The Future of Data Science and Machine Learning at Scale: A Look at MLflow, D...Databricks
Many had dubbed 2020 as the decade of data. This is indeed an era of data zeitgeist.
From code-centric software development 1.0, we are entering software development 2.0, a data-centric and data-driven approach, where data plays a central theme in our everyday lives.
As the volume and variety of data garnered from myriad data sources continue to grow at an astronomical scale and as cloud computing offers cheap computing and data storage resources at scale, the data platforms have to match in their abilities to process, analyze, and visualize at scale and speed and with ease — this involves data paradigm shifts in processing and storing and in providing programming frameworks to developers to access and work with these data platforms.
In this talk, we will survey some emerging technologies that address the challenges of data at scale, how these tools help data scientists and machine learning developers with their data tasks, why they scale, and how they facilitate the future data scientists to start quickly.
In particular, we will examine in detail two open-source tools MLflow (for machine learning life cycle development) and Delta Lake (for reliable storage for structured and unstructured data).
Other emerging tools such as Koalas help data scientists to do exploratory data analysis at scale in a language and framework they are familiar with as well as emerging data + AI trends in 2021.
You will understand the challenges of machine learning model development at scale, why you need reliable and scalable storage, and what other open source tools are at your disposal to do data science and machine learning at scale.
Embarking on building a modern data warehouse in the cloud can be an overwhelming experience due to the sheer number of products that can be used, especially when the use cases for many products overlap others. In this talk I will cover the use cases of many of the Microsoft products that you can use when building a modern data warehouse, broken down into four areas: ingest, store, prep, and model & serve. It’s a complicated story that I will try to simplify, giving blunt opinions of when to use what products and the pros/cons of each.
Modernizing to a Cloud Data ArchitectureDatabricks
Organizations with on-premises Hadoop infrastructure are bogged down by system complexity, unscalable infrastructure, and the increasing burden on DevOps to manage legacy architectures. Costs and resource utilization continue to go up while innovation has flatlined. In this session, you will learn why, now more than ever, enterprises are looking for cloud alternatives to Hadoop and are migrating off of the architecture in large numbers. You will also learn how elastic compute models’ benefits help one customer scale their analytics and AI workloads and best practices from their experience on a successful migration of their data and workloads to the cloud.
Intuit's Data Mesh - Data Mesh Leaning Community meetup 5.13.2021Tristan Baker
Past, present and future of data mesh at Intuit. This deck describes a vision and strategy for improving data worker productivity through a Data Mesh approach to organizing data and holding data producers accountable. Delivered at the inaugural Data Mesh Leaning meetup on 5/13/2021.
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
The document discusses migrating a data warehouse to the Databricks Lakehouse Platform. It outlines why legacy data warehouses are struggling, how the Databricks Platform addresses these issues, and key considerations for modern analytics and data warehousing. The document then provides an overview of the migration methodology, approach, strategies, and key takeaways for moving to a lakehouse on Databricks.
Data Build Tool (DBT) is an open source technology to set up your data lake using best practices from software engineering. This SQL first technology is a great marriage between Databricks and Delta. This allows you to maintain high quality data and documentation during the entire datalake life-cycle. In this talk I’ll do an introduction into DBT, and show how we can leverage Databricks to do the actual heavy lifting. Next, I’ll present how DBT supports Delta to enable upserting using SQL. Finally, we show how we integrate DBT+Databricks into the Azure cloud. Finally we show how we emit the pipeline metrics to Azure monitor to make sure that you have observability over your pipeline.
This document discusses data mesh, a distributed data management approach for microservices. It outlines the challenges of implementing microservice architecture including data decoupling, sharing data across domains, and data consistency. It then introduces data mesh as a solution, describing how to build the necessary infrastructure using technologies like Kubernetes and YAML to quickly deploy data pipelines and provision data across services and applications in a distributed manner. The document provides examples of how data mesh can be used to improve legacy system integration, batch processing efficiency, multi-source data aggregation, and cross-cloud/environment integration.
Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.
Data Warehouse or Data Lake, Which Do I Choose?DATAVERSITY
Today’s data-driven companies have a choice to make – where do we store our data? As the move to the cloud continues to be a driving factor, the choice becomes either the data warehouse (Snowflake et al) or the data lake (AWS S3 et al). There are pro’s and con’s for each approach. While the data warehouse will give you strong data management with analytics, they don’t do well with semi-structured and unstructured data with tightly coupled storage and compute, not to mention expensive vendor lock-in. On the other hand, data lakes allow you to store all kinds of data and are extremely affordable, but they’re only meant for storage and by themselves provide no direct value to an organization.
Enter the Open Data Lakehouse, the next evolution of the data stack that gives you the openness and flexibility of the data lake with the key aspects of the data warehouse like management and transaction support.
In this webinar, you’ll hear from Ali LeClerc who will discuss the data landscape and why many companies are moving to an open data lakehouse. Ali will share more perspective on how you should think about what fits best based on your use case and workloads, and how some real world customers are using Presto, a SQL query engine, to bring analytics to the data lakehouse.
This document provides an overview and summary of the author's background and expertise. It states that the author has over 30 years of experience in IT working on many BI and data warehouse projects. It also lists that the author has experience as a developer, DBA, architect, and consultant. It provides certifications held and publications authored as well as noting previous recognition as an SQL Server MVP.
Snowflake + Power BI: Cloud Analytics for EveryoneAngel Abundez
This document discusses architectures for using Snowflake and Power BI together. It begins by describing the benefits of each technology. It then outlines several architectural scenarios for connecting Snowflake to Power BI, including using a Power BI gateway, without a gateway, and connecting to Analysis Services. The document also provides examples of usage scenarios and developer best practices. It concludes with a section on data governance considerations for architectures with and without a Power BI gateway.
Delta Lake brings reliability, performance, and security to data lakes. It provides ACID transactions, schema enforcement, and unified handling of batch and streaming data to make data lakes more reliable. Delta Lake also features lightning fast query performance through its optimized Delta Engine. It enables security and compliance at scale through access controls and versioning of data. Delta Lake further offers an open approach and avoids vendor lock-in by using open formats like Parquet that can integrate with various ecosystems.
Snowflake: The Good, the Bad, and the UglyTyler Wishnoff
Learn how to solve the top 3 challenges Snowflake customers face, and what you can do to ensure high-performance, intelligent analytics at any scale. Ideal for those currently using Snowflake and those considering it. Learn more at: https://kyligence.io/
Product-thinking is making a big impact in the data world with the rise of Data Products, Data Product Managers, data mesh, and treating “Data as a Product.” But Honest, No-BS: What is a Data Product? And what key questions should we ask ourselves while developing them? Tim Gasper (VP of Product, data.world), will walk through the Data Product ABCs as a way to make treating data as a product way simpler: Accountability, Boundaries, Contracts and Expectations, Downstream Consumers, and Explicit Knowledge.
Achieving Lakehouse Models with Spark 3.0Databricks
It’s very easy to be distracted by the latest and greatest approaches with technology, but sometimes there’s a reason old approaches stand the test of time. Star Schemas & Kimball is one of those things that isn’t going anywhere, but as we move towards the “Data Lakehouse” paradigm – how appropriate is this modelling technique, and how can we harness the Delta Engine & Spark 3.0 to maximise it’s performance?
Tech talk on what Azure Databricks is, why you should learn it and how to get started. We'll use PySpark and talk about some real live examples from the trenches, including the pitfalls of leaving your clusters running accidentally and receiving a huge bill ;)
After this you will hopefully switch to Spark-as-a-service and get rid of your HDInsight/Hadoop clusters.
