Azure Big Data Story
•Télécharger en tant que PPTX, PDF•
3 j'aime•1,880 vues
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.
Signaler
Partager
Signaler
Partager
Recommandé
Database Choices
The document discusses database choices and provides an overview of different types of databases including relational, NoSQL, and Hadoop databases. It compares features of relational databases versus Hadoop/MapReduce and provides demos of various database options like AWS DynamoDB, MongoDB, Neo4j, SQL Server, and AWS Redshift. The document aims to help readers understand the different database choices available and which types may be best suited to different types of data and use cases.
Big data in Azure
This document discusses Big Data solutions in Microsoft Azure. It introduces Azure cloud services and provides an overview of Big Data and how it differs from traditional databases. It then outlines Microsoft's Big Data solutions built on Hortonworks Data Platform, including HDInsight which allows running Hadoop on Azure. HDInsight supports various data storage options and processing tools like Hive, Pig, and Storm. The document also covers designing HDInsight clusters and Azure Data Lake for unlimited storage of structured and unstructured data.
Bleeding Edge Databases
This document provides information on and demonstrations of several bleeding edge database technologies: Aerospike, Algebraix Data, and Google BigQuery. It includes benchmark results, architecture diagrams, pricing and deployment details for each one. Example use cases and instructions for getting started with the technologies are also provided.
Using Premium Data - for Business Analysts
Understanding use cases for free and premium data in Big Data scenarios - uses D&B, Melissa, Quandl and others.
Shown using integration with Microsoft Excel and other tools.
Big Data on azure
The document discusses Big Data on Azure and provides an overview of HDInsight, Microsoft's Apache Hadoop-based data platform on Azure. It describes HDInsight cluster types for Hadoop, HBase, Storm and Spark and how clusters can be automatically provisioned on Azure. Example applications and demos of Storm, HBase, Hive and Spark are also presented. The document highlights key aspects of using HDInsight including storage integration and tools for interactive analysis.
Cloud Big Data Architectures
This document provides an overview of a workshop on cloud big data architectures. The workshop covers:
1. Different types of big data solutions and when to use each, such as Hadoop, NoSQL and big relational databases.
2. Data pipelines, including ETL tools, load testing patterns and connecting clouds.
3. Querying and visualizing data through business analytics, predictive analytics and visualization tools.
4. A brief introduction to IoT and how it relates to big data.
4Developers 2018: Przetwarzanie Big Data w oparciu o architekturę Lambda na p...
Według szacunków do 2020 roku wygenerujmy 40 Zetta byte’ów, a do roku 2025 aż 163 Zetta byte’ów różnego rodzaju danych, a ich dokładna analiza ACpozwali na odkrywanie nowych zjawisk, optymalizacje procesów, czy wspomaganie procesów decyzyjnych. Aby efektywnie przetwarzać tak duże zbiory danych potrzebujemy nowych technik analizy danych oraz innowacyjnych rozwiązań technologicznych. Ważną role pełni tutaj chmura Azure, która oferuje szereg usług, przy użyciu których możemy tworzyć rozwiązania na potrzeby przetwarzania Big Data zarówno w trybie batch’owych jak i ‘near real time’. Podczas sesji stworzymy przykładowe rozwiązanie przetwarzania Big Data oparte o architekturę Lambda , z wykorzystaniem usług platformy Azure, takich jak Azure Data Factory, Azure Stream Analytics, Azure HdInsight, Azure Event (IoT) Hub, czy Azure Data Lake.
Integration Monday - Analysing StackExchange data with Azure Data Lake
Big data is the new big thing where storing the data is the easy part. Gaining insights in your pile of data is something different.
Based on a data dump of the well-known StackExchange websites, we will store & analyse 150+ GB of data with Azure Data Lake Store & Analytics to gain some insights about their users. After that we will use Power BI to give an at a glance overview of our learnings.
If you are a developer that is interested in big data, this is your time to shine! We will use our existing SQL & C# skills to analyse everything without having to worry about running clusters.
Recommandé
Database Choices
The document discusses database choices and provides an overview of different types of databases including relational, NoSQL, and Hadoop databases. It compares features of relational databases versus Hadoop/MapReduce and provides demos of various database options like AWS DynamoDB, MongoDB, Neo4j, SQL Server, and AWS Redshift. The document aims to help readers understand the different database choices available and which types may be best suited to different types of data and use cases.
Big data in Azure
This document discusses Big Data solutions in Microsoft Azure. It introduces Azure cloud services and provides an overview of Big Data and how it differs from traditional databases. It then outlines Microsoft's Big Data solutions built on Hortonworks Data Platform, including HDInsight which allows running Hadoop on Azure. HDInsight supports various data storage options and processing tools like Hive, Pig, and Storm. The document also covers designing HDInsight clusters and Azure Data Lake for unlimited storage of structured and unstructured data.
Bleeding Edge Databases
This document provides information on and demonstrations of several bleeding edge database technologies: Aerospike, Algebraix Data, and Google BigQuery. It includes benchmark results, architecture diagrams, pricing and deployment details for each one. Example use cases and instructions for getting started with the technologies are also provided.
Using Premium Data - for Business Analysts
Understanding use cases for free and premium data in Big Data scenarios - uses D&B, Melissa, Quandl and others.
Shown using integration with Microsoft Excel and other tools.
Big Data on azure
The document discusses Big Data on Azure and provides an overview of HDInsight, Microsoft's Apache Hadoop-based data platform on Azure. It describes HDInsight cluster types for Hadoop, HBase, Storm and Spark and how clusters can be automatically provisioned on Azure. Example applications and demos of Storm, HBase, Hive and Spark are also presented. The document highlights key aspects of using HDInsight including storage integration and tools for interactive analysis.
Cloud Big Data Architectures
This document provides an overview of a workshop on cloud big data architectures. The workshop covers:
1. Different types of big data solutions and when to use each, such as Hadoop, NoSQL and big relational databases.
2. Data pipelines, including ETL tools, load testing patterns and connecting clouds.
3. Querying and visualizing data through business analytics, predictive analytics and visualization tools.
4. A brief introduction to IoT and how it relates to big data.
4Developers 2018: Przetwarzanie Big Data w oparciu o architekturę Lambda na p...
Według szacunków do 2020 roku wygenerujmy 40 Zetta byte’ów, a do roku 2025 aż 163 Zetta byte’ów różnego rodzaju danych, a ich dokładna analiza ACpozwali na odkrywanie nowych zjawisk, optymalizacje procesów, czy wspomaganie procesów decyzyjnych. Aby efektywnie przetwarzać tak duże zbiory danych potrzebujemy nowych technik analizy danych oraz innowacyjnych rozwiązań technologicznych. Ważną role pełni tutaj chmura Azure, która oferuje szereg usług, przy użyciu których możemy tworzyć rozwiązania na potrzeby przetwarzania Big Data zarówno w trybie batch’owych jak i ‘near real time’. Podczas sesji stworzymy przykładowe rozwiązanie przetwarzania Big Data oparte o architekturę Lambda , z wykorzystaniem usług platformy Azure, takich jak Azure Data Factory, Azure Stream Analytics, Azure HdInsight, Azure Event (IoT) Hub, czy Azure Data Lake.
Integration Monday - Analysing StackExchange data with Azure Data Lake
Big data is the new big thing where storing the data is the easy part. Gaining insights in your pile of data is something different.
Based on a data dump of the well-known StackExchange websites, we will store & analyse 150+ GB of data with Azure Data Lake Store & Analytics to gain some insights about their users. After that we will use Power BI to give an at a glance overview of our learnings.
If you are a developer that is interested in big data, this is your time to shine! We will use our existing SQL & C# skills to analyse everything without having to worry about running clusters.
Options for Data Prep - A Survey of the Current Market
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.
Simplifying And Accelerating Data Access for Python With Dremio and Apache Arrow
By Sudheesh Katkam
PyData New York City 2017
Dremio is a new open source project for self-service data fabric. Dremio simplifies and accelerates access to data from any source and any size, including relational databases, NoSQL, Hadoop, Parquet, and text files. We'll show you how you can use Dremio to visually curate data from any source, then access via Pandas or Jupyter notebook for rapid access.
