Amazon Glue
•
0 likes•84 views
What is AWS Glue? AWS Glue is an ETL (Extract, Transform, and Load) data integration solution that is fully managed. The process of preparing and merging data for analytics, machine learning, and application development is known as data integration. It's made to make it simple and inexpensive to not just categorize your data, but also to clean, enhance, and transfer it.
Report
Share
Report
Share
Download to read offline
Recommended
What is AWS Glue
AWS Glue is a serverless data integration service that allows users to discover, prepare, and transform data for analytics and machine learning. It provides a fully managed extract, transform, and load (ETL) service on AWS. AWS Glue crawls data sources, automatically extracts metadata and stores it in a centralized data catalog. It then executes ETL jobs developed by users to clean, enrich and move data between various data stores.
1- Introduction of Azure data factory.pptx
Azure Data Factory is a cloud-based data integration service that allows users to easily construct extract, transform, load (ETL) and extract, load, transform (ELT) processes without code. It offers job scheduling, security for data in transit, integration with source control for continuous delivery, and scalability for large data volumes. The document demonstrates how to create an Azure Data Factory from the Azure portal.
ADF Demo_ppt.pptx
Azure Data Factory (ADF) is a cloud-based data integration service that allows users to easily construct ETL and ELT processes through a code-free visual interface or custom code. ADF can connect to both cloud and on-premises data sources, support data transformation, and also run existing SSIS packages that have been migrated to the cloud. Key components of ADF include storage accounts, containers, linked services, datasets, data pipelines, triggers, and data flows which allow users to move, transform and process data.
10 difference between aws and google cloud by Zareef Ahmed
AWS is market leader in open cloud space but other players are making inroads into its market share. Google cloud is one of them. In this talk, we are going to cover 10 differences between Google Cloud and AWS. Major focus is on the features which covers common use cases. You can take this talk as stepping stone to understand another cloud service provider if you know one of them.
By Zareef Ahmed (https://www.zareef.com)
Deployment Flexibility: Introducing Support for Microsoft Azure
Heading to Azure? We got you. Atlassian and Microsoft are happy to introduce JIRA Software Data Center deployments directly into Microsoft Azure! Learn how you can get setup in Microsoft Azure with a ready to run JIRA Software Data Center Cluster, hear deployment examples for organizations who are making the move to Azure and hear about what's next for Atlassian on Azure.
Lift SSIS package to Azure Data Factory V2
Manjeet Singh gives a presentation on lifting SSIS packages to Azure using Data Factory v2. He discusses how the Integration Runtime in ADF v2 allows existing on-premises SSIS packages to be lifted to the cloud. He demonstrates deploying a SSIS package to an Azure SQL database, running it using SQL Server Management Studio and Azure Data Factory pipelines, and provides tips on using the SSIS Integration Runtime.
Afternoons with Azure - Azure Data Services
What is in a modern BI architecture? In this presentation, we explore PaaS, Azure Active Directory and Storage options including SQL Database and SQL Datawarehouse.
Deploy on AWS from GIT Lab PDF2.pdf
GitLab can be used to deploy applications to various AWS services through its integration with AWS and GitLab CI/CD pipelines. It allows deploying to Lambda, Fargate, EKS, ECS, and EC2. For each service, the document provides instructions on setting up the necessary IAM roles and configuring a GitLab CI pipeline to deploy code. This allows development teams to manage the entire software development lifecycle from a single interface without needing separate tools for each AWS service.
Recommended
What is AWS Glue
AWS Glue is a serverless data integration service that allows users to discover, prepare, and transform data for analytics and machine learning. It provides a fully managed extract, transform, and load (ETL) service on AWS. AWS Glue crawls data sources, automatically extracts metadata and stores it in a centralized data catalog. It then executes ETL jobs developed by users to clean, enrich and move data between various data stores.
1- Introduction of Azure data factory.pptx
Azure Data Factory is a cloud-based data integration service that allows users to easily construct extract, transform, load (ETL) and extract, load, transform (ELT) processes without code. It offers job scheduling, security for data in transit, integration with source control for continuous delivery, and scalability for large data volumes. The document demonstrates how to create an Azure Data Factory from the Azure portal.
ADF Demo_ppt.pptx
Azure Data Factory (ADF) is a cloud-based data integration service that allows users to easily construct ETL and ELT processes through a code-free visual interface or custom code. ADF can connect to both cloud and on-premises data sources, support data transformation, and also run existing SSIS packages that have been migrated to the cloud. Key components of ADF include storage accounts, containers, linked services, datasets, data pipelines, triggers, and data flows which allow users to move, transform and process data.
