talk

Data Warehousing with Spark Streaming at Zalando

Building a Data Science as a Service Platform in Azure with Databricks

Big data bi-mature-oanyc summit

Moving eBay’s Data Warehouse Over to Apache Spark – Spark as Core ETL Platfor...

Big data knolx

Validating credit cards on mobile using deep learning

Life is but a Stream

Big Data Ecosystem- Impetus Technologies

Cloud expo june 2013: Building a Real Time Analytics Platform on Big Data in ...

Operationalizing Machine Learning at Scale at Starbucks

No sql and sql - open analytics summit

Transforming Devon’s Data Pipeline with an Open Source Data Hub—Built on Data...

Scaling ML-Based Threat Detection For Production Cyber Attacks

Scaling Your Skillset with Your Data with Jarrett Garcia (Nielsen)

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

“Full Stack” Data Science with R for Startups: Production-ready with Open-Sou...

IDEAS - Int'l Data Engineering and Science Association

How to Hire and SRE in 5 minutes - Max Timchenko - DevOpsDays Tel Aviv 2017

DevOpsDays Tel Aviv

We’ll talk about the changes in the industry that customers are faced with and how Red Hat Hyperconverged Infrastructure can address those challenges . Our customers are struggling not only to manage the growth of big data (structured and unstructured), but also to reap timely business insights from their data using their existing data infrastructure like monolithic Hadoop clusters. This often leads to alternative approaches that often lead to disappointing results.

Managing data analytics in a hybrid cloud

Karan Singh

Introduction to the source{d} Stack

source{d}

The Path to Truly Understanding your MongoDB Data

MongoDB

Communicating information to each other is at the heart of the human experience. Data, and the analysis of it, often drives this communication in a business setting. This session aims to give you an understanding of how advances in Artificial Intelligence, specifically Natural Language Interaction (NLI), Natural Language Generation coupled with Deep Learning can create new and exciting opportunities for building analytical based chatbots. We will talk about how to design and train an NLI system that map requests to deep learning pipelines to derive insights. You will also learn how to apply NLG templates to help facilitate improved understanding and interaction with the chat-bot.

Conversational Artificial Intelligence with Ben Tomlinson and Wayne Thompson

Databricks

Graph Day 2017 Spring Boot

Christopher Pounds

presented at Web Unleashed 2019 For more info see https://fitc.ca/event/webu19/ Kevin Daly RBC Ventures Every developer has faced the difficult choice of deciding what tech stack they should use for a new project. Should you use the latest tech or something that everyone knows? Which framework is the best for your team? To survive your tech stack, developers must make trade-offs with developing on new tech stacks and the ability to maintain and scale their applications. In this presentation, you’ll learn how to evaluate your tech stack and understand the pros and cons of using bleeding edge technology. Using his past experiences, Kevin will also share his lessons learned and how his team tackles managing their tech stack today.

Surviving Your Tech Stack

FITC

At Monsanto, emerging technologies such as IoT, advanced imaging and geo-spatial platforms; molecular breeding, ancestry and genomics data sets have made us rethink how we approach developing, deploying, scaling and distributing our software to accelerate predictive and prescriptive decisions. We created a Cloud based Data Science platform for the enterprise to address this need. Our primary goals were to perform analytics@scale and integrate analytics with our core product platforms. As part of this talk, we will be sharing our journey of transformation showing how we enabled: a collaborative discovery analytics environment for data science teams to perform model development, provisioning data through APIs, streams and deploying models to production through our auto-scaling big-data compute in the cloud to perform streaming, cognitive, predictive, prescriptive, historical and batch analytics@scale, integrating analytics with our core product platforms to turn data into actionable insights.

Turn Data Into Actionable Insights - StampedeCon 2016

StampedeCon

Take a peek into the future of IT - beyond Serverless Software Development, when Serverless becomes a way to run Internal IT. When ServerlessToronto.org invited Joe Emison - AWS Serverless Hero, we expected to see how he "knocked down the wall" between AWS & Google Clouds (to query Amazon DynamoDB from Google BigQuery) using the Fivetran ELT tool, but we learned so much more... and you will too: https://youtu.be/GK5Ivm6EOlI

Running Business Analytics for a Serverless Insurance Company - Joe Emison & ...

