Is there a SQL for NoSQL?

•Download as PPTX, PDF•

0 likes•68 views

SPARQL is proposed as a SQL-like query language for NoSQL databases that is schema-free, accommodates different data models, uses a familiar SQL-syntax, and is an accepted standard. SPARQL represents data as RDF graphs and can run unmodified on different NoSQL systems that support it, providing interoperability and lowering the barrier to querying diverse data sources. It aims to address how organizations can process, manage and analyze growing volumes of data with fewer resources by offering a common language for querying multiple NoSQL databases.

Data & Analytics

Is SPARQL the SQL for NoSQL?
Arthur Keen
SPARQL City
http://www.sparqlcity.com

Outline
• The Business Case
• Why SPARQL?
• Developer Perspective
• Common Questions and Misconceptions
• Questions

Why Do We Need a SQL for NoSQL?
• How are we going to
• Process far more data (than SQL),
• With fewer people (than SQL),
• With skills fragmented over many databases?

Scarce Resources:
SQL > 15 X NoSQL
*Source LinkedIn
IndividualsReportingskillonLinkedin

Fragmented Skills
IndividualsReportingskillonLinkedin
*Source LinkedIn

Millions Know SQL
IndividualsReportingskillonLinkedin
*Source LinkedIn

Key Features for SQL 4 NoSQL
• Schema-free
• Accommodate different NoSQL Data Models
• SQL-like (Syntax and Features)
– Easier to transition from SQL
– Lower impedance mismatch to existing tools
– Joins, Aggregations, Filters, Sub Queries, …
• An Accepted Standard
• SPARQL fits this bill

What is SPARQL?
SPARQL is a modern schema free SQL-like query
language, originally developed by W3C for the
semantic web, that has found a home behind the
firewall in the Enterprise.
There are > 40 SPARQL implementations
Many exist for most Popular NoSQL systems.
The same SPARQL queries can run unmodified on
different NoSQL systems that support SPARQL.
SPARQL’s Logical Model is an RDF Graph.

SPARQL In Action
a1
a2
a3
a4
b1
b2
b3
b4
c1
c2
c3
c4
a b c
Tabular Results
Graph Results
Algebra
Parse
Optimize
Plan
NoSQL APIs
SPARQL
(Declarative)
Execute
NoSQLFiles
Remote
SPARQL
Federate
WHAT?
HOW?

NoSQL Data Models As Graphs
a1
a2
b1
b2
b3
b4
c1
c2
a1
a2
b1
b2
b3
b4
c1
c2
b5
a2
b6
b5
a2
b6
Tables A, B, and C Graph of Table Structure
A
B
C
CA
B

NoSQL Data Models As Graphs
v1 d2/k2
d1
k1
v4 v5
d2
k3
v2 d1/k1
k2
c1
d1 k1 v1
d2 k3 v4
v5
k2
v5
Documents d1and d2 in collection c1 Graph of Table Cells

Developer Perspective:
Why use SPARQL?
• Ease of Use
– Define What, Let Optimizer determine How
– Libraries for most programming languages
• Performance and Scalability
– Let Optimizer/Planner distribute Query/Data
• Interoperability
– Query Portability
– Access to Common Tools
• Query Generation – Modern Clean Query Syntax

Where
Data
Navigation
Machine
Learning
Graph
Algorithms
Imperative
Languages
Purpose
Built
Analytics
Complex Query
Ad-hoc analytics
SPARQL

Common Questions
• Is SPARQL slow?
• Is SPARQL scalable?
• Isn’t SPARQL just for the Semantics?
• Isn’t SPARQL just for Graphs?
• Can SPARQL query document databases?
• Can SPARQL query property graphs?
• Does SPARQL support analytics?
• Can SPARQL iterate?

