Building an API layer for C* at Coursera
•
1 like•906 views
We built an storage API layer on top of Cassandra exposing a document model to improve developer productivity at Coursera.
Report
Share
Report
Share
Download to read offline
Recommended
Scalding @ Coursera
The document discusses options for analyzing semi-structured event data at Coursera. It considers Hive, Pig, and Scalding. Scalding uses Scala and allows joining different data sources and expressing multiple map-reduce jobs in a succinct way. However, it requires learning Scala. An example shows loading event, course, and topic data and joining them to analyze relationships between the data.
Big Data Overview Part 1
Big Data has become the new buzzword like “Agile” and “Cloud”. Like those two others, it’s a transformative technology. We’ll be discussing:
•What is it?
•Technology key words
•HDFS
•Hadoop
•MapReduce
This will be part 1 of 2 (at least). This first talk will not be overly technical. We’ll go over the concepts and terms you’ll encounter when considering a big data solution.
ElasticSearch for data mining
We went over what Big Data is and it's value. This talk will cover the details of Elasticsearch, a Big Data solution. Elasticsearch is an NoSQL-backed search engine using a HDFS-based filesystem.
We'll cover:
• Elasticsearch basics
• Setting up a development environment
• Loading data
• Searching data using REST
• Searching data using NEST, the .NET interface
• Understanding Scores
Finally, I show a use-case for data mining using Elasticsearch.
You'll walk away from this armed with the knowledge to add Elasticsearch to your data analysis toolkit and your applications.
BigData, NoSQL & ElasticSearch
These slides explain briefly about the big data concept, NoSQL database systems and the ElasticSearch technology which are quite modern concepts
Elasticsearch Arcihtecture & What's New in Version 5
General architectural concepts of Elasticsearch and what's new in version 5? Examples are prepared with our company business therefore these are excluded from presentation.
Presentation: mongo db & elasticsearch & membase
This document provides summaries of NoSQL databases MongoDB, ElasticSearch, and Couchbase. It discusses their key features and uses cases. MongoDB is a document-oriented database that stores data in JSON-like documents. ElasticSearch is a search engine and stores data in JSON documents for real-time search and analytics capabilities. Couchbase is a key-value store that provides high-performance access to data through caching and supports high concurrency.
Mongodb lab
We prepared a small 30 min workshop for the Dutch Java User Group to introduce MongoDB basics. This slideshow contains the mongoDB concepts, which will be workout basic in labs . The labs could be found at: http://mongodb.info/labs/
Azure CosmosDB the new frontier of big data and nosql
Azure Cosmos DB is a globally distributed, massively scalable, multi-model database service. It supports document, key-value, graph, and column-family data models. Cosmos DB provides turnkey global distribution, elastic scale of storage and throughput, guaranteed low latency at the 99th percentile, comprehensive SLAs, and five consistency models. It is designed for data growth and puts data where users are located.
Recommended
Scalding @ Coursera
The document discusses options for analyzing semi-structured event data at Coursera. It considers Hive, Pig, and Scalding. Scalding uses Scala and allows joining different data sources and expressing multiple map-reduce jobs in a succinct way. However, it requires learning Scala. An example shows loading event, course, and topic data and joining them to analyze relationships between the data.
Big Data Overview Part 1
Big Data has become the new buzzword like “Agile” and “Cloud”. Like those two others, it’s a transformative technology. We’ll be discussing:
•What is it?
•Technology key words
•HDFS
•Hadoop
•MapReduce
This will be part 1 of 2 (at least). This first talk will not be overly technical. We’ll go over the concepts and terms you’ll encounter when considering a big data solution.
ElasticSearch for data mining
We went over what Big Data is and it's value. This talk will cover the details of Elasticsearch, a Big Data solution. Elasticsearch is an NoSQL-backed search engine using a HDFS-based filesystem.
We'll cover:
• Elasticsearch basics
• Setting up a development environment
• Loading data
• Searching data using REST
• Searching data using NEST, the .NET interface
• Understanding Scores
Finally, I show a use-case for data mining using Elasticsearch.
You'll walk away from this armed with the knowledge to add Elasticsearch to your data analysis toolkit and your applications.
BigData, NoSQL & ElasticSearch
These slides explain briefly about the big data concept, NoSQL database systems and the ElasticSearch technology which are quite modern concepts
Elasticsearch Arcihtecture & What's New in Version 5
General architectural concepts of Elasticsearch and what's new in version 5? Examples are prepared with our company business therefore these are excluded from presentation.
Presentation: mongo db & elasticsearch & membase
This document provides summaries of NoSQL databases MongoDB, ElasticSearch, and Couchbase. It discusses their key features and uses cases. MongoDB is a document-oriented database that stores data in JSON-like documents. ElasticSearch is a search engine and stores data in JSON documents for real-time search and analytics capabilities. Couchbase is a key-value store that provides high-performance access to data through caching and supports high concurrency.
