Building an AI-Powered Retail Experience with Delta Lake, Spark, and Databricks

•

3 recomendaciones•786 vistas

Zalando SE is Europe’s leading online fashion platform and connects customers, brands and partners. With millions of visitors each month, we have petabytes of purchase, click-stream, product and other data in our data lake. This data is crucial to powering insights on shopper behavior and driving an AI-first strategy to improve site engagement. Over 7 months ago, Zalando adopted Apache Spark, Delta Lake and Databricks as its de-facto computation platform for analytics and machine learning. During this period, we onboarded well over 50 internal teams ranging from BI teams, with no knowledge of Spark or big data running ETL pipelines to AI/ML teams already using EMR and Spark for heavy model training. Provided the spectrum of varied business problems they were trying to solve, we worked with each team individually, understanding their use cases, helping them validate assumptions, developing working code and taking them to production. In this talk we will share best practices for building a unified data and analytics architecture on Databricks, lessons learned rolling it out across the organization and provide a deep dive on AI & Analytics use cases in the fashion ecommerce space.

Datos y análisis

WIFI SSID:Spark+AISummit | Password: UnifiedDataAnalytics

Akhil Dhingra, Zalando
Saurav Verma, Zalando
AI-Powered Retail
Experience with
Databricks
#UnifiedDataAnalytics #SparkAISummit

3#UnifiedDataAnalytics #SparkAISummit
● Founded in 2008 in Berlin.
● Europe's leading online fashion
platform
● Connects customers, brands and
partners.
Zalando SE

About Us
6
Akhil Dhingra
Product Manager, Data Solutions @Zalando
Exp: 7+ Years, Ex-Groupon, Ex-Wingify | MBA
Saurav Verma
Senior Engineer, Data Lake @Zalando
Exp: 9+ Years , Ex-Visa | Masters NUS

Data Platform
8
● Data Lake on top
of S3
Data Sources

Data Platform
9
Data Sources
● Multi-tenant / single compute: more
ingestion pipelines

Many Use Cases
11
Data Sources
Team
B
Team
A

Many Use Cases
12
Data Sources
Team
C
Team
B
Team
A

Too Many Use Cases
13
Data Sources
Team
C
Team
B
Team
N
Team
M
Team
A

Too Many … Compute
14
Data Sources
Team
C
Team
B
Team
N
Team
M
Team
A
Compute
Auto-Scale
Stream
Batch
Training
Python
/ Scala

Too Many … Compute
15
Team
C
Team
B
Team
N
Team
M
Team
A
Compute
Auto-Scale
Stream
Batch
Training
Python
/ Scala
● Cost control problem at Scale
● More Time To Production
● No Best Practices
● Duplication of work / Data
● Dependencies
● Inconsistent Environment
● No Community Knowledge
● Accidental Complexity

Spark as a Service
16
● Foundational piece of Zalando’s
Big Data Infrastructure
● GitOps Management,
Decentralized Clusters
● Security / Compliance / CI-CD
● XX clusters/Jobs
● ~20 teams in production
● Thriving #Databricks community
in Zalando
Team
C
Team
B
Team
N
Team
M
Team
A
Auto-Scale
Stream
Batch
Training
Python
/ Scala

Spark as a Service
17
Migration Projects
ETLs | Data Preparation in
Spark-S3

Spark as a Service
18
Others:
Structured Streams |
Traceability

GDPR and Antitrust
20
Compliance with GDPR and antitrust
laws

GDPR and Antitrust
21
Probe (pilot)
- Use marker event to create heat
map of the data path.
- List of all datasets within the heat
map.

GDPR and Antitrust
22
Pseudonymize/Remove
- Identifier based, on-demand,
in-place record updater with field
precision
- Great for semi-structured formats
like JSON
- Use S3 Inventory + Streaming

Personalized article ranking for
relevance and user engagement.
Search & Ranking
23

Search & Ranking
24
Using Spark in ML
training pipeline !

Search & Ranking
25
Article Scoring and
personalization !
ML Model

Others
• Sizing: Reducing return rates due
to size and fit issues.
• Experimentation @Scale
• Merchant Analytics
• Marketing Services
26

First Impressions
• GitOps | Self Service
27

First Impressions
• Multi-Tiered support system
• Delta Adoption | But few readers outside
Databricks ecosystem
• Communicating pricing downstream
• Exploding Usage is Good
• Fits all Size?
28

29#UnifiedDataAnalytics #SparkAISummit
Thank you.
AI- Powered Retail
Experience with Databricks
Akhil Dhingra
Saurav Verma
www.zalando.com
www.jobs.zalando.com/tech

DON’T FORGET TO RATE
AND REVIEW THE SESSIONS
SEARCH SPARK + AI SUMMIT

Más contenido relacionado

La actualidad más candente

Near real-time analytics has become a common requirement for many data teams as the technology has caught up to the demand. One of the hardest aspects of enabling near-realtime analytics is making sure the source data is ingested and deduplicated often enough to be useful to analysts while writing the data in a format that is usable by your analytics query engine. This is usually the domain of many tools since there are three different aspects of the problem: streaming ingestion of data, deduplication using an ETL process, and interactive analytics. With Spark, this can be done with one tool. This talk with walk you through how to use Spark Streaming to ingest change-log data, use Spark batch jobs to perform major and minor compaction, and query the results with Spark.SQL. At the end of this talk you will know what is required to setup near-realtime analytics at your organization, the common gotchas including file formats and distributed file systems, and how to handle data the unique data integrity issues that arise from near-realtime analytics.

Near Real-Time Analytics with Apache Spark: Ingestion, ETL, and Interactive Q...

