This presentation shows how you can build solutions that follow the modern data warehouse architecture and introduces the .NET for Apache Spark support (https://dot.net/spark, https://github.com/dotnet/spark)
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure. Designed in collaboration with the founders of Apache Spark, Azure Databricks combines the best of Databricks and Azure to help customers accelerate innovation with one-click set up, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts. As an Azure service, customers automatically benefit from the native integration with other Azure services such as Power BI, SQL Data Warehouse, and Cosmos DB, as well as from enterprise-grade Azure security, including Active Directory integration, compliance, and enterprise-grade SLAs.
Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
Big Data Adavnced Analytics on Microsoft AzureMark Tabladillo
This presentation provides a survey of the advanced analytics strengths of Microsoft Azure from an enterprise perspective (with these organizations being the bulk of big data users) based on the Team Data Science Process. The talk also covers the range of analytics and advanced analytics solutions available for developers using data science and artificial intelligence from Microsoft Azure.
Running cost effective big data workloads with Azure Synapse and Azure Data L...Michael Rys
The presentation discusses how to migrate expensive open source big data workloads to Azure and leverage latest compute and storage innovations within Azure Synapse with Azure Data Lake Storage to develop a powerful and cost effective analytics solutions. It shows how you can bring your .NET expertise with .NET for Apache Spark to bear and how the shared meta data experience in Synapse makes it easy to create a table in Spark and query it from T-SQL.
This presentation focuses on the value proposition for Azure Databricks for Data Science. First, the talk includes an overview of the merits of Azure Databricks and Spark. Second, the talk includes demos of data science on Azure Databricks. Finally, the presentation includes some ideas for data science production.
In this session we will delve into the world of Azure Databricks and analyze why it is becoming a tool for data Scientist and/or fundamental data Engineer in conjunction with Azure services
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure. Designed in collaboration with the founders of Apache Spark, Azure Databricks combines the best of Databricks and Azure to help customers accelerate innovation with one-click set up, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts. As an Azure service, customers automatically benefit from the native integration with other Azure services such as Power BI, SQL Data Warehouse, and Cosmos DB, as well as from enterprise-grade Azure security, including Active Directory integration, compliance, and enterprise-grade SLAs.
Want to see a high-level overview of the products in the Microsoft data platform portfolio in Azure? I’ll cover products in the categories of OLTP, OLAP, data warehouse, storage, data transport, data prep, data lake, IaaS, PaaS, SMP/MPP, NoSQL, Hadoop, open source, reporting, machine learning, and AI. It’s a lot to digest but I’ll categorize the products and discuss their use cases to help you narrow down the best products for the solution you want to build.
Big Data Adavnced Analytics on Microsoft AzureMark Tabladillo
This presentation provides a survey of the advanced analytics strengths of Microsoft Azure from an enterprise perspective (with these organizations being the bulk of big data users) based on the Team Data Science Process. The talk also covers the range of analytics and advanced analytics solutions available for developers using data science and artificial intelligence from Microsoft Azure.
Running cost effective big data workloads with Azure Synapse and Azure Data L...Michael Rys
The presentation discusses how to migrate expensive open source big data workloads to Azure and leverage latest compute and storage innovations within Azure Synapse with Azure Data Lake Storage to develop a powerful and cost effective analytics solutions. It shows how you can bring your .NET expertise with .NET for Apache Spark to bear and how the shared meta data experience in Synapse makes it easy to create a table in Spark and query it from T-SQL.
This presentation focuses on the value proposition for Azure Databricks for Data Science. First, the talk includes an overview of the merits of Azure Databricks and Spark. Second, the talk includes demos of data science on Azure Databricks. Finally, the presentation includes some ideas for data science production.