Mongodb lab
We prepared a small 30 min workshop for the Dutch Java User Group to introduce MongoDB basics. This slideshow contains the mongoDB concepts, which will be workout basic in labs . The labs could be found at: http://mongodb.info/labs/
Azure CosmosDB the new frontier of big data and nosql
Azure Cosmos DB is a globally distributed, massively scalable, multi-model database service. It supports document, key-value, graph, and column-family data models. Cosmos DB provides turnkey global distribution, elastic scale of storage and throughput, guaranteed low latency at the 99th percentile, comprehensive SLAs, and five consistency models. It is designed for data growth and puts data where users are located.
Azure CosmosDb - Where we are
Introduce (once again) CosmosDb, comparing with common habit of relational SQL database and try to speak about do and don't with it
473_LightningTalks.pptx
Underscore.js is a JavaScript library for manipulating data and JSON objects. It allows users to narrow down large datasets, sort and group data, and derive new data from existing data. Underscore can be used with other libraries like jQuery and provides powerful features like templating for formatting data. It is open source and can be downloaded from http://underscorejs.org in different versions.
Lightning talk: elasticsearch at Cogenta
This document discusses how Elasticsearch is used by Cogenta to power their real-time retail intelligence platform. It tracks hundreds of eCommerce sites daily, organizing large amounts of data into a high-quality market view. Elasticsearch allows Cogenta to scale their processing and analytics capabilities, provide high availability, and power various use cases like logging, internal analytics, and reporting.
NDC Minnesota - Analyzing StackExchange data with Azure Data Lake
This document discusses analyzing StackExchange data with Azure Data Lake. It introduces Azure Data Lake Store as an enterprise-grade data lake and Azure Data Lake Analytics as a serverless big data analytics service. It demonstrates using these services to acquire, aggregate, analyze and visualize StackExchange data. Key features of Data Lake Store and Analytics are explained, including security, pricing, and extensibility options.
"TextMining with ElasticSearch", Saskia Vola, CEO at textminers.io
This document discusses using ElasticSearch for text mining tasks such as information extraction, sentiment analysis, keyword extraction, classification, and clustering. It describes how ElasticSearch can be used to perform linguistic preprocessing including tokenization, stopword removal, and stemming on text data. Additionally, it mentions plugins for language detection and clustering search results. The document provides an example of training a classification model using an index with content and category fields, and evaluating the model's performance on news text categorization.
Azure CosmosDb
Azure Cosmos DB is a globally distributed, massively scalable, multi-model database service. It provides guaranteed low latency at the 99th percentile, elastic scaling of storage and throughput, comprehensive SLAs, and five consistency models. Cosmos DB offers multiple APIs including SQL, MongoDB, Cassandra, Gremlin, and Table to access and query data.
Elasticsearch
Elasticsearch 1.1.0 includes several new features and improvements such as new aggregation types like cardinality and percentiles, significant terms aggregation, and improvements to terms and multi-field search. It also includes breaking changes to configuration, multi-fields, stopwords, and return values. New features for aggregations include bucketing and metrics aggregations as well as the ability to add sub-aggregations. Backup and restore capabilities were added through repositories and snapshots. The tribe feature allows federation across multiple clusters.
Small intro to Big Data - Old version
Presented on Codemotion Warsaw 2016 and JDD 2016.
Pig, Hive, Flink, Kafka, Zeppelin... if you now wonder if someone just tried to offend you or are those just Pokemon names, then this talk is just for you!
Big Data is everywhere and new tools for it are released almost at the speed of new JavaScript frameworks. During this entry level presentation we will walk though the challenges which Big Data presents, reflect how big is big and introduce currently most fancy and popular (mostly open source) tools.
We'll try to spark off interest in Big Data by showing application areas and by throwing ideas where you can later dive into.
Using ElasticSearch as a fast, flexible, and scalable solution to search occu...
Elasticsearch is an open source search engine that provides fast, flexible, and scalable search of occurrence records and checklists. It allows adding and querying data through a REST API or Java API. Data can be imported from databases or other sources using rivers. Mappings customize indexing and querying. Elasticsearch has been used at Canadensys to index vascular plant names with filters for autocompletion, genus filtering, and epithet hierarchy. It is also used at GBIF France to search biodiversity data from MongoDB with filters and calculate statistics with facets.
NoSQL: Why, When, and How
The document discusses NoSQL databases and CouchDB. It provides an overview of NoSQL, the different types of NoSQL databases, and when each type would be used. It then focuses on CouchDB, explaining its features like document centric modeling, replication, and fail fast architecture. Examples are given of how to interact with CouchDB using its HTTP API and tools like Resty.
