Introducing Azure Databases

This document discusses Azure big data capabilities including the 5 V's of big data: volume, velocity, variety, veracity, and value. It notes that 60% of big data projects fail to move beyond pilot according to Gartner. It then provides details on Azure persistence choices for storing big data including storage, Data Lake, HDInsight, DocumentDB, SQL databases, and Hadoop options. It also discusses load and data cleaning choices on Azure like Stream Analytics, SQL Server, and Azure Machine Learning. Finally, it presents 5 architectural patterns for using Azure big data capabilities.

Bleeding Edge Databases

Move your on prem data to a lake in a Lake in Cloud

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With the boom in data; the volume and its complexity, the trend is to move data to the cloud. Where and How do we do this? Azure gives you the answer. In this session, I will give you an introduction to Azure Data Lake and Azure Data Factory, and why they are good for the type of problem we are talking about. You will learn how large datasets can be stored on the cloud, and how you could transport your data to this store. The session will briefly cover Azure Data Lake as the modern warehouse for data on the cloud,

3 CityNetConf - sql+c#=u-sql

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U-SQL combines SQL and C# to allow for querying and analyzing large amounts of structured and unstructured data stored in Azure Data Lake Store. U-SQL queries can access data across various Azure data services and provide analytics capabilities like window functions and ranking functions. The language also allows for extensibility through user-defined functions, aggregates, and operators written in C#. U-SQL queries are compiled and executed on Azure Data Lake Analytics, which provides a scalable analytics service based on Apache YARN.

Not only SQL - Database Choices

Luan Moreno Medeiros Maciel

This document discusses database choices and provides an overview of different database technologies including relational databases, NoSQL databases, and Hadoop. It highlights key-value, columnar, document, and graph NoSQL databases and provides demos of technologies like DynamoDB, MongoDB, Neo4j, and Hadoop. The document also discusses using these database options on premises or in the cloud with providers like AWS, Google, and Microsoft and how to query data from NoSQL databases.

J1 T1 3 - Azure Data Lake store & analytics 101 - Kenneth M. Nielsen

MS Cloud Summit

This document provides an overview and demonstration of Azure Data Lake Store and Azure Data Lake Analytics. The presenter discusses how Azure Data Lake can store and analyze large amounts of data in its native format. Key capabilities of Azure Data Lake Store like unlimited storage, security features, and support for any data type are highlighted. Azure Data Lake Analytics is presented as an elastic analytics service built on Apache YARN that can process large amounts of data. The U-SQL language for big data analytics is demonstrated, along with using Visual Studio and PowerShell for interacting with Azure Data Lake. The presentation concludes with a question and answer section.

Digital Transformation with Microsoft Azure

This document summarizes digital transformation with Microsoft Azure, including cloud computing, big data, and data lakes. It discusses data lake characteristics such as structured, semi-structured, and unstructured data. Data lakes are used for reporting, visualization, analytics, and machine learning. They provide a single store for raw and processed data ranging from raw copies of source systems to structured data for analytics. The document also briefly mentions Azure Data Lake Analytics, DataBricks, and concludes by thanking the reader.

This 1-day course provides hands-on skills in ingesting, analyzing, transforming and visualizing data using AWS Athena and getting the best performance when using it at scale. Audience: This class is intended for data engineers, analysts and data scientists responsible for: analyzing and visualizing big data, implementing cloud-based big data solutions, deploying or migrating big data applications to the public cloud, implementing and maintaining large-scale data storage environments, and transforming/processing big data.

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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.

Introduction to AWS Glue

AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. You can create and run an ETL job with a few clicks in the AWS Management Console. You simply point AWS Glue to your data stored on AWS, and AWS Glue discovers your data and stores the associated metadata (e.g. table definition and schema) in the AWS Glue Data Catalog. Once cataloged, your data is immediately searchable, queryable, and available for ETL. AWS Glue generates the code to execute your data transformations and data loading processes. Level: Intermediate Speakers: Ryan Malecky - Solutions Architect, EdTech, AWS Rajakumar Sampathkumar - Sr. Technical Account Manager, AWS

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Cloud architectural patterns and Microsoft Azure tools

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This document discusses various cloud architectural patterns and Microsoft Azure services. It provides an overview of data management, resiliency, and messaging patterns. It then demonstrates the Materialized View pattern and how it can improve query performance. Finally, it shows examples of Azure Tables, DocumentDB, and Azure Service Bus queues for messaging between loosely coupled applications.

Options for Data Prep - A Survey of the Current Market

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Data comes in many shapes and sizes, and every company struggles to find ways to transform, validate, and enrich data for multiple purposes. The problem has been around as long as data, and the market has an overwhelming number of options. In this presentation we look at the problem and key options from vendors in the market today. Dremio is a new approach that eliminates the need for stand alone data prep tools.

Strategies for SQL Server Index Analysis

Jason Strate

Properly managing indexes for a database is a common use in many environments. When reviewing an index, should it have a single or multiple columns? Should the table be a heap, a clustered index or clustered columnstore index? These considerations are often at the forefront when analyzing your indexes. In this session, we'll look at easy methods for identifying new indexes, we'll review patterns for index consolidation, and discuss how and when to remove indexes. At the end, you'll have a strategy that helps design indexes to improve performance.