In this session we will delve into the world of Azure Databricks and analyze why it is becoming a tool for data Scientist and/or fundamental data Engineer in conjunction with Azure services
Building Advanced Analytics Pipelines with Azure DatabricksLace Lofranco
Participants will get a deep dive into one of Azure’s newest offering: Azure Databricks, a fast, easy and collaborative Apache® Spark™ based analytics platform optimized for Azure. In this session, we start with a technical overview of Spark and quickly jump into Azure Databricks’ key collaboration features, cluster management, and tight data integration with Azure data sources. Concepts are made concrete via a detailed walk through of an advance analytics pipeline built using Spark and Azure Databricks.
Full video of the presentation: https://www.youtube.com/watch?v=14D9VzI152o
Presentation demo: https://github.com/devlace/azure-databricks-anomaly
These are the slides for my talk "An intro to Azure Data Lake" at Azure Lowlands 2019. The session was held on Friday January 25th from 14:20 - 15:05 in room Santander.
Spark is fast becoming a critical part of Customer Solutions on Azure. Databricks on Microsoft Azure provides a first-class experience for building and running Spark applications. The Microsoft Azure CAT team engaged with many early adopter customers helping them build their solutions on Azure Databricks.
In this session, we begin by reviewing typical workload patterns, integration with other Azure services like Azure Storage, Azure Data Lake, IoT / Event Hubs, SQL DW, PowerBI etc. Most importantly, we will share real-world tips and learnings that you can take and apply in your Data Engineering / Data Science workloads
ITCamp 2019 - Andy Cross - Machine Learning with ML.NET and Azure Data LakeITCamp
ML.NET is an open source, machine learning framework built in .NET and runs on Windows, Linux and macOS. It allows developers to integrate custom machine learning into their applications without any prior expertise in developing or tuning machine learning models. Enhance your .NET apps with sentiment analysis, price prediction, fraud detection and more using custom models built with ML.NET
In this Session, Andy will show not only the core of ML.NET but best practices around Azure Data Lake and data in general when using .NET
Here are the slides for my talk "An intro to Azure Data Lake" at Techorama NL 2018. The session was held on Tuesday October 2nd from 15:00 - 16:00 in room 7.
Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark.
Think of big data as all data, no matter what the volume, velocity, or variety. The simple truth is a traditional on-prem data warehouse will not handle big data. So what is Microsoft’s strategy for building a big data solution? And why is it best to have this solution in the cloud? That is what this presentation will cover. Be prepared to discover all the various Microsoft technologies and products from collecting data, transforming it, storing it, to visualizing it. My goal is to help you not only understand each product but understand how they all fit together, so you can be the hero who builds your companies big data solution.
These slides are a copy of a last Azure Cosmos DB + Gremlin API in Action session which I had the pleasure to present on June 2nd, 2018 at PASS SQL Saturday event in Montreal. The original PowerPoint version contained much more elaborate series of animations. We understand that those had to be flatten for upload in this case. Though I guess you'll get the idea of the logic involved.
Data Analytics Meetup: Introduction to Azure Data Lake Storage CCG
Microsoft Azure Data Lake Storage is designed to enable operational and exploratory analytics through a hyper-scale repository. Journey through Azure Data Lake Storage Gen1 with Microsoft Data Platform Specialist, Audrey Hammonds. In this video she explains the fundamentals to Gen 1 and Gen 2, walks us through how to provision a Data Lake, and gives tips to avoid turning your Data Lake into a swamp.
Learn more about Data Lakes with our blog - Data Lakes: Data Agility is Here Now https://bit.ly/2NUX1H6
Technical session on Databases as Service in Azure
Technical session - Azure SQL DB on Dec 20, 2020
https://youtu.be/Cl4IDpc_0yc
Technical session - 2 on Azure SQL DB - Dec 27, 2020
https://youtu.be/_4lZ54eI3F0
Technical session on Azure Cosmos DB -Dec 27, 2020
https://youtu.be/rtDwX1K_64k
Data Con LA 2020
Description
In this session, I introduce the Amazon Redshift lake house architecture which enables you to query data across your data warehouse, data lake, and operational databases to gain faster and deeper insights. With a lake house architecture, you can store data in open file formats in your Amazon S3 data lake.