Daniel Zivkovic

The Briefing Room with Dr. Robin Bloor and WhereScape Live Webcast on September 30, 2014 Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=bfff40f7c9645fc398770ea11152b148 The fueling of information systems will always require some effort, but a confluence of innovations is fundamentally changing how quickly and accurately it can be done. Gone are long cycle times for development. Today, organizations can embrace a more rapid and collaborative approach for building analytical applications and data warehouses. The key is to have business experts working hand-in-hand with data professionals as the solutions take shape, thus expediting the speed to valuable insights. Register for this episode of The Briefing Room to hear veteran Analyst Dr. Robin Bloor as he explains the changing nature of information design. He’ll be briefed by WhereScape President Mark Budzinski, who will discuss his company’s data warehouse automation solutions and how they enable collaborative development. He will share use cases that illustrate show aligning business and IT, organizations can enable faster and more agile data warehouse development. Visit InsideAnlaysis.com for more information.

Business in the Driver’s Seat – An Improved Model for Integration

Inside Analysis

Twilio is a cloud communication platform supporting 40,000+customers, 1+ Million Developers, handling millions of messages per minute across the globe from various different sectors. There are many regulated industries and parts of the world where data needs to be moved, stored and accessed securely. Twilio provides firm foundation for that and is focused towards providing customers a secure and scalable telecommunication cloud platform. Handling this massive amount of data in secured way is possible because of Kafka and Spark. Twilio’s Data platform team is building a compliance layer on top of Data Pipeline, Data Lake and Bulk Data Transformer to handle different compliance requirements such as GDPR, HIPAA, PCI etc. Secured Data Pipeline is a streaming channel for Data Lake, BI Data Warehouse and Elastic Search whereas Bulk Data Transformer is a ETL channel to transfer and transform bulk data from RDMS. Kafka Connect, Spark SQL and Data frames powers streaming channel and makes data wrangling and de-duping efficient. The Data Compliance layer has various components such as Data Anonymization, Authentication, Authorization, Auditing, Custom Retention and Data Deletion to handle the requirements of Processor and Controller. Anonymization as a service provides redaction, encryption and data obfuscation and is based on the varying needs of compliance and customers. Role based Access Control is applied on Kafka layers and S3 Layers to make sure only valid systems and users can access the critical data and rest of them will access to have only redacted data. Auditing service tracks all the access to various resources both from processor and controller perspective. Distributed Spark executor model makes the petabytes of data deletion efficient after the custom retention period. Thus scalable, fault-tolerant, distributed, secured, audited data governance pipeline is possible through Kafka, Kafka connect and Spark.

Enterprise Data Governance and Compliance at Scale with Sri Eshasubbiah and S...

Databricks

The Tin Can API is having major impacts on the direction of the e-learning industry. Organizations and vendors of various types are rushing to adopt Tin Can because it enables many things they have wanted to do for a long time. Things like mobile delivery, offline delivery, serious games and hosting content outside the LMS were all difficult or impossible with SCORM. These are easy with Tin Can. This webinar lets you get an in-depth look at what Tin Can means to various types of software and organizations, and learn what you need to be doing to make sure that you're keeping up with the trends that Tin Can has enabled in our industry. It features eight companies, each of which will tell you how they're using the Tin Can API, and what it means for their business.

The Impacts of the Tin Can API: How 8 Companies are Using the Tin Can API (xAPI)

Rustici Software

Jeremy will give a taste of how Apigee customers are using API Management to create compelling user experiences with incredible speed. We will quickly zoom through high level general principles like culture on our way to the architectural nitty gritty of why you should make a tailored API for every device you support. http://idc2016.sched.org/event/6AyR/apigee-creating-compelling-user-experiences-through-apis

Creating compelling user experiences through APIs

Jeremy Brown

Synapse NanoApps

Synapse Information Ltd

Data engineering to support reporting and analytics for commercial Lifesciences groups consists of very complex interdependent processing with highly complex business rules (thousands of transformations on hundreds of data sources). We will talk about our experiences in building a very high performance data processing platform powered by Spark that balances the considerations of extreme performance, speed of development, and cost of maintenance. We will touch upon optimizing enterprise grade Spark architecture for data warehousing and data mart type applications, optimizing end to end pipelines for extreme performance, running hundreds of jobs in parallel in Spark, orchestrating across multiple Spark clusters, and some guidelines for high speed platform and application development within enterprises. Key takeaways: – example architecture for complex data warehousing and data mart applications on Spark – architecture to build high performance Spark platforms for enterprises that balance functionality with total cost of ownership – orchestrating multiple elastic Spark clusters while running hundreds of jobs in parallel – business benefits of high performance data engineering, especially for Lifesciences.