Cortana Analytics Workshop: Azure Data Lake
Rajesh Dadhia. This session introduces the newest services in the Cortana Analytics family. Azure Data Lake is a hyper-scale data repository designed for big data analytics workloads. It provides a single place to store any type of data in its native format. In this session, we will show how the HDFS compatibility of Azure Data Lake as a Hadoop File System enables all Hadoop workloads including Azure HDInsight, Hortonworks and Cloudera. Further, we will focus on the key capabilities of the Azure Data Lake that make it an ideal choice for storing, accessing and sharing data for a wide range of analytics applications. Go to https://channel9.msdn.com/ to find the recording of this session.
DBP-010_Using Azure Data Services for Modern Data Applications
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.
Azure Databricks—Apache Spark as a Service with Sascha Dittmann
The driving force behind Apache Spark (Databricks Inc.) and Microsoft have designed a joint service to quickly and easily create Big Data and Advanced Analytics solutions. The combination of the comprehensive Databricks Unified Analytics platform and the powerful capabilities of Microsoft Azure make it easy to analyse data streams or large amounts of data, as well asthe training of AI models. Sascha Dittmann shows in this session how the new Azure service can be set up and used in various real-world scenarios. He also shows, how to connect the various Azure Services to the Azure Databricks service.
Modern Data architecture Design
The document discusses modern data architectures. It presents conceptual models for data ingestion, storage, processing, and insights/actions. It compares traditional vs modern architectures. The modern architecture uses a data lake for storage and allows for on-demand analysis. It provides an example of how this could be implemented on Microsoft Azure using services like Azure Data Lake Storage, Azure Data Bricks, and Azure Data Warehouse. It also outlines common data management functions such as data governance, architecture, development, operations, and security.
Microsoft Machine Learning Smackdown
comparison of Excel add-ins and other solutions for implementing data mining or machine learning solutions on the Microsoft stack - includes coverage of XLMiner, Analysis Services Data Mining and PredixionSoftware
TechDays NL 2016 - Building your scalable secure IoT Solution on Azure
The Internet-of-Things was one of the big hypes in 2015 but it’s more than that – Customers want to build out their own infrastructures and act on their data.
Today we’ll look at how Microsoft Azure helps us to build scalable solutions to process events from thousands of devices in a secure manner and the challenges it has. Once the data is in the cloud we’ll also take a look at ways we can learn from our measurements.
Building Data Lakes with Apache Airflow
Build a simple Data Lake on AWS using a combination of services, including Amazon Managed Workflows for Apache Airflow (Amazon MWAA), AWS Glue, AWS Glue Studio, Amazon Athena, and Amazon S3.
Blog post and link to the video: https://garystafford.medium.com/building-a-data-lake-with-apache-airflow-b48bd953c2b
Finding new Customers using D&B and Excel Power Query
Screencast which shows how to use Excel Power Query with D&B APIs to get company DUNS numbers and other company information from the Windows Azure Marketplace.
Lecture1
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
Getting to 1.5M Ads/sec: How DataXu manages Big Data
DataXu sits at the heart of the all-digital world, providing a data platform that manages tens of millions of dollars of digital advertising investments from Global 500 brands. The DataXu data platform evaluates 1.5 million online ad opportunities every second for our customers, allowing them to manage and optimize their marketing investments across all digital channels. DataXu employs a wide range of AWS services: Cloud Front, Cloud Trail, CloudWatch, Data Pipeline, Direct Connect, Dynamo DB, EC2, EMR, Glacier, IAM, Kinesis, RDS, Redshift, Route53, S3, SNS, SQS, and VPC to run various workloads at scale for DataXu data platform.
In addition, DataXu also uses Qubole Data Service, QDS, to offer a Unified Analytics Interface tool to DataXu customers. Qubole, a member of APN provides self-managing Big data infrastructure in the Cloud which leverages spot pricing for cost-efficiencies, provides fast performance, and most importantly a streamlined user-interface for ease of use.
Attendees will learn how Qubole provided self-managing Hadoop clusters in the AWS Cloud accelerated DataXu’s batch-oriented analysis jobs; and how Qubole integration with Amazon Redshift enabled DataXu to preform low latency and interactive analysis. Further, in the session we'll take a look at how DataXu opened up QDS access to their customers using QDS user interface thereby providing them with a single tool for both batch-oriented and interactive analysis. By using the QDS user interface buyers of the DataXu data service could perform all manner of analysis against the data stored in their AWS S3 bucket.
Speakers:
Scott Ward
Solutions Architect at Amazon Web Services
Ashish Dubey
Solutions Architect at Qubole
Yekesa Kosuru
VP Engineering at DataXu
Big data on AWS
This document discusses big data and AWS tools for managing it. It defines big data as data with high volume, velocity and variety. AWS provides scalable tools like EC2, EMR, Kinesis and Redshift to handle the ingestion, storage, processing and analysis of large and diverse datasets in the cloud. These tools work together in an integrated environment and auto-scale based on demand, providing a cost-effective solution for big data challenges. An example use case of real-time IoT analytics is presented to illustrate how different AWS products interact to build scalable data pipelines.
Bi on Big Data - Strata 2016 in London
There are many options for business intelligence on Hadoop and other Big Data systems. This presentation provides an overview of some of the options.
Using Data Lakes
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Speakers:
Neel Mitra - Solutions Architect, AWS
Roger Dahlstrom - Solutions Architect, AWS
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
A developer's introduction to big data processing with Azure Databricks
The document discusses how companies can use big data analytics and Azure Databricks to improve their customer experiences and grow their business. It provides an overview of how Wide World Importers seeks to expand its customers through an omni-channel strategy using analytics from data across its retail stores, website, and mobile apps. The document also outlines logical architectures for ingesting, storing, preparing, training models on, and serving data using Azure Databricks and other Azure services.
NoSQL for the SQL Server Pro
Lynn Langit is an expert in data and NoSQL databases. She has industry awards from Microsoft, Google, and 10Gen and experience as a technical author, trainer, and architect. Her presentation discusses how business intelligence has evolved from optimized relational databases to incorporating big data from a variety of sources. She explains the big data pipeline of acquiring, processing, storing, querying, and visualizing data. Langit demonstrates several NoSQL database options like MongoDB, HBase, Cassandra, and Neo4j, as well as cloud storage services. She compares relational and NoSQL databases and how they apply to different types and sizes of data.
REDSHIFT - Amazon
Amazon Redshift is a cloud-hosted data warehouse service from AWS that allows for petabyte-scale analytics on large datasets using massive parallel processing. It stores data in a column-oriented format and integrates with other AWS services like S3, DynamoDB, and EMR. Redshift provides features like columnar storage, parallel query processing across multiple nodes, automated backups and restores, encryption, and integration with SQL and BI tools. The document demonstrates using Redshift alongside S3, Pipeline, EC2/MySQL, and Qlik Sense to build a scalable data warehouse solution in the cloud.
Apache Arrow: In Theory, In Practice
This document discusses Apache Arrow, an open source cross-language development platform for in-memory analytics. It provides an overview of Arrow's goals of being cross-language compatible, optimized for modern CPUs, and enabling interoperability between systems. Key components include core C++/Java libraries, integrations with projects like Pandas and Spark, and common message patterns for sharing data. The document also describes how Arrow is implemented in practice in systems like Dremio's Sabot query engine.
Big Data in Azure
Big data is driving transformative changes in traditional data warehousing. Traditional ETL processes and highly structured data schemas are being replaced with schema flexibility to handle all types of data from diverse sources. This allows for real-time experimentation and analysis beyond just operational reporting. Microsoft is applying lessons from its own big data journey to help customers by providing a comprehensive set of Apache big data tools in Azure along with intelligence and analytics services to gain insights from diverse data sources.
Microsoft Azure Big Data Analytics
This is a run-through at a 200 level of the Microsoft Azure Big Data Analytics for the Cloud data platform based on the Cortana Intelligence Suite offerings.
Contenu connexe
Tendances
Options for Data Prep - A Survey of the Current Market
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.
Simplifying And Accelerating Data Access for Python With Dremio and Apache Arrow
By Sudheesh Katkam
PyData New York City 2017
Dremio is a new open source project for self-service data fabric. Dremio simplifies and accelerates access to data from any source and any size, including relational databases, NoSQL, Hadoop, Parquet, and text files. We'll show you how you can use Dremio to visually curate data from any source, then access via Pandas or Jupyter notebook for rapid access.