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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.

Data Warehouses and Data Lakes

This document provides an overview of data warehousing and data lake concepts. It discusses key stages in data collection, storage, analysis and consumption. Different data storage options like Amazon S3, DynamoDB and Redshift are presented along with considerations for which tool to use based on data characteristics. The document also covers stream storage options and best practices for building cost-conscious and decoupled data architectures.

DBP-010_Using Azure Data Services for Modern Data Applications

decode2016

This document discusses using Azure data services for modern data applications based on the Lambda architecture. It covers ingestion of streaming and batch data using services like Event Hubs, IoT Hubs, and Kafka. It describes processing streaming data in real-time using Stream Analytics, Storm, and Spark Streaming, and processing batch data using HDInsight, ADLA, and Spark. It also covers staging data in data lakes, SQL databases, NoSQL databases and data warehouses. Finally, it discusses serving and exploring data using Power BI and enriching data using Azure Data Factory and Machine Learning.

Using Data Lakes

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Manish Singh

This document provides an introduction to a course on big data. It outlines the instructor and TA contact information. The topics that will be covered include data analytics, Hadoop/MapReduce programming, graph databases and analytics. Big data is defined as data sets that are too large and complex for traditional database tools to handle. The challenges of big data include capturing, storing, analyzing and visualizing large, complex data from many sources. Key aspects of big data are the volume, variety and velocity of data. Cloud computing, virtualization, and service-oriented architectures are important enabling technologies for big data. The course will use Hadoop and related tools for distributed data processing and analytics. Assessment will include homework, a group project, and class

Azure Data Lake and Azure Data Lake Analytics

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This document provides an overview and introduction to Azure Data Lake Analytics. It begins with defining big data and its characteristics. It then discusses the history and origins of Azure Data Lake in addressing massive data needs. Key components of Azure Data Lake are introduced, including Azure Data Lake Store for storing vast amounts of data and Azure Data Lake Analytics for performing analytics. U-SQL is covered as the query language for Azure Data Lake Analytics. The document also touches on related Azure services like Azure Data Factory for data movement. Overall it aims to give attendees an understanding of Azure Data Lake and how it can be used to store and analyze large, diverse datasets.

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This document provides an introduction and overview of Azure DocumentDB. It discusses how DocumentDB is a fully managed NoSQL database service that provides fast and predictable performance for JSON data through SQL querying capabilities. It also describes how DocumentDB offers features like elastic scaling, high availability, global distribution and ease of development. The document then provides information on starting with DocumentDB, writing queries, and programming capabilities within DocumentDB like stored procedures and triggers.

Data Warehousing in the Era of Big Data: Deep Dive into Amazon Redshift

by Tony Gibbs, Data Warehouse Specialist SA, AWS Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze all of your data for a fraction of the cost of traditional data warehouses. In this session, we take an in-depth look at data warehousing with Amazon Redshift for big data analytics. We cover best practices to take advantage of Amazon Redshift's columnar technology and parallel processing capabilities to deliver high throughput and query performance. We also discuss how to design optimal schemas, load data efficiently, and use work load management.

Machine Learning on the Microsoft Stack

This document provides an overview of machine learning solutions, including on-premise options using Excel add-ins, SQL Server, and R Studio, as well as cloud solutions on Azure and Predixion. It defines common machine learning roles and algorithms, discusses the R programming language, and compares features of the different solutions such as required infrastructure, complexity, costs, and capabilities.

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Azure DocumentDB 101

The document provides an introduction to Azure DocumentDB, a fully managed NoSQL database service. It discusses key features like schema-free JSON documents, automatic indexing, and the ability to run JavaScript code directly in the database using stored procedures. It also covers how to configure an DocumentDB account, create databases and collections, perform CRUD operations on documents, and write simple stored procedures. The presentation aims to explain the basics of DocumentDB and demonstrates how to interact with it programmatically.

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Modern ETL: Azure Data Factory, Data Lake, and SQL Database

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This document discusses modern Extract, Transform, Load (ETL) tools in Azure, including Azure Data Factory, Azure Data Lake, and Azure SQL Database. It provides an overview of each tool and how they can be used together in a data warehouse architecture with Azure Data Lake acting as the data hub and Azure SQL Database being used for analytics and reporting through the creation of data marts. It also includes two demonstrations, one on Azure Data Factory and another showing Azure Data Lake Store and Analytics.

Build a modern data platform.pptx

The document discusses building a data platform for analytics in Azure. It outlines common issues with traditional data warehouse architectures and recommends building a data lake approach using Azure Synapse Analytics. The key elements include ingesting raw data from various sources into landing zones, creating a raw layer using file formats like Parquet, building star schemas in dedicated SQL pools or Spark tables, implementing alerting using Log Analytics, and loading data into Power BI. Building the platform with Python pipelines, notebooks, and GitHub integration is emphasized for flexibility, testability and collaboration.

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