This is part 1 of an 8 part Data Science for Dummies series:
Databricks for dummies
Titanic survival prediction with Databricks + Python + Spark ML
Titanic with Azure Machine Learning Studio
Titanic with Databricks + Azure Machine Learning Service
Titanic with Databricks + MLS + AutoML
Titanic with Databricks + MLFlow
Titanic with DataRobot
Deployment, DevOps/MLops and Operationalization
The document discusses Azure Data Factory v2. It provides an agenda that includes topics like triggers, control flow, and executing SSIS packages in ADFv2. It then introduces the speaker, Stefan Kirner, who has over 15 years of experience with Microsoft BI tools. The rest of the document consists of slides on ADFv2 topics like the pipeline model, triggers, activities, integration runtimes, scaling SSIS packages, and notes from the field on using SSIS packages in ADFv2.
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsKhalid Salama
In essence, a data lake is commodity distributed file system that acts as a repository to hold raw data file extracts of all the enterprise source systems, so that it can serve the data management and analytics needs of the business. A data lake system provides means to ingest data, perform scalable big data processing, and serve information, in addition to manage, monitor and secure the it environment. In these slide, we discuss building data lakes using Azure Data Factory and Data Lake Analytics. We delve into the architecture if the data lake and explore its various components. We also describe the various data ingestion scenarios and considerations. We introduce the Azure Data Lake Store, then we discuss how to build Azure Data Factory pipeline to ingest the data lake. After that, we move into big data processing using Data Lake Analytics, and we delve into U-SQL.
Next Gen Analytics Going Beyond Data WarehouseDenodo
Watch this Fast Data Strategy session with speakers: Maria Thonn, Enterprise BI Development Manager, T-Mobile & Jonathan Wisgerhof, Smart Data Architect, Kadenza: https://goo.gl/J1qiLj
Your company, like most of your peers, is undoubtedly data-aware and data-driven. However, unless you embrace a modern architecture like data virtualization to deliver actionable insights from your enterprise data, the worth of your enterprise data will diminish to a fraction of its potential.
Attend this session to learn how data virtualization:
• Provides a common semantic layer for business intelligence (BI) and analytical applications
• Enables a more agile, flexible logical data warehouse
• Acts as a single virtual catalog for all enterprise data sources including data lakes
Azure Days 2019: Business Intelligence auf Azure (Marco Amhof & Yves Mauron)Trivadis
In dieser Session stellen wir ein Projekt vor, in welchem wir ein umfassendes BI-System mit Hilfe von Azure Blob Storage, Azure SQL, Azure Logic Apps und Azure Analysis Services für und in der Azure Cloud aufgebaut haben. Wir berichten über die Herausforderungen, wie wir diese gelöst haben und welche Learnings und Best Practices wir mitgenommen haben.
ADV Slides: Building and Growing Organizational Analytics with Data LakesDATAVERSITY
Data lakes are providing immense value to organizations embracing data science.
In this webinar, William will discuss the value of having broad, detailed, and seemingly obscure data available in cloud storage for purposes of expanding Data Science in the organization.
Azure Machine Learning Services provides an end-to-end, scalable platform for operationalizing machine learning models. It allows users to deploy models everywhere from containers and Kubernetes to SQL Datawarehouse and Cosmos DB. It also offers tools to boost data science productivity, increase experimentation, and automate model retraining. The platform seamlessly integrates with Azure services and is built to deploy models globally at scale with high availability and low latency.
Big Data and Data Warehousing Together with Azure Synapse Analytics (SQLBits ...Michael Rys
SQLBits 2020 presentation on how you can build solutions based on the modern data warehouse pattern with Azure Synapse Spark and SQL including demos of Azure Synapse.
CirrusDB provides cloud database and business intelligence services that help companies reduce costs and improve flexibility. Their offerings include managed database services, cloud databases, pre-configured appliances, and professional services. CirrusDB integrates multiple cloud platforms through their Cirrus Enterprise Manager product and claims advantages in scalability, virtualization, and clustering.