Cortana Analytics Workshop: Azure Data Lake
Rajesh Dadhia. This session introduces the newest services in the Cortana Analytics family. Azure Data Lake is a hyper-scale data repository designed for big data analytics workloads. It provides a single place to store any type of data in its native format. In this session, we will show how the HDFS compatibility of Azure Data Lake as a Hadoop File System enables all Hadoop workloads including Azure HDInsight, Hortonworks and Cloudera. Further, we will focus on the key capabilities of the Azure Data Lake that make it an ideal choice for storing, accessing and sharing data for a wide range of analytics applications. Go to https://channel9.msdn.com/ to find the recording of this session.
DBP-010_Using Azure Data Services for Modern Data Applications
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.
Azure Databricks—Apache Spark as a Service with Sascha Dittmann
The driving force behind Apache Spark (Databricks Inc.) and Microsoft have designed a joint service to quickly and easily create Big Data and Advanced Analytics solutions. The combination of the comprehensive Databricks Unified Analytics platform and the powerful capabilities of Microsoft Azure make it easy to analyse data streams or large amounts of data, as well asthe training of AI models. Sascha Dittmann shows in this session how the new Azure service can be set up and used in various real-world scenarios. He also shows, how to connect the various Azure Services to the Azure Databricks service.
Modern Data architecture Design
The document discusses modern data architectures. It presents conceptual models for data ingestion, storage, processing, and insights/actions. It compares traditional vs modern architectures. The modern architecture uses a data lake for storage and allows for on-demand analysis. It provides an example of how this could be implemented on Microsoft Azure using services like Azure Data Lake Storage, Azure Data Bricks, and Azure Data Warehouse. It also outlines common data management functions such as data governance, architecture, development, operations, and security.
Microsoft Machine Learning Smackdown
comparison of Excel add-ins and other solutions for implementing data mining or machine learning solutions on the Microsoft stack - includes coverage of XLMiner, Analysis Services Data Mining and PredixionSoftware
TechDays NL 2016 - Building your scalable secure IoT Solution on Azure
The Internet-of-Things was one of the big hypes in 2015 but it’s more than that – Customers want to build out their own infrastructures and act on their data.
Today we’ll look at how Microsoft Azure helps us to build scalable solutions to process events from thousands of devices in a secure manner and the challenges it has. Once the data is in the cloud we’ll also take a look at ways we can learn from our measurements.
Building Data Lakes with Apache Airflow
Build a simple Data Lake on AWS using a combination of services, including Amazon Managed Workflows for Apache Airflow (Amazon MWAA), AWS Glue, AWS Glue Studio, Amazon Athena, and Amazon S3.
Blog post and link to the video: https://garystafford.medium.com/building-a-data-lake-with-apache-airflow-b48bd953c2b
Finding new Customers using D&B and Excel Power Query
Screencast which shows how to use Excel Power Query with D&B APIs to get company DUNS numbers and other company information from the Windows Azure Marketplace.
Lecture1
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
Getting to 1.5M Ads/sec: How DataXu manages Big Data
DataXu sits at the heart of the all-digital world, providing a data platform that manages tens of millions of dollars of digital advertising investments from Global 500 brands. The DataXu data platform evaluates 1.5 million online ad opportunities every second for our customers, allowing them to manage and optimize their marketing investments across all digital channels. DataXu employs a wide range of AWS services: Cloud Front, Cloud Trail, CloudWatch, Data Pipeline, Direct Connect, Dynamo DB, EC2, EMR, Glacier, IAM, Kinesis, RDS, Redshift, Route53, S3, SNS, SQS, and VPC to run various workloads at scale for DataXu data platform.
In addition, DataXu also uses Qubole Data Service, QDS, to offer a Unified Analytics Interface tool to DataXu customers. Qubole, a member of APN provides self-managing Big data infrastructure in the Cloud which leverages spot pricing for cost-efficiencies, provides fast performance, and most importantly a streamlined user-interface for ease of use.
Attendees will learn how Qubole provided self-managing Hadoop clusters in the AWS Cloud accelerated DataXu’s batch-oriented analysis jobs; and how Qubole integration with Amazon Redshift enabled DataXu to preform low latency and interactive analysis. Further, in the session we'll take a look at how DataXu opened up QDS access to their customers using QDS user interface thereby providing them with a single tool for both batch-oriented and interactive analysis. By using the QDS user interface buyers of the DataXu data service could perform all manner of analysis against the data stored in their AWS S3 bucket.
Speakers:
Scott Ward
Solutions Architect at Amazon Web Services
Ashish Dubey
Solutions Architect at Qubole
Yekesa Kosuru
VP Engineering at DataXu
Big data on AWS
This document discusses big data and AWS tools for managing it. It defines big data as data with high volume, velocity and variety. AWS provides scalable tools like EC2, EMR, Kinesis and Redshift to handle the ingestion, storage, processing and analysis of large and diverse datasets in the cloud. These tools work together in an integrated environment and auto-scale based on demand, providing a cost-effective solution for big data challenges. An example use case of real-time IoT analytics is presented to illustrate how different AWS products interact to build scalable data pipelines.
Bi on Big Data - Strata 2016 in London
There are many options for business intelligence on Hadoop and other Big Data systems. This presentation provides an overview of some of the options.
Using Data Lakes
A data lake can be used as a source for both structured and unstructured data - but how? We'll look at using open standards including Spark and Presto with Amazon EMR, Amazon Redshift Spectrum and Amazon Athena to process and understand data.
Speakers:
Neel Mitra - Solutions Architect, AWS
Roger Dahlstrom - Solutions Architect, AWS
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
A developer's introduction to big data processing with Azure Databricks
The document discusses how companies can use big data analytics and Azure Databricks to improve their customer experiences and grow their business. It provides an overview of how Wide World Importers seeks to expand its customers through an omni-channel strategy using analytics from data across its retail stores, website, and mobile apps. The document also outlines logical architectures for ingesting, storing, preparing, training models on, and serving data using Azure Databricks and other Azure services.
NoSQL for the SQL Server Pro
Lynn Langit is an expert in data and NoSQL databases. She has industry awards from Microsoft, Google, and 10Gen and experience as a technical author, trainer, and architect. Her presentation discusses how business intelligence has evolved from optimized relational databases to incorporating big data from a variety of sources. She explains the big data pipeline of acquiring, processing, storing, querying, and visualizing data. Langit demonstrates several NoSQL database options like MongoDB, HBase, Cassandra, and Neo4j, as well as cloud storage services. She compares relational and NoSQL databases and how they apply to different types and sizes of data.
REDSHIFT - Amazon
Amazon Redshift is a cloud-hosted data warehouse service from AWS that allows for petabyte-scale analytics on large datasets using massive parallel processing. It stores data in a column-oriented format and integrates with other AWS services like S3, DynamoDB, and EMR. Redshift provides features like columnar storage, parallel query processing across multiple nodes, automated backups and restores, encryption, and integration with SQL and BI tools. The document demonstrates using Redshift alongside S3, Pipeline, EC2/MySQL, and Qlik Sense to build a scalable data warehouse solution in the cloud.
Apache Arrow: In Theory, In Practice
This document discusses Apache Arrow, an open source cross-language development platform for in-memory analytics. It provides an overview of Arrow's goals of being cross-language compatible, optimized for modern CPUs, and enabling interoperability between systems. Key components include core C++/Java libraries, integrations with projects like Pandas and Spark, and common message patterns for sharing data. The document also describes how Arrow is implemented in practice in systems like Dremio's Sabot query engine.
Tendances (20)
Options for Data Prep - A Survey of the Current Market
Options for Data Prep - A Survey of the Current Market
Simplifying And Accelerating Data Access for Python With Dremio and Apache Arrow
Simplifying And Accelerating Data Access for Python With Dremio and Apache Arrow
DBP-010_Using Azure Data Services for Modern Data Applications
DBP-010_Using Azure Data Services for Modern Data Applications
Azure Databricks—Apache Spark as a Service with Sascha Dittmann
Azure Databricks—Apache Spark as a Service with Sascha Dittmann
TechDays NL 2016 - Building your scalable secure IoT Solution on Azure
TechDays NL 2016 - Building your scalable secure IoT Solution on Azure
Finding new Customers using D&B and Excel Power Query
Finding new Customers using D&B and Excel Power Query
Getting to 1.5M Ads/sec: How DataXu manages Big Data
Getting to 1.5M Ads/sec: How DataXu manages Big Data
A developer's introduction to big data processing with Azure Databricks
A developer's introduction to big data processing with Azure Databricks
En vedette
Big Data in Azure
Big data is driving transformative changes in traditional data warehousing. Traditional ETL processes and highly structured data schemas are being replaced with schema flexibility to handle all types of data from diverse sources. This allows for real-time experimentation and analysis beyond just operational reporting. Microsoft is applying lessons from its own big data journey to help customers by providing a comprehensive set of Apache big data tools in Azure along with intelligence and analytics services to gain insights from diverse data sources.