Databricks

HP ships millions of PCs, Printers, and other devices every year to customers in all market segments. More customers are seeking services provided with our products enabling new opportunities for HP to create services from the data we can collect from our devices. Every device we ship is an IoT endpoint with powerful CPU to capture rich data. Insights from this data are used internally to improve our products and focus on customer needs. In this presentation, John will focus on HP’s journey to enabling Big Data analytics from within a large enterprise environment. He will review the challenges and how HP decided on AWS, Apache Spark and Databricks as the foundation for their entry into Big Data Analytics. John will also review how HP uses Spark to build analytic services from the data they generate from their devices.

Unlocking Value in Device Data Using Spark: Spark Summit East talk by John La...

Spark Summit

The Spark Listener interface provides a fast, simple and efficient route to monitoring and observing your Spark application - and you can start using it in minutes. In this talk, we'll introduce the Spark Listener interfaces available in core and streaming applications, and show a few ways in which they've changed our world for the better at SpotX. If you're looking for a "Eureka!" moment in monitoring or tracking of your Spark apps, look no further than Spark Listeners and this talk!

Apache Spark Listeners: A Crash Course in Fast, Easy Monitoring

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

We have deployed a hybrid cloud storage solution that leverages compute in the public cloud along with our specialized hardware storage. We will discuss the tradeoffs of hybrid cloud storage, which workloads are best suited for this model, the pipeline we have deployed, and the challenges and best practices we have learned. Spark provides a flexible compute environment that can be used alongside todays cloud compute providers. However in read-heavy workloads that dominate much of analysis and machine learning today, storage costs scale poorly on these same cloud storage models. Hybrid cloud offers an alternative approach to get amortized storage costs over a dedicated link while using elastic compute in the cloud. We are currently running an end to end data science stack with multiple production workloads with this setup – A Spark-based ETL for transforming the real time log data that we ingest from our devices in the field into databases, a scale-out general regular expression search over log files that provides our support engineers real time access to searching for pathologies across our customer base, and a Spark based machine learning system for time series analysis to predict various customer metrics.

An End-to-End Spark-Based Machine Learning Stack in the Hybrid Cloud with Far...

Databricks

Virtualizing Analytics with Apache Spark: Keynote by Arsalan Tavakoli

Spark Summit

Redis accelerates Apache Spark execution by 45 times, when used as a shared distributed in-memory datastore for Spark in analyses like time series data range queries. With the redis module for machine learning, redis-ml, implementation of spark-ml models gains a new real time serving layer that offloads processing of models directly in Redis, allows multiple applications to reuse the same models and speeds up classification and execution of these models by 13x. Join this session to learn more about the Redis Labs’ connector for Apache Spark that enhances production implementations of real-time big data processing.

Analytics at the Real-Time Speed of Business: Spark Summit East talk by Manis...

Spark Summit

Data is increasingly becoming core to many products. Whether to provide recommendations for users, getting insights on how they use the product, or using machine learning to improve the experience. This creates a critical need for reliable data operations and understanding how data is flowing through our systems. Data pipelines must be auditable, reliable, and run on time. This proves particularly difficult in a constantly changing, fast-paced environment. Collecting this lineage metadata as data pipelines are running provides an understanding of dependencies between many teams consuming and producing data and how constant changes impact them. It is the underlying foundation that enables the many use cases related to data operations. The OpenLineage project is an API standardizing this metadata across the ecosystem, reducing complexity and duplicate work in collecting lineage information. It enables many projects, consumers of lineage in the ecosystem whether they focus on operations, governance or security. Marquez is an open source project part of the LF AI & Data foundation which instruments data pipelines to collect lineage and metadata and enable those use cases. It implements the OpenLineage API and provides context by making visible dependencies across organizations and technologies as they change over time.

Observability for Data Pipelines With OpenLineage

Databricks

"Most data practitioners grapple with data quality issues and data pipeline complexities—it's the bane of their existence. Data engineers, in particular, strive to design and deploy robust data pipelines that serve reliable data in a performant manner so that their organizations can make the most of their valuable corporate data assets. Databricks Delta, part of Databricks Runtime, is a next-generation unified analytics engine built on top of Apache Spark. Built on open standards, Delta employs co-designed compute and storage and is compatible with Spark API’s. It powers high data reliability and query performance to support big data use cases, from batch and streaming ingests, fast interactive queries to machine learning. In this tutorial we will discuss the requirements of modern data pipelines, the challenges data engineers face when it comes to data reliability and performance and how Delta can help. Through presentation, code examples and notebooks, we will explain pipeline challenges and the use of Delta to address them. You will walk away with an understanding of how you can apply this innovation to your data architecture and the benefits you can gain. This tutorial will be both instructor-led and hands-on interactive session. Instructions in how to get tutorial materials will be covered in class. WHAT YOU’LL LEARN: – Understand the key data reliability and performance data pipelines challenges – How Databricks Delta helps build robust pipelines at scale – Understand how Delta fits within an Apache Spark™ environment – How to use Delta to realize data reliability improvements – How to deliver performance gains using Delta PREREQUISITES: – A fully-charged laptop (8-16GB memory) with Chrome or Firefox – Pre-register for Databricks Community Edition" Speakers: Steven Yu, Burak Yavuz

Building Robust Production Data Pipelines with Databricks Delta

Databricks

"Combining Databricks, the unified analytics platform with Snowflake, the data warehouse built for the cloud is a powerful combo. Databricks offers the ability to process large amounts of data reliably, including developing scalable AI projects. Snowflake offers the elasticity of a cloud-based data warehouse that centralizes the access to data. Databricks brings the unparalleled utility of being based on a mature distributed big data processing and AI-enabled tool to the table, capable of integrating with nearly every technology, from message queues (e.g. Kafka) to databases (e.g. Snowflake) to object stores (e.g. S3) and AI tools (e.g. Tensorflow). Key Takeaways: How Databricks & Snowflake work; Why they're so powerful; How Databricks + Snowflake symbiotically catalyze analytics and AI initiatives"

Databricks + Snowflake: Catalyzing Data and AI Initiatives

Databricks

This talk will cover the tools we used, the hurdles we faced and the work arounds we developed with the help from Databricks support in our attempt to build a custom machine learning model and use it to predict the TV ratings for different networks and demographics. The Apache Spark machine learning and dataframe APIs make it incredibly easy to produce a machine learning pipeline to solve an archetypal supervised learning problem. In our applications at Cadent, we face a challenge with high dimensional labels and relatively low dimensional features; at first pass such a problem is all but intractable but thanks to a large number of historical records and the tools available in Apache Spark, we were able to construct a multi-stage model capable of forecasting with sufficient accuracy to drive the business application. Over the course of our work we have come across many tools that made our lives easier, and others that forced work around. In this talk we will review our custom multi-stage methodology, review the challenges we faced and walk through the key steps that made our project successful.