10 difference between aws and google cloud by Zareef Ahmed
AWS is market leader in open cloud space but other players are making inroads into its market share. Google cloud is one of them. In this talk, we are going to cover 10 differences between Google Cloud and AWS. Major focus is on the features which covers common use cases. You can take this talk as stepping stone to understand another cloud service provider if you know one of them.
By Zareef Ahmed (https://www.zareef.com)
Deployment Flexibility: Introducing Support for Microsoft Azure
Heading to Azure? We got you. Atlassian and Microsoft are happy to introduce JIRA Software Data Center deployments directly into Microsoft Azure! Learn how you can get setup in Microsoft Azure with a ready to run JIRA Software Data Center Cluster, hear deployment examples for organizations who are making the move to Azure and hear about what's next for Atlassian on Azure.
Lift SSIS package to Azure Data Factory V2
Manjeet Singh gives a presentation on lifting SSIS packages to Azure using Data Factory v2. He discusses how the Integration Runtime in ADF v2 allows existing on-premises SSIS packages to be lifted to the cloud. He demonstrates deploying a SSIS package to an Azure SQL database, running it using SQL Server Management Studio and Azure Data Factory pipelines, and provides tips on using the SSIS Integration Runtime.
Afternoons with Azure - Azure Data Services
What is in a modern BI architecture? In this presentation, we explore PaaS, Azure Active Directory and Storage options including SQL Database and SQL Datawarehouse.
Deploy on AWS from GIT Lab PDF2.pdf
GitLab can be used to deploy applications to various AWS services through its integration with AWS and GitLab CI/CD pipelines. It allows deploying to Lambda, Fargate, EKS, ECS, and EC2. For each service, the document provides instructions on setting up the necessary IAM roles and configuring a GitLab CI pipeline to deploy code. This allows development teams to manage the entire software development lifecycle from a single interface without needing separate tools for each AWS service.
5 Reasons to Move Your BI to the Cloud
Traditional BI promises security and scale, but at what cost? Often, working with data, finding answers and sharing them can be laborious and time intensive. The rapid growth and maturation of cloud technologies offers an easier path.
With Tableau and AWS you can move your BI to the cloud and deliver the security and scale of your traditional BI, but with accessibility, flexibility, and speed. Take a closer look at the benefits of cloud BI, and how you can get started today.
Azure quick-start-for-net-developers
This document provides an overview of Azure services that can be used to run .NET applications and store data. It discusses App Services like Web Apps, Mobile Apps, and Functions for hosting apps. Virtual Machines allow lifting existing apps. Logic Apps and Functions are for process orchestration. Storage services like SQL, Cosmos DB, and Blob storage are covered. Frameworks supported in each service are outlined. The document serves as a quick start guide for .NET developers new to Azure.
cloud computing.pptx
Google App Engine (GAE) is a platform-as-a-service that provides scalable hosting and internet services. GAE requires applications be written in Java or Python and store data in Google Bigtable. GAE provides more infrastructure than other services like Amazon EC2 and eliminates system administration tasks. Google provides free usage of GAE up to certain limits on resources. GAE is a fully managed, serverless platform used to host, build, and deploy web applications where users create accounts, set up software development kits, and write source code leveraging services like Blobstore, Cloud Storage, and Memcache.
Extending your Hadoop Implementation to the Cloud
This document discusses strategies for extending Hadoop implementations to the cloud. It describes using infrastructure as a service (IaaS) to run Hadoop clusters in the cloud, leveraging cloud storage for flexibility, and using Hadoop as a service offerings. Hybrid scenarios involving both on-premises and cloud deployments are also covered. Demonstrations show deploying Hadoop clusters to Microsoft Azure and using Azure HDInsight.
Aswin
Aswin T has 4 years of experience as a DevOps engineer at Apple, where he focuses on automating the deployment of infrastructure to cloud environments across 30+ accounts using tools he has built. His technical skills include Java, Golang, Python, Groovy, Spring, SpringBoot, Angular, MySQL, SQL Server, AWS, Git, Maven, and Gradle. He has experience building automation frameworks in Groovy and Golang to provision AWS resources, as well as Progressive Web Applications to monitor environments and track deployment details. He is AWS Certified as a Solutions Architect Associate and Cloud Practitioner.