Microsoft Azure Big Data Analytics
This is a run-through at a 200 level of the Microsoft Azure Big Data Analytics for the Cloud data platform based on the Cortana Intelligence Suite offerings.
Azure Spark - Big Data - Coresic 2016
Este documento habla sobre Big Data y herramientas para procesar grandes cantidades de datos. Explica que Spark es una tecnología 100 veces más rápida que Hadoop MapReduce para procesar datos en memoria y soporta tolerancia a fallos. También describe a Jupyter como una herramienta popular para científicos de datos que puede usar diferentes lenguajes de programación e integrarse con Spark.
Big data on Azure for Architects
This document discusses big data concepts like volume, velocity, and variety of data. It introduces NoSQL databases as an alternative to relational databases for big data that does not require data cleansing or schema definition. Hadoop is presented as a framework for distributed storage and processing of large datasets across clusters of commodity hardware. Key Hadoop components like HDFS, MapReduce, Hive, Pig and YARN are described at a high level. The document also discusses using Azure services like Azure Storage, HDInsight and Stream Analytics with Hadoop.
Big Data en Azure: Azure Data Lake
Este documento describe el concepto de Big Data y los principales componentes de la plataforma Microsoft Azure Data Lake para procesar y analizar grandes cantidades de datos. Explica que Big Data se refiere a almacenar y encontrar patrones en grandes cantidades de datos de diversas fuentes y formatos. Luego describe las características de volumen, velocidad y variedad de los datos, y algunas de las herramientas clave de Azure Data Lake como Azure Data Lake Store, Azure Data Lake Analytics, HDInsight y U-SQL para procesar y analizar datos a gran escala de
Intorducing Big Data and Microsoft Azure
The purpose of these slides is to give a high-level overview of Big Data concepts and techniques, as well as its related tools and technologies, focusing on Microsoft Azure. It starts by defining what Big Data is, as well as why Big Data platforms are needed. Fundamental components of a Big Data Platform are discussed, followed by a little bit of theory about Distributed Processing & CAP Theorem, and its relevance to how Big Data Solutions compare to Traditional RDBMS. Use case of how Big Data fits in Enterprise Data Platforms are shown. The Hadoop Ecosystem is briefly reviewed before Big Data on Microsoft Azure is discussed. Then some directions of How to get started with Big Data.
Big Data Architectural Patterns
The world is producing an ever increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Presented by: Arie Leeuwesteijn, Principal Solutions Architect, Amazon Web Services
Customer Guest: Sander Kieft, Sanoma
Big Data Use Cases and Solutions in the AWS Cloud
The AWS cloud computing platform has disrupted big data. Managing big data applications used to be for only well-funded research organizations and large corporations, but not any longer. Hear from Ben Butler, Big Data Solutions Marketing Manager for AWS, to learn how our customers are using big data services in the AWS cloud to innovate faster than ever before. Not only is AWS technology available to everyone, but it is self-service, on-demand, and featuring innovative technology and flexible pricing models at low cost with no commitments. Learn from customer success stories, as Ben shares real-world case studies describing the specific big data challenges being solved on AWS. We will conclude with a discussion around the tutorials, public datasets, test drives, and our grants program - all of the resources needed to get you started quickly.
Big Data and Analytics on AWS
This document discusses a presentation about big data and analytics on AWS. It describes what big data is, provides examples of AWS services for ingesting, storing, processing, analyzing and visualizing big data. It also provides examples of industries using AWS for data analysis and discusses Amazon Kinesis for real-time processing of streaming data. Finally, it discusses putting the various AWS services together in an end-to-end big data workflow.
Dive into Spark Streaming
The document discusses Spark Streaming and its execution model. It describes how Spark Streaming receives data streams, divides them into micro-batches, and processes the micro-batches using Spark. It also covers transformations and actions that can be performed on DStreams, window operations, and deployment options for Spark Streaming including local, standalone, and cluster modes.
(BDT310) Big Data Architectural Patterns and Best Practices on AWS
The world is producing an ever increasing volume, velocity, and variety of big data. Consumers and businesses are demanding up-to-the-second (or even millisecond) analytics on their fast-moving data, in addition to classic batch processing. AWS delivers many technologies for solving big data problems. But what services should you use, why, when, and how? In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. Next, we discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. Finally, we provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Big data architectures and the data lake
The document provides an overview of big data architectures and the data lake concept. It discusses why organizations are adopting data lakes to handle increasing data volumes and varieties. The key aspects covered include:
- Defining top-down and bottom-up approaches to data management
- Explaining what a data lake is and how Hadoop can function as the data lake
- Describing how a modern data warehouse combines features of a traditional data warehouse and data lake
- Discussing how federated querying allows data to be accessed across multiple sources
- Highlighting benefits of implementing big data solutions in the cloud
- Comparing shared-nothing, massively parallel processing (MPP) architectures to symmetric multi-processing (
Getting started with microsoft azure in 30 mins
This document provides an overview of resources for getting started with Microsoft Azure cloud computing within 30 minutes. It outlines an agenda for learning Azure including why it's important, identifying career paths, available learning resources, and certification paths. Recommended first steps include exploring official documentation, online courses, hands-on labs, and getting an Azure subscription. Certification preparation timelines are provided for several Azure exams, along with links to practice tests and recorded training content. The goal is to help users efficiently learn Azure and excel in their careers through certification.
Azure Data Lake Analytics Deep Dive
Azure Data Lake Analytics provides a big data analytics service for processing large amounts of data stored in Azure Data Lake Store. It allows users to run analytics jobs using U-SQL, a language that unifies SQL with C# for querying structured, semi-structured and unstructured data. Jobs are compiled, scheduled and run in parallel across multiple Azure Data Lake Analytics Units (ADLAUs). The key components include storage, a job queue, parallelization, and a U-SQL runtime. Partitioning input data improves performance by enabling partition elimination and parallel aggregation of query results.
Microsoft Azure vs Amazon Web Services (AWS) Services & Feature Mapping
If you are a Cloud Architect, Developer, IT Manager, Director or whoever may be, if you are associated with Azure or AWS cloud in some form, I’m sure you must have come across a common question.
“What is the alternate service available in Azure or AWS vice versa and it’s pricing?” I’m sure you will say yes!
Agreed, it’s hard to remember all the services offered by public clouds, i.e. Azure and AWS. Remembering existing services and their benefits itself is a big task, on top of that updating ourselves with the new feature releases and enhancements is another major task.
So I put together a Service & Feature Mappings between Microsoft Azure & AWS for my and colleagues quick reference.
I hope you also find this piece informative.
The AWS Big Data Platform – Overview
The introductory morning session will discuss big data challenges and provide an overview of the AWS Big Data Platform. We will also cover:
• How AWS customers leverage the platform to manage massive volumes of data from a variety of sources while containing costs.
• Reference architectures for popular use cases, including: connected devices (IoT), log streaming, real-time intelligence, and analytics.
• The AWS big data portfolio of services, including Amazon S3, Kinesis, DynamoDB, Elastic MapReduce (EMR) and Redshift.
• The latest relational database engine, Amazon Aurora - a MySQL-compatible, highly-available relational database engine which provides up to five times better performance than MySQL at a price one-tenth the cost of a commercial database.
• Amazon Machine Learning – the latest big data service from AWS provides visualization tools and wizards that guide you through the process of creating machine learning (ML) models without having to learn complex ML algorithms and technology.
Microsoft Cloud Computing - Windows Azure Platform
The document provides an overview of Microsoft's cloud computing platform. It discusses Microsoft's strategy of providing a hybrid cloud that allows customers to run applications both on-premise and in the public cloud. It highlights key services offered, such as compute infrastructure (web and worker roles), SQL Azure database, storage, and AppFabric. Case studies are presented showing how various companies have used the Microsoft cloud platform.