MongoDB
MongoDB is a document database that stores data in BSON format, which is similar to JSON. It is a non-relational, schema-free database that scales easily and supports massive amounts of data and high availability. MongoDB can replace traditional relational databases for certain applications, as it offers dynamic schemas, horizontal scaling, and high performance. Key features include indexing, replication, MapReduce and rich querying of embedded documents.
Introducing Project Alternator - Scylla’s Open-Source DynamoDB-compatible API
ScyllaDB recently announced Project Alternator, a new open source project that will enable Amazon DynamoDB users to easily migrate to an open-source database that runs anywhere — on most cloud platforms, on-premises, on bare-metal, virtual machines or via Kubernetes — all while preserving their investments in their existing application code.
Project Alternator will help DynamoDB users achieve much better and more reliable performance, reduce database costs by 80% - 90%, support large items (10s of MBs) and large partitions (multiple GBs), control the number of replicas, balance cost vs. redundancy, and much more.
Join ScyllaDB founders Avi Kivity and Dor Laor and lead engineer Nadav Har’El for a live webinar on September 25th, where they will share an overview of Project Alternator, including:
Alternator’s design implementation and goals
How to configure Alternator (ok, add alternator_port: 8000 to your scylla.yaml)
Demo how to easily run it from docker/rpm
Run several examples:
Tic-tac-toe based DynamoDB example with Alternator
How to benchmark Scylla Alternator with YCSB and considerations around it
How to run a serverless application along with Alternator
How to migrate DynamoDB data to Alternator using the Spark migrator
Discuss the current limitations of Alternator
Plus we will discuss current limitations of Alternator, describe different consistencies and active-active vs leader model, share the project roadmap, and answer your questions at the end.
Performance comparison: Multi-Model vs. MongoDB and Neo4j
Native multi-model databases combine different data models like documents or graphs in one tool and even allow to mix them in a single query. How can this concept compete with a pure document store like MongoDB or a graph database like Neo4j? I myself and a lot of folks in the community asked that question.
So here are some benchmark results.
Open source big data landscape and possible ITS applications
What is big data, and how open-source big data projects, such as Apache Spark, Kafka and Cassandra can be used in ITS (Intelligent Transport Systems) related projects.
Where Is My Data - ILTAM Session
The document discusses where data is stored in the cloud. It begins with an introduction and agenda. It then covers different types of cloud storage including blob storage, relational databases, NoSQL databases, and MapReduce. Blob storage stores unstructured data like files and is offered by services like Azure Blob and AWS S3. Relational databases like SQL Azure and RDS offer structured storage. NoSQL options include Azure Tables and DynamoDB for flexible schema data. MapReduce frameworks like EMR and HDInsight allow processing large datasets.
ElasticSearch - index server used as a document database
Presentation held on 5.10.2014 on http://2014.webcampzg.org/talks/.
Although ElasticSearch (ES) primary purpose is to be used as index/search server, in its featureset ES overlaps with common NoSql database; better to say, document database.
Why this could be interesting and how this could be used effectively?
Talk overview:
- ES - history, background, philosophy, featureset overview, focus on indexing/search features
- short presentation on how to get started - installation, indexing and search/retrieving
- Database should provide following functions: store, search, retrieve -> differences between relational, document and search databases
- it is not unusual to use ES additionally as an document database (store and retrieve)
- an use-case will be presented where ES can be used as a single database in the system (benefits and drawbacks)
- what if a relational database is introduced in previosly demonstrated system (benefits and drawbacks)
ES is a nice and in reality ready-to-use example that can change perspective of development of some type of software systems.
Introduction to Cassandra (June 2010)
This document provides an overview of Apache Cassandra, including its origins from Amazon Dynamo and Google BigTable, its data model using a ring topology and column families, and how it provides horizontal scalability and eventual consistency through replication. It discusses Cassandra's write path using commit logs and memtables as well as its read path involving caching. It also covers client access, practical considerations, and Cassandra's future direction.
No sq lv1_0
The document discusses NoSQL technologies including Cassandra, MongoDB, and ElasticSearch. It provides an overview of each technology, describing their data models, key features, and comparing them. Example documents and queries are shown for MongoDB and ElasticSearch. Popular use cases for each are also listed.
An Introduction To NoSQL & MongoDB
This document provides an introduction to NoSQL and MongoDB. It discusses that NoSQL is a non-relational database management system that avoids joins and is easy to scale. It then summarizes the different flavors of NoSQL including key-value stores, graphs, BigTable, and document stores. The remainder of the document focuses on MongoDB, describing its structure, how to perform inserts and searches, features like map-reduce and replication. It concludes by encouraging the reader to try MongoDB themselves.
