Microsoft azure documentDB
•Download as PPTX, PDF•
1 like•956 views
Azure DocumentDB is a NoSQL document database service designed from the ground up to natively support JSON and JavaScript directly inside the database engine. It’s the right solution for applications that run in the cloud when predictable throughput, low latency, and flexible query are key. Microsoft consumer applications like MSN use DocumentDB in production to support millions of users.
Report
Share
Report
Share
Recommended
Introduction to Azure DocumentDB
Denny Lee introduced Azure DocumentDB, a fully managed NoSQL database service. DocumentDB provides elastic scaling of throughput and storage, global distribution with low latency reads and writes, and supports querying JSON documents with SQL and JavaScript. Common scenarios that benefit from DocumentDB include storing product catalogs, user profiles, sensor telemetry, and social graphs due to its ability to handle hierarchical and de-normalized data at massive scale.
Data Modeling on Azure for Analytics
When you model data you are making two decisions:
* The location where data will be stored
* How the data will be organized for ease of use
Transitioning to a BI Role
James Serra is a Big Data Evangelist at Microsoft with over 28 years of experience in IT. He has worked in various roles including as a desktop/web/database developer, DBA, BI and DW architect and developer, MDM architect, and PDW developer. Serra is an author, blogger, and presenter who shares his expertise in business intelligence and big data. In his presentation, he provides an overview of the Microsoft BI stack, career opportunities in BI, and lessons from his own transition from DBA to a BI focus.
Wrangling data like a boss
Effective data wrangling starts with data collection. Make sure to think about Front-end validation, limiting free text, GDPR, security, accessibility, inclusion, and designing to minimise the amount of data collected.
The next part is effective data storage. You can store data in relational or non-relational data stores. Relational data solutions in Azure include Azure SQL Database, Azure Database for MariaDB, and Azure Database for PostgreSQL. Non-relational data solutions include Azure Storage and Azure Cosmos DB.
You might wrangle data during a data movement process between systems. A batch process moves multiple records, typically on a schedule. A streaming process operates on each individual event/message as it happens. In ETL (Extract Transform Load) you run a process to retrieve data from a system, process it, and then insert it into a new data store. In ELT (Extract Load Transform) you run a process to retrieve data from a system, insert it into a new data store, and the process it.
The modern data warehouse stack (used to combine data from multiple sources) includes Azure Data Factory, Azure Databricks, Azure Synapse Analytics and Azure Analysis Services.
Power Query is a useful language for processing data and can be used in Excel, Power BI, and Azure Data Factory.
Power BI is a self-service data modelling and visualisation tool that can connect to data stored in lots of different places. It has a data modeling area where Power Query can be used to build a dataset. The reports allow you make multi-page analysis of a dataset. A dashboard combines visuals from one or more reports to provide a broader and simpler view.
SQL is a language that works with many relational and non-relational systems. You can use it interact with data, data structures, and the access model for the data objects.
Finally you can write code to wrangle data with Python, R, and dotnet being common data wrangling languages.
Introduction to PolyBase
First introduced with the Analytics Platform System (APS), PolyBase simplifies management and querying of both relational and non-relational data using T-SQL. It is now available in both Azure SQL Data Warehouse and SQL Server 2016. The major features of PolyBase include the ability to do ad-hoc queries on Hadoop data and the ability to import data from Hadoop and Azure blob storage to SQL Server for persistent storage. A major part of the presentation will be a demo on querying and creating data on HDFS (using Azure Blobs). Come see why PolyBase is the “glue” to creating federated data warehouse solutions where you can query data as it sits instead of having to move it all to one data platform.
Power BI for Big Data and the New Look of Big Data Solutions
New features in Power BI give it enterprise tools, but that does not mean it automatically creates an enterprise solution. In this talk we will cover these new features (composite models, aggregations tables, dataflow) as well as Azure Data Lake Store Gen2, and describe the use cases and products of an individual, departmental, and enterprise big data solution. We will also talk about why a data warehouse and cubes still should be part of an enterprise solution, and how a data lake should be organized.
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
Hitchhiker’s Guide to SharePoint BI
This document provides an overview and agenda for a presentation on business intelligence and big data technologies. The presentation covers tools such as Excel, PowerPivot, Power View, Reporting Services, PerformancePoint, and HDInsight for working with data from sources like SQL Server, Oracle, DB2, and Hadoop. It discusses self-service BI capabilities and how these tools work with the Microsoft BI stack and platform.
Recommended
Introduction to Azure DocumentDB
Denny Lee introduced Azure DocumentDB, a fully managed NoSQL database service. DocumentDB provides elastic scaling of throughput and storage, global distribution with low latency reads and writes, and supports querying JSON documents with SQL and JavaScript. Common scenarios that benefit from DocumentDB include storing product catalogs, user profiles, sensor telemetry, and social graphs due to its ability to handle hierarchical and de-normalized data at massive scale.
Data Modeling on Azure for Analytics
When you model data you are making two decisions:
* The location where data will be stored
* How the data will be organized for ease of use
Transitioning to a BI Role
James Serra is a Big Data Evangelist at Microsoft with over 28 years of experience in IT. He has worked in various roles including as a desktop/web/database developer, DBA, BI and DW architect and developer, MDM architect, and PDW developer. Serra is an author, blogger, and presenter who shares his expertise in business intelligence and big data. In his presentation, he provides an overview of the Microsoft BI stack, career opportunities in BI, and lessons from his own transition from DBA to a BI focus.
Wrangling data like a boss
Effective data wrangling starts with data collection. Make sure to think about Front-end validation, limiting free text, GDPR, security, accessibility, inclusion, and designing to minimise the amount of data collected.
The next part is effective data storage. You can store data in relational or non-relational data stores. Relational data solutions in Azure include Azure SQL Database, Azure Database for MariaDB, and Azure Database for PostgreSQL. Non-relational data solutions include Azure Storage and Azure Cosmos DB.
You might wrangle data during a data movement process between systems. A batch process moves multiple records, typically on a schedule. A streaming process operates on each individual event/message as it happens. In ETL (Extract Transform Load) you run a process to retrieve data from a system, process it, and then insert it into a new data store. In ELT (Extract Load Transform) you run a process to retrieve data from a system, insert it into a new data store, and the process it.
The modern data warehouse stack (used to combine data from multiple sources) includes Azure Data Factory, Azure Databricks, Azure Synapse Analytics and Azure Analysis Services.
Power Query is a useful language for processing data and can be used in Excel, Power BI, and Azure Data Factory.
Power BI is a self-service data modelling and visualisation tool that can connect to data stored in lots of different places. It has a data modeling area where Power Query can be used to build a dataset. The reports allow you make multi-page analysis of a dataset. A dashboard combines visuals from one or more reports to provide a broader and simpler view.