UNIT -IV.docx
The document discusses Azure Synapse Analytics and its core services. It provides an overview of Azure Synapse Analytics, its high-level architecture, components and features. It discusses Azure Synapse Studio, Azure Synapse Data Integration, Synapse SQL Pools, Apache Spark for Azure Synapse and Azure Synapse security. It also discusses Azure HDInsight, its features, architecture, metastore best practices, migration practices and security and DevOps.
Cloud Computing Serverless Architecture
Serverless architecture does not mean that it can be executed sans server but it is a service on its own, and it also means that developer no longer has to worry about the server on which code will get executed.
Aws vs azure bakeoff
This document summarizes a presentation given on AWS vs Azure. It begins with an overview of AWS, describing its many services like S3, EC2, VPC, IAM, ELB, RDS, DynamoDB and more. It notes AWS is an "alphabet soup" but has good documentation. The presentation then covers the goods of AWS like elasticity, programmability, variety of services and large user base. The bads include potential higher costs, complexity and service outages. The uglies include vendor lock-in risks and need to rewrite existing systems. It concludes by providing recommendations for different roles on best getting started with AWS.
Working with azure database services platform
This chapter discusses working with Azure database services and SQL Database. It provides an overview of SQL Database, including that it is a fully managed database service that provides the core capabilities of SQL Server in a cloud platform. It describes key differences between SQL Database and on-premises SQL Server, such as licensing, maintenance, and security being managed by Microsoft in the cloud. The chapter also covers connectivity options, libraries for connecting different programming languages, sizing and performance tiers, designing databases, and approaches to multi-tenancy in SQL Database.
Azure fundamental -Introduction
This document provides an overview of cloud computing concepts and Azure cloud services. It discusses cloud service models including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). It introduces Azure, the Microsoft cloud computing platform, and key Azure services like Azure Storage, Azure Portal, Azure Accounts, Azure Data Factory, and Azure Data Flow. Azure Data Factory allows building data integration solutions using activities, linked services, datasets and triggers without writing code. Azure Data Flow enables visually designing data transformations using a Spark optimizer without code.
Introdcution to Azure
This document provides an introduction to Azure and describes several of its core services. It outlines compute, data, networking and other services available on Azure. Specifically it discusses virtual machines, websites, SQL databases, storage tables and blobs, import/export of large data sets, file services, virtual networks, ExpressRoute for private connections, Traffic Manager for routing, Automation for managing resources via PowerShell runbooks, API Management, backup services, messaging queues, Service Bus Relay for cross-firewall communication, Scheduler for scheduling jobs, caching with Cache service, Content Delivery Network for caching blobs globally, and HDInsight for Hadoop clusters.
When Should You Use AWS Lambda?
AWS Lambda is considered to be a proficient serverless computing service that allows you to run your code for managing servers and containers. The scaling under AWS Lambda is done automatically by measuring the work intensity integrated onto it. There are several use cases to AWS Lambda that define its prime efficacy of executing code within the AWS cloud. Even though AWS Lambda is meant to be used within the cloud, local development attributes can also use it for diverse development needs. For further reading please visit https://www.whizlabs.com/blog/use-of-aws-lambda/
Google apps engine
Google App Engine is a Platform as a Service (PaaS) cloud computing platform that allows developers to build and host web applications in Google's data centers. It provides a scalable and reliable environment for developing applications using popular languages like Java, Python, PHP, and Go. Google App Engine handles tasks like provisioning servers and managing traffic so developers can focus on building their applications. It also includes services for storage, mail delivery, caching, and accessing web resources.