For More Information
• W3C http://www.w3.org/TR/sparql11-query/

NoSQL Query Languages
• UnQL (Couchbase, SQLite)
• Cypher (neo4j)
• Schemaless SQL (Hadapt/Teradata)
• Hive/Pig (Hbase)
• CQL (Cassandra)
• …
• W3C SPARQL (Over 40 implementations)
– Oracle, SQL Server, MySQL, DB2, …
– MongoDB, Redis, SOLR, MemcacheD, Cassandra, Hbase
– Neo4j,…

SPARQL Features
• INSERT/ASK/DESCRIBE/SELECT/CONSTRUCT
• UNION, MINUS
• Aggregations
• Filters
• Sub queries
• Optionals
• Built in functions,
• Expressions
• Casting

Beyond the SPARQL 1.1 Standard
• Text Search
• Streaming
• Geospatial
• Temporal
• Inference/Rules
• Constraint Checking
• Probabilistic
• User Defined Extensions
• Result Formatting
• Workflow/Dataflow
• Stored Procedures
• Windowed Aggregates
• Recursion/iteration
• Graph Algorithms
• Social Network Analysis
• Machine Learning

The NoSQL Landscape
SQL NSQL
NoSQL
Row K-V
D-K-V
Column
Graph
RDF

Scala and Spark are ideal for big data applications. Scala is a functional programming language that runs on the Java Virtual Machine and has strong typing, concise syntax, and supports both object-oriented and functional programming. Spark is an open source cluster computing framework that provides fast, in-memory processing of large datasets across clusters of machines using its Resilient Distributed Datasets (RDDs). Using Scala with Spark provides benefits like leveraging Spark's Scala API and leveraging functional features of Scala that are a natural fit with Spark's programming model.

Intro to Python for C# Developers

Sarah Dutkiewicz

Adf and ala design c sharp corner toronto chapter feb 2019 meetup nik shahriar

Nilesh Shah

The Polyglot Data Scientist - Exploring R, Python, and SQL Server

Sarah Dutkiewicz

This document provides an overview of a presentation on being a polyglot data scientist using multiple languages and tools. It discusses using SQL, R, and Python together in data science work. The presentation covers the challenges of being a polyglot, how SQL Server with R or Python can help solve problems more easily, and examples of analyzing sensor data with these tools. It also discusses resources for learning more about R, Python, and machine learning services in SQL Server.

20160512 apache-spark-for-everyone

Amanda Casari

Spark: The Good, the Bad, and the Ugly

Sarah Guido

Given at Data Day Texas 2016. Apache Spark has been hailed as a trail-blazing new tool for doing distributed data science. However, since it's so new, it can be difficult to set up and hard to use. In this talk, I'll discuss the journey I've had using Spark for data science at Bitly over the past year. I'll talk about the benefits of using Spark, the challenges I've had to overcome, the caveats for using a cutting-edge technology such as this, and my hopes for the Spark project as a whole.

Big data analysing genomics and the bdg project

sree navya

This document discusses a project analyzing genomics using big data techniques. It introduces Spark, a framework for large-scale data analysis, and ADAM, a Spark-based framework for genomic data. Key points covered include an overview of Spark and Hadoop, data types and formats in genomics, and using ADAM and Spark to perform genomic analysis and queries on large datasets like the 1000 Genomes Project.

Agile Lab_BigData_Meetup_AKKA

Paolo Platter

This document discusses reactive and parallel programming using Akka. It introduces Akka, an open-source toolkit for building distributed, concurrent applications on the JVM. Akka supports actors, futures, software transactional memory, and message passing to handle concurrency. Akka cluster allows building fault-tolerant distributed systems. Examples demonstrate using Akka for microservices, streaming engines, authentication platforms, and IoT applications. The document concludes with an example of how Agile Lab uses Akka in their applications.

Given at Data Day Seattle 2015. Bitly generates over 9 billion clicks on shortened links a month, as well as over 100 million unique link shortens. Analyzing data of this scale is not without its challenges. At Bitly, we have started adopting Apache Spark as a way to process our data. In this talk, I’ll elaborate on how I use Spark as part of my data science workflow. I’ll cover how Spark fits into our existing architecture, the kind of problems I’m solving with Spark, and the benefits and challenges of using Spark for large-scale data science.