SQL is a language that works with many relational and non-relational systems. You can use it interact with data, data structures, and the access model for the data objects.
Finally you can write code to wrangle data with Python, R, and dotnet being common data wrangling languages.
Introduction to PolyBase
First introduced with the Analytics Platform System (APS), PolyBase simplifies management and querying of both relational and non-relational data using T-SQL. It is now available in both Azure SQL Data Warehouse and SQL Server 2016. The major features of PolyBase include the ability to do ad-hoc queries on Hadoop data and the ability to import data from Hadoop and Azure blob storage to SQL Server for persistent storage. A major part of the presentation will be a demo on querying and creating data on HDFS (using Azure Blobs). Come see why PolyBase is the “glue” to creating federated data warehouse solutions where you can query data as it sits instead of having to move it all to one data platform.
Power BI for Big Data and the New Look of Big Data Solutions
New features in Power BI give it enterprise tools, but that does not mean it automatically creates an enterprise solution. In this talk we will cover these new features (composite models, aggregations tables, dataflow) as well as Azure Data Lake Store Gen2, and describe the use cases and products of an individual, departmental, and enterprise big data solution. We will also talk about why a data warehouse and cubes still should be part of an enterprise solution, and how a data lake should be organized.
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
Hitchhiker’s Guide to SharePoint BI
This document provides an overview and agenda for a presentation on business intelligence and big data technologies. The presentation covers tools such as Excel, PowerPivot, Power View, Reporting Services, PerformancePoint, and HDInsight for working with data from sources like SQL Server, Oracle, DB2, and Hadoop. It discusses self-service BI capabilities and how these tools work with the Microsoft BI stack and platform.
Big Data and NoSQL for Database and BI Pros
This document provides an agenda and overview for a conference session on Big Data and NoSQL for database and BI professionals held from April 10-12 in Chicago, IL. The session will include an overview of big data and NoSQL technologies, then deeper dives into Hadoop, NoSQL databases like HBase, and tools like Hive, Pig, and Sqoop. There will also be demos of technologies like HDInsight, Elastic MapReduce, Impala, and running MapReduce jobs.
Data Modeling for NoSQL
- Data modeling for NoSQL databases is different than relational databases and requires designing the data model around access patterns rather than object structure. Key differences include not having joins so data needs to be duplicated and modeling the data in a way that works for querying, indexing, and retrieval speed.
- The data model should focus on making the most of features like atomic updates, inner indexes, and unique identifiers. It's also important to consider how data will be added, modified, and retrieved factoring in object complexity, marshalling/unmarshalling costs, and index maintenance.
- The _id field can be tailored to the access patterns, such as using dates for time-series data to keep recent
SQL vs NoSQL
Explaining the most important distinctions and discuss the best SQL and NoSQL database systems available.
Introduction to Microsoft’s Hadoop solution (HDInsight)
Did you know Microsoft provides a Hadoop Platform-as-a-Service (PaaS)? It’s called Azure HDInsight and it deploys and provisions managed Apache Hadoop clusters in the cloud, providing a software framework designed to process, analyze, and report on big data with high reliability and availability. HDInsight uses the Hortonworks Data Platform (HDP) Hadoop distribution that includes many Hadoop components such as HBase, Spark, Storm, Pig, Hive, and Mahout. Join me in this presentation as I talk about what Hadoop is, why deploy to the cloud, and Microsoft’s solution.
High-performance database technology for rock-solid IoT solutions
Clusterpoint is a privately held database software company founded in 2006 with 32 employees. Their product is a hybrid operational database, analytics, and search platform that provides secure, high-performance distributed data management at scale. It reduces total cost of ownership by 80% over traditional relational databases by providing blazing fast performance, unlimited scalability, and bulletproof transactions with instant text search and security. Clusterpoint also offers their database software as a cloud database as a service to instantly scale databases on demand.
Is the traditional data warehouse dead?
With new technologies such as Hive LLAP or Spark SQL, do I still need a data warehouse or can I just put everything in a data lake and report off of that? No! In the presentation I’ll discuss why you still need a relational data warehouse and how to use a data lake and a RDBMS data warehouse to get the best of both worlds. I will go into detail on the characteristics of a data lake and its benefits and why you still need data governance tasks in a data lake. I’ll also discuss using Hadoop as the data lake, data virtualization, and the need for OLAP in a big data solution. And I’ll put it all together by showing common big data architectures.
NoSQL: An Analysis
Andrew Brust gave a presentation on NoSQL databases. He began by explaining why NoSQL databases were developed, noting their ability to handle large volumes of data and preference for availability over consistency. He then covered different NoSQL concepts like indexing, queries, and sharding. The main NoSQL categories - key-value stores, wide column stores, document stores, and graph databases - were described. Brust concluded by discussing using NoSQL with big data and cloud platforms like Windows Azure.
Big Data Strategy for the Relational World
1) Andrew Brust is the CEO of Blue Badge Insights and a big data expert who writes for ZDNet and GigaOM Research.
2) The document discusses trends in databases including the growth of NoSQL databases like MongoDB and Cassandra and Hadoop technologies.
3) It also covers topics like SQL convergence with Hadoop, in-memory databases, and recommends that organizations look at how widely database products are deployed before adopting them to avoid being locked into niche products.
Azure Document Db
This document discusses an IoT Day event hosted by 1nn0va on May 8, 2015. It covers topics like representing data models for IoT using DocumentDB, including embedding vs normalizing data and handling one-to-many relationships. It also discusses partitioning strategies for DocumentDB, consistency levels to trade off speed and availability vs consistency, and using weaker consistency for scenarios like IoT and data analysis.
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.
Data modeling trends for analytics
The document discusses trends in data modeling for analytics. It outlines weaknesses in traditional enterprise data architectures that rely on ETL processes and large centralized data warehouses. A modern approach uses a data lake to store raw data files and enable just-in-time analytics using data virtualization. Key aspects of the data lake include storing data in folders by level of processing (raw, staging, ODS, aggregated), using file formats like Parquet, and creating star schemas and aggregations on top of the stored data.
Finding business value in Big Data
I often hear from clients: “We don’t know much about Big Data – can you tell us what it is and how it can help our business?” Yes! The first step is this vendor-free presentation, where I start with a business level discussion, not a technical one. Big Data is an opportunity to re-imagine our world, to track new signals that were once impossible, to change the way we experience our communities, our places of work and our personal lives. I will help you to identify the business value opportunity from Big Data and how to operationalize it. Yes, we will cover the buzz words: modern data warehouse, Hadoop, cloud, MPP, Internet of Things, and Data Lake, but I will show use cases to better understand them. In the end, I will give you the ammo to go to your manager and say “We need Big Data an here is why!” Because if you are not utilizing Big Data to help you make better business decisions, you can bet your competitors are.
