The document discusses JSON data modeling and accessing data in NoSQL databases. It covers why organizations adopt NoSQL, modeling data in relational versus JSON document models, strategies for modeling different types of data, and methods for accessing data including key-value operations, queries using N1QL and map reduce, and migrating data into NoSQL databases from relational sources. The presentation aims to help attendees understand how to design their data model and choose the best approach to working with data in a NoSQL database like Couchbase.
JSON Data Modeling - July 2018 - Tulsa TechfestMatthew Groves
If you’re thinking about using a document database, it can be intimidating to start. A flexible data model gives you a lot of choices, but which way is the right way? Is a document database even the right tool? In this session we’ll go over the basics of data modeling using JSON. We’ll compare and contrast with traditional RDBMS modeling. Impact on application code will be discussed, as well as some tooling that could be helpful along the way. The examples use the free, open-source Couchbase Server document database, but the principles from this session can also be applied to CosmosDb, Mongo, RavenDb, etc.
Bringing SQL to NoSQL: Rich, Declarative Query for NoSQLKeshav Murthy
Abstract
NoSQL databases bring the benefits of schema flexibility and
elastic scaling to the enterprise. Until recently, these benefits have
come at the expense of giving up rich declarative querying as
represented by SQL.
In today’s world of agile business, developers and organizations need
the benefits of both NoSQL and SQL in a single platform. NoSQL
(document) databases provide schema flexibility; fast lookup; and
elastic scaling. SQL-based querying provides expressive data access
and transformation; separation of querying from modeling and storage;
and a unified interface for applications, tools, and users.
Developers need to deliver applications that can easily evolve,
perform, and scale. Otherwise, the cost, effort, and delay in keeping
up with changing business needs will become significant disadvantages.
Organizations need sophisticated and rapid access to their operational data, in
order to maintain insight into their business. This access should
support both pre-defined and ad-hoc querying, and should integrate
with standard analytical tools.
This talk will cover how to build applications that combine the
benefits of NoSQL and SQL to deliver agility, performance, and
scalability. It includes:
- N1QL, which extends SQL to JSON
- JSON data modeling
- Indexing and performance
- Transparent scaling
- Integration and ecosystem
You will walk away with an understanding of the design patterns and
best practices for effective utilization of NoSQL document
databases - all using open-source technologies.
Agile Data Engineering: Introduction to Data Vault 2.0 (2018)Kent Graziano
(updated slides used for North Texas DAMA meetup Oct 2018) As we move more and more towards the need for everyone to do Agile Data Warehousing, we need a data modeling method that can be agile with us. Data Vault Data Modeling is an agile data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is a hybrid approach using the best of 3NF and dimensional modeling. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for over 15 years and is now growing in popularity. The purpose of this presentation is to provide attendees with an introduction to the components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics:
• What the basic components of a DV model are
• How to build, and design structures incrementally, without constant refactoring
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL. Join this webinar to learn why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships.
Ryan Boyd, Developer Relations at Neo4j
Ryan is a SF-based software engineer focused on helping developers understand the power of graph databases. Previously he was a product manager for architectural software, built applications and web hosting environments for higher education, and worked in developer relations for twenty products during his 8 years at Google. He enjoys cycling, sailing, skydiving, and many other adventures when not in front of his computer.
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A native graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL.
This webinar explains why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships.
Couchbase and Apache Kafka - Bridging the gap between RDBMS and NoSQLDATAVERSITY
Thousands of companies, from Uber and Netflix to Goldman Sachs and Cisco, use Apache Kafka to transform and reshape their data architectures. Kafka is frequently used as the bridge between legacy RDBMS and new NoSQL database systems, effectively transforming SQL table data into JSON documents and vice versa. Many companies also use Kafka for business-critical applications that drive real-time stream processing and analytics, intersystem messaging, high-volume data ingestion, and operational metrics collection.
Couchbase and Kafka can be used together to address high throughput, distributed data management, and transformation challenges.
In this webinar we’ll explore:
Where Kafka fits into the big data ecosystem
How companies are using Kafka for both real-time processing and as a bus for data exchange
An example of how Kafka can bridge legacy RDBMS and new NoSQL database systems
Several real-world use case architectures
JSON Data Modeling - July 2018 - Tulsa TechfestMatthew Groves
If you’re thinking about using a document database, it can be intimidating to start. A flexible data model gives you a lot of choices, but which way is the right way? Is a document database even the right tool? In this session we’ll go over the basics of data modeling using JSON. We’ll compare and contrast with traditional RDBMS modeling. Impact on application code will be discussed, as well as some tooling that could be helpful along the way. The examples use the free, open-source Couchbase Server document database, but the principles from this session can also be applied to CosmosDb, Mongo, RavenDb, etc.
Bringing SQL to NoSQL: Rich, Declarative Query for NoSQLKeshav Murthy
Abstract
NoSQL databases bring the benefits of schema flexibility and
elastic scaling to the enterprise. Until recently, these benefits have
come at the expense of giving up rich declarative querying as
represented by SQL.
In today’s world of agile business, developers and organizations need
the benefits of both NoSQL and SQL in a single platform. NoSQL
(document) databases provide schema flexibility; fast lookup; and
elastic scaling. SQL-based querying provides expressive data access
and transformation; separation of querying from modeling and storage;
and a unified interface for applications, tools, and users.
Developers need to deliver applications that can easily evolve,
perform, and scale. Otherwise, the cost, effort, and delay in keeping
up with changing business needs will become significant disadvantages.
Organizations need sophisticated and rapid access to their operational data, in
order to maintain insight into their business. This access should
support both pre-defined and ad-hoc querying, and should integrate
with standard analytical tools.
This talk will cover how to build applications that combine the
benefits of NoSQL and SQL to deliver agility, performance, and
scalability. It includes:
- N1QL, which extends SQL to JSON
- JSON data modeling
- Indexing and performance
- Transparent scaling
- Integration and ecosystem
You will walk away with an understanding of the design patterns and
best practices for effective utilization of NoSQL document
databases - all using open-source technologies.