May 2021 Spark Testing ... or how to farm reputation on StackOverflow

Adam Doyle

Артем Логинов «NoSQL DBMSs review and non-relational approaches to store data»

Anna Shymchenko

This document provides an overview of NoSQL database management systems (DBMS) and non-relational approaches to storing data. It discusses the differences between SQL and NoSQL, considerations for choosing between them, the CAP theorem, common NoSQL data structures including key-value, columnar, document and graph models. Examples of DBMS using each data structure are given. The document also questions whether NoSQL systems are ready for production use.

Architecture - why so serious?

Barbara Fusinska

This document discusses architecture patterns for ASP.Net MVC web applications. It covers topics like software components, domain design, infrastructure, ORM strategies including micro ORMs and full-fledged ORMs. It also discusses repositories, database schema, separating read models from the domain, using commands to isolate business logic in the domain, and managing transactions per request. The presentation demonstrates an app called "Let's Go Out" and summarizes key points like using IoC containers, ORMs, separating read and domain models, implementing commands, and handling transactions.

Supercharge your RDBMS with Elasticsearch

Arthur Gimpel

Neo4j tms

_mdev_

Zeppelin at twitter (sf data science meetup, july 2016)

Prasad Wagle

Zeppelin is a data analysis notebook tool that Twitter adopted in late 2015. It is now used widely at Twitter with over 850 users creating 860 notebooks containing 4000 paragraphs. Zeppelin integrates with Twitter's various data platforms and has custom authentication and security features. Twitter engineers have contributed improvements to Zeppelin's stability, operations, and interpreters. Ongoing work focuses on further enhancing security, usability, and integration with Twitter systems.

5 Popular Choices for NoSQL on a Microsoft Platform - Tulsa - July 2018

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.

Data Science at Scale with Apache Spark and Zeppelin Notebook

Carolyn Duby

Distributed End-to-End Drug Similarity Analytics and Visualization Workflow w...

Databricks

The majority of a data scientist’s time is spent cleaning and organizing data before insights can be derived. Frequently, with large datasets, a lack of integration with visualization tools makes it hard to know what’s most interesting in the data and also creates challenges for validating numerical insights from models. Given the vast number of tools available in the ecosystem, it is hard to experiment with different tools to pick the most suitable one, especially given the complexity involved in integrating them with one’s solution. The speakers will present an easy to use workflow that solves this integration challenge by combining various open source libraries, databases (e.g. Hive, Postgres, MySQL, HBase etc.) and visualization with distributed analytics. Intel developed a highly scalable library built over Apache Spark with novel graph, statistical and machine learning algorithms that also enhances the user experience of Apache Spark via easier to use APIs. This session will showcase how to address the above mentioned issues for a drug similarity use case. We’ll go from ETL operations on raw drug data to deriving relevant features from the drug’s chemical structure using statistical and graph algorithms, using techniques to identify best model and parameters for this data to derive insights, and then demonstrating the ease of connectivity to different databases and visualization tools.

Apache Arrow: Cross-language Development Platform for In-memory Data

Wes McKinney

Apache Arrow is an open standard for in-memory columnar data and an analytical data processing platform. It aims to simplify system architectures, improve interoperability between systems, and enable data and algorithms to be reused across different programming languages. Arrow provides a portable in-memory data format and computational libraries to build analytical data processing systems. It is language-independent and supports data sharing and algorithm reuse between libraries and processes via shared memory with near-zero overhead.

LF_APIStrat17_Don't Repeat Yourself - Your API is Your Documentation

LF_APIStrat

"If you find yourself maintaining a set of documents explaining the use of your API, you haven't finished it's design yet. This talk will compare various strategies with examples, while discussing ways to determine the most appropriate method for your API. We will explore OAS (Swagger), Json-LD, Schema.org, HAL, Hydra, Siren, Semantic profiles, and other formats while comparing their relative strengths and weaknesses. After these options, we will arm you with a series of questions which direct you to the appropriate tool for your API. Discover how you can save considerable time and headaches by incorporating a self documenting method in your API design."