Data Lake Overview
The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
How to build your career
In three years I went from a complete unknown to a popular blogger, speaker at PASS Summit, a SQL Server MVP, and then joined Microsoft. Along the way I saw my yearly income triple. Is it because I know some secret? Is it because I am a genius? No! It is just about laying out your career path, setting goals, and doing the work.
I'll cover tips I learned over my career on everything from interviewing to building your personal brand. I'll discuss perm positions, consulting, contracting, working for Microsoft or partners, hot fields, in-demand skills, social media, networking, presenting, blogging, salary negotiating, dealing with recruiters, certifications, speaking at major conferences, resume tips, and keys to a high-paying career.
Your first step to enhancing your career will be to attend this session! Let me be your career coach!
Master Meta Data
Organize & manage master meta data centrally, built upon kong, cassandra, neo4j & elasticsearch. Managing master & meta data is a very common problem with no good opensource alternative as far as I know, so initiating this project – MasterMetaData.
How to Radically Simplify Your Business Data Management
Relational databases were designed for tabular data storage model. It requires complex software: schemas, encoded data, inflexible relations, sophisticated indexes. Complexity of your IT systems increases over your database life-time many-fold. Your costs too. Yet, we have a solution for this.
Designing a modern data warehouse in azure
This document discusses designing a modern data warehouse in Azure. It provides an overview of traditional vs. self-service data warehouses and their limitations. It also outlines challenges with current data warehouses around timeliness, flexibility, quality and findability. The document then discusses why organizations need a modern data warehouse based on criteria like customer experience, quality assurance and operational efficiency. It covers various approaches to ingesting, storing, preparing, modeling and serving data on Azure. Finally, it discusses architectures like the lambda architecture and common data models.
How to boost your datamanagement with Dremio ?
Works with any source. Relational, non-relational, 3rd party apps. 5 years ago nobody was using Hadoop, MongoDB, and 5 years from now there will be new products. You need a solution that is future proof.
Works with any BI tool. In every company multiple tools are in use. Each department has their favorite. We need to work with all of them.
No ETL, data warehouse, cubes. This would need to give you a really good alternative to these options.
Makes data self-service, collaborative. Probably most important of all, we need to change the dynamic between the business and IT. We need to make it so business users can get the data they want, in the shape they want it, without waiting on IT.
Makes Big Data feels small. It needs to make billions of rows feel like a spreadsheet on your desktop.
Open source. It’s 2017, so we think this has to be open source.
Big Data Analytics in the Cloud with Microsoft Azure
Big Data Analytics in the Cloud using Microsoft Azure services was discussed. Key points included:
1) Azure provides tools for collecting, processing, analyzing and visualizing big data including Azure Data Lake, HDInsight, Data Factory, Machine Learning, and Power BI. These services can be used to build solutions for common big data use cases and architectures.
2) U-SQL is a language for preparing, transforming and analyzing data that allows users to focus on the what rather than the how of problems. It uses SQL and C# and can operate on structured and unstructured data.
3) Visual Studio provides an integrated environment for authoring, debugging, and monitoring U-SQL scripts and jobs. This allows
The Value of Explicit Schema for Graph Use Cases
A look at the many facets of schema-less approaches vs a rich schema approach, ranging from performance and query support to heterogeneity and code/data migration issues. Presented by Nick Quinn, Principal Engineer, InfiniteGraph
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.
Scheduling Algorithms-R.D.Sivakumar
This document discusses various CPU scheduling algorithms. It describes non-preemptive and preemptive scheduling and covers scheduling algorithms like first-come first-served (FCFS), shortest job first (SJF), priority scheduling, round robin, multi-level queue scheduling, and multi-processor scheduling. It provides details on how each algorithm works and its advantages and disadvantages.
More Related Content
What's hot
Big Data and NoSQL for Database and BI Pros
This document provides an agenda and overview for a conference session on Big Data and NoSQL for database and BI professionals held from April 10-12 in Chicago, IL. The session will include an overview of big data and NoSQL technologies, then deeper dives into Hadoop, NoSQL databases like HBase, and tools like Hive, Pig, and Sqoop. There will also be demos of technologies like HDInsight, Elastic MapReduce, Impala, and running MapReduce jobs.
Data Modeling for NoSQL
- Data modeling for NoSQL databases is different than relational databases and requires designing the data model around access patterns rather than object structure. Key differences include not having joins so data needs to be duplicated and modeling the data in a way that works for querying, indexing, and retrieval speed.
- The data model should focus on making the most of features like atomic updates, inner indexes, and unique identifiers. It's also important to consider how data will be added, modified, and retrieved factoring in object complexity, marshalling/unmarshalling costs, and index maintenance.
- The _id field can be tailored to the access patterns, such as using dates for time-series data to keep recent
SQL vs NoSQL
Explaining the most important distinctions and discuss the best SQL and NoSQL database systems available.
Introduction to Microsoft’s Hadoop solution (HDInsight)
Did you know Microsoft provides a Hadoop Platform-as-a-Service (PaaS)? It’s called Azure HDInsight and it deploys and provisions managed Apache Hadoop clusters in the cloud, providing a software framework designed to process, analyze, and report on big data with high reliability and availability. HDInsight uses the Hortonworks Data Platform (HDP) Hadoop distribution that includes many Hadoop components such as HBase, Spark, Storm, Pig, Hive, and Mahout. Join me in this presentation as I talk about what Hadoop is, why deploy to the cloud, and Microsoft’s solution.
High-performance database technology for rock-solid IoT solutions
Clusterpoint is a privately held database software company founded in 2006 with 32 employees. Their product is a hybrid operational database, analytics, and search platform that provides secure, high-performance distributed data management at scale. It reduces total cost of ownership by 80% over traditional relational databases by providing blazing fast performance, unlimited scalability, and bulletproof transactions with instant text search and security. Clusterpoint also offers their database software as a cloud database as a service to instantly scale databases on demand.
Is the traditional data warehouse dead?
With new technologies such as Hive LLAP or Spark SQL, do I still need a data warehouse or can I just put everything in a data lake and report off of that? No! In the presentation I’ll discuss why you still need a relational data warehouse and how to use a data lake and a RDBMS data warehouse to get the best of both worlds. I will go into detail on the characteristics of a data lake and its benefits and why you still need data governance tasks in a data lake. I’ll also discuss using Hadoop as the data lake, data virtualization, and the need for OLAP in a big data solution. And I’ll put it all together by showing common big data architectures.