Speaker
Antje Barth, Amazon Web Services, Sr. Developer Advocate, AI and Machine Learning
Build Big Data Enterprise solutions faster on Azure HDInsightDataWorks Summit
Hadoop and Spark are big data frameworks used to extract useful span a variety of scenarios from ingestion, data prep, data management, processing, analyzing and visualizing data. Each step requires specialized toolsets to be productive. In this talk I will share solution examples in the Big Data ecosystem such as Cask, StreamSets, Datameer, AtScale, Dataiku on Microsoft’s Azure HDInsight that simplify your Big Data solutions. Azure HDInsight is a cloud Spark and Hadoop service for the enterprise and take advantage of all the benefits of HDInsight giving you the best of both worlds. Join this session for practical information that will enable faster time to insights for you and your business.
Data Con LA 2020
Description
Data warehouses are not enough. Data lakes are the backbone of a modern data environment. Data Lakes are best built leveraging unique services of the cloud provider to reduce operations complexity. This session will explain why everyone's talking about data lakes, break down the best services in Azure to build a Data Lake, and walk through code for querying and loading with Azure Databricks and Event Hubs for Kafka. Attendees will leave the session with a firm grasp of why we build data lakes and how Azure Databricks fits in for ETL and querying.
Speaker
Dustin Vannoy, Dustin Vannoy Consulting, Principal Data Engineer
Jupyter Notebooks and Apache Spark are first class citizens of the Data Science space, a truly requirement for the "modern" data scientist. Now with Azure Synapse these two computing powers are available to the .NET Developer. And .NET is available for all data scientists. Let's look what .net can do for notebooks and spark inside Azure Synapse and what are Synapse, notebooks and spark.
Building Advanced Analytics Pipelines with Azure DatabricksLace Lofranco
Participants will get a deep dive into one of Azure’s newest offering: Azure Databricks, a fast, easy and collaborative Apache® Spark™ based analytics platform optimized for Azure. In this session, we start with a technical overview of Spark and quickly jump into Azure Databricks’ key collaboration features, cluster management, and tight data integration with Azure data sources. Concepts are made concrete via a detailed walk through of an advance analytics pipeline built using Spark and Azure Databricks.
Full video of the presentation: https://www.youtube.com/watch?v=14D9VzI152o
Presentation demo: https://github.com/devlace/azure-databricks-anomaly
These are the slides for my talk "An intro to Azure Data Lake" at Azure Lowlands 2019. The session was held on Friday January 25th from 14:20 - 15:05 in room Santander.
Spark is fast becoming a critical part of Customer Solutions on Azure. Databricks on Microsoft Azure provides a first-class experience for building and running Spark applications. The Microsoft Azure CAT team engaged with many early adopter customers helping them build their solutions on Azure Databricks.
In this session, we begin by reviewing typical workload patterns, integration with other Azure services like Azure Storage, Azure Data Lake, IoT / Event Hubs, SQL DW, PowerBI etc. Most importantly, we will share real-world tips and learnings that you can take and apply in your Data Engineering / Data Science workloads
ITCamp 2019 - Andy Cross - Machine Learning with ML.NET and Azure Data LakeITCamp
ML.NET is an open source, machine learning framework built in .NET and runs on Windows, Linux and macOS. It allows developers to integrate custom machine learning into their applications without any prior expertise in developing or tuning machine learning models. Enhance your .NET apps with sentiment analysis, price prediction, fraud detection and more using custom models built with ML.NET
In this Session, Andy will show not only the core of ML.NET but best practices around Azure Data Lake and data in general when using .NET
Here are the slides for my talk "An intro to Azure Data Lake" at Techorama NL 2018. The session was held on Tuesday October 2nd from 15:00 - 16:00 in room 7.