Introduction to Scala for Java Developers

Solix JJ

The document summarizes a Scala for Java developers meetup that covered why Scala, an introduction to basic Scala concepts, and a quick demo of Akka actors. Some key differences between Scala and Java highlighted include Scala's support for both object-oriented and functional programming, its ability to reduce lines of code through type inference, and its treatment of functions and numbers as objects. The meetup also covered Scala classes, case classes, pattern matching, traits, generics, and the Akka framework for building highly concurrent distributed applications on the JVM.

Bringing Deep Learning into production

Paolo Platter

- The document discusses deep learning frameworks and how to choose one for a given environment. It summarizes the strengths, weaknesses, opportunities and threats of popular frameworks like TensorFlow, Theano, Torch, Caffe, DeepLearning4J and H2O. - It recommends H2O as a good choice for enterprise environments due to its ease of use, scalability on big data, integration with Spark, Java/Scala support and commercial support. DeepLearning4J is also recommended for more advanced deep neural networks and multi-dimensional arrays. - The document proposes using Spark as a middleware to leverage multiple frameworks and avoid vendor lock-in, and describes Agile Lab's recommended stack for enterprises which combines H

C# Advanced L06-SQL Server+LINQ to SQL

Mohammad Shaker

This document discusses LINQ to SQL and how to query databases using LINQ. It provides examples of using LINQ to SQL to retrieve data from database tables and perform queries. Key topics covered include adding LINQ to SQL classes to a project, dragging and dropping database tables to generate mappings, and examples of LINQ queries to get teacher names and current lectures from database tables. The document also briefly mentions SQL Server Management Studio, database configuration in Visual Studio, and ORM mapping with LINQ to SQL.

Kafka Streams VS Spark Structured Streaming - Modern Stream Processing Engin...

Jacek Laskowski

DataMass Summit 2019 Edition --> http://summit.datamass.io There is quite a bit to learn about any stream processing engine. But at a reasonably high level they actually are very similar and have lots in common. Not only do all have to offer a high-level stream processing API to describe distributed streaming dataflows, but also a low-level API for more sophisticated streaming topologies. The engines translate the dataflow description into their internal runtime representation. That’s where the differences are and where we’ll be looking at. This talk compares two modern stream processing engines — Kafka Streams and Spark Structured Streaming. We’ll be talking about their internals and how the engines manage stateless and stateful streams. You will learn about their similarities and differences that should shed more light on the question when to use which engine.

Deep Learning and Streaming in Apache Spark 2.x with Matei Zaharia

Jen Aman

2017 continues to be an exciting year for Apache Spark. I will talk about new updates in two major areas in the Spark community this year: stream processing with Structured Streaming, and deep learning with high-level libraries such as Deep Learning Pipelines and TensorFlowOnSpark. In both areas, the community is making powerful new functionality available in the same high-level APIs used in the rest of the Spark ecosystem (e.g., DataFrames and ML Pipelines), and improving both the scalability and ease of use of stream processing and machine learning.

OpenSearchLab and the Lucene Ecosystem

Grant Ingersoll

Grant Ingersoll discussed using open source projects like Lucene for building an open search lab (OSL). Lucene is part of a large ecosystem of open source projects including Solr, Hadoop, Mahout, and others. It provides functionality for indexing, searching, and analyzing large amounts of data. The OSL could use a service-oriented architecture with Lucene and related projects to build a distributed, scalable system for content acquisition, storage, search and machine learning. Lucene is well-suited for information retrieval and data structure research.

Drupal for programmers

Michael Shahov

In this presentation we are talking about Drupal? What does it mean to a programmer? And basically we want to get the answer for "What's in it for me?" when we use Drupal. The presentation also shows some of the main highlights of Drupal? How is Drupal structured and how does the information flow? Also we have a list of the most commonly used modules and a list of some of the key new features of Drupal 8.

Scala and Spark are Ideal for Big Data - Data Science Pop-up Seattle

Domino Data Lab

Scala and Spark are each great tools for data processing and they work well together. They can process data via small simple interactive queries as well as in very large highly-available and scalable production systems. They provide an integrated framework for an ever growing wide range of data processing capabilities. We examine the reasons for this and also look a couple of simple data processing examples written in Scala. Presented by John Nestor, Sr Architect at 47 Degrees.