NoSQL: An Analysis
Andrew Brust gave a presentation on NoSQL databases. He began by explaining why NoSQL databases were developed, noting their ability to handle large volumes of data and preference for availability over consistency. He then covered different NoSQL concepts like indexing, queries, and sharding. The main NoSQL categories - key-value stores, wide column stores, document stores, and graph databases - were described. Brust concluded by discussing using NoSQL with big data and cloud platforms like Windows Azure.
Big Data Strategy for the Relational World
1) Andrew Brust is the CEO of Blue Badge Insights and a big data expert who writes for ZDNet and GigaOM Research.
2) The document discusses trends in databases including the growth of NoSQL databases like MongoDB and Cassandra and Hadoop technologies.
3) It also covers topics like SQL convergence with Hadoop, in-memory databases, and recommends that organizations look at how widely database products are deployed before adopting them to avoid being locked into niche products.
Azure Document Db
This document discusses an IoT Day event hosted by 1nn0va on May 8, 2015. It covers topics like representing data models for IoT using DocumentDB, including embedding vs normalizing data and handling one-to-many relationships. It also discusses partitioning strategies for DocumentDB, consistency levels to trade off speed and availability vs consistency, and using weaker consistency for scenarios like IoT and data analysis.
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.
Data modeling trends for analytics
The document discusses trends in data modeling for analytics. It outlines weaknesses in traditional enterprise data architectures that rely on ETL processes and large centralized data warehouses. A modern approach uses a data lake to store raw data files and enable just-in-time analytics using data virtualization. Key aspects of the data lake include storing data in folders by level of processing (raw, staging, ODS, aggregated), using file formats like Parquet, and creating star schemas and aggregations on top of the stored data.
Finding business value in Big Data
I often hear from clients: “We don’t know much about Big Data – can you tell us what it is and how it can help our business?” Yes! The first step is this vendor-free presentation, where I start with a business level discussion, not a technical one. Big Data is an opportunity to re-imagine our world, to track new signals that were once impossible, to change the way we experience our communities, our places of work and our personal lives. I will help you to identify the business value opportunity from Big Data and how to operationalize it. Yes, we will cover the buzz words: modern data warehouse, Hadoop, cloud, MPP, Internet of Things, and Data Lake, but I will show use cases to better understand them. In the end, I will give you the ammo to go to your manager and say “We need Big Data an here is why!” Because if you are not utilizing Big Data to help you make better business decisions, you can bet your competitors are.
Data Lake Overview
The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
How to build your career
In three years I went from a complete unknown to a popular blogger, speaker at PASS Summit, a SQL Server MVP, and then joined Microsoft. Along the way I saw my yearly income triple. Is it because I know some secret? Is it because I am a genius? No! It is just about laying out your career path, setting goals, and doing the work.
I'll cover tips I learned over my career on everything from interviewing to building your personal brand. I'll discuss perm positions, consulting, contracting, working for Microsoft or partners, hot fields, in-demand skills, social media, networking, presenting, blogging, salary negotiating, dealing with recruiters, certifications, speaking at major conferences, resume tips, and keys to a high-paying career.
Your first step to enhancing your career will be to attend this session! Let me be your career coach!
Master Meta Data
Organize & manage master meta data centrally, built upon kong, cassandra, neo4j & elasticsearch. Managing master & meta data is a very common problem with no good opensource alternative as far as I know, so initiating this project – MasterMetaData.
How to Radically Simplify Your Business Data Management
Relational databases were designed for tabular data storage model. It requires complex software: schemas, encoded data, inflexible relations, sophisticated indexes. Complexity of your IT systems increases over your database life-time many-fold. Your costs too. Yet, we have a solution for this.
Designing a modern data warehouse in azure
This document discusses designing a modern data warehouse in Azure. It provides an overview of traditional vs. self-service data warehouses and their limitations. It also outlines challenges with current data warehouses around timeliness, flexibility, quality and findability. The document then discusses why organizations need a modern data warehouse based on criteria like customer experience, quality assurance and operational efficiency. It covers various approaches to ingesting, storing, preparing, modeling and serving data on Azure. Finally, it discusses architectures like the lambda architecture and common data models.
How to boost your datamanagement with Dremio ?
Works with any source. Relational, non-relational, 3rd party apps. 5 years ago nobody was using Hadoop, MongoDB, and 5 years from now there will be new products. You need a solution that is future proof.
Works with any BI tool. In every company multiple tools are in use. Each department has their favorite. We need to work with all of them.
No ETL, data warehouse, cubes. This would need to give you a really good alternative to these options.
Makes data self-service, collaborative. Probably most important of all, we need to change the dynamic between the business and IT. We need to make it so business users can get the data they want, in the shape they want it, without waiting on IT.
Makes Big Data feels small. It needs to make billions of rows feel like a spreadsheet on your desktop.
Open source. It’s 2017, so we think this has to be open source.
Big Data Analytics in the Cloud with Microsoft Azure
Big Data Analytics in the Cloud using Microsoft Azure services was discussed. Key points included:
1) Azure provides tools for collecting, processing, analyzing and visualizing big data including Azure Data Lake, HDInsight, Data Factory, Machine Learning, and Power BI. These services can be used to build solutions for common big data use cases and architectures.
2) U-SQL is a language for preparing, transforming and analyzing data that allows users to focus on the what rather than the how of problems. It uses SQL and C# and can operate on structured and unstructured data.
3) Visual Studio provides an integrated environment for authoring, debugging, and monitoring U-SQL scripts and jobs. This allows
The Value of Explicit Schema for Graph Use Cases
A look at the many facets of schema-less approaches vs a rich schema approach, ranging from performance and query support to heterogeneity and code/data migration issues. Presented by Nick Quinn, Principal Engineer, InfiniteGraph
What's hot (20)
Introduction to Microsoft’s Hadoop solution (HDInsight)
Introduction to Microsoft’s Hadoop solution (HDInsight)
High-performance database technology for rock-solid IoT solutions
High-performance database technology for rock-solid IoT solutions
How to Radically Simplify Your Business Data Management
How to Radically Simplify Your Business Data Management
Big Data Analytics in the Cloud with Microsoft Azure
Big Data Analytics in the Cloud with Microsoft Azure
Viewers also liked
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.
Scheduling Algorithms-R.D.Sivakumar
This document discusses various CPU scheduling algorithms. It describes non-preemptive and preemptive scheduling and covers scheduling algorithms like first-come first-served (FCFS), shortest job first (SJF), priority scheduling, round robin, multi-level queue scheduling, and multi-processor scheduling. It provides details on how each algorithm works and its advantages and disadvantages.