Apache Spark for Machine Learning with High Dimensional Labels: Spark Summit ...

Spark Summit

Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Comcast, GrubHub, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.

Presto: Fast SQL-on-Anything (including Delta Lake, Snowflake, Elasticsearch ...

Databricks

Multi Model Machine Learning by Maximo Gurmendez and Beth Logan

Spark Summit

Healthcare Claim Reimbursement using Apache Spark

Databricks

"In the oil and gas industry, utilizing vast amounts of data has long been identified as an important indicator of operational performance. The measurement of key performance indicators is a routine practice in well construction, but a systematic way of statistically analyzing performance against a large data bank of offset wells is not a common practice. The performance of statistical analysis in real-time is even less common. With the adoption of distributed computing platforms, like Apache Spark, new analysis opportunities become available to leverage large-scale time-series data sets to optimize performance. Two case studies are presented in this talk: the rate of penetration (ROP) and the amount of vibration per run. By collecting real-time, telemetry data and comparing it with historic sample datasets within the Databricks Unified Analytics Platform, the optimization team was able to quickly determine whether the performance being delivered matched or exceeded past performance with statistical certainty. This is extremely important while trying new techniques with data that is highly variable. By substituting anecdotal evidence with statistical analysis, decision making is more precise and better informed. In this talk we'll share how we accomplished this and the lessons learned along the way."

Unifying Streaming and Historical Telemetry Data For Real-time Performance Re...

Databricks

This talk is a case-study on how Apache Spark and the Spark-Solr library is being used at Flipp for driving search relevancy. Flipp is a Toronto based digital flyer and ecommerce company which helps shoppers save money on weekly shopping. Our customers have the option of browsing through our 5+ million products from the brick-and-mortar retailers in North America. This makes Search a very challenging function in our app. How to show the most relevant and personalized search results to users on a query? The talk will focus on using user signals such as Click Through Rate (CTR) and Impressions to increase search relevancy. I will also talk about how PySpark is used to create the Flipp Search ETL platform for collecting user signals and reading product data from Solr. The problem scenario will be explained in which keyword search and basic relevancy algorithms become ineffective when dealing with a large product database. The solutions will cover the following implementations being used at Flipp to drive relevancy: – Utilizing user clicks and popularity data to derive and index normalized item weights to implement the Search Crowd Curation models in Apache Solr – How around 5+ million items are classified into Google Categories in real time using Keras and Apache Spark to power product category curation in Solr. – How to create a crowd sourced query intent categorizer in Solr using the Spark-Solr library. – The use of offline and online metrics at Flipp for evaluating changes in search relevancy. – Future plans for incorporating Kafka-connect in Apache Solr with structured streaming to perform real-time product indexing with Spark-Solr library.

Using Spark-Solr at Scale: Productionizing Spark for Search with Apache Solr...

Databricks

The state of the art in productionizing machine Learning models today primarily addresses building RESTful APIs. In the Digital Ecosystem, RESTful APIs are a necessary, but not sufficient, part of the complete solution for productionizing ML models. And according to recent research by the McKinsey Global Institute, applying AI in marketing and sales has the most potential value. In the digital ecosystem, productionizing ML models at an accelerated pace becomes easy with: Feature Store with commonly used features that is available for all data scientists Feature Stores that distill visitor behavior is ready to use feature vectors in a semi supervised manner Data pipeline that can support the challenging demands of the digital ecosystem to feed the Feature Store on an ongoing basis Pipeline templates that support the challenging demands of the digital ecosystem that feed feature store, predict and distribute predictions on an ongoing basis. With these, a major electronics manufacturer was able to build and productionize a new model in 3 weeks. The use case for the model is retargeting advertising; it analyzes the behavior of website visitors and builds customized audiences of the visitors that are most likely to purchase 9 different products. Using the model, this manufacturer was able to maintain the same level of purchases with half of the retargeting media spend -increasing the efficiency of their marketing spend by 100%.

No REST till Production – Building and Deploying 9 Models to Production in 3 ...

Databricks

Spark Summit EU talk by Shaun Klopfenstein and Neelesh Shastry

Spark Summit

Translating Models to Medicine an Example of Managing Visual Communications

Databricks

At SpotX, we have built and maintained a portfolio of Spark Streaming applications -- all of which process records in the millions per minute. From pure data ingestion, to ETL, to real-time reporting, to live customer-facing products and features, continuous applications are in our DNA. Come along with us as we outline our journey from square one to present in the world of Spark Streaming. We'll detail what we've learned about efficient processing and monitoring, reliability and stability, and long term support of a streaming app. Come learn from our mistakes, and leave with some handy settings and designs you can implement in your own streaming apps.

Headaches and Breakthroughs in Building Continuous Applications

Databricks

La actualidad más candente (20)

Near Real-Time Analytics with Apache Spark: Ingestion, ETL, and Interactive Q...

Unlocking Value in Device Data Using Spark: Spark Summit East talk by John La...

Apache Spark Listeners: A Crash Course in Fast, Easy Monitoring

Virtualizing Analytics with Apache Spark: Keynote by Arsalan Tavakoli

An End-to-End Spark-Based Machine Learning Stack in the Hybrid Cloud with Far...