High Performance Enterprise Data Processing with Apache Spark with Sandeep Va...

Spark Summit

"Walking on water and developing software from a specification are easy if both are frozen." - Edward V. Berard Decisions are context dependent. A right decision at the beginning may seem like a wrong decision 4 years later. This talk will compare the effect of architecture decisions of a small versus a mature project, how to make the "right" decision, and how to deal with problems when the right decision isn't right anymore.

The world is not black and white – Impact of decisions over the lifetime of a...

Eric Reiche

Oracle in the 2014 edition of its Open World rolled out new database public cloud service with its DBaaS offerings, but this is just a piece in each company's technological architecture. Businesses still have the need to create a Private cloud and discover the driver to create it; Wether it is a measured service,consolidation or rapid provisioning, finding this driver will be the initial building block for it. This presentation will give you an insight on how a Private Cloud is architected, how the service catalog is the most important brick and how get the benefit of this upcoming era of Databases.

Architecting Your Own DBaaS in a Private Cloud with EM12c

Gustavo Rene Antunez

The Challenges of Bringing Machine Learning to the Masses

Alice Zheng

In Between (A mobile App for couples, downloaded 20M in Global), from daily batch for extracting metrics, analysis and dashboard. Spark is widely used by engineers and data analysts in Between, thanks to the performance and expendability of Spark, data operating has become extremely efficient. Entire team including Biz Dev, Global Operation, Designers are enjoying data results so Spark is empowering entire company for data driven operation and thinking. Kevin, Co-founder and Data Team leader of Between will be presenting how things are going in Between. Listeners will know how small and agile team is living with data (how we build organization, culture and technical base) after this presentation.

Powering a Startup with Apache Spark with Kevin Kim

Spark Summit

Ähnlich wie How Apache Spark Changed the Way We Hire People with Tomasz Magdanski (20)

“Full Stack” Data Science with R for Startups: Production-ready with Open-Sou...

How to Hire and SRE in 5 minutes - Max Timchenko - DevOpsDays Tel Aviv 2017

Managing data analytics in a hybrid cloud

Introduction to the source{d} Stack

The Path to Truly Understanding your MongoDB Data

Conversational Artificial Intelligence with Ben Tomlinson and Wayne Thompson

Graph Day 2017 Spring Boot

Surviving Your Tech Stack

Turn Data Into Actionable Insights - StampedeCon 2016

Running Business Analytics for a Serverless Insurance Company - Joe Emison & ...

Business in the Driver’s Seat – An Improved Model for Integration

Enterprise Data Governance and Compliance at Scale with Sri Eshasubbiah and S...

The Impacts of the Tin Can API: How 8 Companies are Using the Tin Can API (xAPI)

Creating compelling user experiences through APIs

Synapse NanoApps

High Performance Enterprise Data Processing with Apache Spark with Sandeep Va...

The world is not black and white – Impact of decisions over the lifetime of a...

Architecting Your Own DBaaS in a Private Cloud with EM12c

The Challenges of Bringing Machine Learning to the Masses

Powering a Startup with Apache Spark with Kevin Kim

Mehr von Databricks

DW Migration Webinar-March 2022.pptx

Databricks

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 1

Databricks

Data Lakehouse Symposium | Day 1 | Part 2

Databricks

Data Lakehouse Symposium | Day 2

Databricks

Data Lakehouse Symposium | Day 4

Databricks

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.

5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

Databricks

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

Democratizing Data Quality Through a Centralized Platform

Databricks

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.

Learn to Use Databricks for Data Science

Databricks

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.

Why APM Is Not the Same As ML Monitoring

Databricks

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.