Azure document DB
Azure Document DB is a fully managed, highly scalable, queryable, schema-free JSON document database delivered as a service. It allows for the production and consumption of data at high volumes while requiring fast response times. Document DB supports developing applications iteratively with concurrent versions and evolving data models, and handles unpredictable growth through scalability. It addresses the lack of querying and transactions in some NoSQL databases.
Introduction to Azure DocumentDB
This document discusses DocumentDB, a NoSQL database offered by Microsoft Azure. It provides an overview of DocumentDB's data model, which uses JSON-like documents with a RESTful API. It also covers best practices for modeling data relations in DocumentDB through embedding or referencing other documents. The document also explores indexing and querying capabilities in DocumentDB, as well as consistency models and techniques for scaling collections in the database.
Analyze and visualize non-relational data with DocumentDB + Power BI
The session will show how to do Analyze and visualize non-relational data with DocumentDB + Power BI. We are in the midst of a paradigm shift on how we store and analyze data. Unstructured or flexible schema data represents a large portion of data within an organization. Everyone is obsessed to turn this data into meaningful business information. Unstructured data analytics do not need to be time consuming and complex. Come learn how to analyze and visualize unstructured data in DocumentDB.
Developing Solutions with Azure DocumentDB
• How to build a web app, and explore how Azure Search integrates DocumentDB?
• Build a Mobile App with Azure Mobile Apps and DocumentDB
• How it fits into the greater Azure ecosystem?
• Things you can do to get the best out of DocumentDB
Modeling JSON data for NoSQL document databases
Modeling data in a relational database is easy, we all know how to do it because that's what we've always been taught; But what about NoSQL Document Databases?
Document databases take (much) of what you know and flip it upside down. This talk covers some common patterns for modeling data and how to approach things when working with document stores such as Azure DocumentDB
Azure DocumentDB Overview
DocumentDB is a fast, globally distributed, multi-model NoSQL database service. It provides automatic scaling of storage and throughput, high availability across regions, flexible data models, and developer productivity with support for SQL and JavaScript queries. Customers can use DocumentDB for building scalable applications that need to handle large volumes of data across any number of regions worldwide with low latency and high availability.
Azure DocumentDB
This document discusses creating a database and document collection in Azure Cosmos DB. It shows code to instantiate a DocumentClient, create a database called "Sales", and then create a document collection within that database called "SalesCollection". Links are also provided to the author's blog and the Azure Cosmos DB documentation.
Scheduling algo(by HJ)
CPU scheduling decides which processes run when multiple are ready. It aims to make the system efficient, fast and fair. There are different scheduling algorithms like first come first serve (FCFS), shortest job first (SJF), priority scheduling, and round robin. Multi-level feedback queue scheduling uses multiple queues and allows processes to move between queues based on their CPU usage to prioritize shorter interactive processes.
Benjamin Guinebertière - Microsoft Azure: Document DB and other noSQL databas...
When deploying your service to Microsoft Azure, you have a number of options in terms of noSQL: you can install databases on Linux or Windows virtual machines by yourself, or via the marketplace, or you can use open source databases available as a service like HBase or proprietary and managed databases like Document DB. After showing these options, we'll show Document DB in more details. This is a noSQL database as a service that stores JSON.
Microsoft Azure DocumentDB - Global Azure Bootcamp 2016
Microsoft Azure DocumentDB is a fully managed NoSQL database service that supports JSON documents and SQL queries. It provides tunable consistency levels from strong to eventual, excellent search capabilities without SQL, and a REST API. Documents are stored in collections and addressed through a unique ID. Operations include CRUD and querying documents. DocumentDB also supports server-side JavaScript for stored procedures, triggers, and user-defined functions.
Viewers also liked (12)
Analyze and visualize non-relational data with DocumentDB + Power BI
Analyze and visualize non-relational data with DocumentDB + Power BI
Benjamin Guinebertière - Microsoft Azure: Document DB and other noSQL databas...
Benjamin Guinebertière - Microsoft Azure: Document DB and other noSQL databas...
Microsoft Azure DocumentDB - Global Azure Bootcamp 2016
Microsoft Azure DocumentDB - Global Azure Bootcamp 2016
Similar to Microsoft azure documentDB
Online MongoDB Training by Easylearning.guru
Learn MongoDB online at Easylearning.guru. We offer instructor led online training and Life Time LMS (Learning Management System) Access. Join Our Free Live Demo Classes of MongoDB.
UNIT-2.pptx
NoSQL is a non-relational database approach that accommodates a wide variety of data models. It is non-relational, distributed, flexible and scalable. The four main types of NoSQL databases are document databases, key-value stores, column-oriented databases, and graph databases. MongoDB is an example of a document-oriented NoSQL database. NoSQL databases offer benefits over relational databases like flexible schemas, horizontal scalability, and fast queries. Hadoop is an open source framework for distributed storage and processing of large datasets across clusters of computers. It uses MapReduce as its parallel programming model and the Hadoop Distributed File System for storage.
5 Best Mobile App Databases
Discover the 5 best mobile app database for 2023. Explore MySQL, PostgreSQL, MongoDB, AWS DynamoDB, and Couchbase for optimal performance and scalability.
Ijaprr vol1-2-6-9naseer
The document summarizes key aspects of NOSQL databases for interactive applications. It discusses how NOSQL databases provide better scalability, performance, and availability compared to traditional databases, which is important for applications that need to handle large amounts of data and users. The document also outlines some important criteria for choosing a database for interactive applications, including scalability, performance, availability, and architecture. It concludes that NOSQL databases are well-suited for these criteria and are becoming more popular for enterprises due to their ability to address issues with traditional databases.
[「RDB技術者のためのNoSQLガイド」出版記念セミナー] Azure DocumentDB
Microsoft Azure DocumentDB is a NoSQL document database service that is part of Microsoft Azure. It allows for the storage and querying of JSON documents and offers rich query capabilities over schema-free data using SQL and JavaScript. DocumentDB provides scalability, availability, and predictable performance for cloud applications.
Ijaprr vol1-2-6-9naseer
This document discusses the suitability of NoSQL databases for interactive applications. It notes that traditional databases do not provide the necessary scalability and performance for interactive applications that require reading and writing huge amounts of data swiftly. NoSQL databases are better suited for these tasks as they are scalable, fast, and robust. The document provides an overview of NoSQL databases and how their flexible schema, high performance, availability, and ability to scale horizontally make them a better fit than traditional databases for modern, large-scale, interactive applications.