En vedette (17)
(BDT310) Big Data Architectural Patterns and Best Practices on AWS
(BDT310) Big Data Architectural Patterns and Best Practices on AWS
Microsoft Azure vs Amazon Web Services (AWS) Services & Feature Mapping
Microsoft Azure vs Amazon Web Services (AWS) Services & Feature Mapping
Microsoft Cloud Computing - Windows Azure Platform
Microsoft Cloud Computing - Windows Azure Platform
Similaire à Azure Big Data Story
[WITH THE VISION 2017] IoT/AI時代を生き抜くためのデータ プラットフォーム (Leveraging Azure Data Se...
The document discusses the Microsoft data platform and its capabilities for building applications in the IoT/AI era. It outlines various Azure data services for relational, non-relational, caching, and search needs. Examples are provided for when to use SQL Database, Table Storage, Cosmos DB, HBase on HDInsight, Redis Cache, and Azure Search. The platform allows capturing, managing, transforming, analyzing, and visualizing data to power intelligent applications.
Amazon QuickSight
Amazon QuickSight is a fast, cloud-powered business intelligence (BI) service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. In this session, we demonstrate how you can point Amazon QuickSight to AWS data stores, flat files, or other third-party data sources and begin visualizing your data in minutes. We also introduce SPICE - a new Super-fast, Parallel, In-memory, Calculation Engine in Amazon QuickSight, which performs advanced calculations and render visualizations rapidly without requiring any additional infrastructure, SQL programming, or dimensional modeling, so you can seamlessly scale to hundreds of thousands of users and petabytes of data. Lastly, you will see how Amazon QuickSight provides you with smart visualizations and graphs that are optimized for your different data types, to ensure the most suitable and appropriate visualization to conduct your analysis, and how to share these visualization stories using the built-in collaboration tools.
Presented by: Matthew McClean, AWS Partner Solutions Architect, Amazon Web Services
Building Big Data Solutions with Azure Data Lake.10.11.17.pptx
The document discusses Microsoft's use of a data lake approach to better leverage large amounts of data from various sources using tools like Azure Data Lake Store, Azure Data Lake Analytics, HDInsight, and Spark. It provides an overview of how Microsoft built their own data lake to handle exabytes of data from different parts of the company and support analytics, machine learning, and real-time streaming. Common patterns for using Azure Data Lake tools for ingesting, storing, analyzing, and visualizing data are also presented.
Data Platform Overview
The document discusses how businesses need to build a data strategy and modernize their data platforms to harness the power of data from diverse and growing sources. It provides examples of how organizations like healthcare and energy companies are using technologies like machine learning, real-time analytics, and predictive modeling on data from various sources to improve outcomes, predict trends, and drive business decisions. The Microsoft data platform is positioned as helping businesses manage both traditional and new forms of data, gain insights faster, and transform into data-driven organizations through offerings like SQL Server, Azure, Power BI, and the Internet of Things.
Accelerating Business Intelligence Solutions with Microsoft Azure pass
Business Intelligence (BI) solutions need to move at the speed of business. Unfortunately, roadblocks related to availability of resources and deployment often present an issue. What if you could accelerate the deployment of an entire BI infrastructure to just a couple hours and start loading data into it by the end of the day. In this session, we'll demonstrate how to leverage Microsoft tools and the Azure cloud environment to build out a BI solution and begin providing analytics to your team with tools such as Power BI. By end of the session, you'll gain an understanding of the capabilities of Azure and how you can start building an end to end BI proof-of-concept today.
利用 Amazon QuickSight 視覺化分析服務剖析資料
Amazon QuickSight is a fast, cloud-powered business analytics service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. Using our cloud-based service you can easily connect to your data, perform advanced analysis, and create stunning visualizations and rich dashboards that can be accessed from any browser or mobile device.
Azure Data Platform Overview.pdf
Dustin Vannoy is a field data engineer at Databricks and co-founder of Data Engineering San Diego. He specializes in Azure, AWS, Spark, Kafka, Python, data lakes, cloud analytics, and streaming. The document provides an overview of various Azure data and analytics services including Azure SQL DB, Cosmos DB, Blob Storage, Data Lake Storage Gen 2, Databricks, Synapse Analytics, Data Factory, Event Hubs, Stream Analytics, and Machine Learning. It also includes a reference architecture and recommends Microsoft Learn paths and community resources for learning.
Getting Started with Amazon QuickSight
Amazon QuickSight is a fast, cloud-powered business intelligence (BI) service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. In this session, we demonstrate how you can point Amazon QuickSight to AWS data stores, flat files, or other third-party data sources and begin visualizing your data in minutes. We also introduce you to SPICE - a Super-fast, Parallel, In-memory, Calculation Engine in Amazon QuickSight, which performs advanced calculations and render visualizations rapidly without requiring any additional infrastructure, SQL programming, or dimensional modeling, so you can seamlessly scale to hundreds of thousands of users and petabytes of data. Lastly, you will see how Amazon QuickSight provides you with smart visualizations and graphs that are optimized for your different data types, to ensure the most suitable and appropriate visualization to conduct your analysis, and how to share these visualization stories using the built-in collaboration tools.
Lakehouse in Azure
In this session, Sergio covered the Lakehouse concept and how companies implement it, from data ingestion to insight. He showed how you could use Azure Data Services to speed up your Analytics project from ingesting, modelling and delivering insights to end users.
Getting Started with Amazon QuickSight
Amazon QuickSight is a fast, cloud-powered business intelligence (BI) service that makes it easy to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. In this session, we demonstrate how you can point Amazon QuickSight to AWS data stores, flat files, or other third-party data sources and begin visualizing your data in minutes. We also introduce you to SPICE - a Super-fast, Parallel, In-memory, Calculation Engine in Amazon QuickSight, which performs advanced calculations and render visualizations rapidly without requiring any additional infrastructure, SQL programming, or dimensional modeling, so you can seamlessly scale to hundreds of thousands of users and petabytes of data. Lastly, you will see how Amazon QuickSight provides you with smart visualizations and graphs that are optimized for your different data types, to ensure the most suitable and appropriate visualization to conduct your analysis, and how to share these visualization stories using the built-in collaboration tools.
5 Comparing Microsoft Big Data Technologies for Analytics
In this session, we spend time comparing Microsoft Big Data Technologies for Analytics. he technology includes HDInsight, Apache Spark and Power BI.
Pitfalls of Data Warehousing_2019-04-24
This document discusses trends in data warehousing and analytics. It provides an overview of the evolution of data warehousing from its origins in the 1980s to modern approaches. Key stages discussed include the rise of data marts and ETL in the 1990s-2000s, the emergence of big data and Hadoop in the 2010s, and current approaches like logical data warehousing, data lakes, and machine learning/AI. It also examines ongoing challenges around data volume, complexity, legacy systems, and others.
AWS October Webinar Series - Introducing Amazon QuickSight
The document provides an overview of Amazon QuickSight, Amazon's business intelligence service. It discusses how QuickSight allows users to easily explore and analyze data from various AWS sources for a low cost. Key features highlighted include fast insights using SPICE technology, intuitive visualizations, mobile access, and easy data sharing capabilities. The document also demonstrates QuickSight through an example analysis of a fitness promotion's performance.
AzureDay - Introduction Big Data Analytics.
AzureDay North 2016. Conference about cloud solutions.
What is Analytics? What is Big Data? Why Big Data we have in the cloud. What offer Microsoft for Big Data Analytics. How to start with Big Data Analytics or Advanced Analytics? Session introduce fundamentals for Big Data and Advanced Analytics.
By Data Scientist as a Service
Introducing DocumentDB
DocumentDB is a powerful NoSQL solution. It provides elastic scale, high performance, global distribution, a flexible data model, and is fully managed. If you are looking for a scaled OLTP solution that is too much for SQL Server to handle (i.e. millions of transactions per second) and/or will be using JSON documents, DocumentDB is the answer.
How does Microsoft solve Big Data?
So you got a handle on what Big Data is and how you can use it to find business value in your data. Now you need an understanding of the Microsoft products that can be used to create a Big Data solution. Microsoft has many pieces of the puzzle and in this presentation I will show how they fit together. How does Microsoft enhance and add value to Big Data? From collecting data, transforming it, storing it, to visualizing it, I will show you Microsoft’s solutions for every step of the way
Azure Data.pptx
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.