Virtualizing Analytics with Apache Spark: Keynote by Arsalan Tavakoli

Analytics at the Real-Time Speed of Business: Spark Summit East talk by Manis...

Observability for Data Pipelines With OpenLineage

Building Robust Production Data Pipelines with Databricks Delta

Databricks + Snowflake: Catalyzing Data and AI Initiatives

Apache Spark for Machine Learning with High Dimensional Labels: Spark Summit ...

Presto: Fast SQL-on-Anything (including Delta Lake, Snowflake, Elasticsearch ...

Multi Model Machine Learning by Maximo Gurmendez and Beth Logan

Healthcare Claim Reimbursement using Apache Spark

Unifying Streaming and Historical Telemetry Data For Real-time Performance Re...

Using Spark-Solr at Scale: Productionizing Spark for Search with Apache Solr...

No REST till Production – Building and Deploying 9 Models to Production in 3 ...

Spark Summit EU talk by Shaun Klopfenstein and Neelesh Shastry

Translating Models to Medicine an Example of Managing Visual Communications

Headaches and Breakthroughs in Building Continuous Applications

Similar a Building an AI-Powered Retail Experience with Delta Lake, Spark, and Databricks

Roadmap for Enterprise Graph Strategy

Neo4j

To disrupt and innovate, you need access to data. All of your data. The challenge for many organisations is that the data they need is locked away in a variety of silos. And there's perhaps no bigger silo than one of the most a widely deployed business application: SAP. Bringing together all your data for analytics and machine learning unlocks new insights and business value. Together, Cloudera and Datavard hold the key to breaking SAP data out of its silo, providing access to unlimited and untapped opportunities that currently lay hidden.

When SAP alone is not enough

Cloudera, Inc.

7 inspiring Big Data factories in AWS

Sebastien BONNOTTE

Societe Generale is one of the major banks in France and has many data science teams across the globe. After years of explorations and prototyping, it is time for the company to really deploy machine learning projects at scale to the production environment. To achieve that goal, we have been working hard to define a standard process of collaboration between data engineers and data scientists. And we also designed and deployed an infrastructure for productionizing machine learning. During this presentation, you will be looking at the following points of our adventure: 1. Difficulties that we had for putting ML applications into production, such as lack of model registry; hard to deploy ML libraries to our Hadoop cluster; collaboration between data scientists and data engineers etc. ? 2. How did we deploy MLflow as a key technical component to our production hadoop environment given different security constraints. 3. How did we build a CI/CD pipeline to deploy ML applications automatically. MLflow plays an important role in this piepline. 4. A first and concrete production project developed on top of this infrastructure with MLflow, Spark streaming, Sklearn and CI/CD. The key takeaways of this presentation would be: 1. To productionize machine learning in a big structure like Société Générale, a process of collaboration should be clearly defined. 2. A ML model registry is key to ML productionization. MLflow is the best solution we found. 3. A CI/CD pipeline is essential to the success of a machine learning application.

Machine Learning at Scale with MLflow and Apache Spark

Databricks

Your Roadmap for An Enterprise Graph Strategy

Neo4j

How to reinvent your organization in an iterative and pragmatic way? This is the result of using our digital toolbox. It allows you to transform your business model, expand your ecosystem by setting up your digital platform. This reinvention is also supported by the adaptation of your governance allowing you to innovate while guaranteeing the performance of your organization. For any information / suggestion / collaboration - william.poos@nrb.be Comment réinventer votre organisation de manière itérative et pragmatique ? C'est le résultat de l'utilisation de notre boîte à outils digitale. Elle vous permet de transformer votre modèle métier, d'étendre votre écosystème en mettant en place votre plateforme digitale. Cette réinvention est également supportée par l'adaptation de votre gouvernance vous permettant d'innover tout en garantissant la performance de votre organisation. Pour toute information / suggestion / collaboration - william.poos@nrb.be

Digital Reinvention by NRB

William Poos

Watch full webinar here: https://bit.ly/3oah4ng Gartner a récemment qualifié la Data Virtualisation comme étant une pièce maitresse des architectures d’intégration de données. Découvrez : - Les bénéfices d’une plateforme de virtualisation de données - La multiplication des usages : Lakehouse, Data Science, Big Data, Data Service & IoT - La création d’une vue unifiée de votre patrimoine de données sans transiger sur la performance - La construction d’une architecture d’intégration Agile des données : on-premise, dans le cloud ou hybride

Virtualisation de données : Enjeux, Usages & Bénéfices

Denodo

Your Roadmap for An Enterprise Graph Strategy

Neo4j

Interested in learning how Showtime is leveraging the power of Spark to transform a traditional premium cable network into a data-savvy analytical competitor? The growth in our over-the-top (OTT) streaming subscription business has led to an abundance of user-level data not previously available. To capitalize on this opportunity, we have been building and evolving our unified platform which allows data scientists and business analysts to tap into this rich behavioral data to support our business goals. We will share how our small team of data scientists is creating meaningful features which capture the nuanced relationships between users and content; productionizing machine learning models; and leveraging MLflow to optimize the runtime of our pipelines, track the accuracy of our models, and log the quality of our data over time. From data wrangling and exploration to machine learning and automation, we are augmenting our data supply chain by constantly rolling out new capabilities and analytical products to help the organization better understand our subscribers, our content, and our path forward to a data-driven future. Authors: Josh McNutt, Keria Bermudez-Hernandez

Data-Driven Transformation: Leveraging Big Data at Showtime with Apache Spark

Databricks

Dynniq & GoDataDriven - Shaping the future of traffic with IoT and AI

BigDataExpo

Knowledge Graph for Machine Learning and Data Science

Cambridge Semantics

NGSI-LD and Smart Data Models: Standard Access to Digital Twin Data - 15 July 2020 Corresponding webinar recording: https://youtu.be/MBx23ypORLk Understanding the basis of context information management, NGSI-LD and smart Data Models Chapter: Core Difficulty: 2 Audience: Any Technical Speaker: Juanjo Hierro (CTO, FIWARE Foundation), Alberto Abella (Data Modeling Expert and Technical Evangelist, FIWARE Foundation)

FIWARE Wednesday Webinars - NGSI-LD and Smart Data Models: Standard Access to...