Agile Data Engineering: Introduction to Data Vault 2.0 (2018)Kent Graziano
(updated slides used for North Texas DAMA meetup Oct 2018) As we move more and more towards the need for everyone to do Agile Data Warehousing, we need a data modeling method that can be agile with us. Data Vault Data Modeling is an agile data modeling technique for designing highly flexible, scalable, and adaptable data structures for enterprise data warehouse repositories. It is a hybrid approach using the best of 3NF and dimensional modeling. It is not a replacement for star schema data marts (and should not be used as such). This approach has been used in projects around the world (Europe, Australia, USA) for over 15 years and is now growing in popularity. The purpose of this presentation is to provide attendees with an introduction to the components of the Data Vault Data Model, what they are for and how to build them. The examples will give attendees the basics:
• What the basic components of a DV model are
• How to build, and design structures incrementally, without constant refactoring
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL. Join this webinar to learn why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships.
Ryan Boyd, Developer Relations at Neo4j
Ryan is a SF-based software engineer focused on helping developers understand the power of graph databases. Previously he was a product manager for architectural software, built applications and web hosting environments for higher education, and worked in developer relations for twenty products during his 8 years at Google. He enjoys cycling, sailing, skydiving, and many other adventures when not in front of his computer.
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A native graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL.
This webinar explains why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships.
Couchbase and Apache Kafka - Bridging the gap between RDBMS and NoSQLDATAVERSITY
Thousands of companies, from Uber and Netflix to Goldman Sachs and Cisco, use Apache Kafka to transform and reshape their data architectures. Kafka is frequently used as the bridge between legacy RDBMS and new NoSQL database systems, effectively transforming SQL table data into JSON documents and vice versa. Many companies also use Kafka for business-critical applications that drive real-time stream processing and analytics, intersystem messaging, high-volume data ingestion, and operational metrics collection.
Couchbase and Kafka can be used together to address high throughput, distributed data management, and transformation challenges.
In this webinar we’ll explore:
Where Kafka fits into the big data ecosystem
How companies are using Kafka for both real-time processing and as a bus for data exchange
An example of how Kafka can bridge legacy RDBMS and new NoSQL database systems
Several real-world use case architectures
[Given at DAMA WI, Nov 2018] With the increasing prevalence of semi-structured data from IoT devices, web logs, and other sources, data architects and modelers have to learn how to interpret and project data from things like JSON. While the concept of loading data without upfront modeling is appealing to many, ultimately, in order to make sense of the data and use it to drive business value, we have to turn that schema-on-read data into a real schema! That means data modeling! In this session I will walk through both simple and complex JSON documents, decompose them, then turn them into a representative data model using Oracle SQL Developer Data Modeler. I will show you how they might look using both traditional 3NF and data vault styles of modeling. In this session you will:
1. See what a JSON document looks like
2. Understand how to read it
3. Learn how to convert it to a standard data model
Presentation at Data/Graph Day Texas Conference.
Austin, Texas
January 14, 2017
This talk grew out Juan Sequeda's office hours following the Seattle Graph Meetup. Some of the questions posed were: How do I recognize problem best solved with a graph solution? How do I determine the best type of graph to solve the problem? How do I manage the data where both graph and relational operations will be performed? Juan did such a great job of explaining the options, we asked him to develop his responses into a formal talk.
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL. Join this webinar to learn why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships
Virtualizing Relational Databases as Graphs: a multi-model approachJuan Sequeda
Talk given at Smart Data 2017
Relational Databases are inflexible due to the rigid constraints of the relational data model. If you have new data that doesn’t fit your schema, you will need to alter your schema (add a column or a new table). This is a task that is not always possible. IT departments don't have time, or they won't allow it - just more nulls that can lead to query performance degradation, etc.
A goal of graph databases is to address this problem with their schema-less graph data model. However, many businesses have large investments in commercial RDBMSs and their associated applications and can't expect to move all of their data to a graph database.
In this talk, I will present a multi-model graph/relational architecture solution. Keep your relational data where it is, virtualize it as a graph, and then connect it with additional data stored in a graph database. This way, both graph and relational technologies can seamlessly interact together.
Integrating Semantic Web in the Real World: A Journey between Two Cities Juan Sequeda
Keynote at The 9th International Conference on Knowledge Capture (KCAP2017), Austin, Texas, Dec 2017
An early vision in Computer Science has been to create intelligent systems capable of reasoning on large amounts of data. Today, this vision can be delivered by integrating Relational Databases with the Semantic Web using the W3C standards: a graph data model (RDF), ontology language (OWL), mapping language (R2RML) and query language (SPARQL). The research community has successfully been showing how intelligent systems can be created with Semantic Web technologies, dubbed now as Knowledge Graphs.
However, where is the mainstream industry adoption? What are the barriers to adoption? Are these engineering and social barriers or are they open scientific problems that need to be addressed?
This talk will chronicle our journey of deploying Semantic Web technologies with real world users to address Business Intelligence and Data Integration needs, describe technical and social obstacles that are present in large organizations, and scientific challenges that require attention.
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.
Graph Query Languages: update from LDBCJuan Sequeda
The Linked Data Benchmark Council (LDBC) is a non-profit organization dedicated to establishing benchmarks, benchmark practices and benchmark results for graph data management software. The Graph Query Language task force of LDBC is studying query languages for graph data management systems, and specifically those systems storing so-called Property Graph data. The goals of the GraphQL task force are to:
Devise a list of desired features and functionalities of a graph query language.
Evaluate a number of existing languages (i.e. Cypher, Gremlin, PGQL, SPARQL, SQL), and identify possible issues.
Provide a better understanding of the design space and state-of-the-art.
Develop proposals for changes to existing query languages or even a new graph query language.
This query language should cover the needs of the most important use-cases for such systems, such as social network and Business Intelligence workloads.
This talk will present an update of the work accomplished by the LDBC GraphQL task force. We also look for input from the graph community.
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But, oftentimes with RDBMS, performance degrades with the increasing number and levels of data relationships and data size.
A graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL.
This webinar explains why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships.
Slides: NoSQL Data Modeling Using JSON Documents – A Practical ApproachDATAVERSITY
After three decades of relational data modeling, everyone’s pretty comfortable with schemas, tables, and entity-relationships. As more and more Global 2000 companies choose NoSQL databases to power their Digital Economy applications, they need to think about how to best model their data. How do they move from a constrained, table-driven model to an agile, flexible data model based on JSON documents?