Databricks is a Software-as-a-Service-like experience (or Spark-as-a-service) that is a tool for curating and processing massive amounts of data and developing, training and deploying models on that data, and managing the whole workflow process throughout the project. It is for those who are comfortable with Apache Spark as it is 100% based on Spark and is extensible with support for Scala, Java, R, and Python alongside Spark SQL, GraphX, Streaming and Machine Learning Library (Mllib). It has built-in integration with many data sources, has a workflow scheduler, allows for real-time workspace collaboration, and has performance improvements over traditional Apache Spark.
Think of big data as all data, no matter what the volume, velocity, or variety. The simple truth is a traditional on-prem data warehouse will not handle big data. So what is Microsoft’s strategy for building a big data solution? And why is it best to have this solution in the cloud? That is what this presentation will cover. Be prepared to discover all the various Microsoft technologies and products from collecting data, transforming it, storing it, to visualizing it. My goal is to help you not only understand each product but understand how they all fit together, so you can be the hero who builds your companies big data solution.
These slides are a copy of a last Azure Cosmos DB + Gremlin API in Action session which I had the pleasure to present on June 2nd, 2018 at PASS SQL Saturday event in Montreal. The original PowerPoint version contained much more elaborate series of animations. We understand that those had to be flatten for upload in this case. Though I guess you'll get the idea of the logic involved.
Data Analytics Meetup: Introduction to Azure Data Lake Storage CCG
Microsoft Azure Data Lake Storage is designed to enable operational and exploratory analytics through a hyper-scale repository. Journey through Azure Data Lake Storage Gen1 with Microsoft Data Platform Specialist, Audrey Hammonds. In this video she explains the fundamentals to Gen 1 and Gen 2, walks us through how to provision a Data Lake, and gives tips to avoid turning your Data Lake into a swamp.
Learn more about Data Lakes with our blog - Data Lakes: Data Agility is Here Now https://bit.ly/2NUX1H6
Technical session on Databases as Service in Azure
Technical session - Azure SQL DB on Dec 20, 2020
https://youtu.be/Cl4IDpc_0yc
Technical session - 2 on Azure SQL DB - Dec 27, 2020
https://youtu.be/_4lZ54eI3F0
Technical session on Azure Cosmos DB -Dec 27, 2020
https://youtu.be/rtDwX1K_64k
Data Con LA 2020
Description
In this session, I introduce the Amazon Redshift lake house architecture which enables you to query data across your data warehouse, data lake, and operational databases to gain faster and deeper insights. With a lake house architecture, you can store data in open file formats in your Amazon S3 data lake.
Speaker
Antje Barth, Amazon Web Services, Sr. Developer Advocate, AI and Machine Learning
Build Big Data Enterprise solutions faster on Azure HDInsightDataWorks Summit
Hadoop and Spark are big data frameworks used to extract useful span a variety of scenarios from ingestion, data prep, data management, processing, analyzing and visualizing data. Each step requires specialized toolsets to be productive. In this talk I will share solution examples in the Big Data ecosystem such as Cask, StreamSets, Datameer, AtScale, Dataiku on Microsoft’s Azure HDInsight that simplify your Big Data solutions. Azure HDInsight is a cloud Spark and Hadoop service for the enterprise and take advantage of all the benefits of HDInsight giving you the best of both worlds. Join this session for practical information that will enable faster time to insights for you and your business.
Data Con LA 2020
Description
Data warehouses are not enough. Data lakes are the backbone of a modern data environment. Data Lakes are best built leveraging unique services of the cloud provider to reduce operations complexity. This session will explain why everyone's talking about data lakes, break down the best services in Azure to build a Data Lake, and walk through code for querying and loading with Azure Databricks and Event Hubs for Kafka. Attendees will leave the session with a firm grasp of why we build data lakes and how Azure Databricks fits in for ETL and querying.
Speaker
Dustin Vannoy, Dustin Vannoy Consulting, Principal Data Engineer
Jupyter Notebooks and Apache Spark are first class citizens of the Data Science space, a truly requirement for the "modern" data scientist. Now with Azure Synapse these two computing powers are available to the .NET Developer. And .NET is available for all data scientists. Let's look what .net can do for notebooks and spark inside Azure Synapse and what are Synapse, notebooks and spark.