Sa introduction to big data pipelining with cassandra & spark west mins...

Simon Ambridge

This document provides an overview and outline of a 1-hour introduction to building a big data pipeline using Docker, Cassandra, Spark, Spark-Notebook and Akka. The introduction is presented as a half-day workshop at Devoxx November 2015. It uses a data pipeline environment from Data Fellas and demonstrates how to use scalable distributed technologies like Docker, Spark, Spark-Notebook and Cassandra to build a reactive, repeatable big data pipeline. The key takeaway is understanding how to construct such a pipeline.

Spark SQL

Caserta

The document provides an agenda and overview for a Big Data Warehousing meetup hosted by Caserta Concepts. The meetup agenda includes an introduction to SparkSQL with a deep dive on SparkSQL and a demo. Elliott Cordo from Caserta Concepts will provide an introduction and overview of Spark as well as a demo of SparkSQL. The meetup aims to share stories in the rapidly changing big data landscape and provide networking opportunities for data professionals.

What's hot

Data Science at Scale: Using Apache Spark for Data Science at Bitly

Sarah Guido

May 2021 Spark Testing ... or how to farm reputation on StackOverflow

Adam Doyle

Артем Логинов «NoSQL DBMSs review and non-relational approaches to store data»

Anna Shymchenko

Architecture - why so serious?

Barbara Fusinska

Supercharge your RDBMS with Elasticsearch

Arthur Gimpel

Neo4j tms

_mdev_

Zeppelin at twitter (sf data science meetup, july 2016)

Prasad Wagle

5 Popular Choices for NoSQL on a Microsoft Platform - Tulsa - July 2018

Matthew Groves

Data Science at Scale with Apache Spark and Zeppelin Notebook

Carolyn Duby

Distributed End-to-End Drug Similarity Analytics and Visualization Workflow w...

Databricks

Apache Arrow: Cross-language Development Platform for In-memory Data

Wes McKinney

LF_APIStrat17_Don't Repeat Yourself - Your API is Your Documentation

LF_APIStrat

Introduction to Scala for Java Developers

Solix JJ

Bringing Deep Learning into production

Paolo Platter

C# Advanced L06-SQL Server+LINQ to SQL

Mohammad Shaker

Kafka Streams VS Spark Structured Streaming - Modern Stream Processing Engin...

Jacek Laskowski

Deep Learning and Streaming in Apache Spark 2.x with Matei Zaharia

Jen Aman

OpenSearchLab and the Lucene Ecosystem

Grant Ingersoll

Drupal for programmers

Michael Shahov

What's hot (19)

Data Science at Scale: Using Apache Spark for Data Science at Bitly

May 2021 Spark Testing ... or how to farm reputation on StackOverflow

Артем Логинов «NoSQL DBMSs review and non-relational approaches to store data»

Architecture - why so serious?

Supercharge your RDBMS with Elasticsearch

Neo4j tms

Zeppelin at twitter (sf data science meetup, july 2016)

5 Popular Choices for NoSQL on a Microsoft Platform - Tulsa - July 2018

Data Science at Scale with Apache Spark and Zeppelin Notebook

Distributed End-to-End Drug Similarity Analytics and Visualization Workflow w...

Apache Arrow: Cross-language Development Platform for In-memory Data

LF_APIStrat17_Don't Repeat Yourself - Your API is Your Documentation

Introduction to Scala for Java Developers

Bringing Deep Learning into production

C# Advanced L06-SQL Server+LINQ to SQL

Kafka Streams VS Spark Structured Streaming - Modern Stream Processing Engin...

Deep Learning and Streaming in Apache Spark 2.x with Matei Zaharia

OpenSearchLab and the Lucene Ecosystem

Drupal for programmers

Similar to Is there a SQL for NoSQL?

Scala and Spark are Ideal for Big Data - Data Science Pop-up Seattle

Domino Data Lab

Sa introduction to big data pipelining with cassandra & spark west mins...