A Tale of Two BI Standards
Data lakes often fail because they are only accessible by highly-skilled data scientists and not by business users. But BI tools have been able to access data warehouses for years, so what gives?
In this talk, we’ll discuss:
- Why existing BI tools are architected well for data warehouses, but not data lakes.
- The pros and cons of each architecture.
- Why every organization should have two BI standards: one for data warehouses and one for data lakes.
BDA305 Building Data Lakes and Analytics on AWS
Over 90% of today’s data was generated in the last 2 years, and the rate of data growth isn’t slowing down. In this session, we’ll step through the challenges and best practices on how to capture all the data that is being generated, understand what data you have, and start driving insights and even predict the future using purpose built AWS Services. We’ll frame the session and demonstrations around common pitfalls of building Data Lakes and how to successful drive analytics and insights from the data. This session will focus on the architecture patterns bringing together key AWS Services and rather than a deep dive on any single service. We’ll show how services such as Amazon S3, Amazon Glue, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, and Amazon Kinesis, and Amazon Machine Learning services are put together to build a successful data lake for various role including both data scientists and business users.
Azure synapse by usama whaba khan
This document provides an overview of Azure Synapse Analytics and its key capabilities. Azure Synapse Analytics is a limitless analytics service that brings together enterprise data warehousing and big data analytics. It allows querying data on-demand or at scale using serverless or provisioned resources. The document outlines Synapse's integrated data platform capabilities for business intelligence, artificial intelligence and continuous intelligence. It also describes the different types of analytics workloads that Synapse supports and key architectural components like the dedicated SQL pool and massively parallel processing concepts.
Similaire à Azure Big Data Story (20)
[WITH THE VISION 2017] IoT/AI時代を生き抜くためのデータ プラットフォーム (Leveraging Azure Data Se...
[WITH THE VISION 2017] IoT/AI時代を生き抜くためのデータ プラットフォーム (Leveraging Azure Data Se...
Building Big Data Solutions with Azure Data Lake.10.11.17.pptx
Building Big Data Solutions with Azure Data Lake.10.11.17.pptx
Accelerating Business Intelligence Solutions with Microsoft Azure pass
Accelerating Business Intelligence Solutions with Microsoft Azure pass
5 Comparing Microsoft Big Data Technologies for Analytics
5 Comparing Microsoft Big Data Technologies for Analytics
AWS October Webinar Series - Introducing Amazon QuickSight
AWS October Webinar Series - Introducing Amazon QuickSight
Plus de Lynn Langit
VariantSpark on AWS
Slides from Broad Institute SoftEng retreat poster session - moving CSIRO bioinformatics team's VariantSpark algorithm on AWS using EMR or EKS
Serverless Architectures
This document discusses serverless computing and compares different cloud computing services. It outlines how serverless computing can be used for various workloads including web requests, queue messages, transactions, infrequent tasks, and scheduled jobs. It also compares common services between AWS and GCP for virtual machines, containers, functions, storage, databases, machine learning, IoT, and more. Serverless architectures are suggested to isolate and scale dynamic workloads without needing to manage servers.
10+ Years of Teaching Kids Programming
Experience report of teaching kids to code by two organizations. They are TKP (Teaching Kids Programming) and TKPLabs.
Blastn plus jupyter on Docker
Examples from bioinformatics - using containers for bioinformatics tools (such as blastn), plus example Jupyter notebooks
Testing in Ballerina Language
The document discusses different types of tests for microservices including unit tests, service tests, services composition tests, deployment tests, and modern approaches to testing microservices. It provides examples of testing functions, services, interactions, and deploying to containers using tools like Docker. It emphasizes the importance of testing at the unit, integration, deployment levels as well as testing documentation, full stack setups, and chaos engineering.
Teaching Kids to create Alexa Skills
Introduction to voice-user interface applications and Alexa skills development. Designed for use for middle-school teachers.
Practical cloud
Core deck on the state of the public cloud. Focuses on patterns (i.e. VMs, Serverless, Containers), Vendors (AWS, GCP) and practices.
Understanding Jupyter notebooks using bioinformatics examples
Understanding Jupyter notebooks using examples and tools from the CSRIO bioinformatics team - VariantSpark and GT-Scan2
Genome-scale Big Data Pipelines
This document discusses genomic-scale data pipelines. It introduces Dr. Denis Bauer and his transformational bioinformatics team. It describes how genomic data and research will grow exponentially to exabytes by 2025. It outlines genomic research workflows and challenges like processing, analyzing, and visualizing large variant call format (VCF) data. It presents two cloud data pipeline patterns used by the team: 1) A Spark server cluster pipeline for machine learning on large genomic datasets. 2) A serverless pipeline using AWS Lambda and Step Functions for scalable genomic searches.
Teaching Kids Programming
Lynn Langit was introduced to programming at age 16 through Teaching Kids Programming (TKP) events. She went on to teach TKP events while studying computer science and bioinformatics in college. After interning at Microsoft Research and working at Pivotal Labs, she created TKP Java courseware to teach programming to middle school students. The courseware includes 80 coding lessons and bridges students to AP Computer Science courses using a teacher-led, puzzle-based approach. Langit advocates for developers to create educational courseware, work with K-12 teachers, and be visible technical role models to help address the lack of gender diversity in computer science fields.
Practical Cloud
Talk on public cloud architectures, particularly for data pipelines. Includes Serverless and Server-based architectures built on AWS and GCP.
Serverless Reality
deck from talk as ASAS conference in Arnhem, Netherlands on cloud serverless architecture patterns and vendor comparison for aws, gcp and azure
Genomic Scale Big Data Pipelines
deck from talk at YOW Data in Sydney, covers VariantSpark, custom Apache Spark Machine Learning library and also GT-Scan2 using AWS Lambda architecture for bioinformatics
VariantSpark - a Spark library for genomics
VariantSpark a customer Apache Spark library for genomic data. Customer wide random forest machine learning algorithm, designed for workloads with millions of features.
Bioinformatics Data Pipelines built by CSIRO on AWS
The document discusses cancer genomics data pipelines and CSIRO's solutions. CSIRO has developed variant-spark, an open-source Apache Spark library for scalable genomic analysis. Variant-spark allows analysis of large genomic datasets up to 80% faster than other tools. CSIRO recommends using cloud data pipelines with serverless architectures, Apache Spark on AWS, and SaaS tools like Databricks for scalable, fast cancer genomics analysis. Their solutions provide reusable patterns for ingesting, processing, analyzing and visualizing genomic data in the cloud.
Serverless Reality
This document discusses serverless computing and compares it to traditional server-based computing. It defines serverless computing and provides examples of serverless technologies like AWS Lambda. It also outlines common use cases for serverless computing like handling dynamic workloads and scheduled tasks. Finally, it compares different services between server-based and serverless models like compute, files, databases, data pipelines, machine learning, and IoT.
Beyond Relational
The document discusses building data pipelines in the cloud. It covers serverless data pipeline patterns using services like BigQuery, Cloud Storage, Cloud Dataflow, and Cloud Pub/Sub. It also compares Cloud Dataflow and Cloud Dataproc for ETL workflows. Key questions around ingestion and ETL are discussed, focusing on volume, variety, velocity and veracity of data. Cloud vendor offerings for streaming and ETL are also compared.
New AWS Services for Bioinformatics
New AWS services were announced at re:Invent 2016 including Athena, Step Functions, Batch, Glue, and QuickSight that could be useful for scaling bioinformatics pipelines. Athena allows SQL queries on data stored in S3, Step Functions allows creating serverless visual workflows using Lambda functions, and Batch provides fully managed batch processing at scale across AWS services. Glue provides serverless ETL capabilities, and QuickSight allows creating quick data dashboards. Examples were shown of using these services for genomics workflows, running jobs on unmanaged compute environments, and processing genomic data.
Google Cloud and Data Pipeline Patterns
1. The document discusses various Google Cloud Platform products and patterns for data pipelines, including virtual machines, storage, data warehousing, streaming analytics, machine learning, internet of things, and bioinformatics.
2. Demos and examples are provided of storage, virtual machines, BigQuery, Cloud Spanner, and machine learning on the Google Cloud Platform.