Come può .NET contribuire alla Data Science? Cosa è .NET Interactive? Cosa c'entrano i notebook? E Apache Spark? E il pythonismo? E Azure? Vediamo in questa sessione di mettere in ordine le idee.
.net developer for Jupyter Notebook and Apache Spark and viceversaMarco Parenzan
Jupyter Notebooks and Apache Spark are first class citizens of the Data Science space, a truly requirement for the "modern" data scientist. But there was a requirement: being a python developer. Now Microsoft is investing on C# as another first class citizen in this space. Let's look what .net can do for notebooks and spark and what are notebooks and spark.
This introductory workshop is aimed at data analysts & data engineers new to Apache Spark and exposes them how to analyze big data with Spark SQL and DataFrames.
In this partly instructor-led and self-paced labs, we will cover Spark concepts and you’ll do labs for Spark SQL and DataFrames
in Databricks Community Edition.
Toward the end, you’ll get a glimpse into newly minted Databricks Developer Certification for Apache Spark: what to expect & how to prepare for it.
* Apache Spark Basics & Architecture
* Spark SQL
* DataFrames
* Brief Overview of Databricks Certified Developer for Apache Spark
Author: Stefan Papp, Data Architect at “The unbelievable Machine Company“. An overview of Big Data Processing engines with a focus on Apache Spark and Apache Flink, given at a Vienna Data Science Group meeting on 26 January 2017. Following questions are addressed:
• What are big data processing paradigms and how do Spark 1.x/Spark 2.x and Apache Flink solve them?
• When to use batch and when stream processing?
• What is a Lambda-Architecture and a Kappa Architecture?
• What are the best practices for your project?
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Azure Synapse Analytics is Azure SQL Data Warehouse evolved: a limitless analytics service, that brings together enterprise data warehousing and Big Data analytics into a single service. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources, at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs. This is a huge deck with lots of screenshots so you can see exactly how it works.
Teaching Apache Spark: Demonstrations on the Databricks Cloud PlatformYao Yao
Yao Yao Mooyoung Lee
https://github.com/yaowser/learn-spark/tree/master/Final%20project
https://www.youtube.com/watch?v=IVMbSDS4q3A
https://www.academia.edu/35646386/Teaching_Apache_Spark_Demonstrations_on_the_Databricks_Cloud_Platform
https://www.slideshare.net/YaoYao44/teaching-apache-spark-demonstrations-on-the-databricks-cloud-platform-86063070/
Apache Spark is a fast and general engine for big data analytics processing with libraries for SQL, streaming, and advanced analytics
Cloud Computing, Structured Streaming, Unified Analytics Integration, End-to-End Applications
A sharing in a meetup of the AWS Taiwan User Group.
The registration page: https://bityl.co/7yRK
The promotion page: https://www.facebook.com/groups/awsugtw/permalink/4123481584394988/
Running cost effective big data workloads with Azure Synapse and ADLS (MS Ign...Michael Rys
Presentation by James Baker and myself on Running cost effective big data workloads with Azure Synapse and Azure Datalake Storage (ADLS) at Microsoft Ignite 2020. Covers Modern Data warehouse architecture supported by Azure Synapse, integration benefits with ADLS and some features that reduce cost such as Query Acceleration, integration of Spark and SQL processing with integrated meta data and .NET For Apache Spark support.
Apache Spark™ + IBM Watson + Twitter DataPalooza SF 2015Mike Broberg
Use Apache Spark Streaming in with IBM Watson on Bluemix to perform sentiment analysis and track how a conversation is trending on Twitter.