Azure from scratch part 3 By Girish Kalamati
Azure Identity (AD,ADFS 2.0,AAD,ADB2C,OAuth,OpenID,PingID,AD Custom Policies) ,
Azure PaaS (Azure Functions, Serverless computing, Azure Comsos DB, Webhooks, API Apps, Logic Apps, Kudu, Azure Websites), Azure Functions, Lamda Function, Event Functions, Serverless architecture, Implementing azure functions on GIT HUB comment feature, Why Azure Functions, Azure Virtual Machines, Azure Cloud Services, Azure Web Apps & WebJobs, Service Fabric, Consumption Plans, Billing Model, Benefits of Azure Functions, What is serverless, Implementing bigger solutions into smaller azure functions, Microservices, Use cases, Function App, Implementation storing unstructured data using Azure functions into Cosmos DB, Cosmos DB, Custom Azure functions, Azure Cosmos DB, IOTS, Document DB, Doc DB, How to setup a Jenkins build server and automatically trigger code from Visual studio online,Azure App Service, App service Environment, Azure Stack, Managing Azure App services, Azure Powershell, Azure CLI, REST APIS, Azure Portal, Templates, Kudu Console access, Run GIT Commands on Kudu Console, Locking Azure Resources, Configuring Custom Domains, Adding Extensions to Azure Web App/Websites, App service Deployment options, Data Services in Azure , Azure SQL, Azure SQL server, Azure SQL database vs SQL server in a Azure VM, SQL Tiers, DTU, Data Transactional Unit, Planning & provisioning azure SQL databases,Migrating SQL Databases, Azure SQL Server, SQL server transactional replication, Deploy database to Microsoft Azure Database Wizard, DAC package, DAC, SQL compatibility issues, Migrating SQL with downtime, DMA, Data Migration Assistant, Database Snapshot, Migrating SQL without downtime, DTU, Data Transactional Unit, Recommendations for best performance during SQL Import Process, Transactional Replication, T-SQL, Task to implement what ever you learnt till now,
Google apps engine
Google App Engine is a Platform as a Service (PaaS) cloud computing platform that allows developers to build and host web applications in Google's data centers. It provides a scalable and reliable environment for developing applications using popular languages like Java, Python, PHP, and Go. App Engine handles tasks like provisioning servers and managing traffic so developers can focus on their code. It also includes services for storage, mail delivery, caching, and accessing web resources. App Engine is well-suited for applications with unpredictable traffic spikes or those where developers don't want to manage their own servers.
Cloud service providers
The document discusses and compares several major cloud service providers, including Amazon Web Services (AWS), Google Cloud Platform, Microsoft Azure, Oracle Cloud Infrastructure, SAP Cloud Platform, and Salesforce Service Cloud. It provides an overview of the services offered by each provider such as compute, storage, databases, machine learning, and describes some of their key features and histories. A table is included that compares AWS, Azure, and GCP across several categories like data management, app development, SMB analytics, and machine learning products.
Aws community day pune 2020 v3
This document provides an overview of AWS Glue, a fully managed ETL service for preparing and loading big data for analytics. It discusses what big data and analytics are, and describes the key components of Glue including the crawler, catalog, architecture and ETL jobs. Glue allows for easy, fast and cost-effective processing of vast amounts of data from sources like Amazon S3. It can be used to build data warehouses, run queries against data lakes, create event-driven pipelines, and understand data assets. The document provides examples of how Glue components like the crawler, catalog, and ETL jobs work together in a workflow.
Inside Microsoft Azure
A Web architect who doesn't usually use Microsoft technologies explains Microsoft's cloud offering, Azure, in terms that hopefully other UNIX/open source people will understand.
Exploring Microsoft Azure Infrastructures
This document provides an agenda and summary for a Data Analytics Meetup (DAM) on March 27, 2018. The agenda covers topics such as disruption opportunities in a changing data landscape, transitioning from traditional to modern BI architectures using Azure, Azure SQL Database vs Data Warehouse, data integration with Azure Data Factory and SSIS, Analysis Services, Power BI reporting, and a wrap-up. The document discusses challenges around data growth, digital transformation, and the shrinking time for companies to adapt to disruption. It provides overviews and comparisons of Azure SQL Database, Data Warehouse, and related Azure services to help modernize analytics architectures.
Serverless Extract-transform-load (ETL) on AWS Webinar
Previously, ETL meant using proprietary products with commercial databases and users with specialist skills. Learn how to create ETL data pipelines that can securely consume data at scale while using open source technologies and languages to enable your organisation, team, and data.
Speaker: Paul Macey, Big Data Specialist, AWS
Amazon Lake Formation
What is AWS Lake Formation?
AWS Lake Formation is a fully managed service that makes creating, maintaining, and managing data
lakes a breeze. AWS Lake Formation automates most of the data lake construction process, reducing
the time it takes from months to weeks. The service acts as a single point of control for identifying,
retrieving, cleaning, and transforming data from thousands of sources, as well as enforcing security
regulations across various services and acquiring and managing fresh data. AWS Lake Formation is
controlled via a single dashboard from which you can configure and alter all data lake lifecycle phases
and operations.
Amazon Data Pipeline
What is Amazon Data Pipeline?