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

Databricks

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.

Stage Level Scheduling Improving Big Data and AI Integration

Databricks

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

Mehr von Databricks (20)

DW Migration Webinar-March 2022.pptx

Data Lakehouse Symposium | Day 1 | Part 1

Data Lakehouse Symposium | Day 1 | Part 2

Data Lakehouse Symposium | Day 2

Data Lakehouse Symposium | Day 4

5 Critical Steps to Clean Your Data Swamp When Migrating Off of Hadoop

Democratizing Data Quality Through a Centralized Platform

Learn to Use Databricks for Data Science

Why APM Is Not the Same As ML Monitoring

The Function, the Context, and the Data—Enabling ML Ops at Stitch Fix

Stage Level Scheduling Improving Big Data and AI Integration

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Scaling your Data Pipelines with Apache Spark on Kubernetes

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

Kürzlich hochgeladen

Model Call Girl Services in Delhi reach out to us at 🔝 9953056974 🔝✔️✔️ Our agency presents a selection of young, charming call girls available for bookings at Oyo Hotels. Experience high-class escort services at pocket-friendly rates, with our female escorts exuding both beauty and a delightful personality, ready to meet your desires. Whether it's Housewives, College girls, Russian girls, Muslim girls, or any other preference, we offer a diverse range of options to cater to your tastes. We provide both in-call and out-call services for your convenience. Our in-call location in Delhi ensures cleanliness, hygiene, and 100% safety, while our out-call services offer doorstep delivery for added ease. We value your time and money, hence we kindly request pic collectors, time-passers, and bargain hunters to refrain from contacting us. Our services feature various packages at competitive rates: One shot: ₹2000/in-call, ₹5000/out-call Two shots with one girl: ₹3500/in-call, ₹6000/out-call Body to body massage with sex: ₹3000/in-call Full night for one person: ₹7000/in-call, ₹10000/out-call Full night for more than 1 person: Contact us at 🔝 9953056974 🔝. for details Operating 24/7, we serve various locations in Delhi, including Green Park, Lajpat Nagar, Saket, and Hauz Khas near metro stations. For premium call girl services in Delhi 🔝 9953056974 🔝. Thank you for considering us!

CHEAP Call Girls in Saket (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE

9953056974 Low Rate Call Girls In Saket, Delhi NCR

FESE Capital Markets Fact Sheet 2024 Q1.pdf

MarinCaroMartnezBerg

Mg Road Call Girls Service: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore Escorts Service Booking Contact Details :- WhatsApp Chat :- +91-7737669865 4-May-2024(SMW) Call Girls In Model Towh +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in , Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service NCRWelcome To Escorts Service – An All Over New Very Sexy Hot Call Girls Agency Service Escorts In South NCR’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Mg Road Call Girls Service: 🍓 7737669865 🍓 High Profile Model Escorts | Banga...

amitlee9823

Call Girls Jalahalli Just Call 👗 7737669865 👗 Top Class Call Girl Service Bangalore Booking Contact Details :- WhatsApp Chat :- +91-7737669865 2-May-2024(SMW) Call Girls In Model Towh Bangalore +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in Bangalore NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in Bangalore, Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service Bangalore NCRWelcome To Bangalore Escorts Service – An All Over New Bangalore Very Sexy Hot Call Girls Agency Service Escorts In South BangaloreNCRBangalore’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Bangalore Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Call Girls Jalahalli Just Call 👗 7737669865 👗 Top Class Call Girl Service Ban...