An introduction to Nosql
This document provides an introduction to NoSQL databases. It describes NoSQL as non-relational, distributed, open-source databases that are horizontally scalable with no predefined schema. It lists the main types of NoSQL databases as document stores, graph stores, key-value stores, and wide-column stores. The document gives MongoDB as an example of a document database and explains that sharding allows horizontal scaling by storing data records across multiple machines.
EWD 3 Training Course Part 26: Event-driven Indexing
This presentation is Part 26 of the EWD 3 Training Course. It explains how indexing of Global Storage can be defined in an event-driven, modular way
IaaS with ARM templates for Azure
The document discusses infrastructure as code using ARM templates on Azure. ARM templates allow infrastructure to be defined in code and treated like application code in version control. Key points covered include:
- ARM templates are JSON files that define Azure resources and deployment parameters
- Templates support parameters, variables, functions and outputs to define flexible and repeatable deployments
- Common elements like resources, properties, and tags can be authored in templates
- Templates are deployed via tools like Powershell or Azure CLI to create the defined infrastructure
More Related Content
What's hot
Azure CosmosDb - Where we are
Introduce (once again) CosmosDb, comparing with common habit of relational SQL database and try to speak about do and don't with it
473_LightningTalks.pptx
Underscore.js is a JavaScript library for manipulating data and JSON objects. It allows users to narrow down large datasets, sort and group data, and derive new data from existing data. Underscore can be used with other libraries like jQuery and provides powerful features like templating for formatting data. It is open source and can be downloaded from http://underscorejs.org in different versions.
Lightning talk: elasticsearch at Cogenta
This document discusses how Elasticsearch is used by Cogenta to power their real-time retail intelligence platform. It tracks hundreds of eCommerce sites daily, organizing large amounts of data into a high-quality market view. Elasticsearch allows Cogenta to scale their processing and analytics capabilities, provide high availability, and power various use cases like logging, internal analytics, and reporting.
NDC Minnesota - Analyzing StackExchange data with Azure Data Lake
This document discusses analyzing StackExchange data with Azure Data Lake. It introduces Azure Data Lake Store as an enterprise-grade data lake and Azure Data Lake Analytics as a serverless big data analytics service. It demonstrates using these services to acquire, aggregate, analyze and visualize StackExchange data. Key features of Data Lake Store and Analytics are explained, including security, pricing, and extensibility options.
"TextMining with ElasticSearch", Saskia Vola, CEO at textminers.io
This document discusses using ElasticSearch for text mining tasks such as information extraction, sentiment analysis, keyword extraction, classification, and clustering. It describes how ElasticSearch can be used to perform linguistic preprocessing including tokenization, stopword removal, and stemming on text data. Additionally, it mentions plugins for language detection and clustering search results. The document provides an example of training a classification model using an index with content and category fields, and evaluating the model's performance on news text categorization.
Azure CosmosDb
Azure Cosmos DB is a globally distributed, massively scalable, multi-model database service. It provides guaranteed low latency at the 99th percentile, elastic scaling of storage and throughput, comprehensive SLAs, and five consistency models. Cosmos DB offers multiple APIs including SQL, MongoDB, Cassandra, Gremlin, and Table to access and query data.
Elasticsearch
Elasticsearch 1.1.0 includes several new features and improvements such as new aggregation types like cardinality and percentiles, significant terms aggregation, and improvements to terms and multi-field search. It also includes breaking changes to configuration, multi-fields, stopwords, and return values. New features for aggregations include bucketing and metrics aggregations as well as the ability to add sub-aggregations. Backup and restore capabilities were added through repositories and snapshots. The tribe feature allows federation across multiple clusters.
Small intro to Big Data - Old version
Presented on Codemotion Warsaw 2016 and JDD 2016.
Pig, Hive, Flink, Kafka, Zeppelin... if you now wonder if someone just tried to offend you or are those just Pokemon names, then this talk is just for you!
Big Data is everywhere and new tools for it are released almost at the speed of new JavaScript frameworks. During this entry level presentation we will walk though the challenges which Big Data presents, reflect how big is big and introduce currently most fancy and popular (mostly open source) tools.
We'll try to spark off interest in Big Data by showing application areas and by throwing ideas where you can later dive into.
Using ElasticSearch as a fast, flexible, and scalable solution to search occu...
Elasticsearch is an open source search engine that provides fast, flexible, and scalable search of occurrence records and checklists. It allows adding and querying data through a REST API or Java API. Data can be imported from databases or other sources using rivers. Mappings customize indexing and querying. Elasticsearch has been used at Canadensys to index vascular plant names with filters for autocompletion, genus filtering, and epithet hierarchy. It is also used at GBIF France to search biodiversity data from MongoDB with filters and calculate statistics with facets.