FIWARE

Your Roadmap for An Enterprise Graph Strategy

Neo4j

Improve Store Expansion (Territory Management Featuring)

Esri España

Google Cloud Machine Learning

India Quotient

Watch here: https://bit.ly/3i2iJbu You will often hear that "data is the new gold". In this context, data management is one of the areas that has received more attention by the software community in recent years. From Artificial Intelligence and Machine Learning to new ways to store and process data, the landscape for data management is in constant evolution. From the privileged perspective of an enterprise middleware platform, we at Denodo have the advantage of seeing many of these changes happen. Join us for an exciting session that will cover: - The most interesting trends in data management. - Our predictions on how those trends will change the data management world. - How these trends are shaping the future of data virtualization and our own software.

What is the future of data strategy?

Denodo

At Sams Club we have a long history of using Apache Spark and Hadoop. Projects from all parts of the company use Apache Spark, from fraud detection to product recommendations. Because of the scale of our business with billions of transactions and trillions of events it is often essential to use big data technologies. Until recently all of this work has run on several large on-premise Hadoop clusters. As part of our transition to public cloud we needed to build out an enterprise scale data platform. Azure Databricks is a key component of this platform giving our data scientist, engineers, and business users the ability to easily work with the companies data. We will discuss our architecture considerations that lead to using multiple Databricks workspaces and external Azure blob storage. We will also discuss how we move massive amounts of data to Azure on a daily basis with Airflow. Further we will discuss the self-service tools that we created to help users get their data to Azure and for us to manage the platform. Finally we will discuss our security considerations and how that played out in our architecture. Authors: Andrew Ray, Craig Covey

Building an Enterprise Data Platform with Azure Databricks to Enable Machine ...

Databricks

Are you considering cloud options or even looking at expanding your e-procurement tools and trying to sort through the cloud jargon? Are you unsure of the change management impacts with cloud solutions? This session will focus on how to take a holistic view of your existing procurement systems and understand the value and challenges of transitioning to the Cloud. We will explain the key considerations and challenges by module that companies encounter when transitioning to the Cloud. Overall, this session will be a great opportunity to learn about migrating to the cloud and how you can evaluate and optimize this transition while setting the right solution architecture for your company.

Evaluating the Business and Change Management Implications of a Cloud Transition

SAP Ariba

Integration is a challenge that has become even more urgent with the move to the cloud that all organizations are making or are about to make. Whether SaaS applications have to be enabled (linked to other SaaS applications or to custom apps) or IoT is used to integrate the physical world into enterprise IT or whether microservices (on premises) have to collaborate with microservices (in the cloud) - integration is at the heart of enterprise IT. This presentation discusses the move to the cloud, a number of common integration use cases and the key components in Oracle PaaS Portfolio for tackling these challenges. The presentation was delivered at the Oracle Cloud Day 2017 in Nieuwegein, The Netherlands

Integrating Applications and Data (with Oracle PaaS Cloud) - Oracle Cloud Day...

Lucas Jellema

ICP for Data- Enterprise platform for AI, ML and Data Science

Karan Sachdeva

Similar a Building an AI-Powered Retail Experience with Delta Lake, Spark, and Databricks (20)

Roadmap for Enterprise Graph Strategy

When SAP alone is not enough

7 inspiring Big Data factories in AWS

Machine Learning at Scale with MLflow and Apache Spark

Your Roadmap for An Enterprise Graph Strategy

Digital Reinvention by NRB

Virtualisation de données : Enjeux, Usages & Bénéfices

Your Roadmap for An Enterprise Graph Strategy

Data-Driven Transformation: Leveraging Big Data at Showtime with Apache Spark

Dynniq & GoDataDriven - Shaping the future of traffic with IoT and AI

Knowledge Graph for Machine Learning and Data Science

FIWARE Wednesday Webinars - NGSI-LD and Smart Data Models: Standard Access to...

Your Roadmap for An Enterprise Graph Strategy

Improve Store Expansion (Territory Management Featuring)

Google Cloud Machine Learning

What is the future of data strategy?

Building an Enterprise Data Platform with Azure Databricks to Enable Machine ...

Evaluating the Business and Change Management Implications of a Cloud Transition

Integrating Applications and Data (with Oracle PaaS Cloud) - Oracle Cloud Day...

ICP for Data- Enterprise platform for AI, ML and Data Science

Más de 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

Más de 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

Último

Top profile Call Girls In Chandrapur [ 7014168258 ] Call Me For Genuine Models We are available 24*7 Booking Contact Details :- WhatsApp Chat :- +91-7014168258 If you're looking for India Call girls you've come to the right place. You'll find some of the most beautiful call girls in our location with. These ladies have pleasing personalities, hot figures, and a passion for physical pleasure. Call girls in India Lucknow Many men have booked them for their erotic and soul-mixing performances, which are sure to leave you with unforgettable memories. #K09 Escort Service India is available in the city for men and women of all ages. They can satisfy your sexual needs and will make your experience even more enjoyable and memorable. Whether you're looking for a blow-job, stripping, lovemaking, or other dirty acts, you'll be able to find a match for your tastes and budget. These highly trained professionals will help you have an unforgettable night. 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 —7014168258 We are available 24*7 all days of the year. Call us — 7014168258 Thank you for Visiting.