3. The core Google Cloud Platform products discussed for various data and analytics use cases include Cloud Storage, BigQuery, Cloud Dataflow, Compute Engine, Cloud Pub/Sub, and Bigtable.
Scaling Galaxy on Google Cloud Platform
Deck from Galaxy 2017 conference in Australia on scaling Galaxy bioinformatics tools via GCP or Google Cloud Platform
Plus de Lynn Langit (20)
Understanding Jupyter notebooks using bioinformatics examples
Understanding Jupyter notebooks using bioinformatics examples
Bioinformatics Data Pipelines built by CSIRO on AWS
Bioinformatics Data Pipelines built by CSIRO on AWS
Dernier
Unveiling the Advantages of Agile Software Development.pdf
Learn about Agile Software Development's advantages. Simplify your workflow to spur quicker innovation. Jump right in! We have also discussed the advantages.
Odoo ERP Vs. Traditional ERP Systems – A Comparative Analysis
When it is all about ERP solutions, companies typically meet their needs with common ERP solutions like SAP, Oracle, and Microsoft Dynamics. These big players have demonstrated that ERP systems can be either simple or highly comprehensive. This remains true today, but there are new factors to consider, including a promising new contender in the market that’s Odoo. This blog compares Odoo ERP with traditional ERP systems and explains why many companies now see Odoo ERP as the best choice.
What are ERP Systems?
An ERP, or Enterprise Resource Planning, system provides your company with valuable information to help you make better decisions and boost your ROI. You should choose an ERP system based on your company’s specific needs. For instance, if you run a manufacturing or retail business, you will need an ERP system that efficiently manages inventory. A consulting firm, on the other hand, would benefit from an ERP system that enhances daily operations. Similarly, eCommerce stores would select an ERP system tailored to their needs.
Because different businesses have different requirements, ERP system functionalities can vary. Among the various ERP systems available, Odoo ERP is considered one of the best in the ERp market with more than 12 million global users today.
Odoo is an open-source ERP system initially designed for small to medium-sized businesses but now suitable for a wide range of companies. Odoo offers a scalable and configurable point-of-sale management solution and allows you to create customised modules for specific industries. Odoo is gaining more popularity because it is built in a way that allows easy customisation, has a user-friendly interface, and is affordable. Here, you will cover the main differences and get to know why Odoo is gaining attention despite the many other ERP systems available in the market.
GreenCode-A-VSCode-Plugin--Dario-Jurisic
Presentation about a VSCode plugin from Dario Jurisic at the GSD Community Stage meetup
E-Invoicing Implementation: A Step-by-Step Guide for Saudi Arabian Companies
Explore the seamless transition to e-invoicing with this comprehensive guide tailored for Saudi Arabian businesses. Navigate the process effortlessly with step-by-step instructions designed to streamline implementation and enhance efficiency.
Top Benefits of Using Salesforce Healthcare CRM for Patient Management.pdf
Salesforce Healthcare CRM, implemented by VALiNTRY360, revolutionizes patient management by enhancing patient engagement, streamlining administrative processes, and improving care coordination. Its advanced analytics, robust security, and seamless integration with telehealth services ensure that healthcare providers can deliver personalized, efficient, and secure patient care. By automating routine tasks and providing actionable insights, Salesforce Healthcare CRM enables healthcare providers to focus on delivering high-quality care, leading to better patient outcomes and higher satisfaction. VALiNTRY360's expertise ensures a tailored solution that meets the unique needs of any healthcare practice, from small clinics to large hospital systems.
For more info visit us https://valintry360.com/solutions/health-life-sciences
Using Query Store in Azure PostgreSQL to Understand Query Performance
Microsoft has added an excellent new extension in PostgreSQL on their Azure Platform. This session, presented at Posette 2024, covers what Query Store is and the types of information you can get out of it.
UI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions
The UI5 tooling is the development and build tooling of UI5. It is built in a modular and extensible way so that it can be easily extended by your needs. This session will showcase various tooling extensions which can boost your development experience by far so that you can really work offline, transpile your code in your project to use even newer versions of EcmaScript (than 2022 which is supported right now by the UI5 tooling), consume any npm package of your choice in your project, using different kind of proxies, and even stitching UI5 projects during development together to mimic your target environment.
ALGIT - Assembly Line for Green IT - Numbers, Data, Facts
Presentation from Ghazal Aakel and Eric Jochum at the GSD Community Stage Meetup on June 6th, 2024
一比一原版(UMN毕业证)明尼苏达大学毕业证如何办理
UMN硕士毕业证成绩单【微信95270640】购买（明尼苏达大学毕业证成绩单硕士学历）Q微信95270640代办UMN学历认证留信网伪造明尼苏达大学学位证书精仿明尼苏达大学本科/硕士文凭证书补办明尼苏达大学 diplomaoffer,Transcript购买明尼苏达大学毕业证成绩单购买UMN假毕业证学位证书购买伪造明尼苏达大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
#毕业证成绩单 #毕业証 #成绩单 #學生卡 #OFFER录取通知书 #雅思#托福等……
国外大学明尼苏达大学明尼苏达大学毕业证offer制作方法（一对一专业服务）
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
— — 制作工艺 【高仿真】— —
凭借多年的制作经验本公司制作明尼苏达大学明尼苏达大学毕业证offer《激光》《水印》《钢印》《烫金》《紫外线》凹凸版uv版等防伪技术一流高精仿度几乎跟学校100%相同！让您绝对满意。
— — -公司理念 【诚信为主】— — —
我們以質量求生存.以服务求发展有雄厚的实力专业的团队咨询顾问为您细心解答可详谈是真是假眼见为实让您真正放心平凡人生,尽我所能助您一臂之力让我們携手圆您梦想!
此贴长年有效【贴心专线/微-信: 95270640】敬请保留此联系方式以备用！如有不在线请给我们留言！我们将在第一时间给您回复!上散发着一抹抹的光晕而这每处自然形成的细节融合在一起浑然天成的美实在令人心生愉悦小道的周边无秩序的生长着几株艳丽的野花红的粉的紫的虽混乱无章却给这幅美景更增添一份性感夹杂着一份纯洁的妖娆毫无违和感实在给人带来一份悠然幸福的心情如果说现在的审美已经断然拒绝了无声的话那么在树林间飞掠而过的小鸟叽叽咋咋的叫声是否就是这最后的点睛之笔悠然走在林间的小路上宁静与清香一丝丝的盛夏气息吸入身体昔日生活里的繁忙多
All you need to know about Spring Boot and GraalVM
All you need to know about Spring Boot and GraalVM 🐰
Artificia Intellicence and XPath Extension Functions
The purpose of this presentation is to provide an overview of how you can use AI from XSLT, XQuery, Schematron, or XML Refactoring operations, the potential benefits of using AI, and some of the challenges we face.
在线购买加拿大英属哥伦比亚大学毕业证本科学位证书原版一模一样
原版一模一样【微信：741003700 】【加拿大英属哥伦比亚大学毕业证本科学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。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，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：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、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
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
How to write a program in any programming language
How to write a program in any programming language
socradar-q1-2024-aviation-industry-report.pdf
SOCRadar's Aviation Industry Q1 Incident Report is out now!
The aviation industry has always been a prime target for cybercriminals due to its critical infrastructure and high stakes. In the first quarter of 2024, the sector faced an alarming surge in cybersecurity threats, revealing its vulnerabilities and the relentless sophistication of cyber attackers.
SOCRadar’s Aviation Industry, Quarterly Incident Report, provides an in-depth analysis of these threats, detected and examined through our extensive monitoring of hacker forums, Telegram channels, and dark web platforms.
Malibou Pitch Deck For Its €3M Seed Round
French start-up Malibou raised a €3 million Seed Round to develop its payroll and human resources
management platform for VSEs and SMEs. The financing round was led by investors Breega, Y Combinator, and FCVC.
Mobile app Development Services | Drona Infotech
Drona Infotech is one of the Best Mobile App Development Company In Noida Maintenance and ongoing support. mobile app development Services can help you maintain and support your app after it has been launched. This includes fixing bugs, adding new features, and keeping your app up-to-date with the latest
Visit Us For :
Enums On Steroids - let's look at sealed classes !