This webinar is intended for architects and application developers who want to learn about new JSON document data modeling approaches, techniques, and best practices. This webinar will show you how to get started building a JSON document data model, how to migrate a table-based data model to JSON documents, and how to optimize your design to enable fast query performance.
This webinar will provide practical, experience-based advice and best practices for modeling JSON documents, including:
- When to embed or not embed objects in your JSON document
- Data modeling using a practical data access pattern approach
- Indexing your JSON documents
- Querying your data using N1QL (SQL for JSON)
JSON Data Modeling - GDG Indy - April 2020Matthew Groves
Presented virtually at GDG Indy - https://www.meetup.com/indy-gdg/events/269467916/
If you’re thinking about using a document database, it can be intimidating to start. A flexible data model gives you a lot of choices, but which way is the right way? Is a document database even the right tool? In this session we’ll go over the basics of data modeling using JSON. We’ll compare and contrast with traditional RDBMS modeling. Impact on application code will be discussed, as well as some tooling that could be helpful along the way. The examples use the free, open-source Couchbase Server document database, but the principles from this session can also be applied to CosmosDb, Mongo, RavenDb, etc.
[Given at DAMA WI, Nov 2018] With the increasing prevalence of semi-structured data from IoT devices, web logs, and other sources, data architects and modelers have to learn how to interpret and project data from things like JSON. While the concept of loading data without upfront modeling is appealing to many, ultimately, in order to make sense of the data and use it to drive business value, we have to turn that schema-on-read data into a real schema! That means data modeling! In this session I will walk through both simple and complex JSON documents, decompose them, then turn them into a representative data model using Oracle SQL Developer Data Modeler. I will show you how they might look using both traditional 3NF and data vault styles of modeling. In this session you will:
1. See what a JSON document looks like
2. Understand how to read it
3. Learn how to convert it to a standard data model
Presentation at Data/Graph Day Texas Conference.
Austin, Texas
January 14, 2017
This talk grew out Juan Sequeda's office hours following the Seattle Graph Meetup. Some of the questions posed were: How do I recognize problem best solved with a graph solution? How do I determine the best type of graph to solve the problem? How do I manage the data where both graph and relational operations will be performed? Juan did such a great job of explaining the options, we asked him to develop his responses into a formal talk.
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL. Join this webinar to learn why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships
Virtualizing Relational Databases as Graphs: a multi-model approachJuan Sequeda
Talk given at Smart Data 2017
Relational Databases are inflexible due to the rigid constraints of the relational data model. If you have new data that doesn’t fit your schema, you will need to alter your schema (add a column or a new table). This is a task that is not always possible. IT departments don't have time, or they won't allow it - just more nulls that can lead to query performance degradation, etc.
A goal of graph databases is to address this problem with their schema-less graph data model. However, many businesses have large investments in commercial RDBMSs and their associated applications and can't expect to move all of their data to a graph database.
In this talk, I will present a multi-model graph/relational architecture solution. Keep your relational data where it is, virtualize it as a graph, and then connect it with additional data stored in a graph database. This way, both graph and relational technologies can seamlessly interact together.
Integrating Semantic Web in the Real World: A Journey between Two Cities Juan Sequeda
Keynote at The 9th International Conference on Knowledge Capture (KCAP2017), Austin, Texas, Dec 2017
An early vision in Computer Science has been to create intelligent systems capable of reasoning on large amounts of data. Today, this vision can be delivered by integrating Relational Databases with the Semantic Web using the W3C standards: a graph data model (RDF), ontology language (OWL), mapping language (R2RML) and query language (SPARQL). The research community has successfully been showing how intelligent systems can be created with Semantic Web technologies, dubbed now as Knowledge Graphs.
However, where is the mainstream industry adoption? What are the barriers to adoption? Are these engineering and social barriers or are they open scientific problems that need to be addressed?
This talk will chronicle our journey of deploying Semantic Web technologies with real world users to address Business Intelligence and Data Integration needs, describe technical and social obstacles that are present in large organizations, and scientific challenges that require attention.
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.
Graph Query Languages: update from LDBCJuan Sequeda
The Linked Data Benchmark Council (LDBC) is a non-profit organization dedicated to establishing benchmarks, benchmark practices and benchmark results for graph data management software. The Graph Query Language task force of LDBC is studying query languages for graph data management systems, and specifically those systems storing so-called Property Graph data. The goals of the GraphQL task force are to:
Devise a list of desired features and functionalities of a graph query language.
Evaluate a number of existing languages (i.e. Cypher, Gremlin, PGQL, SPARQL, SQL), and identify possible issues.
Provide a better understanding of the design space and state-of-the-art.
Develop proposals for changes to existing query languages or even a new graph query language.
This query language should cover the needs of the most important use-cases for such systems, such as social network and Business Intelligence workloads.
This talk will present an update of the work accomplished by the LDBC GraphQL task force. We also look for input from the graph community.
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But, oftentimes with RDBMS, performance degrades with the increasing number and levels of data relationships and data size.
A graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL.
This webinar explains why companies are shifting away from RDBMS towards graphs to unlock the business value in their data relationships.
Slides: NoSQL Data Modeling Using JSON Documents – A Practical ApproachDATAVERSITY
After three decades of relational data modeling, everyone’s pretty comfortable with schemas, tables, and entity-relationships. As more and more Global 2000 companies choose NoSQL databases to power their Digital Economy applications, they need to think about how to best model their data. How do they move from a constrained, table-driven model to an agile, flexible data model based on JSON documents?
This webinar is intended for architects and application developers who want to learn about new JSON document data modeling approaches, techniques, and best practices. This webinar will show you how to get started building a JSON document data model, how to migrate a table-based data model to JSON documents, and how to optimize your design to enable fast query performance.