Simon Ambridge

Spark SQL

Caserta

No sql and sql - open analytics summit

Open Analytics

NoSQL and SQL databases can work together to handle real-time big data needs. Apache Drill is an open source tool that allows interactive analysis of big data using standard SQL queries across NoSQL, Hadoop, and relational data sources. It provides low-latency queries, full ANSI SQL support, and flexibility to handle rapidly evolving schemas and data in different systems. By enabling analysis of all data together using a common interface, it helps tackle challenges of combining operational and decision support systems on big, diverse datasets.

NoSQL Now! Webinar Series: Innovations in NoSQL Query Languages

DATAVERSITY

This webinar will cover the latest trends in advanced query languages for NoSQL databases. We’ll look at how innovations in vendor-independent standardized query languages allow NoSQL developers to query multiple types of data and multiple NoSQL databases using a single query language. We’ll see how using the right NoSQL query language promotes portability across multiple NoSQL databases, avoids vendor lock-in, and keeps your developers productive at the same time. We will be interviewing Matthias Brantner from 28msec and see on how they use JSONiq as a basis for a modern ETL framework that works on a diverse number of data sources.

Apache Spark in Industry

Dorian Beganovic

1. Apache Spark is an open source cluster computing framework for large-scale data processing. It is compatible with Hadoop and provides APIs for SQL, streaming, machine learning, and graph processing. 2. Over 3000 companies use Spark, including Microsoft, Uber, Pinterest, and Amazon. It can run on standalone clusters, EC2, YARN, and Mesos. 3. Spark SQL, Streaming, and MLlib allow for SQL queries, streaming analytics, and machine learning at scale using Spark's APIs which are inspired by Python/R data frames and scikit-learn.

Your Big Data Stack is Too Big!: Presented by Timothy Potter, Lucidworks

Lucidworks

Timothy Potter presented at a Big Data conference in Boston from October 11-14, 2016. He discussed how Lucidworks Fusion provides an alternative to traditional big data stacks that emphasizes fast access, agility and automation over integration. Fusion allows for common access patterns like fast lookups, ranked retrieval and distributed scans while integrating technologies like Solr, Spark, HDFS and more. It provides tools for data ingestion, time-based partitioning, analytics, machine learning and more to solve business problems rather than focus on infrastructure.

8th TUC Meeting - Zhe Wu (Oracle USA). Bridging RDF Graph and Property Graph...

LDBC council

Big Data for Data Scientists - WeCloudData

WeCloudData

Apache Spark Fundamentals

Zahra Eskandari

This document provides an overview of the Apache Spark framework. It covers Spark fundamentals including the Spark execution model using Resilient Distributed Datasets (RDDs), basic Spark programming, and common Spark libraries and use cases. Key topics include how Spark improves on MapReduce by operating in-memory and supporting general graphs through its directed acyclic graph execution model. The document also reviews Spark installation and provides examples of basic Spark programs in Scala.

Nashville analytics summit aug9 no sql mike king dell v1.5

Mike King

Here are potential solutions for the NoSQL use cases with explanations: UC1 - Email: MongoDB. Email data is document-oriented with a well-defined schema. MongoDB would allow flexible schema and easy querying of email metadata and contents. UC2 - Twitter: Cassandra. Twitter data arrives continuously in JSON format and relationships are many-to-many. Cassandra is a good fit as a wide column store for high write throughput and querying across hashtags and users. UC3 - Householding: HBase. Large volumes of batch data from different sources and formats need to be joined on customer keys with no strict schema. HBase is optimized for random read/write access to structured and semi-structured data at scale

Oracle OpenWo2014 review part 03 three_paa_s_database

Getting value from IoT, Integration and Data Analytics

This document provides a summary of Oracle OpenWorld 2014 discussions on database cloud, in-memory database, native JSON support, big data, and Internet of Things (IoT) technologies. Key points include: - Database Cloud on Oracle offers pay-as-you-go pricing and self-service provisioning similar to on-premise databases. - Oracle Database 12c includes an in-memory option that can provide up to 100x faster analytics queries and 2-4x faster transaction processing. - Native JSON support in 12c allows storing and querying JSON documents within the database. - Big data technologies like Oracle Big Data SQL and Oracle Big Data Discovery help analyze large and diverse data sets from sources like

Building data pipelines for modern data warehouse with Apache® Spark™ and .NE...