Amazon Data Pipeline is a web service that enables you to process and transport data across AWS
computing and storage services, as well as on-premises data sources, in a reliable manner. It
automates the extraction, transformation, combination, validation, and uploading of data for further
analysis and display. It can handle many data streams at the same time and delivers end-to-end
speed by removing mistakes or overcoming delays.
More Related Content
Similar to Amazon Glue
5 Reasons to Move Your BI to the Cloud
Traditional BI promises security and scale, but at what cost? Often, working with data, finding answers and sharing them can be laborious and time intensive. The rapid growth and maturation of cloud technologies offers an easier path.
With Tableau and AWS you can move your BI to the cloud and deliver the security and scale of your traditional BI, but with accessibility, flexibility, and speed. Take a closer look at the benefits of cloud BI, and how you can get started today.
Azure quick-start-for-net-developers
This document provides an overview of Azure services that can be used to run .NET applications and store data. It discusses App Services like Web Apps, Mobile Apps, and Functions for hosting apps. Virtual Machines allow lifting existing apps. Logic Apps and Functions are for process orchestration. Storage services like SQL, Cosmos DB, and Blob storage are covered. Frameworks supported in each service are outlined. The document serves as a quick start guide for .NET developers new to Azure.
cloud computing.pptx
Google App Engine (GAE) is a platform-as-a-service that provides scalable hosting and internet services. GAE requires applications be written in Java or Python and store data in Google Bigtable. GAE provides more infrastructure than other services like Amazon EC2 and eliminates system administration tasks. Google provides free usage of GAE up to certain limits on resources. GAE is a fully managed, serverless platform used to host, build, and deploy web applications where users create accounts, set up software development kits, and write source code leveraging services like Blobstore, Cloud Storage, and Memcache.
Extending your Hadoop Implementation to the Cloud
This document discusses strategies for extending Hadoop implementations to the cloud. It describes using infrastructure as a service (IaaS) to run Hadoop clusters in the cloud, leveraging cloud storage for flexibility, and using Hadoop as a service offerings. Hybrid scenarios involving both on-premises and cloud deployments are also covered. Demonstrations show deploying Hadoop clusters to Microsoft Azure and using Azure HDInsight.
Aswin
Aswin T has 4 years of experience as a DevOps engineer at Apple, where he focuses on automating the deployment of infrastructure to cloud environments across 30+ accounts using tools he has built. His technical skills include Java, Golang, Python, Groovy, Spring, SpringBoot, Angular, MySQL, SQL Server, AWS, Git, Maven, and Gradle. He has experience building automation frameworks in Groovy and Golang to provision AWS resources, as well as Progressive Web Applications to monitor environments and track deployment details. He is AWS Certified as a Solutions Architect Associate and Cloud Practitioner.
UNIT -IV.docx
The document discusses Azure Synapse Analytics and its core services. It provides an overview of Azure Synapse Analytics, its high-level architecture, components and features. It discusses Azure Synapse Studio, Azure Synapse Data Integration, Synapse SQL Pools, Apache Spark for Azure Synapse and Azure Synapse security. It also discusses Azure HDInsight, its features, architecture, metastore best practices, migration practices and security and DevOps.
Cloud Computing Serverless Architecture
Serverless architecture does not mean that it can be executed sans server but it is a service on its own, and it also means that developer no longer has to worry about the server on which code will get executed.
Aws vs azure bakeoff
This document summarizes a presentation given on AWS vs Azure. It begins with an overview of AWS, describing its many services like S3, EC2, VPC, IAM, ELB, RDS, DynamoDB and more. It notes AWS is an "alphabet soup" but has good documentation. The presentation then covers the goods of AWS like elasticity, programmability, variety of services and large user base. The bads include potential higher costs, complexity and service outages. The uglies include vendor lock-in risks and need to rewrite existing systems. It concludes by providing recommendations for different roles on best getting started with AWS.
Working with azure database services platform
This chapter discusses working with Azure database services and SQL Database. It provides an overview of SQL Database, including that it is a fully managed database service that provides the core capabilities of SQL Server in a cloud platform. It describes key differences between SQL Database and on-premises SQL Server, such as licensing, maintenance, and security being managed by Microsoft in the cloud. The chapter also covers connectivity options, libraries for connecting different programming languages, sizing and performance tiers, designing databases, and approaches to multi-tenancy in SQL Database.