Selecting best NoSQL
The document discusses factors to consider when selecting a NoSQL database management system (DBMS). It provides an overview of different NoSQL database types, including document databases, key-value databases, column databases, and graph databases. For each type, popular open-source options are described, such as MongoDB for document databases, Redis for key-value, Cassandra for columnar, and Neo4j for graph databases. The document emphasizes choosing a NoSQL solution based on application needs and recommends commercial support for production systems.
nodejs.pptx
Node.js is a JavaScript runtime environment that allows JavaScript to run on the server side. It uses the V8 engine and is cross-platform, high-performance, and allows for building scalable network applications. MongoDB is a scalable, flexible, high-performance NoSQL database that stores data in flexible, JSON-like documents. An Object Document Mapper (ODM) allows mapping between objects in application code and documents in MongoDB. The MERN stack uses MongoDB, Express, React, and Node.js and allows building single-page web applications that are high-performance, scalable, flexible, efficient, and secure.
Mongodb Introduction
- Modern data workloads like big data, agile development, and cloud computing are driving new requirements for database management systems that relational databases can't meet.
- NoSQL databases like MongoDB were created to address these new requirements by providing horizontal scalability, flexible schemas, and compatibility with cloud environments.
- MongoDB scales across multiple servers, allows dynamic schema changes, and runs well on commodity hardware and virtual infrastructures, making it well-suited for modern applications.
A Brief Introduction: MongoDB
Big data, agile development, and cloud computing
are driving new requirements for database
management systems. These requirements are in turn
driving the next phase of growth in the database
industry, mirroring the evolution of the OLAP
industry. This document describes this evolution, the
new application workload, and how MongoDB is
uniquely suited to address these challenges.
Introduction to databases (1).pptx
MongoDB and SQL are two popular database management systems. MongoDB is a document-oriented and schemaless database that stores data in flexible JSON-like documents, while SQL is a relational database that stores data in rigidly defined tables. MongoDB uses a powerful query language based on JavaScript and scales horizontally very well. In contrast, SQL uses the standardized SQL language and typically scales vertically. MongoDB generally performs better for large, unstructured datasets like those from IoT and big data applications, while SQL is better suited for structured data needing complex transactions, analytics or regulatory compliance.
Literature Survey on Mobile Database Tools
This document provides an overview of mobile database tools, including why mobile databases are needed, common database architectures, and features of mobile database tools. It reviews several specific mobile database tools (SQLAnywhere, SolidDB, SQL Server Compact, Oracle Database Lite, SQLite, DB4o, MongoDB), discussing their strengths, weaknesses, and technical details. The document concludes that as mobile device usage increases, mobile databases need to efficiently store and synchronize data across platforms using small memory footprints and secure connectivity.
Introducing NoSQL and MongoDB to complement Relational Databases (AMIS SIG 14...
This presentation gives an brief overview of the history of relational databases, ACID and SQL and presents some of the key strentgths and potential weaknesses. It introduces the rise of NoSQL - why it arose, what is entails, when to use it. The presentation focuses on MongoDB as prime example of NoSQL document store and it shows how to interact with MongoDB from JavaScript (NodeJS) and Java.
SQL & NoSQL
This document provides an overview of SQL and NoSQL databases. It defines SQL as a language used to communicate with relational databases, allowing users to query, manipulate, and retrieve data. NoSQL databases are defined as non-relational and allow for flexible schemas. The document compares key aspects of SQL and NoSQL such as data structure, querying, scalability and provides examples of popular SQL and NoSQL database systems. It concludes that both SQL and NoSQL databases will continue to be important with polyglot persistence, using the best database for each storage need.
MongoDB
This document provides an overview of MongoDB and its suitability for handling IoT data. MongoDB is a document-oriented NoSQL database that uses a flexible document data model and scales horizontally. It can handle the high volume and varied structures of sensor data generated by IoT devices in real-time without expensive ETL processes. MongoDB addresses the challenges of IoT data by allowing rapid iteration of data schemas, scaling to billions of documents, and performing analytics directly on the database.
DDS-to-JSON and DDS Real-time Data Storage with MongoDB
The document discusses using MongoDB to store time-series sensor data transmitted using the Data Distribution Service (DDS) standard. DDS is used to acquire real-time data from sensors and devices, which is then converted to JSON format and stored as documents in MongoDB collections. MongoDB is suitable for this application because it can manage flexible data structures and scale to large volumes of sensor data. The system provides benefits like replay capability, querying, analysis and integration testing using the recorded sensor information.
A beginners guide to Cloudera Hadoop
In this document, we will present a very brief introduction to BigData (what is BigData?), Hadoop (how does Hadoop fits the picture?) and Cloudera Hadoop (what is the difference between Cloudera Hadoop and regular Hadoop?).
Please note that this document is for Hadoop beginners looking for a place to start.
Development of a Web based Shopping Cart using the Mongo DB Database for Huma...
The databases in use today are of SQL-type. This has its drawbacks such as unnecessary complex queries, rigid schema, non-asynchronous persistence and they are definitely not object oriented. Moreover, SQL-shopping cart is expensive by requiring more programs to function. Therefore, the development of a modern shopping cart using MongoDB will eradicate these set backs. The main aim of this study is to design and execute a modern e-commerce shopping cart using MongoDB database. The method used here is the agile development methodology. Stages involved here include: Brainstorm, Design, development stage, Quality Assurance, deployment and Cycle. The User interface is written with HTML, CSS and JavaScript. The HTML (Hyper Text markup language) is used to create the web pages involved, including the forms through which the user supplies input to the system. Each item in the web page is well labeled to optimize user friendliness. The CSS (cascading Style Sheet) is used to create a mobile-friendly, responsive interface to enable mobile devices to seamlessly use the system.The developed shopping cart will save time and effort for programmers rather than using SQL tools with all the labors with it.
BPM und SOA machen mobil - Ein Architekturüberblick
http://www.opitz-consulting.com/go/3-5-898
Smartphones haben unsere Welt im Schnellgang erobert. Die Tablets folgen nicht minder schnell nach. Was fasziniert uns so daran? Welche neuen Möglichkeiten bieten sich für das Business? Welchen Einfluss wird das allgegenwärtige HTML5 haben? Wie bekomme ich mobile Lösungen architektonisch optimal in meine SOA-Landschaft integriert, und welche Vorteile gewinne ich bei der Prozessautomatisierung? Diese Session liefert sowohl einen Überblick als auch Antworten für eine neue Klasse von Architekturfragen.
Die SOA-Experten Torsten Winterberg und Guido Schmutz hielten diesen Fachvortrag bei der DOAG Konferenz und Ausstellung am 20.11.2013 in Nürnberg.
--
Über uns:
Als führender Projektspezialist für ganzheitliche IT-Lösungen tragen wir zur Wertsteigerung der Organisationen unserer Kunden bei und bringen IT und Business in Einklang. Mit OPITZ CONSULTING als zuverlässigem Partner können sich unsere Kunden auf ihr Kerngeschäft konzentrieren und ihre Wettbewerbsvorteile nachhaltig absichern und ausbauen.