NoSQL: Why, When, and How
The document discusses NoSQL databases and CouchDB. It provides an overview of NoSQL, the different types of NoSQL databases, and when each type would be used. It then focuses on CouchDB, explaining its features like document centric modeling, replication, and fail fast architecture. Examples are given of how to interact with CouchDB using its HTTP API and tools like Resty.
MongoDB
MongoDB is a document database that stores data in BSON format, which is similar to JSON. It is a non-relational, schema-free database that scales easily and supports massive amounts of data and high availability. MongoDB can replace traditional relational databases for certain applications, as it offers dynamic schemas, horizontal scaling, and high performance. Key features include indexing, replication, MapReduce and rich querying of embedded documents.
Introducing Project Alternator - Scylla’s Open-Source DynamoDB-compatible API
ScyllaDB recently announced Project Alternator, a new open source project that will enable Amazon DynamoDB users to easily migrate to an open-source database that runs anywhere — on most cloud platforms, on-premises, on bare-metal, virtual machines or via Kubernetes — all while preserving their investments in their existing application code.
Project Alternator will help DynamoDB users achieve much better and more reliable performance, reduce database costs by 80% - 90%, support large items (10s of MBs) and large partitions (multiple GBs), control the number of replicas, balance cost vs. redundancy, and much more.
Join ScyllaDB founders Avi Kivity and Dor Laor and lead engineer Nadav Har’El for a live webinar on September 25th, where they will share an overview of Project Alternator, including:
Alternator’s design implementation and goals
How to configure Alternator (ok, add alternator_port: 8000 to your scylla.yaml)
Demo how to easily run it from docker/rpm
Run several examples:
Tic-tac-toe based DynamoDB example with Alternator
How to benchmark Scylla Alternator with YCSB and considerations around it
How to run a serverless application along with Alternator
How to migrate DynamoDB data to Alternator using the Spark migrator
Discuss the current limitations of Alternator
Plus we will discuss current limitations of Alternator, describe different consistencies and active-active vs leader model, share the project roadmap, and answer your questions at the end.
Performance comparison: Multi-Model vs. MongoDB and Neo4j
Native multi-model databases combine different data models like documents or graphs in one tool and even allow to mix them in a single query. How can this concept compete with a pure document store like MongoDB or a graph database like Neo4j? I myself and a lot of folks in the community asked that question.
So here are some benchmark results.
Open source big data landscape and possible ITS applications
What is big data, and how open-source big data projects, such as Apache Spark, Kafka and Cassandra can be used in ITS (Intelligent Transport Systems) related projects.
Where Is My Data - ILTAM Session
The document discusses where data is stored in the cloud. It begins with an introduction and agenda. It then covers different types of cloud storage including blob storage, relational databases, NoSQL databases, and MapReduce. Blob storage stores unstructured data like files and is offered by services like Azure Blob and AWS S3. Relational databases like SQL Azure and RDS offer structured storage. NoSQL options include Azure Tables and DynamoDB for flexible schema data. MapReduce frameworks like EMR and HDInsight allow processing large datasets.
ElasticSearch - index server used as a document database
Presentation held on 5.10.2014 on http://2014.webcampzg.org/talks/.
Although ElasticSearch (ES) primary purpose is to be used as index/search server, in its featureset ES overlaps with common NoSql database; better to say, document database.
Why this could be interesting and how this could be used effectively?
Talk overview:
- ES - history, background, philosophy, featureset overview, focus on indexing/search features
- short presentation on how to get started - installation, indexing and search/retrieving
- Database should provide following functions: store, search, retrieve -> differences between relational, document and search databases
- it is not unusual to use ES additionally as an document database (store and retrieve)
- an use-case will be presented where ES can be used as a single database in the system (benefits and drawbacks)
- what if a relational database is introduced in previosly demonstrated system (benefits and drawbacks)
ES is a nice and in reality ready-to-use example that can change perspective of development of some type of software systems.
Introduction to Cassandra (June 2010)
This document provides an overview of Apache Cassandra, including its origins from Amazon Dynamo and Google BigTable, its data model using a ring topology and column families, and how it provides horizontal scalability and eventual consistency through replication. It discusses Cassandra's write path using commit logs and memtables as well as its read path involving caching. It also covers client access, practical considerations, and Cassandra's future direction.
No sq lv1_0
The document discusses NoSQL technologies including Cassandra, MongoDB, and ElasticSearch. It provides an overview of each technology, describing their data models, key features, and comparing them. Example documents and queries are shown for MongoDB and ElasticSearch. Popular use cases for each are also listed.
An Introduction To NoSQL & MongoDB
This document provides an introduction to NoSQL and MongoDB. It discusses that NoSQL is a non-relational database management system that avoids joins and is easy to scale. It then summarizes the different flavors of NoSQL including key-value stores, graphs, BigTable, and document stores. The remainder of the document focuses on MongoDB, describing its structure, how to perform inserts and searches, features like map-reduce and replication. It concludes by encouraging the reader to try MongoDB themselves.