These are slides for my session"Enums On Steroids - let's look at sealed classes !" - delivered among others, on Devoxx UK 2024 conference
一比一原版(USF毕业证)旧金山大学毕业证如何办理
USF硕士毕业证成绩单【微信95270640】一比一伪造旧金山大学文凭@假冒USF毕业证成绩单+Q微信95270640办理USF学位证书@仿造USF毕业文凭证书@购买旧金山大学毕业证成绩单USF真实使馆认证/真实留信认证回国人员证明
#一整套旧金山大学文凭证件办理#—包含旧金山大学旧金山大学本科毕业证成绩单学历认证|使馆认证|归国人员证明|教育部认证|留信网认证永远存档教育部学历学位认证查询办理国外文凭国外学历学位认证#我们提供全套办理服务。
一整套留学文凭证件服务：
一：旧金山大学旧金山大学本科毕业证成绩单毕业证 #成绩单等全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站永久可查
四：留信认证留学生信息网站永久可查
国外毕业证学位证成绩单办理方法：
1客户提供办理旧金山大学旧金山大学本科毕业证成绩单信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
教育部文凭学历认证认证的用途：
如果您计划在国内发展那么办理国内教育部认证是必不可少的。事业性用人单位如银行国企公务员在您应聘时都会需要您提供这个认证。其他私营 #外企企业无需提供！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帮您快速整合材料让您少走弯路。
实体公司专业为您服务如有需要请联系我: 微信95270640奈一次次令他失望山娃今年岁上五年级识得很多字从走出小屋开始山娃就知道父亲的家和工地共有一个很动听的名字——天河工地的底层空空荡荡很宽阔很凉爽在地上铺上报纸和水泥袋父亲和工人们中午全睡在地上地面坑坑洼洼山娃曾多次绊倒过也曾有长铁钉穿透凉鞋刺在脚板上但山娃不怕工地上也常有五六个从乡下来的小学生他们的父母亲也是高楼上的建筑工人小伙伴来自不同省份都操着带有浓重口音的普通话可不知为啥山娃不仅很快与他们熟识了
Hand Rolled Applicative User Validation Code Kata
Could you use a simple piece of Scala validation code (granted, a very simplistic one too!) that you can rewrite, now and again, to refresh your basic understanding of Applicative operators <*>, <*, *>?
The goal is not to write perfect code showcasing validation, but rather, to provide a small, rough-and ready exercise to reinforce your muscle-memory.
Despite its grandiose-sounding title, this deck consists of just three slides showing the Scala 3 code to be rewritten whenever the details of the operators begin to fade away.
The code is my rough and ready translation of a Haskell user-validation program found in a book called Finding Success (and Failure) in Haskell - Fall in love with applicative functors.
Energy consumption of Database Management - Florina Jonuzi
Presentation from Florina Jonuzi at the GSD Community Stage Meetup on June 06, 2024
Dernier (20)
Unveiling the Advantages of Agile Software Development.pdf
Unveiling the Advantages of Agile Software Development.pdf
Odoo ERP Vs. Traditional ERP Systems – A Comparative Analysis
Odoo ERP Vs. Traditional ERP Systems – A Comparative Analysis
E-Invoicing Implementation: A Step-by-Step Guide for Saudi Arabian Companies
E-Invoicing Implementation: A Step-by-Step Guide for Saudi Arabian Companies
Top Benefits of Using Salesforce Healthcare CRM for Patient Management.pdf
Top Benefits of Using Salesforce Healthcare CRM for Patient Management.pdf
Using Query Store in Azure PostgreSQL to Understand Query Performance
Using Query Store in Azure PostgreSQL to Understand Query Performance
UI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions
UI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions
ALGIT - Assembly Line for Green IT - Numbers, Data, Facts
ALGIT - Assembly Line for Green IT - Numbers, Data, Facts
All you need to know about Spring Boot and GraalVM
All you need to know about Spring Boot and GraalVM
Artificia Intellicence and XPath Extension Functions
Artificia Intellicence and XPath Extension Functions
How to write a program in any programming language
How to write a program in any programming language
Enums On Steroids - let's look at sealed classes !
Enums On Steroids - let's look at sealed classes !
Energy consumption of Database Management - Florina Jonuzi
Energy consumption of Database Management - Florina Jonuzi
Azure Big Data Story
- 1. Azure Big Data Story @LynnLangit
- 2. I love to…Learn & Build
- 4. Azure Big Data by the V’s Value Volume Velocity Variety Veracity
- 5. Big Data = Business Value ? 60% of Big Data projects FAIL to go beyond pilot and will be abandoned (through 2017) - Gartner
- 6. Volume How big is Big Data?
- 7. Variety Is my Data rectangular?
- 10. Azure Persistence Choices • Storage • Store Simple Files • Data Lake • HDInsight • Cloudera Hadoop • MANY… NoSQL • SQL Azure • SQL Azure DW • SQL Server on Azure VM Relational
- 11. Drilling In: Relational AND NoSQL
- 12. Azure Persistence Choices Detailed • Storage • Store Simple Files • Data Lake • HDInsight • Cloudera Hadoop • Redis Caching • DataStax Enterprise • Document DB • Mongo Labs • Graph Engine NoSQL • SQL Azure • SQL Azure DW • SQL Server on Azure VM Relational
- 13. Velocity How fast is my Data?
- 14. Veracity How clean is my Data?
- 16. Azure Load Choices Load Libraries StreamEvent Hub Batch Stream Analytics
- 18. Azure Data Cleaning Choices ETL • SQL Server VM • Data Pipeline Client • Power BI • Power Query ML • Azure ML • Data Marketplace • SQL Server DQS
- 19. Data Pipelines
- 23. Data Model On Premise SQL Server+ Cloud Azure+ Key-Value Queues None Windows Queues Azure Redis Cache Azure Queues Wide Sparse Columns Columnstore Index SSAS Tabular Models Azure Tables DataStax Enterprise (Cassandra) Files FileTable, Filestream XML data type Azure BLOB Store StoreSimple JSON or Graph SQL Server 2016 None Azure DocumentDB / Graph Engine (beta) Hosted MongoDB or Neo4J Large Relational SQL Server Enterprise PDW SQL Analysis Services SQL Database (basic, standard, premium) APS SQL Data Warehouse Hadoop Hortonworks HDInsight/ Data Lake, Hosted Cloudera Other Stream Insight Event Hub, StreamAnalytics, ML Marketplace
- 25. Value How useful is my Big Data?
- 26. Big Data = Business Value ? 60% of Big Data projects FAIL to go beyond pilot and will be abandoned (through 2017) - Gartner
- 29. Architecture 1- File Storage / Backup
- 30. Architecture 2- Data Warehouse
- 31. Architecture 3 – Operational Database
- 32. Old becomes New
- 33. Architecture 4 – Small Big Data
- 34. MORE DATA
- 35. Architecture 5 – Big Data
- 37. Azure Big Data Story @LynnLangit
Notes de l'éditeur
- https://about.me/LynnLangit
- ScottGu Blog - http://weblogs.asp.net/scottgu Azure Big Data -- http://azure.microsoft.com/blog/topics/big-data/ Data Factory Pipeline Sample (Blog) -- http://azure.microsoft.com/blog/2015/04/24/azure-data-factory-update-simplified-sample-deployment/
- http://www.bain.com/publications/articles/three-promises-and-perils-of-big-data.aspx
- http://azure.microsoft.com/en-us/documentation/infographics/building-real-world-cloud-apps/
- Image credit - http://tapoueh.org/images/pipeline.png Azure Data Factory tutorial - http://azure.microsoft.com/en-us/documentation/articles/data-factory-get-started-using-editor/
- Image credit - http://tapoueh.org/images/pipeline.png Azure Data Factory tutorial - http://azure.microsoft.com/en-us/documentation/articles/data-factory-get-started-using-editor/
- ScottGu Blog - http://weblogs.asp.net/scottgu Azure Big Data -- http://azure.microsoft.com/blog/topics/big-data/ Data Factory Pipeline Sample (Blog) -- http://azure.microsoft.com/blog/2015/04/24/azure-data-factory-update-simplified-sample-deployment/
- http://recode.net/2014/04/15/microsofts-big-data-angle-office-as-a-friendly-front-end/
- http://www.bain.com/publications/articles/three-promises-and-perils-of-big-data.aspx
- http://www.wsj.com/articles/ubers-new-funding-values-it-at-over-41-billion-1417715938
- http://www.nytimes.com/newsgraphics/2013/08/18/reshaping-new-york/
- www.thingful.net