This webinar will provide practical, experience-based advice and best practices for modeling JSON documents, including:
- When to embed or not embed objects in your JSON document
- Data modeling using a practical data access pattern approach
- Indexing your JSON documents
- Querying your data using N1QL (SQL for JSON)
JSON Data Modeling - GDG Indy - April 2020Matthew Groves
Presented virtually at GDG Indy - https://www.meetup.com/indy-gdg/events/269467916/
If you’re thinking about using a document database, it can be intimidating to start. A flexible data model gives you a lot of choices, but which way is the right way? Is a document database even the right tool? In this session we’ll go over the basics of data modeling using JSON. We’ll compare and contrast with traditional RDBMS modeling. Impact on application code will be discussed, as well as some tooling that could be helpful along the way. The examples use the free, open-source Couchbase Server document database, but the principles from this session can also be applied to CosmosDb, Mongo, RavenDb, etc.
JSON Data Modeling in Document DatabaseDATAVERSITY
Making the move to a document database can be intimidating. Yes, its flexible data model gives you a lot of choices, but it also raises questions: Which way is the right way? Is a document database even the right tool?
Join this live session on the basics of data modeling with JSON to learn:
- How a document database compares to a traditional RDBMS
- What JSON data modeling means for your application code
- Which tools might be helpful along the way
Making the move to a document database can be intimidating. Yes, its flexible data model gives you a lot of choices, but it also raises questions: Which way is the right way? Is a document database even the right tool? Join this live session on the basics of data modeling with JSON to learn:
- How a document database compares to a traditional RDBMS
- What JSON data modeling means for your application code
- Which tools might be helpful along the way
The examples in this session use the free, open-source Couchbase Server document database, but the principles you’ll learn can also be applied to Cosmos DB, MongoDB, RavenDB, and others.
SQL for JSON: Rich, Declarative Querying for NoSQL Databases and Applications Keshav Murthy
In today’s world of agile business, Java developers and organizations benefit when JSON-based NoSQL databases and SQL-based querying come together. NoSQL provides schema flexibility and elastic scaling. SQL provides expressive, independent data access. Java developers need to deliver apps that readily evolve, perform, and scale with changing business needs. Organizations need rapid access to their operational data, using standard analytical tools, for insight into their business. In this session, you will learn to build apps that combine NoSQL and SQL for agility, performance, and scalability. This includes
• JSON data modeling
• Indexing
• Tool integration
Querying NoSQL with SQL - KCDC - August 2017Matthew Groves
Until recently, agile business had to choose between the benefits of JSON-based NoSQL databases and the benefits of SQL-based querying. NoSQL provides schema flexibility, high performance, and elastic scaling, while SQL provides expressive, independent data access. Recent convergence allows developers and organizations to have the best of both worlds.
Developers need to deliver apps that readily evolve, perform, and scale, all to match changing business needs. Organizations need rapid access to their operational data, using standard analytical tools, for insight into their business. In this session, you will learn the ways that SQL can be applied to NoSQL databases (N1QL, SQL++, ODBC, JDBC, and others), and what additional features are needed to deal with JSON documents. SQL for JSON, JSON data modeling, indexing, and tool integration will be covered.
From SQL to NoSQL: Structured Querying for JSONKeshav Murthy
Can SQL be used to query JSON? SQL is the universally known structured query language, used for well defined, uniformly structured data; while JSON is the lingua franca of flexible data management, used to define complex, variably structured data objects.
Yes! SQL can most-definitely be used to query JSON with Couchbase's SQL query language for JSON called N1QL (verbalized as Nickel.)
In this session, we will explore how N1QL extends SQL to provide the flexibility and agility inherent in JSON while leveraging the universality of SQL as a query language.
We will discuss utilizing SQL to query complex JSON objects that include arrays, sets and nested objects.
You will learn about the powerful query expressiveness of N1QL, including the latest features that have been added to the language. We will cover how using N1QL can solve your real-world application challenges, based on the actual queries of Couchbase end-users.
The Data & Analytics Journey – Why it’s more attainable for your company than...John Head
The typical perception of Big Data, Analytics, and Predicative/AI is that only the big companies can reap the benefits. Many believe they need a data warehouse, expensive reporting software, & an army of data scientists to get any value out of effort and cost. This session will explore and debunk that myth and showcase how companies of any size can participate. While there are many maturity models available, most are not designed to be practical guides to solving common business problems. Because of the explosion in cloud services, the barrier to entry has eroded significantly. We will look at some practical steps to access these capabilities and provide examples to where market-leading and growth companies have seen large benefits. Attendees will walk away with broader understanding of what’s possible to move their company through the journey in 2017. We will take a close look at IBM Watson solutions and how they integrate with IBM Collaboration and Social solutions.
The Data & Analytics Journey – Why it’s more attainable for your company than...LetsConnect
The typical perception of Big Data, Analytics, and Predicative/AI is that only the big companies can reap the benefits. Many believe they need a data warehouse, expensive reporting software, & an army of data scientists to get any value out of effort and cost. This session will explore and debunk that myth and showcase how companies of any size can participate. While there are many maturity models available, most are not designed to be practical guides to solving common business problems. Because of the explosion in cloud services, the barrier to entry has eroded significantly. We will look at some practical steps to access these capabilities and provide examples to where market-leading and growth companies have seen large benefits. Attendees will walk away with broader understanding of what’s possible to move their company through the journey in 2017. We will take a close look at IBM Watson solutions and how they integrate with IBM Collaboration and Social solutions.
MWLUG2017 - The Data & Analytics Journey 2.0John Head
The typical perception of Big Data, Analytics, and Predicative/AI is that only the big companies can reap the benefits. Many believe they need a data warehouse, expensive reporting software, & an army of data scientists to get any value out of effort and cost. This session will explore and debunk that myth and showcase how companies of any size can participate. While there are many maturity models available, most are not designed to be practical guides to solving common business problems. Because of the explosion in cloud services, the barrier to entry has eroded significantly. We will look at some practical steps to access these capabilities and provide examples to where market-leading and growth companies have seen large benefits. Attendees will walk away with broader understanding of what’s possible to move their company through the journey in 2017. We will take a close look at IBM Watson solutions and how they integrate with IBM Collaboration and Social solutions.
During this Big Data Warehousing Meetup, Caserta Concepts and Databricks addressed the number one operational and analytic goal of nearly every organization today – to have complete view of every customer. Customer Data Integration (CDI) must be implemented to cleanse and match customer identities within and across various data systems. CDI has been a long-standing data engineering challenge, not just one of logic and complexity but also of performance and scalability.