This document provides an overview of cloud computing concepts and Azure cloud services. It discusses cloud service models including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). It introduces Azure, the Microsoft cloud computing platform, and key Azure services like Azure Storage, Azure Portal, Azure Accounts, Azure Data Factory, and Azure Data Flow. Azure Data Factory allows building data integration solutions using activities, linked services, datasets and triggers without writing code. Azure Data Flow enables visually designing data transformations using a Spark optimizer without code.
Introduces the Microsoft’s Data Platform for on premise and cloud. Challenges businesses are facing with data and sources of data. Understand about Evolution of Database Systems in the modern world and what business are doing with their data and what their new needs are with respect to changing industry landscapes.
Dive into the Opportunities available for businesses and industry verticals: the ones which are identified already and the ones which are not explored yet.
Understand the Microsoft’s Cloud vision and what is Microsoft’s Azure platform is offering, for Infrastructure as a Service or Platform as a Service for you to build your own offerings.
Introduce and demo some of the Real World Scenarios/Case Studies where Businesses have used the Cloud/Azure for creating New and Innovative solutions to unlock these potentials.
Enabling Next Gen Analytics with Azure Data Lake and StreamSetsStreamsets Inc.
This document discusses enabling next generation analytics with Azure Data Lake. It provides definitions of big data and discusses how big data is a cornerstone of Cortana Intelligence. It also discusses challenges with big data like obtaining skills and determining value. The document then discusses Azure HDInsight and how it provides a cloud Spark and Hadoop service. It also discusses StreamSets and how it can be used for data movement and deployment on Azure VM or local machine. Finally, it discusses a use case of StreamSets at a major bank to move data from on-premise to Azure Data Lake and consolidate migration tools.
The new Microsoft Azure SQL Data Warehouse (SQL DW) is an elastic data warehouse-as-a-service and is a Massively Parallel Processing (MPP) solution for "big data" with true enterprise class features. The SQL DW service is built for data warehouse workloads from a few hundred gigabytes to petabytes of data with truly unique features like disaggregated compute and storage allowing for customers to be able to utilize the service to match their needs. In this presentation, we take an in-depth look at implementing a SQL DW, elastic scale (grow, shrink, and pause), and hybrid data clouds with Hadoop integration via Polybase allowing for a true SQL experience across structured and unstructured data.
Best Practices in the Cloud for Data Management (US)Denodo
Watch here: https://bit.ly/2Npt82U
If you have data, you are engaged in data management—be sure to do it effectively.
As organizations are assessing how COVID-19 has impacted their operations, new possibilities and uncharted routes are becoming the norm for many businesses. While exploring and implementing different deployment and operational models, the question of data management naturally surfaces while considering how these changes impact your data. Is this the right time to focus on data management? The reality is that if you have data, you are engaged in data management and so the real question is, are you doing it well?
Join Brice Giesbrecht from Caserta and Mitesh Shah from Denodo to explore data management challenges and solutions facing data driven organizations.
This document provides an overview of a course on implementing a modern data platform architecture using Azure services. The course objectives are to understand cloud and big data concepts, the role of Azure data services in a modern data platform, and how to implement a reference architecture using Azure data services. The course will provide an ARM template for a data platform solution that can address most data challenges.
The document discusses how organizations can leverage cloud, data, and AI to gain competitive advantages. It notes that 80% of organizations now adopt cloud-first strategies, AI investment increased 300% in 2017, and data is expected to grow dramatically. The document promotes Microsoft's cloud-based analytics services for harnessing data at scale from various sources and types. It provides examples of how companies have used these services to improve customer experience, reduce costs, speed up insights, and gain operational efficiencies.
SQL Saturday Redmond 2019 ETL Patterns in the CloudMark Kromer
This document discusses ETL patterns in the cloud using Azure Data Factory. It covers topics like ETL vs ELT, scaling ETL in the cloud, handling flexible schemas, and using ADF for orchestration. Key points include staging data in low-cost storage before processing, using ADF's integration runtime to process data both on-premises and in the cloud, and building resilient data flows that can handle schema drift.