An introduction to Nosql
This document provides an introduction to NoSQL databases. It describes NoSQL as non-relational, distributed, open-source databases that are horizontally scalable with no predefined schema. It lists the main types of NoSQL databases as document stores, graph stores, key-value stores, and wide-column stores. The document gives MongoDB as an example of a document database and explains that sharding allows horizontal scaling by storing data records across multiple machines.
What's hot (20)
NDC Minnesota - Analyzing StackExchange data with Azure Data Lake
NDC Minnesota - Analyzing StackExchange data with Azure Data Lake
"TextMining with ElasticSearch", Saskia Vola, CEO at textminers.io
"TextMining with ElasticSearch", Saskia Vola, CEO at textminers.io
Using ElasticSearch as a fast, flexible, and scalable solution to search occu...
Using ElasticSearch as a fast, flexible, and scalable solution to search occu...
Introducing Project Alternator - Scylla’s Open-Source DynamoDB-compatible API
Introducing Project Alternator - Scylla’s Open-Source DynamoDB-compatible API
Performance comparison: Multi-Model vs. MongoDB and Neo4j
Performance comparison: Multi-Model vs. MongoDB and Neo4j
Open source big data landscape and possible ITS applications
Open source big data landscape and possible ITS applications
ElasticSearch - index server used as a document database
ElasticSearch - index server used as a document database
Similar to Building an API layer for C* at Coursera
EWD 3 Training Course Part 26: Event-driven Indexing
This presentation is Part 26 of the EWD 3 Training Course. It explains how indexing of Global Storage can be defined in an event-driven, modular way
IaaS with ARM templates for Azure
The document discusses infrastructure as code using ARM templates on Azure. ARM templates allow infrastructure to be defined in code and treated like application code in version control. Key points covered include:
- ARM templates are JSON files that define Azure resources and deployment parameters
- Templates support parameters, variables, functions and outputs to define flexible and repeatable deployments
- Common elements like resources, properties, and tags can be authored in templates
- Templates are deployed via tools like Powershell or Azure CLI to create the defined infrastructure
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.
Extensible RESTful Applications with Apache TinkerPop
This document discusses building a graph database and domain-specific language (DSL) for analyzing Slack data. It defines entities like messages, users, and channels as graph nodes and their relationships as edges. A REST API is created to ingest and query the graph using TinkerPop and remote traversals. Custom traversal sources and classes define shorthand traversals and business logic to build the DSL, adding structure and meaning to queries over the Slack data graph.
From Kafka to BigQuery - Strata Singapore
Describing the journey made by MyHeritage, streaming billions of events, every day, for data analysis
Managing the Life Cycle of IT Products
In this session, you’ll learn how you can incorporate your IT product lifecycle into the cloud where you can define, publish, monitor, and manage your products. Central IT can enable end-users in their organizations to easily discover and provision these products, from a personalized portal. We will demonstrate using AWS services that enable IT to retain control of resources provisioned in the AWS cloud, track configuration changes and audit user activities. We will also show AWS Marketplace, that helps you find third-party software that you need, buy it, and easily deploy it in the AWS cloud.
Repository As A Service (RaaS) at ICPSR
Since 1962, ICPSR has been an integral part of the infrastructure of social science research with its vast digital archive supporting over 700 member institutions worldwide. With the release of our new digital assets management system “Archonnex,” ICPSR continues this tradition by extending our expertise and digital technology capabilities as a service to the larger community. For the first time researchers, institutions, organizations, and even nations will be able to host their own repositories and setup data services for their members. We call it RaaS - Repository as a Service.
윈도 닷넷 개발자를 위한 솔루션 클라우드 데브옵스 솔루션
This document discusses DevOps concepts and practices including:
- DevOps aims to improve collaboration between development and operations teams through practices like continuous integration, deployment automation, and infrastructure as code.
- The five pillars of DevOps are: microservices, infrastructure as code, automation and configuration management, continuous integration and continuous delivery, and logging and monitoring.
- Specific DevOps practices discussed include building infrastructure templates with CloudFormation, implementing continuous integration and delivery pipelines with CodePipeline/CodeBuild/CodeDeploy, and automating infrastructure provisioning and configuration changes.
Exploring an API with Blocks
The document discusses the development of an API request builder tool that allows users to visually construct API requests using blocks. It describes how the tool uses the Google Blockly library and recursion to build requests and auto-generate code in multiple programming languages from an API's OpenAPI specification file. The tool addresses goals like easy request creation for nested objects, including documentation, viewing requests as JSON, and auto-programming SDKs. An early version received positive feedback, and a public release is planned for the summer.