The speakers brought together best practice techniques with Apache Spark to achieve complete CDI.
Speakers:
Joe Caserta, President, Caserta Concepts
Kevin Rasmussen, Big Data Engineer, Caserta Concepts
Vida Ha, Lead Solutions Engineer, Databricks
The sessions covered a series of problems that are adequately solved with Apache Spark, as well as those that are require additional technologies to implement correctly. Topics included:
· Building an end-to-end CDI pipeline in Apache Spark
· What works, what doesn’t, and how do we use Spark we evolve
· Innovation with Spark including methods for customer matching from statistical patterns, geolocation, and behavior
· Using Pyspark and Python’s rich module ecosystem for data cleansing and standardization matching
· Using GraphX for matching and scalable clustering
· Analyzing large data files with Spark
· Using Spark for ETL on large datasets
· Applying Machine Learning & Data Science to large datasets
· Connecting BI/Visualization tools to Apache Spark to analyze large datasets internally
The speakers also touched on data governance, on-boarding new data rapidly, how to balance rapid agility and time to market with critical decision support and customer interaction. They also shared examples of problems that Apache Spark is not optimized for.
For more information on the services offered by Caserta Concepts, visit our website: http://casertaconcepts.com/
This introduction to graph databases is specifically designed for Enterprise Architects who need to map business requirements to architectural components like graph databases. It explains how and why graphs matter for Enterprise Architecture and reviews the architectural differences between relational and graph models.
Architecting Data For The Modern Enterprise - Data Summit 2017, Closing KeynoteCaserta
The “Big Data era” has ushered in an avalanche of new technologies and approaches for delivering information and insights to business users. What is the role of the cloud in your analytical environment? How can you make your migration as seamless as possible? This closing keynote, delivered by Joe Caserta, a prominent consultant who has helped many global enterprises adopt Big Data, provided the audience with the inside scoop needed to supplement data warehousing environments with data intelligence—the amalgamation of Big Data and business intelligence.
This presentation was given as the closing keynote at DBTA's annual Data Summit in NYC.
Webinar: Building a Multi-Cloud Strategy with Data Autonomy featuring 451 Res...DataStax
Data autonomy goes hand-in-hand with building a powerful, multi-cloud data management strategy. Enterprises today are rethinking their data management tactics in light of what they can achieve with the correct usage of the public clouds. In this on-demand webcast, guest speaker James Curtis, Senior Analyst, Data Platforms & Analytics, 451 Research, discussed how enterprises are getting valuable data autonomy and building game-changing multi-cloud database management strategies.
View recording: https://youtu.be/RMoEaATgGO8
Explore all DataStax webinars: https://www.datastax.com/resources/webinars
Business-centric data models are key to gaining a clear view of the data that drives the business – from customers to products to invoices and more. They offer a clear, visual way for both business and technical stakeholders to communicate around the crucial business rules and definitions that drive both operational usage of data as well as analytics and reporting. This webinar will provide practical, concrete steps in creating valuable, business-centric data models that can show immediate value to the organization, while at the same time building towards a full-enterprise view.
Data Pipelines and Tools to Integrate with Power BI and Spotfire.pdfGregKreutzer2
The goal is to highlight the business value of utilizing these skills in oil and gas applications while creating a workshop that can still accommodate and provide value to a larger audience that has a wide range of analytic and data science skills, roles and interests.
The Why, When, and How of NoSQL - A Practical ApproachDATAVERSITY
More and more Fortune 1000 companies like Marriott, Cars.com, Gannett, and PayPal are choosing NoSQL over relational databases like Oracle, SQL Server, and DB2 to power their web, mobile, and IoT applications. Why? Lower costs, higher performance and availability, better agility, and easier scalability. According to The Forrester Wave™: Big Data NoSQL, Q3 2016 report, “NoSQL is no longer an option.” Come see why.
This webinar is intended for developers, architects, and database engineers who are considering NoSQL as an alternative to relational databases. If you’re looking to add NoSQL to your environment, this webinar will show you how to get started and avoid potential pitfalls.
You’ll get practical advice, including:
•Key considerations in moving from relational to NoSQL
•How to identify applications that benefit most from NoSQL
•Data modeling and querying with NoSQL
•Migrating your data to NoSQL
•Best practices for making the switch
Similar to Json data modeling june 2017 - pittsburgh tech fest (20)
CREAM - That Conference Austin - January 2024.pptxMatthew Groves
Caching can bring speed to the slow systems in your enterprise. In this session, we'll explore how to avoid expensive computations and reduce database (or other systems) load with caching. Considerations include when to cache (cache policy), how to cache, and when/how to evict. This session will also explore and discuss problems and gotchas like cache sizing, eviction mistakes, and where your cache should live. Finally, we'll look at how caching is implemented in the "memory-first" Couchbase architecture.
I just made a change to the database schema, but now the team needs it for my feature to work. How can I keep track of my database changes and communicate them to the rest of the team? Migrations give a structured way to structurally alter your database structure as your application evolves . . . structurally. They also provide a way for everyone on the team: developers, testers, CI admins, DBAs, etc, to apply the latest changes wherever they are needed - with uniformity and low friction. Fluent Migrations for .NET provide a discoverable, human readable API that supports dozens of different databases (including SQL Server, PostgreSQL, Oracle). Topics covered in this session:
* Why you should use migrations
* How to write fluent migrations
* A look behind the scenes of how fluent migrations work
* Drawbacks/downsides to using migrations
* Other migration options for EF and NoSQL (Couchbase)
Ad hoc SQL scripts make you want to flip a desk? Keep your team on the same page with fluent migrations.
(This session will briefly mention EF Migrations, but is not primarily about EF).
Cache Rules Everything Around Me - DevIntersection - December 2022Matthew Groves
Caching can bring speed to the slow systems in your enterprise. In this session, we'll explore how to avoid expensive computations and reduce database (or other systems) load with caching. Considerations include when to cache (cache policy), how to cache, and when/how to evict. This session will also explore and discuss problems and gotchas like cache sizing, eviction mistakes, and where your cache should live. Finally, we'll look at how caching is implemented in the "memory-first" Couchbase architecture.