amitlee9823

Al Barsha Escorts $#$ O565212860 $#$ Escort Service In Al Barsha Call girls in dubai Call girls at dubai Dubai Call girl mistaken. Just Call girl dubai Call girl in dubai Indian Call girls dubai Indian Call girl dubai Pakistan Call girls in dubai the Pakistani Call girl dubai https://services.tochat.be/whatsapp-business-directory/b22b3d16-5b26-4e59-b6c1-db53f14dfea0?utm_medium=social&utm_source=heylink.me Dubai Call girls service Dubai Call girl services Call girl service in dubai Dubai Call girl agency Dubai Call girls agency Verified Call girls dubai correct motivation is required. Her smile enlarges as if she were Young Call girls in dubai Marina Call girls Dubai marina Call girls Jumeirah Call girls Dubai Jumeirah Call girls Bur dubai Call girls Indian Call girls in bur dubai Call girls bur dubai hiding a tremendous secret. Al qusais Call girls Al nahda dubai Call girls Independent Call girls dubai Independent Call girl dubai Russian Call girls in dubai Dubai russian Call girls Young Call girls in dubai Dubai young Call girls Call girls numbers in dubai How about leaving your father's home, being wealthy, and being able to help your sister? Even though I know what she is going to say won't be good, my ears are ringing. To have this chat, I waited until Dubai Call girls number Call girls near me dubai Call girls near my hotel Cute Call girls in dubai Model Call girl in dubai Rent a girlfriend dubai you were eighteen years old. Do you understand what I do, Eden? Since I have no idea, I shake my head and my mind races. She must be some kind of successful businesswoman, I suppose. "I own a business. Do you recognize that? Knowing my best. She left. She said that Dad told her that Dubai Call girls Call girls dubai Call girls in dubai Call girls at dubai we didn’t need her anymore when he came home. I was sad.Dubai Call girl Call girl dubai Call girl in dubai Indian Call girls dubai Indian Call girl dubai Can you tell her to come back? I like her.” Her little face is Pakistan Call girls in dubai Pakistani Call girl dubai Dubai Call girls service Dubai Call girl services all pinched. So sweet. Call girl service in dubai Dubai Call girl agency Dubai Call girls agency Verified Call girls dubai But I'm pissed off. How can he Young Call girls in dubai Marina Call girls Dubai marina Call girls Jumeirah Call girls Dubai Jumeirah Call girls Bur dubai Call girls Indian Call girls in bur dubai Call girls bur dubai turn down someone I'm paying for? “So, who's here with you?” I ask her,Al qusais Call girls Al nahda dubai Call girls Independent Call girls dubai Independent Call girl dubai Russian Call girls in dubai Dubai russian Call girls fervently hoping she wasn’t here alone. “Dad's downstairs, I think Young Call girls in dubai Dubai young Call girls Call girls numbers in dubai Dubai Call girls number Call girls near me dubai Call girls near my hotel Cute Call girls in dubai Model Call girl in dubai Rent a girlfriend

Al Barsha Escorts $#$ O565212860 $#$ Escort Service In Al Barsha

AroojKhan71

Edukaciniai dropshipping via API with DroFx

olyaivanovalion

Call Girl In Dwarka ☎92055#41914 ¶¶ Indian,Russian Best Quality full Educated And Full Cooperative Independent Call Girls Escort Services In New Delhi- I Have Extremely Beautiful Broad Minded Cute Sexy & Hot Call Girls and Escorts, We Are Located in 3* 4* 5* Hotels in Delhi. Safe & Secure High Class Services Affordable Rate 100% Satisfaction, Unlimited Enjoyment. Any Time for Model/Teens Escort in Delhi High class luxury and premium escorts agency Indian Russian Call Girls In Delhi Booking Good High Profile Escorts (Call Girls) In Delhi 5 Star Hotel ,Incall Service,OutCall Service, We provide services by Call Girls,College Girls,Modals Get High Profile queens,Well Educated,Good Looking,Full Cooperative Model, Russian Models,Punjabi Girls Kashmeri Girls Services etc… We Provide Hottest Female With Safe And Consensual With Most Limits Respected Complete Satisfaction Guaranteed…Service. Call Me Spacial For Including Incall//outcall Service In New Delhi Indian Russian Escorts Service

Call Girls in Sarai Kale Khan Delhi 💯 Call Us 🔝9205541914 🔝( Delhi) Escorts S...

Delhi Call girls

Vip Mumbai Call Girls Marol Naka Call On 9920725232 With Body to body massage with sex Booking Contact Details :- WhatsApp Chat :- +91-9920725232 4-May-2024(SMW) Call Girls In Model Towh +91-9920725232 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in NCR 24 Hours Available Service Call Girls, Contact Us +91-9920725232 (Any Time. Any Where) Call Girls in , Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service NCRWelcome To Escorts Service – An All Over New Very Sexy Hot Call Girls Agency Service Escorts In South NCR’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —9920725232 We are available 24*7 all days of the year. Call us — 9920725232 Thank you for Visiting.