Bridging the Last Mile: Getting Data to the People Who Need It (APAC)Denodo
Watch full webinar here: https://bit.ly/34iCruM
Many organizations are embarking on strategically important journeys to embrace data and analytics. The goal can be to improve internal efficiencies, improve the customer experience, drive new business models and revenue streams, or – in the public sector – provide better services. All of these goals require empowering employees to act on data and analytics and to make data-driven decisions. However, getting data – the right data at the right time – to these employees is a huge challenge and traditional technologies and data architectures are simply not up to this task. This webinar will look at how organizations are using Data Virtualization to quickly and efficiently get data to the people that need it.
Attend this session to learn:
- The challenges organizations face when trying to get data to the business users in a timely manner
- How Data Virtualization can accelerate time-to-value for an organization’s data assets
- Examples of leading companies that used data virtualization to get the right data to the users at the right time
Azure Days 2019: Grösser und Komplexer ist nicht immer besser (Meinrad Weiss)Trivadis
«Moderne» Data Warehouse/Data Lake Architekturen strotzen oft nur von Layern und Services. Mit solchen Systemen lassen sich Petabytes von Daten verwalten und analysieren. Das Ganze hat aber auch seinen Preis (Komplexität, Latenzzeit, Stabilität) und nicht jedes Projekt wird mit diesem Ansatz glücklich.
Der Vortrag zeigt die Reise von einer technologieverliebten Lösung zu einer auf die Anwender Bedürfnisse abgestimmten Umgebung. Er zeigt die Sonnen- und Schattenseiten von massiv parallelen Systemen und soll die Sinne auf das Aufnehmen der realen Kundenanforderungen sensibilisieren.
Data Driven Advanced Analytics using Denodo Platform on AWSDenodo
The document discusses challenges with data-driven cloud modernization and how the Denodo platform can help address them. It outlines Denodo's capabilities like universal connectivity, data services APIs, security and governance features. Example use cases are presented around real-time analytics, centralized access control and transitioning to the cloud. Key benefits of the Denodo data virtualization approach are that it provides a logical view of data across sources and enables self-service analytics while reducing costs and IT dependencies.
Denodo Partner Connect: A Review of the Top 5 Differentiated Use Cases for th...Denodo
Watch full webinar here: https://buff.ly/46pRfV7
This Denodo session explores the power of data virtualization, shedding light on its architecture, customer value, and a diverse range of use cases. Attendees will discover how the Denodo Platform enables seamless connectivity to various data sources while effortlessly combining, cleansing, and delivering data through 5 differentiated use cases.
Architecture: Delve into the core architecture of the Denodo Platform and learn how it empowers organizations to create a unified virtual data layer. Understand how data is accessed, integrated, and delivered in a real-time, agile manner.
Value for the Customer: Explore the tangible benefits that Denodo offers to its customers. From cost savings to improved decision-making, discover how the Denodo Platform helps organizations derive maximum value from their data assets.
Five Different Use Cases: Uncover five real-world use cases where Denodo's data virtualization platform has made a significant impact. From data governance to analytics, Denodo proves its versatility across a variety of domains.
- Logical Data Fabric
- Self Service Analytics
- Data Governance
- 360 degree of Entities
- Hybrid/Multi-Cloud Integration
Watch this illuminating session to gain insights into the transformative capabilities of the Denodo Platform.
This document provides an agenda and summary for a Data Analytics Meetup (DAM) on March 27, 2018. The agenda covers topics such as disruption opportunities in a changing data landscape, transitioning from traditional to modern BI architectures using Azure, Azure SQL Database vs Data Warehouse, data integration with Azure Data Factory and SSIS, Analysis Services, Power BI reporting, and a wrap-up. The document discusses challenges around data growth, digital transformation, and the shrinking time for companies to adapt to disruption. It provides overviews and comparisons of Azure SQL Database, Data Warehouse, and related Azure services to help modernize analytics architectures.