Presented at DevIntersection 2022 in Las Vegas
Putting the SQL Back in NoSQL - October 2022 - All Things OpenMatthew Groves
Do you like the familiarity of SQL, but need the speed and flexibility of JSON data that NoSQL databases can provide? You don't have to choose anymore. SQL++ is an emerging standard to apply SQL to JSON data. In this session, you'll learn how SQL++ eases the transition to building an application with modern NoSQL technology. The basics of SQL++ and the necessary extensions to working with JSON technology will be covered. Finally, you'll learn how to start using a SQL++ implementation in production with Couchbase Capella, a cloud DBaaS with one of the top SQL++ implementations available.
Cache Rules Everything Around Me - Momentum - October 2022.pptxMatthew Groves
Caching can bring speed to the slow systems in your enterprise. In this session, we'll explore how to avoid expensive computations and reduce database (or other systems) load with caching. Considerations include when to cache (cache policy), how to cache, and when/how to evict. This session will also explore and discuss problems and gotchas like cache sizing, eviction mistakes, and where your cache should live. Finally, we'll look at how caching is implemented in the "memory-first" Couchbase architecture.
NoSQL document databases provide unique capabilities of scaling, flexibility, and performance for a wide variety of use cases. However, many developers from relational backgrounds are understandably nervous (for a variety of reasons) about using NoSQL in their next project. This session will address one of those reasons: ACID transactions (or lack thereof). This session will start with some background about why NoSQL databases didn’t (initially) have full ACID capabilities. Next, we’ll look at why lack of ACID may not be a big deal and some of the data modeling and querying techniques to use instead. Finally, we’ll look at the more recent trend of document databases adding distributed multi-document ACID capabilities and show a live demo of a NoSQL transaction. You’ll leave this session with a better understanding of how ACID works and when to use it.
ACID and NoSQL are no longer exclusive. This session explains what ACID is, the reasons why it's difficult for distributed NoSQL, some of the workarounds that are still relevant, and how to use it today.
Demystifying NoSQL - All Things Open - October 2020Matthew Groves
We’ve been using relational databases like SQL Server, Postgres, MySQL, and Oracle for a long time. Tables are practically ingrained into our thought processes. But many organizations and businesses are turning to NoSQL options to solve problems of scale, performance, and flexibility. What is a long-time relational database-using developer supposed to do? Do I just forget about all that SQL that I learned? (Spoiler alert: NO). Come to this session with all your burning questions about data modeling, transactions, schema, migration, how to get started, and more. Let’s find out if a NoSQL tool like Couchbase, CosmosDb, Mongo, etc, is the right fit for your next project.
Autonomous Microservices - Manning - July 2020Matthew Groves
Everybody loves Microservices, but we all know how difficult it is to make it right. Distributed systems are much more complex to develop and maintain, and over time, we even miss the simplicity of old monoliths. In this talk, I propose a combination of infrastructure, architecture, and design principles to make your microservices bulletproof and easy to maintain with a combination of high scalability, elasticity, fault tolerance, and resilience. This session will also include a discussion about some microservices blueprints like asynchronous communications, how to avoid cascading failures in synchronous calls, and why you should use different storages according to the use case: Document Databases to speed up your performance, RDBMS for transactions, Graphs for recommendations, etc.
Background Tasks Without a Separate Service: Hangfire for ASP.NET - KCDC - Ju...Matthew Groves
If you’re a web developer, eventually you’ll need to do some background processing. This has often meant running separate daemons, services, or Cron jobs, potentially complicating your integration and deployment. With Hangfire, you can create background tasks that run right inside the same .NET or .NET Core application. Hangfire background tasks can scale easily to multiple servers and can use a variety of durable storage options. You even get a monitoring UI right out of the box. In this session, we’ll look at the basics of setting up Hangfire, and how to perform fire-and-forget, delayed, recurring, and continuations of background tasks. We’ll also look at possible gotchas: debugging, failed jobs, cloud deployment.
Autonomous Microservices - CodeMash - January 2019Matthew Groves
Everybody loves microservices, but it's difficult to do it right. Distributed systems are much more complex to develop and maintain. Over time, you may even miss the simplicity of old monoliths. In this session, I propose a combination of infrastructure, architecture, and design principles to bulletproof your microservices and make them easy to maintain with a combination of high scalability, elasticity, fault tolerance, and resilience. This session will include a discussion of microservices blueprints like: asynchronous communications, avoiding cascading failures in synchronous calls, and how distributed NoSQL databases become valuable in terms of scalability and performance when combined with your microservices in a Kubernetes deployment.
5 Popular Choices for NoSQL on a Microsoft Platform - Tulsa - July 2018Matthew Groves
If you are thinking of trying out a NoSQL document database, there are many good options available to Microsoft-oriented developers. In this session, we’ll compare some of the more popular databases, including: CosmosDb, Couchbase, MongoDb, CouchDb, and RavenDb. We’ll look at the strengths and weaknesses of each system. Querying, scaling, usability, speed, deployment, support and flexibility will all be covered. This session will include a discussion about when NoSQL is right for your project and give you an idea of which technology to pursue for your use case.
If you are thinking of trying out a NoSQL document database, there are many good options available to Microsoft-oriented developers. In this session, we’ll compare some of the more popular databases, including: CosmosDb, Couchbase, MongoDb, CouchDb, and RavenDb. We’ll look at the strengths and weaknesses of each system. Querying, scaling, usability, speed, deployment, support and flexibility will all be covered. This session will include a discussion about when NoSQL is right for your project and give you an idea of which technology to pursue for your use case.
Full stack development with node and NoSQL - All Things Open - October 2017Matthew Groves
What is different about this generation of web applications? A solid development approach must consider latency, throughput, and interactivity demanded by users users across mobile devices, web browsers, and IoT. These applications often use NoSQL to support a flexible data model and easy scalability required for modern development.
A full stack application (composed of Couchbase, WebAPI, Angular2, and ASP.NET/ASP.NET Core) will be demonstrated in this session. The individual parts of a stack may vary, but the overall design is the focus.