Über unsere IT-Beratung: http://www.opitz-consulting.com/go/3-8-10
Unser Leistungsangebot: http://www.opitz-consulting.com/go/3-8-874
Karriere bei OPITZ CONSULTING: http://www.opitz-consulting.com/go/3-8-5
BPM and SOA are going mobile - An architectural perspective
http://www.opitz-consulting.com/go/3-5-898
Smartphones and tablets conquered our world. Which new opportunities are there for our businesses? Which influence has the omnipresent HTML5? How can I integrate mobile solutions in an optimal architectural way in my SOA landscapes and which kind of advantages do I gain for business process automation? This session delivers answers and puts current buzzwords like Big Data, Cloud, internet of things, HTML5 and mobile in the context of BPM and integration. Thereby we derive a reference architecture for Oracle SOA Suite, OSB, BPM Suite, Enterprise Gateway, Webcenter, ADF Mobile, etc., which makes all the buzzwords easily manageable in our daily IT work and prevents you from making mistakes others already did.
Torsten Winterberg und Guido Schmutz, both well-respected SOA Experts, presented this session at German Oracle User Communities’s Conference (DOAG Konferenz) at nov 20th 2013 in Nuremberg, Germany.
--
- - -
About us:
OPITZ CONSULTING is a leading project specialist for custom-build applications and individual business intelligence solutions in the German market. The company's ambition is to help organizations to be better than their competitors. To achieve this OPITZ CONSULTING analyses the individual competitive edge the customer has, optimizes business processes for process automation and IT-support, chooses and designs appropriate system architectures, develops and implements solutions and guarantees a 24/7 support and application maintenance. To ensure the necessary skill and qualification OPITZ CONSULTING has established a training center for customers and the internal staff.
Since 1990 over 600 customers have a long lasting and successful business relationship with OPITZ CONSULTING. Over 2/3 of the German stock index (DAX) companies rely on services from the 400+ OPITZ CONSULTING consultants. OPITZ CONSULTING maintains offices in Bad Homburg, Berlin, Essen, Gummersbach, Hamburg, Munich, Nuremberg and Kraków and Warsawa (Poland).
About us: http://www.opitz-consulting.com/en/about_us
Services: http://www.opitz-consulting.com/en/leistungsangebot
Career: http://www.opitz-consulting.com/en/career
Similar to Microsoft azure documentDB (20)
Introducing NoSQL and MongoDB to complement Relational Databases (AMIS SIG 14...
Introducing NoSQL and MongoDB to complement Relational Databases (AMIS SIG 14...
DDS-to-JSON and DDS Real-time Data Storage with MongoDB
DDS-to-JSON and DDS Real-time Data Storage with MongoDB
Development of a Web based Shopping Cart using the Mongo DB Database for Huma...
Development of a Web based Shopping Cart using the Mongo DB Database for Huma...
BPM und SOA machen mobil - Ein Architekturüberblick
BPM und SOA machen mobil - Ein Architekturüberblick
BPM and SOA are going mobile - An architectural perspective
BPM and SOA are going mobile - An architectural perspective
Recently uploaded
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.
Energy consumption of Database Management - Florina Jonuzi
Presentation from Florina Jonuzi at the GSD Community Stage Meetup on June 06, 2024
DDS-Security 1.2 - What's New? Stronger security for long-running systems
DDS Security Version 1.2 was adopted in 2024. This revision strengthens support for long runnings systems adding new cryptographic algorithms, certificate revocation, and hardness against DoS attacks.
KuberTENes Birthday Bash Guadalajara - Introducción a Argo CD
Charla impartida en el evento de "KuberTENes Birthday Bash Guadalajara" para celebrar el 10mo. aniversario de Kubernetes #kuberTENes #celebr8k8s #k8s
What is Master Data Management by PiLog Group
PiLog Group's Master Data Record Manager (MDRM) is a sophisticated enterprise solution designed to ensure data accuracy, consistency, and governance across various business functions. MDRM integrates advanced data management technologies to cleanse, classify, and standardize master data, thereby enhancing data quality and operational efficiency.
What is Augmented Reality Image Tracking
Augmented Reality (AR) Image Tracking is a technology that enables AR applications to recognize and track images in the real world, overlaying digital content onto them. This enhances the user's interaction with their environment by providing additional information and interactive elements directly tied to physical images.
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.
E-commerce Application Development Company.pdf
Your business can reach new heights with our assistance as we design solutions that are specifically appropriate for your goals and vision. Our eCommerce application solutions can digitally coordinate all retail operations processes to meet the demands of the marketplace while maintaining business continuity.
Oracle 23c New Features For DBAs and Developers.pptx
Most important New features of Oracle 23c for DBAs and Developers. You can get more idea from my youtube channel video from https://youtu.be/XvL5WtaC20A
Oracle Database 19c New Features for DBAs and Developers.pptx
Oracle Database 19c New Features for DBAs and Developers. This video you can watch from my youtube channel at https://youtu.be/m-F-mZA3MkU
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
Why Choose Odoo 17 Community & How it differs from Odoo 17 Enterprise Edition
Why Choose Odoo 17 Community & How it differs from Odoo 17 Enterprise EditionEnvertis Software Solutions
Odoo ERP software
Odoo ERP software, a leading open-source software for Enterprise Resource Planning (ERP) and business management, has recently launched its latest version, Odoo 17 Community Edition. This update introduces a range of new features and enhancements designed to streamline business operations and support growth.
The Odoo Community serves as a cost-free edition within the Odoo suite of ERP systems. Tailored to accommodate the standard needs of business operations, it provides a robust platform suitable for organisations of different sizes and business sectors. Within the Odoo Community Edition, users can access a variety of essential features and services essential for managing day-to-day tasks efficiently.
This blog presents a detailed overview of the features available within the Odoo 17 Community edition, and the differences between Odoo 17 community and enterprise editions, aiming to equip you with the necessary information to make an informed decision about its suitability for your business.How to write a program in any programming language
How to write a program in any programming language
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.
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
Measures in SQL (SIGMOD 2024, Santiago, Chile)
SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
Revolutionizing Visual Effects Mastering AI Face Swaps.pdf
The quest for the best AI face swap solution is marked by an amalgamation of technological prowess and artistic finesse, where cutting-edge algorithms seamlessly replace faces in images or videos with striking realism. Leveraging advanced deep learning techniques, the best AI face swap tools meticulously analyze facial features, lighting conditions, and expressions to execute flawless transformations, ensuring natural-looking results that blur the line between reality and illusion, captivating users with their ingenuity and sophistication.