Azure fundamental -Introduction
This document provides an overview of cloud computing concepts and Azure cloud services. It discusses cloud service models including infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). It introduces Azure, the Microsoft cloud computing platform, and key Azure services like Azure Storage, Azure Portal, Azure Accounts, Azure Data Factory, and Azure Data Flow. Azure Data Factory allows building data integration solutions using activities, linked services, datasets and triggers without writing code. Azure Data Flow enables visually designing data transformations using a Spark optimizer without code.
Introdcution to Azure
This document provides an introduction to Azure and describes several of its core services. It outlines compute, data, networking and other services available on Azure. Specifically it discusses virtual machines, websites, SQL databases, storage tables and blobs, import/export of large data sets, file services, virtual networks, ExpressRoute for private connections, Traffic Manager for routing, Automation for managing resources via PowerShell runbooks, API Management, backup services, messaging queues, Service Bus Relay for cross-firewall communication, Scheduler for scheduling jobs, caching with Cache service, Content Delivery Network for caching blobs globally, and HDInsight for Hadoop clusters.
When Should You Use AWS Lambda?
AWS Lambda is considered to be a proficient serverless computing service that allows you to run your code for managing servers and containers. The scaling under AWS Lambda is done automatically by measuring the work intensity integrated onto it. There are several use cases to AWS Lambda that define its prime efficacy of executing code within the AWS cloud. Even though AWS Lambda is meant to be used within the cloud, local development attributes can also use it for diverse development needs. For further reading please visit https://www.whizlabs.com/blog/use-of-aws-lambda/
Google apps engine
Google App Engine is a Platform as a Service (PaaS) cloud computing platform that allows developers to build and host web applications in Google's data centers. It provides a scalable and reliable environment for developing applications using popular languages like Java, Python, PHP, and Go. Google App Engine handles tasks like provisioning servers and managing traffic so developers can focus on building their applications. It also includes services for storage, mail delivery, caching, and accessing web resources.
Azure from scratch part 3 By Girish Kalamati
Azure Identity (AD,ADFS 2.0,AAD,ADB2C,OAuth,OpenID,PingID,AD Custom Policies) ,
Azure PaaS (Azure Functions, Serverless computing, Azure Comsos DB, Webhooks, API Apps, Logic Apps, Kudu, Azure Websites), Azure Functions, Lamda Function, Event Functions, Serverless architecture, Implementing azure functions on GIT HUB comment feature, Why Azure Functions, Azure Virtual Machines, Azure Cloud Services, Azure Web Apps & WebJobs, Service Fabric, Consumption Plans, Billing Model, Benefits of Azure Functions, What is serverless, Implementing bigger solutions into smaller azure functions, Microservices, Use cases, Function App, Implementation storing unstructured data using Azure functions into Cosmos DB, Cosmos DB, Custom Azure functions, Azure Cosmos DB, IOTS, Document DB, Doc DB, How to setup a Jenkins build server and automatically trigger code from Visual studio online,Azure App Service, App service Environment, Azure Stack, Managing Azure App services, Azure Powershell, Azure CLI, REST APIS, Azure Portal, Templates, Kudu Console access, Run GIT Commands on Kudu Console, Locking Azure Resources, Configuring Custom Domains, Adding Extensions to Azure Web App/Websites, App service Deployment options, Data Services in Azure , Azure SQL, Azure SQL server, Azure SQL database vs SQL server in a Azure VM, SQL Tiers, DTU, Data Transactional Unit, Planning & provisioning azure SQL databases,Migrating SQL Databases, Azure SQL Server, SQL server transactional replication, Deploy database to Microsoft Azure Database Wizard, DAC package, DAC, SQL compatibility issues, Migrating SQL with downtime, DMA, Data Migration Assistant, Database Snapshot, Migrating SQL without downtime, DTU, Data Transactional Unit, Recommendations for best performance during SQL Import Process, Transactional Replication, T-SQL, Task to implement what ever you learnt till now,
Google apps engine
Google App Engine is a Platform as a Service (PaaS) cloud computing platform that allows developers to build and host web applications in Google's data centers. It provides a scalable and reliable environment for developing applications using popular languages like Java, Python, PHP, and Go. App Engine handles tasks like provisioning servers and managing traffic so developers can focus on their code. It also includes services for storage, mail delivery, caching, and accessing web resources. App Engine is well-suited for applications with unpredictable traffic spikes or those where developers don't want to manage their own servers.