5 Popular Choices for NoSQL on a Microsoft Platform - All Things Open - Octob...Matthew Groves
If you are thinking of trying out a NoSQL document database, there are many good options available to Microsoft-oriented developers. In this session, we’ll compare some of the more popular databases, including: CosmosDb, Couchbase, MongoDb, CouchDb, and RavenDb. We’ll look at the strengths and weaknesses of each system. Querying, scaling, usability, speed, deployment, support and flexibility will all be covered. This session will include a discussion about when NoSQL is right for your project and give you an idea of which technology to pursue for your use case.
My toaster stores data without SQL and without tables. But making a choice based on what something doesn’t have isn’t terribly useful. “NoSQL” is an increasingly inaccurate catch-all term that covers a lot of different types of data storage. Let’s make more sense of this new breed of database management systems and go beyond the buzzword. In this session, the four main data models that make up the NoSQL movement will be covered: key-value, document, columnar and graph. How they differ and when you might want to use each one will be discussed.
This session will be looking at the whole ecosystem, with a more detailed focus on Couchbase, Cassandra, Riak KV, and Neo4j.
I Have a NoSQL Toaster - Troy .NET User Group - July 2017Matthew Groves
My toaster stores data without SQL and without tables. But making a choice based on what something doesn’t have isn’t terribly useful. “NoSQL” is an increasingly inaccurate catch-all term that covers a lot of different types of data storage. Let’s make more sense of this new breed of database management systems and go beyond the buzzword. In this session, the four main data models that make up the NoSQL movement will be covered: key-value, document, columnar and graph. How they differ and when you might want to use each one will be discussed.
This session will be looking at the whole ecosystem, with a more detailed focus on Couchbase, Cassandra, Riak KV, and Neo4j.
I have a NoSQL Toaster - ConnectJS - October 2016Matthew Groves
NoSQL is a catch-all term that covers a lot of different types of data storage. Is it really helpful to group them together by one thing they don't have? Think about it like this: my toaster is as much NoSQL as any database! So, how can we make more sense of this new breed of database management systems?
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
For more Tendenci AMS events, check out www.tendenci.com/events
How Recreation Management Software Can Streamline Your Operations.pptxwottaspaceseo
Recreation management software streamlines operations by automating key tasks such as scheduling, registration, and payment processing, reducing manual workload and errors. It provides centralized management of facilities, classes, and events, ensuring efficient resource allocation and facility usage. The software offers user-friendly online portals for easy access to bookings and program information, enhancing customer experience. Real-time reporting and data analytics deliver insights into attendance and preferences, aiding in strategic decision-making. Additionally, effective communication tools keep participants and staff informed with timely updates. Overall, recreation management software enhances efficiency, improves service delivery, and boosts customer satisfaction.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
Enhancing Project Management Efficiency_ Leveraging AI Tools like ChatGPT.pdfJay Das
With the advent of artificial intelligence or AI tools, project management processes are undergoing a transformative shift. By using tools like ChatGPT, and Bard organizations can empower their leaders and managers to plan, execute, and monitor projects more effectively.
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
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For more details, visit us : https://informapuae.com/field-staff-tracking/
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Spend just a little time on why people are using NoSQL
Talk about how data is modeled differently in JSON
Let’s talk about why SQL is good and why SQL for JSON is needed
Let’s talk about the exciting stuff happening in the database ecosystem
Including but not limited to the stuff Couchbase is doing
If we have time, we’ll look at how a .NET developer (or Java developer, etc) would interact with SQL for JSON
This session is a WIP. It’s based on my knowledge of Couchbase, SQL server experience, and David Segleau’s engagement and lessons learned with customers, all combined into an hour presentation.
David likes bullet points, I like to break up bullet points and use lots of pictures.
David works with customers, I work with dev community.
So you’re going to see a meshing of that, hopefully it works.
What’s also interesting is that we’re seeing the use of NoSQL expand inside many of these companies. Orbitz, the online travel company, is a great example – they started using Couchbase to store their hotel rate data, and now they use Couchbase in many other ways.
Same with ebay, they recently presented at the Couchbase conference with a chart tracking how many instances of various nosql databases are in use, and we see growth in Cassandra, mongo, and couchbase has actually surpassed them within ebay
SQL (relational) databases are great. They give you LOT OF functionality.
Great set of abstractions (tables, columns, data types, constraints, triggers, SQL, ACID TRANSACTIONS, stored procedures and more) at a highly reasonable cost.
Change is inevitable
One thing RDBMS does not handle well is CHANGE.
Change of schema (both logical and physical), change of hardware, change of capacity.
NoSQL databases ESPECIALLY ONES DESIGNED TO BE DISTRIBUTED tend to help solve problems with: agility, scalability, performance, and availability
Let’s talk about what NoSQL is, first.
NoSQL generally refers to databases which lack SQL or don’t use a relational model
Once the SQL language, transaction became optional, flurry of databases were created using distinct approaches for common use-cases.
KEY-Value simply provided quick access to data for a given KEY.
Wide Column databases can store large number of arbitrary columns in each row
Graph databases store data and relationships as first class concepts
Document databases aggregate data into a hierarchical structure.
With JSON is a means to the end. Document databases provide flexible schema,built-in data types, rich structure, implicit relationships using JSON.
When we look at document databases, they originally came with a
Minimal set of APIs and features
But as they continue to mature, we’re seeing more features being added
And generally I’m seeing a convergent trend between SQL and NoSQL
But anyway, this set of minimal features, lacking a SQL language and tables gives us the buzzword “nosql”
Elastic scaling
Size your cluster for today
Scale out on demand
Cost effective scaling
Commodity hardware
On premise or on cloud
Scale OUT instead of Scale UP
[example: changing the channel to a soccer game or Game of Thrones, everyone makes the same API request in the same 5 minutes]
[example: TV show lets watchers vote during some period of the week, so you can scale up during that period of time]
[example: black Friday]
Schema flexibility
Easier management of change in the business requirements
Easier management of change in the structure of the data
Sometimes you're pulling together data, integrating from different sources (e.g. ELT) and that flexibility helps
Document database means that you have no rigid schema. You can do whatever the heck you want.
That being said, you SHOULDN’T. You should still have discipline about your data.