Vital AI MetaQL: Queries Across NoSQL, SQL, Sparql, and Spark
This document provides an overview of MetaQL, which allows composing queries across NoSQL, SQL, SPARQL, and Spark databases using a domain model. Key points include:
- MetaQL uses a domain model to define concepts and compose typed queries in code that can execute across different databases.
- This separates concerns and improves developer efficiency over managing schemas and databases separately.
- Examples demonstrate MetaQL queries in graph, path, select, and aggregation formats across SQL, NoSQL, and RDF implementations.
Building APIs in an easy way using API Platform
The document discusses building APIs in an easy way using API Platform. It describes how API Platform makes it simple to create APIs that support JSON-LD, Hydra, and HAL formats. API Platform is built on Symfony and integrates with common Symfony tools like Doctrine ORM. It provides features like CRUD operations, serialization groups, validation, pagination and extensions out of the box. The document also provides examples of creating a player resource and implementing authentication with JSON Web Tokens.
Deep Dive - Infrastructure as Code
Many of our customers have adopted DevOps for faster and reliable software delivery. Applying software engineering best practices such as revision control and continuous delivery to your infrastructure is essential for adopting DevOps.
In this session, find out how AWS CloudFormation and the associated AWS tools enable DevOps by allowing you to treat infrastructure as code and applying those software engineering best practices to your infrastructure.
Speakers:
Steven Bryen, AWS Solutions Architect
Bruce Jackson, Chief Technology Officer, Myriad Group
Rajpal Singh Wilkhu,Principal Engineer, Just Eat
Improving Infrastructure Governance on AWS by Henrik Johansson, Solutions Ar...
This document discusses improving infrastructure governance on AWS. It recommends using policy as code, infrastructure standardization through code, self-service environments, and logging/auditing infrastructure changes. AWS services like CloudFormation, Service Catalog, CloudTrail, and Config can help implement these recommendations by treating infrastructure as code, enabling self-service provisioning, auditing API calls, and monitoring configuration changes.
REST easy with API Platform
The web has changed! Users spend more time on mobile than on desktops and they expect to have an amazing user experience on both platforms. APIs are the heart of the new web as the central point of access data, encapsulating logic and providing the same data and same features for desktops and mobiles.
In this talk, I will show you how in only 45 minutes we can create full REST API, with documentation and admin application build with React.
Improving Infrastructure Governance on AWS - AWS June 2016 Webinar Series
As your teams and infrastructure grow, it becomes more difficult to track IT resource changes as well as identify who made changes and when. It also becomes harder to enforce standards for your infrastructure resources, resulting in configuration drift and potential security issues. On AWS, you can easily standardize infrastructure configurations for commonly used IT services while also enabling self-service provisioning for your company. Once these resources are provisioned, you can then track how these resources are connected and monitor configuration changes and drift. In this session, we will discuss how you can achieve a sophisticated level of standardization, configuration compliance, and monitoring using a combination of AWS Service Catalog, AWS Config, and AWS CloudTrail.
Learning Objectives:
Understand how to use AWS services to enable governance while providing self-service
Learn to codify your business policies to promote compliance
How to improve security without sacrificing developer productivity
Azure Data Lake and U-SQL
Introduction to Azure Data Lake and U-SQL presented at Seattle Scalability Meetup, January 2016. Demo code available at https://github.com/Azure/usql/tree/master/Examples/TweetAnalysis
Please signup for the preview at http://www.azure.com/datalake. Install Visual Studio Community Edition and the Azure Datalake Tools (http://aka.ms/adltoolvs) to use U-SQL locally for free.
RDF Validation in a Linked Data World - A vision beyond structural and value ...
RDF Validation in a Linked Data World - A vision beyond structural and value ...Nandana Mihindukulasooriya
This document discusses RDF validation in a Linked Data context. It outlines factors to consider in designing an RDF validation process, including data source dynamics, publication strategy, and access control. It also covers procedural factors like the number of data sources and validation scope. Context factors like the validation purpose and data provenance must also be taken into account. The conclusion is that RDF validation for Linked Data needs to accommodate the particularities of the data sources, processes, and context involved.Improving Infrastructure Governance on AWS
Learn how you can achieve a sophisticated level of standardization, configuration compliance, and monitoring using a combination of AWS Service Catalog, AWS Config, and AWS CloudTrail.
Socialite, the Open Source Status Feed
Building a complete social networking platform presents many challenges at scale. Socialite is a reference architecture and open source Java implementation of a scalable social feed service built on DropWizard and MongoDB. We'll provide an architectural overview of the platform, explaining how you can store an infinite timeline of data while optimizing indexing and sharding configuration for access to the most recent window of data. We'll also dive into the details of storing a social user graph in MongoDB.