Amazon DocumentDB(MongoDB와 호환됨)는 빠르고 안정적이며 완전 관리형 데이터베이스 서비스입니다. Amazon DocumentDB를 사용하면 클라우드에서 MongoDB 호환 데이터베이스를 쉽게 설치, 운영 및 규모를 조정할 수 있습니다. Amazon DocumentDB를 사용하면 MongoDB에서 사용하는 것과 동일한 애플리케이션 코드를 실행하고 동일한 드라이버와 도구를 사용하는 것을 실습합니다.
How We Added Replication to QuestDB - JonTheBeachjavier ramirez
Building a database that can beat industry benchmarks is hard work, and we had to use every trick in the book to keep as close to the hardware as possible. In doing so, we initially decided QuestDB would scale only vertically, on a single instance.
A few years later, data replication —for horizontally scaling reads and for high availability— became one of the most demanded features, especially for enterprise and cloud environments. So, we rolled up our sleeves and made it happen.
Today, QuestDB supports an unbounded number of geographically distributed read-replicas without slowing down reads on the primary node, which can ingest data at over 4 million rows per second.
In this talk, I will tell you about the technical decisions we made, and their trade offs. You'll learn how we had to revamp the whole ingestion layer, and how we actually made the primary faster than before when we added multi-threaded Write Ahead Logs to deal with data replication. I'll also discuss how we are leveraging object storage as a central part of the process. And of course, I'll show you a live demo of high-performance multi-region replication in action.
Cómo hemos implementado semántica de "Exactly Once" en nuestra base de datos ...javier ramirez
Los sistemas distribuidos son difíciles. Los sistemas distribuidos de alto rendimiento, más. Latencias de red, mensajes sin confirmación de recibo, reinicios de servidores, fallos de hardware, bugs en el software, releases problemáticas, timeouts... hay un montón de motivos por los que es muy difícil saber si un mensaje que has enviado se ha recibido y procesado correctamente en destino. Así que para asegurar mandas el mensaje otra vez.. y otra... y cruzas los dedos para que el sistema del otro lado tenga tolerancia a los duplicados.
QuestDB es una base de datos open source diseñada para alto rendimiento. Nos queríamos asegurar de poder ofrecer garantías de "exactly once", deduplicando mensajes en tiempo de ingestión. En esta charla, te cuento cómo diseñamos e implementamos la palabra clave DEDUP en QuestDB, permitiendo deduplicar y además permitiendo Upserts en datos en tiempo real, añadiendo solo un 8% de tiempo de proceso, incluso en flujos con millones de inserciones por segundo.
Además, explicaré nuestra arquitectura de log de escrituras (WAL) paralelo y multithread. Por supuesto, todo esto te lo cuento con demos, para que veas cómo funciona en la práctica.
Applications of Data Science in Various IndustriesIABAC
The wide-ranging applications of data science across industries.
From healthcare to finance, data science drives innovation and efficiency by transforming raw data into actionable insights.
Learn how data science enhances decision-making, boosts productivity, and fosters new advancements in technology and business. Explore real-world examples of data science applications today.
2. Migration to Azure Cloud
Introduction
Azure SQL Data Warehouse is a cloud-based Enterprise Data Warehouse (EDW) that leverages Massively Parallel Processing (MPP) to quickly run
complex queries across petabytes of data. Its columnar storage in relational tables significantly reduces data storage cost and improves query performance.
It can run analytics near real time at massive scale using Azure Databricks streaming Dataframes.
SQL Datawarehouse uses PolyBase (T-SQL compliance) to query data from big data sources. Azure SQL DW migration provide utilities/services like Azure
Data Factory, Data Migration Assistant, SSIS and data migration service to make migration more streamlined.