Vip Mumbai Call Girls Marol Naka Call On 9920725232 With Body to body massage...

amitlee9823

ELKO dropshipping via API with DroFx.pptx

olyaivanovalion

Call Girls Bannerghatta Road Just Call 👗 7737669865 👗 Top Class Call Girl Service Bangalore Booking Contact Details :- WhatsApp Chat :- +91-7737669865 2-May-2024(SMW) Call Girls In Model Towh Bangalore +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in Bangalore NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in Bangalore, Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service Bangalore NCRWelcome To Bangalore Escorts Service – An All Over New Bangalore Very Sexy Hot Call Girls Agency Service Escorts In South BangaloreNCRBangalore’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Bangalore Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Call Girls Bannerghatta Road Just Call 👗 7737669865 👗 Top Class Call Girl Ser...

amitlee9823

Probability Grade 10 Third Quarter Lessons

JoseMangaJr1

ALSO dropshipping via API with DroFx.pptx

olyaivanovalion

This project aims to predict whether a loan application will be approved or denied based on various factors such as applicant's income, credit score, loan amount, etc. Using a dataset containing historical loan application data, we employed machine learning algorithms to build a predictive model. The model was trained on features such as applicant's income, credit history, loan amount, loan term, and others. After training the model, we evaluated its performance using metrics like accuracy, precision, recall, and F1 score. The insights from this project can help financial institutions streamline their loan approval process and make informed decisions. Visit for more information: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Predicting Loan Approval: A Data Science Project

Boston Institute of Analytics

April 2024 - Crypto Market Report's Analysis

manisha194592

Accredited-Transport-Cooperatives-Jan-2021-Web.pdf

adriantubila

Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% Secure

Pooja Nehwal

Digital advertising, or paid media, encompasses the strategic deployment of online advertisements to reach target audiences efficiently and effectively. This includes any digital platform that supports advertising to deliver unique messages for any objective. Understanding the mechanics of digital advertising platforms, along with insights into audience behaviors and preferences, allows marketers to optimize their ad spend and achieve significant engagement and conversion rates. This lecture is for Advanced Digital & Social Media Strategy (MGMTX 466.05) at UCLA Extension.

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

Valters Lauzums

VidaXL dropshipping via API with DroFx.pptx

olyaivanovalion

Halmar dropshipping via API with DroFx

olyaivanovalion

Midocean dropshipping via API with DroFx

olyaivanovalion

Kürzlich hochgeladen (20)

CHEAP Call Girls in Saket (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE

FESE Capital Markets Fact Sheet 2024 Q1.pdf

Mg Road Call Girls Service: 🍓 7737669865 🍓 High Profile Model Escorts | Banga...

Call Girls Jalahalli Just Call 👗 7737669865 👗 Top Class Call Girl Service Ban...

Al Barsha Escorts $#$ O565212860 $#$ Escort Service In Al Barsha

Edukaciniai dropshipping via API with DroFx

Call Girls in Sarai Kale Khan Delhi 💯 Call Us 🔝9205541914 🔝( Delhi) Escorts S...

Vip Mumbai Call Girls Marol Naka Call On 9920725232 With Body to body massage...

ELKO dropshipping via API with DroFx.pptx

Call Girls Bannerghatta Road Just Call 👗 7737669865 👗 Top Class Call Girl Ser...

Probability Grade 10 Third Quarter Lessons

ALSO dropshipping via API with DroFx.pptx

Predicting Loan Approval: A Data Science Project

April 2024 - Crypto Market Report's Analysis

Accredited-Transport-Cooperatives-Jan-2021-Web.pdf

Call me @ 9892124323 Cheap Rate Call Girls in Vashi with Real Photo 100% Secure

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

VidaXL dropshipping via API with DroFx.pptx

Halmar dropshipping via API with DroFx

Midocean dropshipping via API with DroFx

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

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

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Was ist angesagt?

Was ist angesagt? (20)

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

Ähnlich wie How Apache Spark Changed the Way We Hire People with Tomasz Magdanski (20)

Mehr von Databricks

Mehr von Databricks (20)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

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