Building Highly Flexible, High Performance Query Engines
The document discusses Apache Drill, an open source SQL query engine for analysis of data in Hadoop. It provides an overview of Drill, highlighting its ability to handle flexible schemas, analyze semi-structured and nested data from NoSQL sources, and integrate with existing business intelligence tools through a familiar SQL interface. The document also notes that traditional SQL approaches do not always work well for new big data applications and data models, and that Drill aims to address these challenges.
Similar to Building an API layer for C* at Coursera (20)
EWD 3 Training Course Part 26: Event-driven Indexing
EWD 3 Training Course Part 26: Event-driven Indexing
Benjamin Guinebertière - Microsoft Azure: Document DB and other noSQL databas...
Benjamin Guinebertière - Microsoft Azure: Document DB and other noSQL databas...
Extensible RESTful Applications with Apache TinkerPop
Extensible RESTful Applications with Apache TinkerPop
Vital AI MetaQL: Queries Across NoSQL, SQL, Sparql, and Spark
Vital AI MetaQL: Queries Across NoSQL, SQL, Sparql, and Spark
Improving Infrastructure Governance on AWS by Henrik Johansson, Solutions Ar...
Improving Infrastructure Governance on AWS by Henrik Johansson, Solutions Ar...
Improving Infrastructure Governance on AWS - AWS June 2016 Webinar Series
Improving Infrastructure Governance on AWS - AWS June 2016 Webinar Series
RDF Validation in a Linked Data World - A vision beyond structural and value ...
RDF Validation in a Linked Data World - A vision beyond structural and value ...
Building Highly Flexible, High Performance Query Engines
Building Highly Flexible, High Performance Query Engines
Recently uploaded
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Azure API Management to expose backend services securely
How to use Azure API Management to expose backend service securely
A Comprehensive Guide to DeFi Development Services in 2024
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
This case study explores designing a scalable e-commerce platform, covering key requirements, system components, and best practices.
Trusted Execution Environment for Decentralized Process Mining
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Dandelion Hashtable: beyond billion requests per second on a commodity server
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Public CyberSecurity Awareness Presentation 2024.pptx
Cyber security awareness slides for a busisness by TreeTop Security
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
AWS Cloud Cost Optimization Presentation.pptx
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
GNSS spoofing via SDR (Criptored Talks 2024)
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays, June 2024 with Maria Copot 20
Recently uploaded (20)
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Azure API Management to expose backend services securely
Azure API Management to expose backend services securely
WeTestAthens: Postman's AI & Automation Techniques
WeTestAthens: Postman's AI & Automation Techniques
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
A Comprehensive Guide to DeFi Development Services in 2024
A Comprehensive Guide to DeFi Development Services in 2024
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
Trusted Execution Environment for Decentralized Process Mining
Trusted Execution Environment for Decentralized Process Mining
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
Dandelion Hashtable: beyond billion requests per second on a commodity server
Dandelion Hashtable: beyond billion requests per second on a commodity server
Public CyberSecurity Awareness Presentation 2024.pptx
Public CyberSecurity Awareness Presentation 2024.pptx
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
Building an API layer for C* at Coursera
- 1. Daniel Chia Infrastructure Engineer @DanielChiaJH Courral Storage API layer
- 2. Agenda ❖ Brief History ❖ Quick Run Through ❖ Wins / Problems ❖ Future Directions
- 4. History App KVS Library App KVS Library App 3 KVS Library Indexing?
- 5. Courral ❖ Document Model ❖ Key -> Value ❖ Declarative indexing
- 6. Example: Product Ownership ❖ User X owns Product Y during ❖ [2016-01-01, 2016-03-01] ❖ [2016-05-01, 2016-08-01] ❖ Query by ❖ (user, product) ❖ (user)
- 7. Document Collection { "clusterName": "cass_prod_1", "idSchema": "org.coursera.product.ownership.UserProductOwnershipId", "dataSchema": "org.coursera.product.ownership.StoredProductOwnership", "indexes": [ { "name": "byUser", "indexScheme": "reverse", "targets": [{ "targetType": "id", "path": ["userId"] }] } ] }
- 8. Schema Language (Courier) record UserProductOwnershipId { userId: UserId, productType: ProductType productItemId: ProductItemId } http://coursera.github.io/courier/
- 9. API documentClient.get(id).map(_.document) documentClient.set(id, ownership) documentClient .list( “byUser”, List(userId), limit, start.map(ListStart(_), fetchSize = 50)
- 10. Self-service
- 11. Wins ❖ Deeper integration with our schema system.
- 12. Wins ❖ Better monitoring / insight
- 13. Problems ❖ New “single” point of failure ❖ Additional latency / CPU utilization
- 14. Future Work ❖ Automatic index backfilling ❖ Integrated caching ❖ Multi-tenant safeguards
- 15. Questions?