Enablers in Retail
Azure is preferred by many Retailers as it isn’t viewed as a competitor but an enabler for digital transformation, providing more regions than other Cloud
providers, one of the best platforms with TCO, easy to use IoT ecosystem, strategic partnerships for data lifecycle such as Snowflake, strong AI / ML
capabilities, and greater control to build custom applications. TBD (i.e. advantages such as TCO, accelerators (ML frameworks, data pipeline frameworks,
data visualization)
• 10x - increase in the number of data sets that can be effectively handled
• 1 day to 15 minutes – develop granular data analytics reports
• $800 k-cost efficiencies from data analytics resulted in significant annual savings
• 158% - Average ROI for customers who modernized with Azure SQL DW
• $533K – In annual savings from enhanced IT team productivity
• $1 M + - Less per year thanks to simplified DW deployment and management
• $120K - In saved data replication costs by moving failover to Azure SQL DW
• $100K – Cost of backup DR data warehouse that was avoided with Azure SQL DW
• Fewer vulnerabilities - With Always Encrypted and standard endpoints
*Customers who modernized with Azure SQL DW…
*Reference: Forrester TEI Commissioned By Microsoft December 2017; https://pdfs.semanticscholar.org/presentation/318e/d2faf8df5c441637a3000cfa74f50cbb57cd.pdf
3. Azure DW Reference Architecture
Storage
Process Orchestration Data Governance
Job Scheduler
Workflow ADF
Azure Data
Catalogue
Data Profiling
Data Lifecycle
Metadata
Management
Audit
User roles and
Security
PII information
Compliance
ADF
Logic Apps
Event Hub
Data Producers / Source Systems
Structured Data
Merchandise
Planning
Sales
Master Data
Store Profile
• Supply Chain
• POS
• Sales
• Marketing
• Customer
Experience
Data
Acquisition
Batch
Integration tools
Native connectors
Real
time/Near
Real time
Unstructured Data
Logs
• Sensor Data
• Social Media
• Emails
• Clickstreams
External
Data
Source EDW Systems
Data Processing
Batch Realtime Advanced Analytics Layer
Join
Calculate
Aggregate
Azure Data Factory
Logic Apps
Stream analytics
Parse Validate
Cleanse
Transform
Semantic Analytics Layer
Pattern Mining
ML workflows
Classify
Analyze
Predict
Prepare
Train
Correlate
Data Storage (Target EDW)
Azure SQL DWH
Staging
Dynamic Layer
Azure HD Insight
Aggregated
Data Store
Data
Distribution
ContentDeliveryDataAbstractionAPIGateway
Data Consumption
(BI Tool)
Big Data
Connections
Data
Federation
Self Service Reports
and visualizations
Customer
360
Operational
Reporting
Next Best
Action
Churn
Propensity
Event Hub
Azure Blob
Storage
Azure Databricks
Azure ML
Deep Learning
Cognitive AI
Azure Analysis
Services
Azure AI
services
PolyBase
Azure AD
ADF
Event Hub
ETL
Logic Apps
Retail Analytics
4. EDW to Azure – Migration Strategies & Decision tree
Current
State
Lift and
Shift
Review &
Refine
Rearchitect
Data Models &
Taxonomy
MOM maturity
Valuation
Data Characteristics
(Quality, Volumes)
Sharding
Workload
Characteristics
Optimized
data
Needs
Optimization
Flawed
Data
By User Groups
By Pain Points
Batch
Real Time /
Near Real
time
Structured
Semi Structured
Unstructured
Current State Methodology Criteria Migration
Strategy
Mode Data Types
Schema Data / Tables
Logic Apps
Event Hub
Azure Data
Factory
Data Transfer
Data
Movement
ETL Tool
CDC
Metadata
Remodel
Aggregate
Join
Transform
Replicate
Azure
Functions
Stream
Analytics
Blob
Storage
Azure
SQL
DWH
PolyBase
Test
Validate
Operationalize
Migration Paths
Migrate
5. Retail eCommerce Value Chain
Advanced retail analytics
solutions spanning descriptive,
predictive, & prescriptive
modeling accelerating ROI