Web:- https://undressbaby.com/
Empowering Growth with Best Software Development Company in Noida - Deuglo
Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
Requirement — Collecting the Requirements is the first Phase in the SSLC process.
Feasibility Study — after completing the requirement process they move to the design phase.
Design — in this phase, they start designing the software.
Coding — when designing is completed, the developers start coding for the software.
Testing — in this phase when the coding of the software is done the testing team will start testing.
Installation — after completion of testing, the application opens to the live server and launches!
Maintenance — after completing the software development, customers start using the software.
Webinar On-Demand: Using Flutter for Embedded
Flutter is a popular open source, cross-platform framework developed by Google. In this webinar we'll explore Flutter and its architecture, delve into the Flutter Embedder and Flutter’s Dart language, discover how to leverage Flutter for embedded device development, learn about Automotive Grade Linux (AGL) and its consortium and understand the rationale behind AGL's choice of Flutter for next-gen IVI systems. Don’t miss this opportunity to discover whether Flutter is right for your project.
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.
Recently uploaded (20)
Using Query Store in Azure PostgreSQL to Understand Query Performance
Using Query Store in Azure PostgreSQL to Understand Query Performance
Energy consumption of Database Management - Florina Jonuzi
Energy consumption of Database Management - Florina Jonuzi
DDS-Security 1.2 - What's New? Stronger security for long-running systems
DDS-Security 1.2 - What's New? Stronger security for long-running systems
KuberTENes Birthday Bash Guadalajara - Introducción a Argo CD
KuberTENes Birthday Bash Guadalajara - Introducción a Argo CD
Oracle 23c New Features For DBAs and Developers.pptx
Oracle 23c New Features For DBAs and Developers.pptx
Oracle Database 19c New Features for DBAs and Developers.pptx
Oracle Database 19c New Features for DBAs and Developers.pptx
ALGIT - Assembly Line for Green IT - Numbers, Data, Facts
ALGIT - Assembly Line for Green IT - Numbers, Data, Facts
Why Choose Odoo 17 Community & How it differs from Odoo 17 Enterprise Edition
Why Choose Odoo 17 Community & How it differs from Odoo 17 Enterprise Edition
How to write a program in any programming language
How to write a program in any programming language
Artificia Intellicence and XPath Extension Functions
Artificia Intellicence and XPath Extension Functions
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...
Revolutionizing Visual Effects Mastering AI Face Swaps.pdf
Revolutionizing Visual Effects Mastering AI Face Swaps.pdf
Empowering Growth with Best Software Development Company in Noida - Deuglo
Empowering Growth with Best Software Development Company in Noida - Deuglo
Unveiling the Advantages of Agile Software Development.pdf
Unveiling the Advantages of Agile Software Development.pdf
Microsoft azure documentDB
- 1. MICROSOFT AZURE DOCUMENTDB The JSON database for rapid development By: Mohamed Elkhodary
- 3. WHAT IS A DOCUMENT DATABASE?
- 4. WHAT IS A DOCUMENT DATABASE? o Growth with the use of the term NoSQL itself. o Designed for storing, retrieving, and managing document-oriented information. o Get their type information from the data itself, normally store all related information together, and allow every instance of data to be different from any other. o Attractive for programming modern web applications, which are subject to continual change in place, and speed of deployment is an important issue.
- 5. WHAT IS A DOCUMENT DATABASE?
- 6. Come as you are Data normalization WHAT IS A DOCUMENT DATABASE?
- 7. WHY USING DOCUMENTDB? Why it is different?
- 8. WHY?
- 9. WHY?
- 10. WHY?
- 11. WHY?
- 12. WHY?
- 13. WHY?
- 14. WHY?
- 15. fully managed, scalable, queryable, schemafree JSON document database service for modern applications transactional processing rich query managed as a service elastic scale internet accessible http/rest schema-free data model arbitrary data formats WHY?
- 16. WHY? Fully-managed, highly-scalable, NoSQL document database service query over schema- free JSON multi-document transactions tunable, high performance fully managed and designed for massive scale JS { }SQ L
- 17. WHY? o Promotes code first development. o Resilient to iterative schema changes. o Provide rich queries. o Low impedance as object/JSON store. No ORM required. o It just works. o It is fast.
- 18. USING DOCUMENTDB More details
- 19. DOCUMENTDB RESOURCES { “id” : “123” “name” : “joe” “age” : 30 “address” : { “street” : “some st” } }
- 20. SO COLLECTION IS A TABLE?
- 21. COLLECTION IS NOT A TABLE
- 22. SCALE UP VS SCALE OUT Scale Up – add bigger resources Scale Out – add more of the same
- 23. SCALING UP WITH DOCUMENTDB 10G B 1000 RU/sec S2 $0.067/hour ~ $50 per month 10G B 250 RU/sec $0.034/hour ~ $25 per month S1 10G B 2,500 RU/sec $0.134/hour ~ $100 per month S3
- 24. LET’S GET STARTED Using Azure Portal and .NET SDK
- 25. USING AZURE PORTAL o Create DocumentDB database account. o Add, edit and delete documents. o Query documents. o Explore Query Playground.
- 26. USING .NET SDK o Add, edit, query and delete documents.
- 27. Thank you
Editor's Notes
- Definitely not this kind of documents! If you want to store this kind of documents use SharePoint. You can use metadata of these documents and use it as attachments. It’s OK.
- In an RDB, every instance of data has the same format as every other, and changing that format is generally difficult.
- In an RDB, every instance of data has the same format as every other, and changing that format is generally difficult.
- User demands evolves. Agile development and rapid deployment and shorter release cycle. As a result, data sources needs to be evolved as well to match the needs.
- How data will be structured? How much data to handle? How many users, transactions, queries? You always need to define a schema up front. You do know some things. (Cloud - fast deployment, low cost, scalability). Need powerful queries and atomic transactions. You could use a relational database (SQL database on azure or database server of VM machine).
- NoSQL introduced to ease this development challenges.
- NoSQL solutions are hard to setup, manage and scale. Unless you have a team of qualified experts to help.
- - Document DB is here to help. - DocumentDB is part of the NoSql family that is available on azure. - Built for simplicity, scalability and performance.
- JSON and Javascript. Offers predictable performance to meet every aspect of the application.
- - DocumentDB can excute queries using familiar SQL.
- Microsoft Azure Data Services
- I skipped it at first, why this kind of thing is going to work. But the fact that it works and it is better.
- First you create a database account. Consists of a set of databases. … A database also has associated users, each with a set of permissions.
- No
- Boundary for queries or a partition or bag
- You can scale up and you can scale out by created too many collections using the same specs.
- http://www.documentdb.com/sql/demo