Cloud service providers
The document discusses and compares several major cloud service providers, including Amazon Web Services (AWS), Google Cloud Platform, Microsoft Azure, Oracle Cloud Infrastructure, SAP Cloud Platform, and Salesforce Service Cloud. It provides an overview of the services offered by each provider such as compute, storage, databases, machine learning, and describes some of their key features and histories. A table is included that compares AWS, Azure, and GCP across several categories like data management, app development, SMB analytics, and machine learning products.
Aws community day pune 2020 v3
This document provides an overview of AWS Glue, a fully managed ETL service for preparing and loading big data for analytics. It discusses what big data and analytics are, and describes the key components of Glue including the crawler, catalog, architecture and ETL jobs. Glue allows for easy, fast and cost-effective processing of vast amounts of data from sources like Amazon S3. It can be used to build data warehouses, run queries against data lakes, create event-driven pipelines, and understand data assets. The document provides examples of how Glue components like the crawler, catalog, and ETL jobs work together in a workflow.
Inside Microsoft Azure
A Web architect who doesn't usually use Microsoft technologies explains Microsoft's cloud offering, Azure, in terms that hopefully other UNIX/open source people will understand.
Exploring Microsoft Azure Infrastructures
This document provides an agenda and summary for a Data Analytics Meetup (DAM) on March 27, 2018. The agenda covers topics such as disruption opportunities in a changing data landscape, transitioning from traditional to modern BI architectures using Azure, Azure SQL Database vs Data Warehouse, data integration with Azure Data Factory and SSIS, Analysis Services, Power BI reporting, and a wrap-up. The document discusses challenges around data growth, digital transformation, and the shrinking time for companies to adapt to disruption. It provides overviews and comparisons of Azure SQL Database, Data Warehouse, and related Azure services to help modernize analytics architectures.
Serverless Extract-transform-load (ETL) on AWS Webinar
Previously, ETL meant using proprietary products with commercial databases and users with specialist skills. Learn how to create ETL data pipelines that can securely consume data at scale while using open source technologies and languages to enable your organisation, team, and data.
Speaker: Paul Macey, Big Data Specialist, AWS
Similar to Amazon Glue (20)
Serverless Extract-transform-load (ETL) on AWS Webinar
Serverless Extract-transform-load (ETL) on AWS Webinar
More from Elif Nurber Karakaş
Amazon Lake Formation
What is AWS Lake Formation?
AWS Lake Formation is a fully managed service that makes creating, maintaining, and managing data
lakes a breeze. AWS Lake Formation automates most of the data lake construction process, reducing
the time it takes from months to weeks. The service acts as a single point of control for identifying,
retrieving, cleaning, and transforming data from thousands of sources, as well as enforcing security
regulations across various services and acquiring and managing fresh data. AWS Lake Formation is
controlled via a single dashboard from which you can configure and alter all data lake lifecycle phases
and operations.
Amazon Data Pipeline
What is Amazon Data Pipeline?
Amazon Data Pipeline is a web service that enables you to process and transport data across AWS
computing and storage services, as well as on-premises data sources, in a reliable manner. It
automates the extraction, transformation, combination, validation, and uploading of data for further
analysis and display. It can handle many data streams at the same time and delivers end-to-end
speed by removing mistakes or overcoming delays.
Amazon Quicksight
Amazon QuickSight is a cloud-based machine learning-powered business analytics tool provided by
Amazon Web Services. It allows businesses to make more informed, data-driven decisions.
Businesses may use Amazon QuickSight BI to generate and analyze data visualizations and receive
easy-to-understand insights to help them make better business decisions. These dynamic dashboards
may be integrated into a variety of apps, portals, and websites with ease. Because Amazon
QuickSight is scalable, it can handle tens of thousands of users without the need for extra
infrastructure or capacity planning. It is also device agnostic.
Amazon Redshift
What is Amazon Redshift?
Amazon Redshift is a fully managed, petabyte-scale, cloud-based data warehouse product designed for large-scale dataset storage and analysis. It is also used to perform large-scale database migrations. Amazon Redshift helps you predict your costs by providing all of them at a price performance that is up to three times better than other cloud data warehouses from the start.
Amazon OpenSearch Service
What is Amazon OpenSearch Service?
OpenSearch is a distributed, open-source search and analytics package that may be used for real-
time application monitoring, log analysis, and internet search, among other things. With OpenSearch
Dashboards, an integrated visualization tool that makes it easy for users to examine their data,
OpenSearch provides a highly scalable solution for quick access and reaction to massive amounts of
data. The Apache Lucene search library, as well as OpenSearch, Elasticsearch, and Apache Solr,
support it. Elasticsearch 7.10.2 and Kibana 7.10.2 were used to create OpenSearch and OpenSearch
Dashboards. The Apache License Version 2.0 applies to all software in the OpenSearch project (ALv2).