Top profile Call Girls In Chandrapur [ 7014168258 ] Call Me For Genuine Model...

gajnagarg

Top profile Call Girls In Latur [ 7014168258 ] Call Me For Genuine Models We are available 24*7 Booking Contact Details :- WhatsApp Chat :- +91-7014168258 If you're looking for India Call girls you've come to the right place. You'll find some of the most beautiful call girls in our location with. These ladies have pleasing personalities, hot figures, and a passion for physical pleasure. Call girls in India Lucknow Many men have booked them for their erotic and soul-mixing performances, which are sure to leave you with unforgettable memories. #K09 Escort Service India is available in the city for men and women of all ages. They can satisfy your sexual needs and will make your experience even more enjoyable and memorable. Whether you're looking for a blow-job, stripping, lovemaking, or other dirty acts, you'll be able to find a match for your tastes and budget. These highly trained professionals will help you have an unforgettable night. 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 —7014168258 We are available 24*7 all days of the year. Call us — 7014168258 Thank you for Visiting.

Top profile Call Girls In Latur [ 7014168258 ] Call Me For Genuine Models We ...

gajnagarg

Lecture_2_Deep_Learning_Overview-newone1

ranjankumarbehera14

Charbagh + Female Escorts Service in Lucknow | Starting ₹,5K To @25k with A/C 9548273370 Escort Service CALL GIRL IN Lucknow 9548273370 ❤CALL GIRLS IN ESCORT SERVICE❤CALL GIRL IN #j11 We are Providing :- ● – Private independent collage Going girls . ● – independent Models . ● – House Wife’s . ● – Private Independent House Wife’s ● – Corporate M.N.C Working Profiles . ● – Call Center Girls . ● – Live Band Girls . ●- Foreigners & Many More . Service type: 1.In call 2.out call 3. full Lip to Lip kiss 4.69 5.b-job without Condom 6. Hard Core sex & Much More. 7 Body to Body Touch 8 Kissing 9 Sucking Boobs and More 10 Enjoy by Hand 11 Relax By Oral 12 Sex with Happy Ending • In Call and Out Call Service • 3* 5* 7* Hotels Service • 24 Hours Available • Indian, Russian, Punjabi, Kashmiri Escorts • Real Models, College Girls, House Wife, Also Available • Short Time and Full Time Service Available • Hygienic Full AC Neat and Clean Rooms Avail. In Hotel 24 hours • Daily Escorts Staff Available • Minimum to Maximu m Range Available.c

Charbagh + Female Escorts Service in Lucknow | Starting ₹,5K To @25k with A/C...

HyderabadDolls

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

Jodhpur Park | Call Girls in Kolkata Phone No 8005736733 Elite Escort Service Available 24/7 Hire Our agency presents a selection of young, charming call girls #J11 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,#J11 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 Kolkata 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 🔝 8005736733 🔝. for details Operating 24/7, we serve various locations in Kolkata, including Green Park, near metro stations. For premium call girl services in Kolkata 🔝 8005736733 🔝. Thank you for considering us!

Jodhpur Park | Call Girls in Kolkata Phone No 8005736733 Elite Escort Service...

HyderabadDolls

Discover how TrafficWave Generator can drive targeted, engaging visitors to your online assets and propel your business to new heights. Unlock the power of free, set-and-forget traffic today. Featuring a user-friendly interface, set-and-forget functionality, and the ability to monetize the traffic with your affiliate links or product offers, TrafficWave Generator is a game-changer for online entrepreneurs, bloggers, and e-commerce store owners alike. Backed by a 30-day money-back guarantee and comprehensive bonus offerings, this software has the potential to transform your traffic-generation and content-creation efforts, unlocking new levels of success in the digital marketing landscape.

TrafficWave Generator Will Instantly drive targeted and engaging traffic back...

SOFTTECHHUB

SAC 25 Final National, Regional & Local Angel Group Investing Insights 2024 0...

Elaine Werffeli

Digital Transformation Playbook by Graham Ware

Graham Ware

Top profile Call Girls In Bihar Sharif [ 7014168258 ] Call Me For Genuine Models We are available 24*7 Booking Contact Details :- WhatsApp Chat :- +91-7014168258 If you're looking for India Call girls you've come to the right place. You'll find some of the most beautiful call girls in our location with. These ladies have pleasing personalities, hot figures, and a passion for physical pleasure. Call girls in India Lucknow Many men have booked them for their erotic and soul-mixing performances, which are sure to leave you with unforgettable memories. #K09 Escort Service India is available in the city for men and women of all ages. They can satisfy your sexual needs and will make your experience even more enjoyable and memorable. Whether you're looking for a blow-job, stripping, lovemaking, or other dirty acts, you'll be able to find a match for your tastes and budget. These highly trained professionals will help you have an unforgettable night. 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 —7014168258 We are available 24*7 all days of the year. Call us — 7014168258 Thank you for Visiting.

Top profile Call Girls In Bihar Sharif [ 7014168258 ] Call Me For Genuine Mod...

nirzagarg

Top profile Call Girls In Hapur [ 7014168258 ] Call Me For Genuine Models We are available 24*7 Booking Contact Details :- WhatsApp Chat :- +91-7014168258 If you're looking for India Call girls you've come to the right place. You'll find some of the most beautiful call girls in our location with. These ladies have pleasing personalities, hot figures, and a passion for physical pleasure. Call girls in India Lucknow Many men have booked them for their erotic and soul-mixing performances, which are sure to leave you with unforgettable memories. #K09 Escort Service India is available in the city for men and women of all ages. They can satisfy your sexual needs and will make your experience even more enjoyable and memorable. Whether you're looking for a blow-job, stripping, lovemaking, or other dirty acts, you'll be able to find a match for your tastes and budget. These highly trained professionals will help you have an unforgettable night. 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 —7014168258 We are available 24*7 all days of the year. Call us — 7014168258 Thank you for Visiting.