By David Taieb: https://twitter.com/DTAIEB55
Video: https://youtu.be/KLc_wazud3s
Tutorial: https://developer.ibm.com/clouddataservices/sentiment-analysis-of-twitter-hashtags/
Scala and Spark are Ideal for Big Data - Data Science Pop-up SeattleDomino Data Lab
Scala and Spark are each great tools for data processing and they work well together. They can process data via small simple interactive queries as well as in very large highly-available and scalable production systems. They provide an integrated framework for an ever growing wide range of data processing capabilities. We examine the reasons for this and also look a couple of simple data processing examples written in Scala. Presented by John Nestor, Sr Architect at 47 Degrees.
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.
Modernizing ETL with Azure Data Lake: Hyperscale, multi-format, multi-platfor...Michael Rys
More and more customers who are looking to modernize analytics needs are exploring the data lake approach in Azure. Typically, they are most challenged by a bewildering array of poorly integrated technologies and a variety of data formats, data types not all of which are conveniently handled by existing ETL technologies. In this session, we’ll explore the basic shape of a modern ETL pipeline through the lens of Azure Data Lake. We will explore how this pipeline can scale from one to thousands of nodes at a moment’s notice to respond to business needs, how its extensibility model allows pipelines to simultaneously integrate procedural code written in .NET languages or even Python and R, how that same extensibility model allows pipelines to deal with a variety of formats such as CSV, XML, JSON, Images, or any enterprise-specific document format, and finally explore how the next generation of ETL scenarios are enabled though the integration of Intelligence in the data layer in the form of built-in Cognitive capabilities.
Best Practices and Performance Tuning of U-SQL in Azure Data Lake (SQL Konfer...Michael Rys
When processing TB and PB of data, running your Big Data queries at scale and having them perform at peak is essential. In this session, we show you some state-of-the art tools on how to analyze U-SQL job performances and we discuss in-depth best practices on designing your data layout both for files and tables and writing performing and scalable queries using U-SQL. You will learn how to analyze performance and scale bottlenecks and will learn several tips on how to make your big data processing scripts both faster and scale better.
Bring your code to explore the Azure Data Lake: Execute your .NET/Python/R co...Michael Rys
Big data processing increasingly needs to address not just querying big data but needs to apply domain specific algorithms to large amounts of data at scale. This ranges from developing and applying machine learning models to custom, domain specific processing of images, texts, etc. Often the domain experts and programmers have a favorite language that they use to implement their algorithms such as Python, R, C#, etc. Microsoft Azure Data Lake Analytics service is making it easy for customers to bring their domain expertise and their favorite languages to address their big data processing needs. In this session, I will showcase how you can bring your Python, R, and .NET code and apply it at scale using U-SQL.
Best practices on Building a Big Data Analytics Solution (SQLBits 2018 Traini...Michael Rys
From theory to implementation - follow the steps of implementing an end-to-end analytics solution illustrated with some best practices and examples in Azure Data Lake.
During this full training day we will share the architecture patterns, tooling, learnings and tips and tricks for building such services on Azure Data Lake. We take you through some anti-patterns and best practices on data loading and organization, give you hands-on time and the ability to develop some of your own U-SQL scripts to process your data and discuss the pros and cons of files versus tables.
This were the slides presented at the SQLBits 2018 Training Day on Feb 21, 2018.
U-SQL Killer Scenarios: Custom Processing, Big Cognition, Image and JSON Proc...Michael Rys
When analyzing big data, you often have to process data at scale that is not rectangular in nature and you would like to scale out your existing programs and cognitive algorithms to analyze your data. To address this need and make it easy for the programmer to add her domain specific code, U-SQL includes a rich extensibility model that allows you to process any kind of data, ranging from CSV files over JSON and XML to image files and add your own custom operators. In this presentation, we will provide some examples on how to use U-SQL to process interesting data formats with custom extractors and functions, including JSON, images, use U-SQL’s cognitive library and finally show how U-SQL allows you to invoke custom code written in Python and R.
Slides for SQL Saturday 635, Vancouver BC presentation, Vancouver BC. Aug 2017.