Amazon Managed Streaming for Apache Kafka
Amazon MSK is a fully managed service that makes it easy for you to build and run applications that use Apache Kafka to process streaming data. Apache Kafka is an open-source platform for building real-time streaming data pipelines and applications. With Amazon MSK, you can use native Apache Kafka APIs to populate data lakes, stream changes to and from databases, and power machine learning and analytics applications.
Amazon Kinesis Data Streams
Amazon Kinesis Data Streams is a scalable, long-lasting, and low-cost streaming data solution from
Amazon. Kinesis Data Streams can gather terabytes of data every second from tens of thousands of
sources, including internet clickstreams, database event streams, financial transactions, social media
feeds, IT logs, and location-tracking events. Real-time dashboards, real-time anomaly detection, and
dynamic pricing are all possible with the collected data, which is available in milliseconds.
Amazon CloudSearch
Amazon CloudSearch is a search service that is completely controlled. It's easy to set up, use, and it's a cost-
effective search option. The fundamental text search engine of Amazon CloudSearch is Apache Solr. Full-text search, faceted search, real-time indexing, dynamic clustering, database integration, NoSQL
capabilities, and productive document handling are all supported by it.
Amazon Elastic MapReduce
Amazon EMR is an Amazon-managed cluster platform that makes it easier to handle and
analyze massive amounts of data using big data frameworks like Apache Hadoop and Apache
Spark on AWS. EMR may be used to process data for analytical and business intelligence
tasks in combination with Apache Hive and Apache Pig. EMR lets you transform and move
large amounts of data across AWS data stores and databases.
Amazon athena
Amazon Athena is a new serverless query service that makes it easy to analyze data in Amazon S3, using standard SQL. With Athena, there is no infrastructure to setup or manage, and you can start analyzing your data immediately. You don’t even need to load your data into Athena, it works directly with data stored in S3.
More from Elif Nurber Karakaş (10)
Recently uploaded
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Mind map of terminologies used in context of Generative AI
Mind map of common terms used in context of Generative AI.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
20 Comprehensive Checklist of Designing and Developing a Website
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
Securing your Kubernetes cluster_ a step-by-step guide to success !
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.Essentials of Automations: The Art of Triggers and Actions in FME
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
“I’m still / I’m still / Chaining from the Block”
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
How to Get CNIC Information System with Paksim Ga.pptx
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Recently uploaded (20)
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Mind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AI
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring Java Application Security with JDK Tools and JFR Events
Monitoring Java Application Security with JDK Tools and JFR Events
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
20 Comprehensive Checklist of Designing and Developing a Website
20 Comprehensive Checklist of Designing and Developing a Website
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...
Securing your Kubernetes cluster_ a step-by-step guide to success !
Securing your Kubernetes cluster_ a step-by-step guide to success !
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
Essentials of Automations: The Art of Triggers and Actions in FME
Essentials of Automations: The Art of Triggers and Actions in FME
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to Production
How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
Amazon Glue
- 1. AMAZON GLUE Elif Nurber KARAKAS
- 2. AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, machine learning, and application development. AWS Glue provides all the capabilities needed for data integration so that you can start analyzing your data and putting it to use in minutes instead of months. What is AWS Glue?
- 3. USE CASES Glue can integrate with Snowflake data warehouse to help manage the data integration process. AWS data lake can integrate with Glue. AWS Glue can integrate with Athena to create schemas. ETL code can be used for Glue on GitHub as well.
- 4. Benefits of using Glue Fault-tolerance: Failed jobs in Glue are retrievable, and logs in Glue can be debugged. Filtering: Filters for bad data. Support: Supports several non-native Java Database Connectivity (JDBC) data sources. Maintenance and deployment: Simple maintenance and deployment, because the service is completely managed by AWS.
- 5. Drawbacks of Using Glue Limited compatibility: While AWS Glue does work with a variety of commonly used data sources, it only works with services running on AWS. Organizations may need a third-party ETL service if sources are not AWS-based. No incremental data sync: All data is staged on S3 first, so Glue is not the best option for real-time ETL jobs. Learning curve: Teams using Glue should have a strong understanding of Apache spark. Relational database queries: Glue has limited support for queries of traditional relational databases, only SQL queries.