Top profile Call Girls In Hapur [ 7014168258 ] Call Me For Genuine Models We ...

nirzagarg

#Dubai Call Girls Agency +971525547819 #Indian And Pakistani Call Girls Dubai #Dubai Indian Call Girls Agency Class Call Girls In Dubai #First Class Call Girls In Dubai #Full Massage Services Call Girls In Dubai #Al Jaddaf,Al Jaffiliya,Business Bay,Al Karama,Bur Dubai,Deira,Dubai,Palm Jumeirah,Al Wasl,Trade Centre,Dubai Mall,JBR,JVC,JLT,Discovery Garden #Dubai Call Girls Services Provide In Ajman_Dubai_RAK_UMQ_Fujairah_Abu_Dhabi#Indian #Tamil #Kerala #Russian #Philippine #Morocco #Thailand #English Models In Dubai #If You Want Serv#Dubai Pakistani Call Girls Agency #Beautiful Call Girls in Dubai #High ices Just Send Me Text On Whatsapp +971525547819 #Website Link http://Dubaicallgirls.pro https://chatwith.io/s/65d1df48b2992

Dubai Call Girls Peeing O525547819 Call Girls Dubai

kojalkojal131

Saudi Arabia [ Abortion pills) Jeddah/riaydh/dammam/+966572737505☎️] cytotec tablets uses abortion pills 💊💊 How effective is the abortion pill? 💊💊 +966572737505) "Abortion pills in Jeddah" how to get cytotec tablets in Riyadh " Abortion pills in dammam*💊💊 The abortion pill is very effective. If you’re taking mifepristone and misoprostol, it depends on how far along the pregnancy is, and how many doses of medicine you take:💊💊 +966572737505) how to buy cytotec pills At 8 weeks pregnant or less, it works about 94-98% of the time. +966572737505[ 💊💊💊 At 8-9 weeks pregnant, it works about 94-96% of the time. +966572737505) At 9-10 weeks pregnant, it works about 91-93% of the time. +966572737505)💊💊 If you take an extra dose of misoprostol, it works about 99% of the time. At 10-11 weeks pregnant, it works about 87% of the time. +966572737505) If you take an extra dose of misoprostol, it works about 98% of the time. In general, taking both mifepristone and+966572737505 misoprostol works a bit better than taking misoprostol only. +966572737505 Taking misoprostol alone works to end the+966572737505 pregnancy about 85-95% of the time — depending on how far along the+966572737505 pregnancy is and how you take the medicine. +966572737505 The abortion pill usually works, but if it doesn’t, you can take more medicine or have an in-clinic abortion. +966572737505 When can I take the abortion pill?+966572737505 In general, you can have a medication abortion up to 77 days (11 weeks)+966572737505 after the first day of your last period. If it’s been 78 days or more since the first day of your last+966572737505 period, you can have an in-clinic abortion to end your pregnancy.+966572737505 Why do people choose the abortion pill? Which kind of abortion you choose all depends on your personal+966572737505 preference and situation. With+966572737505 medication+966572737505 abortion, some people like that you don’t need to have a procedure in a doctor’s office. You can have your medication abortion on your own+966572737505 schedule, at home or in another comfortable place that you choose.+966572737505 You get to decide who you want to be with during your abortion, or you can go it alone. Because+966572737505 medication abortion is similar to a miscarriage, many people feel like it’s more “natural” and less invasive. And some+966572737505 people may not have an in-clinic abortion provider close by, so abortion pills are more available to+966572737505 them. +966572737505 Your doctor, nurse, or health center staff can help you decide which kind of abortion is best for you. +966572737505 More questions from patients: Saudi Arabia+966572737505 CYTOTEC Misoprostol Tablets. Misoprostol is a medication that can prevent stomach ulcers if you also take NSAID medications. It reduces the amount of acid in your stomach, which protects your stomach lining. The brand name of this medication is Cytotec®.+966573737505) Unwanted Kit is a combination of two medicin

Abortion pills in Jeddah | +966572737505 | Get Cytotec

Abortion pills in Riyadh +966572737505 get cytotec

Top Call Girls in Balaghat 9332606886Call Girls Advance Cash On Delivery Service Booking Now open +91- 9332606886 Pune VIP Call Girls is highly qualified professionals. #S10 They will offer only top quality services and never try to scam or drain you of money; in fact, they strive to give complete satisfaction so that each cent spent was worth its worth. Furthermore, these professionals pride themselves in keeping customer details confidential; any information gleaned will never be shared with any third party.$V15 Two shots with one girl: ₹4500/in-call, ₹7500/out-call Body to body massage with : ₹5500/in-call Full night for one person: ₹7000/in-call, ₹12000/out-call •𝐂𝐨𝐥𝐥𝐞𝐠𝐞 𝐆𝐢𝐫𝐥𝐬 •𝐇𝐨𝐮𝐬𝐞𝐰𝐢𝐯𝐞𝐬 •𝐇𝐢𝐠𝐡 𝐏𝐫𝐨𝐟𝐢𝐥𝐞 𝐆𝐢𝐫𝐥𝐬 •𝐑𝐚𝐦𝐩 𝐌𝐨𝐝𝐞𝐥𝐬 •𝐅𝐨𝐫𝐞𝐢𝐠𝐧𝐞𝐫 𝐆𝐢𝐫𝐥𝐬 𝐎𝐮𝐫 𝐞𝐬𝐜𝐨𝐫𝐭 𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 •𝐇𝐉 (𝐇𝐚𝐧𝐝 𝐉𝐨𝐛) •𝐁𝐉 (𝐁𝐥𝐨𝐰𝐣𝐨𝐛) # Completion # (Oral To Completion) # Special Massage # O-Level (Oral ) # Oral With A Noncondom) # COB (Come On Body) # Extraball (Have Many Times) # All Meetings 100%Safe

Top Call Girls in Balaghat 9332606886Call Girls Advance Cash On Delivery Ser...

kumargunjan9515

原版定制【微信:176555708】【圣地亚哥州立大学毕业证（SDSU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！

怎样办理圣地亚哥州立大学毕业证（SDSU毕业证书）成绩单学校原版复制

vexqp

Top profile Call Girls In Begusarai [ 7014168258 ] Call Me For Genuine Models We are available 24*7 Booking Contact Details :- WhatsApp Chat :- +91-7014168258 If you're looking for India Call girls you've come to the right place. You'll find some of the most beautiful call girls in our location with. These ladies have pleasing personalities, hot figures, and a passion for physical pleasure. Call girls in India Lucknow Many men have booked them for their erotic and soul-mixing performances, which are sure to leave you with unforgettable memories. #K09 Escort Service India is available in the city for men and women of all ages. They can satisfy your sexual needs and will make your experience even more enjoyable and memorable. Whether you're looking for a blow-job, stripping, lovemaking, or other dirty acts, you'll be able to find a match for your tastes and budget. These highly trained professionals will help you have an unforgettable night. 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 —7014168258 We are available 24*7 all days of the year. Call us — 7014168258 Thank you for Visiting.