Introduction to Azure Data Lake and U-SQL for SQL users (SQL Saturday 635)Michael Rys
Data Lakes have become a new tool in building modern data warehouse architectures. In this presentation we will introduce Microsoft's Azure Data Lake offering and its new big data processing language called U-SQL that makes Big Data Processing easy by combining the declarativity of SQL with the extensibility of C#. We will give you an initial introduction to U-SQL by explaining why we introduced U-SQL and showing with an example of how to analyze some tweet data with U-SQL and its extensibility capabilities and take you on an introductory tour of U-SQL that is geared towards existing SQL users.
slides for SQL Saturday 635, Vancouver BC, Aug 2017
Killer Scenarios with Data Lake in Azure with U-SQLMichael Rys
Presentation from Microsoft Data Science Summit 2016
Presents 4 examples of custom U-SQL data processing: Overlapping Range Aggregation, JSON Processing, Image Processing and R with U-SQL
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
6. The data lake approach
Ingest all data
regardless of
requirements
Store all data
in native format
without schema
definition
Do analysis
Schematize for
scenario, run scale
out analysis
Interactive queries
Batch queries
Machine Learning
Data warehouse
Real-time analytics
Devices
7. The modern data warehouse extends the scope of the data warehouse to serve big data
that’s prepared with techniques beyond relational ETL
Modern data warehousing
“We want to integrate all our
data—including big data—with
our data warehouse”
Advanced analytics
Advanced analytics is the process
of applying machine learning
and deep learning techniques to
data for the purpose of creating
predictive and prescriptive
insights
“We’re trying to predict when
our customers churn”
Real-time analytics
“We’re trying to get insights from
our devices in real-time”
8. INGEST STORE PREP & TRAIN MODEL & SERVE
Azure Data Lake Storage
Logs, files and media
(unstructured)
Azure SQL Data
Warehouse
Azure Data Factory
Azure Analysis
Services
Azure Databricks
(Python, Scala, Spark SQL,
.NET for Apache Spark)
Polybase
Business/custom apps
(Structured)
Power BI
Azure also supports other Big Data services like Azure HDInsight and Azure Data Lake to allow customers to tailor the above architecture to meet their unique needs.
ORCHESTRATION & DATA FLOW ETL
Azure Data Factory
9.
10. Apache Spark is an OSS fast analytics engine for big data and machine learning
Improves efficiency through:
General computation graphs beyond map/reduce
In-memory computing primitives
Allows developers to scale out their user code & write in their language of
choice
Rich APIs in Java, Scala, Python, R, SparkSQL etc.
Batch processing, streaming and interactive shell
Available on Azure via
Azure Databricks
Azure HDInsight
IaaS/Kubernetes
11. A lot of big data-usable business logic (millions
of lines of code) is written in .NET!
Expensive and difficult to translate into
Python/Scala/Java!
Locked out from big data processing due to
lack of .NET support in OSS big data solutions
In a recently conducted .NET Developer survey (> 1000 developers), more than 70%
expressed interest in Apache Spark!
Would like to tap into OSS eco-system for: Code libraries, support, hiring
12. Goal: .NET for Apache Spark is aimed at providing
.NET developers a first-class experience when
working with Apache Spark.
Non-Goal: Converting existing Scala/Python/Java
Spark developers.