NoSQL systems are optimized for specific access patterns
Low response time for web & mobile user experience
Millisecond latency
Consistently high throughput to handle growth
[perf measures can be subjective – talk about architecture, integrated cache, maybe mention MDS too]
If one machine goes down, customers can still use the other.
Or if you need to perform maintenance, upgrade, etc, you don't have to take the whole system down
This is related to scaling
Built-in replication and fail-over
No application downtime when hardware fails
Online maintenance & upgrade
No application downtime
Let’s talk about data modeling a bit, because storing data in JSON
Is different that storing in tables.
So I want to compare the approaches over 4 key areas.
I’m going to fill in this table, traditional SQL on the left and JSON on the right
Let’s look at modeling Customer data. This is an example of what a customer might look like
There is a rich structure: attributes, potentially sub-attributes (first name and last name)
Relationships: to other data (other customers, to products perhaps)
Value evolution: Maybe we’d start with one connection, change to multiple (data is updated)
Structure evolution: Maybe we start without connections and add those later, or we evolve name field to be more than first and last name (data is reshaped)
Rich Structure
In relational database, this customers data would be stored in five normalized tables.
Each time you want to construct a customer object, you JOIN the data in these tables;
Each time you persist, you find the appropriate rows in relevant tables and insert/update.
Relationship
Enforcement is via referential constraints. Objects are constructed by JOINS, EACH time.
Value Evolution
Additional values of the SAME TYPE (e.g. additional phone, additional address) is managed by additional ROWS in one of the tables.
Customer:contacts will have 1:n relationship.
Structure Evolution:
Imagine we didn't start with a billing table.
This is the most difficult part. Changing the structure is difficult, within a table, across tables.
While you can do these via ALTER TABLE, requires downtime, migration and application versioning.
This is one of the problem document databases try to handle by representing data in JSON.
Let’s see how to represent customer data in JSON.
The primary (CustomerID) becomes the DocumentKey
Column name-Column value becomes KEY-VALUE pair.
We aren’t normal form anymore
Rich Structure & Relationships
Billing information is stored as a sub-document
There could be more than a single credit card. So, use an array.
Value evolution
Simply add additional array element or update a value.
Structure evolution
Simply add new key-value pairs
No downtime to add new KV pairs
Applications can validate data
Structure evolution over time.
Relations via Reference
So, finally, you have a JSON document that represents a CUSTOMER.
In a single JSON document, relationship between the data is implicit by use of sub-structures and arrays and arrays of sub-structures.
Reference slide
What types of relationships are being modeled?
How are the relationships accessed?
Let’s talk about data modeling a bit, because storing data in JSON
Is different that storing in tables.
We’ll focus on N1QL for now.
Notice I’m using Guid
That may not be a good idea
N1QL is powerful in it's flexibility, declarative nature, familiar to developers, JOINs, etc.
Indexing is very important, as it's not as performant as key/value or map/reduce
(Maybe talk about indexing on a SQL table vs indexing on a whole bucket)
Couchbase 5.0 has introduced some tools for analyzing query performance
So you can see what indexes are being used, where the biggest costs are in the query
And so on.
There are a lot of different types of indexes for N1QL
This is kinda like a materialized view
It's powerful in that it can be run in parallel, can use JavaScript to do filtering/mapping, great for aggregation.
It's limited in that it can't do anything like a JOIN, can't get input from other views, and more
Let’s talk about data modeling a bit, because storing data in JSON
Is different that storing in tables.
Are you going to take the time to clean up the data? Do you need to?
Do you need to enrich or restructure the data to take advantage of Json?
Duration v resources: how long is it going to take? What tools and resources are available to you?
Data governance: what are the rules for moving data, auditing, etc?
Duration v resources: how long is it going to take? What tools and resources are available to you?
What’s your biggest constraint – time or resources? Do you need to get the migration done in 1 hr (and have it use as many parallel resources as needed) or do you need to minimize/manage the resource impact on the existing system and it doesn’t matter how long it takes?
Data governance: what are the rules for moving data, auditing, etc?
Do you need to keep track of where the data came from and who is allowed to access it? Many newer systems need to track where sensitive data originated.
A whole bunch at a time, or one at a time
Single threaded – easier
Multi-threaded – faster, complicated
is the migration a one-time event or does it need to happen incrementally (every day or over a 2-3 month period where both the old system and new system are both operating in parallel)? Do you plan to do the data migration as a single thread (read all the data, write all of the data) or using a multi-threaded or multi-process approach where each thread or process reads some percentage of the data.
If you're writing your own, Entity Framework can be helpful, because it can do the mapping of aggregate root C# objects for you, which you can then write to a document database
So if you already have EF mappings created, you're part way there.
KISS: Either export to CVS and use N1QL to do any ETL that’s required (assuming that it’s Simple) or use SQL to do simple ETL on export and then just import into CB.
Basically keep it as simple as you can and plan for failure. Developers often think of the migration process as “One and Done”, but the reality is that data migration is often an ongoing headache that DevOps needs to monitor and manage in a production environment. Make everyone’s life easier by thinking about the long game as much as possible.
From NoSQL to relational
From relational to NoSQL:
Goldendate is from oracle
Cdata for SSIS and Couchbase
https://github.com/mahurtado/CouchbaseGoldenGateAdapter
https://www.cdata.com/drivers/couchbase
Make it part of your application directly
May or may not be reusable
This is a lot of work, so make sure you have a good reason
Let’s talk about data modeling a bit, because storing data in JSON
Is different that storing in tables.
Focus, Success Criteria, Review Architecture
consider using a tool like Hackolade to define models rigorously and collaboratively
Start the animation
Mongo: Features N1QL, XDCR, Full Text Search, Mobile & Sync. Memory-first architecture and proven, easy scaling.
CouchDb: Couchbase started as a whole new piece of software that was basically a combination of memcache and CouchDb a long time ago, but has grown far beyond that. Couchbase isn’t a fork or vice versa. They share an acronym and they are both NoSQL. Like MySQL and SQL Server, for instance.
Open source apache license for community edition, enterprise edition on a faster release schedule, some advanced features, and support license.
Couchbase is software you can run in the cloud on a VM or on your own data center. CosmosDb is a manage cloud service, but there is a emulator you can run locally.
Transactions: if you can use nested modeling, you don't need multi-document transactions.