Top profile Call Girls In Begusarai [ 7014168258 ] Call Me For Genuine Models...

nirzagarg

20240412-SmartCityIndex-2024-Full-Report.pdf

khraisr

5CL-ADBA,5cladba, Chinese supplier, safety is guaranteed

amy56318795

Sonagachi * best call girls in Kolkata | ₹,9500 Pay Cash 8005736733 Free Home Delivery Escorts Service Our agency presents a selection of young, charming call girls #J11 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,#J11 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 Kolkata 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 🔝 8005736733 🔝. for details Operating 24/7, we serve various locations in Kolkata, including Green Park, near metro stations. For premium call girl services in Kolkata 🔝 8005736733 🔝. Thank you for considering us!

Sonagachi * best call girls in Kolkata | ₹,9500 Pay Cash 8005736733 Free Home...

HyderabadDolls

Computer science Sql cheat sheet.pdf.pdf

SayantanBiswas37

Building an AI-Powered Retail Experience with Delta Lake, Spark, and Databricks

1. WIFI SSID:Spark+AISummit | Password: UnifiedDataAnalytics

2. Akhil Dhingra, Zalando Saurav Verma, Zalando AI-Powered Retail Experience with Databricks #UnifiedDataAnalytics #SparkAISummit

3. 3#UnifiedDataAnalytics #SparkAISummit ● Founded in 2008 in Berlin. ● Europe's leading online fashion platform ● Connects customers, brands and partners. Zalando SE

4. Zalando SE 4

5. Big-Data Stack @ Zalando 5

6. About Us 6 Akhil Dhingra Product Manager, Data Solutions @Zalando Exp: 7+ Years, Ex-Groupon, Ex-Wingify | MBA Saurav Verma Senior Engineer, Data Lake @Zalando Exp: 9+ Years , Ex-Visa | Masters NUS

7. Data Platform 7 Data Sources

8. Data Platform 8 ● Data Lake on top of S3 Data Sources

9. Data Platform 9 Data Sources ● Multi-tenant / single compute: more ingestion pipelines

10. Many Use Cases 10 Data Sources Team A

11. Many Use Cases 11 Data Sources Team B Team A

12. Many Use Cases 12 Data Sources Team C Team B Team A

13. Too Many Use Cases 13 Data Sources Team C Team B Team N Team M Team A

14. Too Many … Compute 14 Data Sources Team C Team B Team N Team M Team A Compute Auto-Scale Stream Batch Training Python / Scala

15. Too Many … Compute 15 Team C Team B Team N Team M Team A Compute Auto-Scale Stream Batch Training Python / Scala ● Cost control problem at Scale ● More Time To Production ● No Best Practices ● Duplication of work / Data ● Dependencies ● Inconsistent Environment ● No Community Knowledge ● Accidental Complexity

16. Spark as a Service 16 ● Foundational piece of Zalando’s Big Data Infrastructure ● GitOps Management, Decentralized Clusters ● Security / Compliance / CI-CD ● XX clusters/Jobs ● ~20 teams in production ● Thriving #Databricks community in Zalando Team C Team B Team N Team M Team A Auto-Scale Stream Batch Training Python / Scala

17. Spark as a Service 17 Migration Projects ETLs | Data Preparation in Spark-S3

18. Spark as a Service 18 Others: Structured Streams | Traceability

19. Spectrum of use cases 19

20. GDPR and Antitrust 20 Compliance with GDPR and antitrust laws

21. GDPR and Antitrust 21 Probe (pilot) - Use marker event to create heat map of the data path. - List of all datasets within the heat map.

22. GDPR and Antitrust 22 Pseudonymize/Remove - Identifier based, on-demand, in-place record updater with field precision - Great for semi-structured formats like JSON - Use S3 Inventory + Streaming

23. Personalized article ranking for relevance and user engagement. Search & Ranking 23

24. Search & Ranking 24 Using Spark in ML training pipeline !

25. Search & Ranking 25 Article Scoring and personalization ! ML Model

26. Others • Sizing: Reducing return rates due to size and fit issues. • Experimentation @Scale • Merchant Analytics • Marketing Services 26

27. First Impressions • GitOps | Self Service 27

28. First Impressions • Multi-Tiered support system • Delta Adoption | But few readers outside Databricks ecosystem • Communicating pricing downstream • Exploding Usage is Good • Fits all Size? 28

29. 29#UnifiedDataAnalytics #SparkAISummit Thank you. AI- Powered Retail Experience with Databricks Akhil Dhingra Saurav Verma www.zalando.com www.jobs.zalando.com/tech

30. DON’T FORGET TO RATE AND REVIEW THE SESSIONS SEARCH SPARK + AI SUMMIT

Building an AI-Powered Retail Experience with Delta Lake, Spark, and Databricks

Recomendados

Recomendados

Más contenido relacionado

La actualidad más candente

La actualidad más candente (20)

Similar a Building an AI-Powered Retail Experience with Delta Lake, Spark, and Databricks

Similar a Building an AI-Powered Retail Experience with Delta Lake, Spark, and Databricks (20)

Más de Databricks

Más de Databricks (20)

Último

Último (20)

Building an AI-Powered Retail Experience with Delta Lake, Spark, and Databricks