13. • Interop layer for .NET (Scala-side)
• Potentially optimizing Python and R interop layers
• Technical documentation, blogs and articles
• End-to-end scenarios
• Performance benchmarking (cluster)
• Production workloads
• Out of Box with Azure HDInsight, easy to use with Azure Databricks
• C# (and F#) language extensions using .NET
• Performance benchmarking (Interop)
• Portability aspects (e.g., cross-platform .NET Standard)
• Tooling (e.g., Apache Jupyter, Visual Studio, Visual Studio Code)
Microsoft is committed…
14. Contributions to foundational OSS projects:
• Apache arrow: ARROW-4997, ARROW-5019, ARROW-4839, ARROW-
4502, ARROW-4737, ARROW-4543, ARROW-4435
• Pyrolite (pickling library): Improve pickling/unpickling performance, Add
a Strong Name to Pyrolite
.NET for Apache Spark was open sourced @Spark+AI Summit 2019
• Website: https://dot.net/spark
• GitHub: https://github.com/dotnet/spark
Spark project improvement proposals:
• Interop support for Spark language extensions: SPARK-26257
• .NET bindings for Apache Spark: SPARK-27006
15. Spark DataFrames
with SparkSQL
works with
Spark v2.3.x/v2.4.[0/1]
and includes
~300 SparkSQL
functions
.NET Spark UDFs
Batch &
streaming
including
Spark Structured
Streaming and all
Spark-supported data
sources
.NET Standard 2.0
works with
.NET Framework v4.6.1+
and .NET Core v2.1+
and includes C#/F#
support
.NET
Standard
Machine Learning
Including access to
ML.NET
Speed &
productivity
Performance optimized
interop, as fast or faster
than pySpark
https://github.com/dotnet/spark/examples
16. var spark = SparkSession.Builder().GetOrCreate();
var dataframe =
spark.Read().Json(“input.json”);
dataframe.Filter(df["age"] > 21)
.Select(concat(df[“age”], df[“name”]).Show();
var concat =
Udf<int?, string, string>((age, name)=>name+age);
25. • Spark .NET Project
creation
• Dependency packaging
• Language service
• Sample code
Author
• Reference management
• Spark local run
• Spark cluster run (e.g. HDInsight)
Run
• DebugFix
Extension to VSCode
Tap into VSCode for C# programming
Automate Maven and Spark dependency
for environment setup
Facilitate first project success through
project template and sample code
Support Spark local run and cluster run
Integrate with Azure for HDInsight clusters
navigation
Azure Databricks integration planned
26. More
programming
experiences in
.NET
(UDAF, UDT
support, multi-
language UDFs)
Spark data
connectors in
.NET
(e.g., Apache Kafka,
Azure Blob Store,
Azure Data Lake)
Tooling
experiences
(e.g., Jupyter, VS
Code, Visual
Studio, others?)
Idiomatic
experiences
for C# and F#
(LINQ, Type
Provider)
Go to https://github.com/dotnet/spark and let us know what is important to you!
Out-of-Box
Experiences
(Azure HDInsight,
Azure Databricks,
Cosmos DB
Spark, SQL 2019
BDC, …)
27.
28.
29. CREATE EXTERNAL DATA SOURCE MyADLSGen2
WITH (TYPE = Hadoop,
LOCATION = ‘abfs://<filesys>@<account_name>.dfs.core.windows.net’,
CREDENTIAL = <Database scoped credential>);
CREATE EXTERNAL FILE FORMAT ParquetFile
WITH ( FORMAT_TYPE = PARQUET,
DATA_COMPRESSION = 'org.apache.hadoop.io.compress.GzipCodec’,
FORMAT_OPTIONS (FIELD_TERMINATOR ='|', USE_TYPE_DEFAULT = TRUE));
CREATE EXTERNAL TABLE [dbo].[Customer_import] (
[SensorKey] int NOT NULL,
int NOT NULL,
[Speed] float NOT NULL)
WITH (LOCATION=‘/Dimensions/customer',
DATA_SOURCE = MyADLSGen2,
FILE_FORMAT = ParquetFile)
Once per store
account (WASB,
ADLS G1, ADLS G2)
Once per file
format, supports
Parquet (snappy or
Gzip), ORC, RC,
CSV/TSV
Folder path
30. CREATE TABLE [dbo].[Customer] WITH
( DISTRIBUTION = ROUND_ROBIN
, CLUSTERED INDEX (customerid)
)
AS SELECT * FROM [dbo].[Customer_import]
INSERT INTO [dbo].[Customer]
SELECT *
FROM [dbo].[Customer_import]
WHERE <predicate to determine new data>