Michael Rys

Introducing NoSQL and MongoDB to complement Relational Databases (AMIS SIG 14...

Lucas Jellema

Transitioning from Java to Scala for Spark - March 13, 2019

Gravy Analytics

Gravy Analytics ingests ~17 billion records daily of data and improve and refine that data into many data products at various levels of aggregation. To meet the challenges of our product requirements and scale we constantly evaluate new technologies. Spark has become central to our ability to process ever increasing amounts of data through our data factory. In late 2017 and throughout 2018, we have improved our ability to work with Spark by migrating all Spark jobs to Scala. In this discussion, we’ll cover areas which were more difficult from a Spark perspective to develop in Java than Scala as well as some of the challenges we met along the way.

Spark - The Ultimate Scala Collections by Martin Odersky

Spark Summit

Spark is a domain-specific language for working with collections that is implemented in Scala and runs on a cluster. While similar to Scala collections, Spark differs in that it is lazy and supports additional functionality for paired data. Scala can learn from Spark by adding views to make laziness clearer, caching for persistence, and pairwise operations. Types are important for Spark as they prevent logic errors and help with programming complex functional operations across a cluster.

MySQL Cluster as Transactional NoSQL (KVS)

Ryusuke Kajiyama

Инструменты программиста

Andrew Fadeev

Apache drill

MapR Technologies

Oracle's Take On NoSQL

Alexander Shopov

This document provides an overview of NoSQL databases and Oracle's perspective on them. It begins by explaining that NoSQL databases aim to be highly available and able to scale horizontally. It then discusses some of the origins and types of NoSQL databases, including key-value stores, document databases, column family stores, and graph databases. It also covers Brewer's CAP theorem and how NoSQL databases sacrifice consistency for availability and partition tolerance. Finally, it discusses how Oracle has incorporated some NoSQL concepts into its own database technologies over time.

Similar to Is there a SQL for NoSQL? (20)

Scala and Spark are Ideal for Big Data - Data Science Pop-up Seattle

Sa introduction to big data pipelining with cassandra & spark west mins...

Spark SQL

No sql and sql - open analytics summit

NoSQL Now! Webinar Series: Innovations in NoSQL Query Languages

Apache Spark in Industry

Your Big Data Stack is Too Big!: Presented by Timothy Potter, Lucidworks

8th TUC Meeting - Zhe Wu (Oracle USA). Bridging RDF Graph and Property Graph...

Big Data for Data Scientists - WeCloudData

Apache Spark Fundamentals

Nashville analytics summit aug9 no sql mike king dell v1.5

Oracle OpenWo2014 review part 03 three_paa_s_database

Building data pipelines for modern data warehouse with Apache® Spark™ and .NE...

Introducing NoSQL and MongoDB to complement Relational Databases (AMIS SIG 14...

Transitioning from Java to Scala for Spark - March 13, 2019

Spark - The Ultimate Scala Collections by Martin Odersky

MySQL Cluster as Transactional NoSQL (KVS)

Инструменты программиста

Apache drill

Oracle's Take On NoSQL

Recently uploaded

一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理

g4dpvqap0

毕业原版【微信:41543339】【(爱大毕业证书)爱丁堡大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才留信网服务项目： 1、留学生专业人才库服务（留信分析） 2、国（境）学习人员提供就业推荐信服务 3、留学人员区块链存储服务 → 【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

Learn SQL from basic queries to Advance queries

manishkhaire30

Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively. Key Highlights: Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation. Advanced Queries: Learn to craft complex queries to uncover deep insights from your data. Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets. Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios. Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making. Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data! #DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics

Global Situational Awareness of A.I. and where its headed

vikram sood

You can see the future first in San Francisco. Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum. The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war. Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change. Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride. Let me tell you what we see.

STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...

sameer shah

一比一原版(UO毕业证)渥太华大学毕业证如何办理

aqzctr7x

UO毕业证录取书【微信95270640】购买（渥太华大学毕业证成绩单硕士学历）Q微信95270640代办UO学历认证留信网伪造渥太华大学学位证书精仿渥太华大学本科/硕士文凭证书补办渥太华大学 diplomaoffer,Transcript购买渥太华大学毕业证成绩单购买UO假毕业证学位证书购买伪造渥太华大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。文凭办理流程： 1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：微信95270640我们有专业老师帮你查询）； 2开始安排制作毕业证成绩单电子图； 3毕业证成绩单电子版做好以后发送给您确认； 4毕业证成绩单电子版您确认信息无误之后安排制作成品； 5成品做好拍照或者视频给您确认； 6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。 7完成交易删除客户资料高精端提供以下服务：一：渥太华大学渥太华大学毕业证文凭证书全套材料从防伪到印刷水印底纹到钢印烫金二：真实使馆认证（留学人员回国证明）使馆存档三：真实教育部认证教育部存档教育部留服网站可查四：留信认证留学生信息网站可查五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信95270640） A.为什么留学生需要操作留信认证? 留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询! B.为什么我们提供的毕业证成绩单具有使用价值？查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃的头说还好醒得及时要不家早被人掏空了到时连电视也没得看啰不过父亲还是夸山娃能临危不乱随机应变有胆有谋山娃笑笑说那都是书上学的看童话和小说时多

一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理

74nqk8xf

毕业原版【微信:41543339】【(Coventry毕业证书)考文垂大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才留信网服务项目： 1、留学生专业人才库服务（留信分析） 2、国（境）学习人员提供就业推荐信服务 3、留学人员区块链存储服务 → 【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

My burning issue is homelessness K.C.M.O.

rwarrenll

Palo Alto Cortex XDR presentation .......

Sachin Paul

办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样

apvysm8

原版一模一样【微信：741003700 】【(uts毕业证书)悉尼科技大学毕业证学历证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma 1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700 留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才

06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...

Timothy Spann

06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI Discussion on Vector Databases, Unstructured Data and AI https://www.meetup.com/unstructured-data-meetup-new-york/ This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.

一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理

nyfuhyz

毕业原版【微信:176555708】【(UMN毕业证书)明尼苏达大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才留信网服务项目： 1、留学生专业人才库服务（留信分析） 2、国（境）学习人员提供就业推荐信服务 3、留学人员区块链存储服务 → 【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

A presentation that explain the Power BI Licensing

AlessioFois2

Analysis insight about a Flyball dog competition team's performance

roli9797

一比一原版(Harvard毕业证书)哈佛大学毕业证如何办理

zsjl4mimo

毕业原版【微信:41543339】【(Harvard毕业证书)哈佛大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才留信网服务项目： 1、留学生专业人才库服务（留信分析） 2、国（境）学习人员提供就业推荐信服务 3、留学人员区块链存储服务 → 【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

Everything you wanted to know about LIHTC

Roger Valdez

Experts live - Improving user adoption with AI

jitskeb

一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理

nuttdpt

毕业原版【微信:176555708】【(UCSF毕业证书)旧金山分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才留信网服务项目： 1、留学生专业人才库服务（留信分析） 2、国（境）学习人员提供就业推荐信服务 3、留学人员区块链存储服务 → 【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理

nuttdpt

毕业原版【微信:176555708】【(UCSB毕业证书)圣芭芭拉分校毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】【业务选择办理准则】一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才留信网服务项目： 1、留学生专业人才库服务（留信分析） 2、国（境）学习人员提供就业推荐信服务 3、留学人员区块链存储服务 → 【关于价格问题（保证一手价格）】我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子我给客户的都是第一手的代理价格，因为我想坦诚对待大家不想跟大家在价格方面浪费时间对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

The Ipsos - AI - Monitor 2024 Report.pdf

Social Samosa

Population Growth in Bataan: The effects of population growth around rural pl...

Bill641377

Recently uploaded (20)

一比一原版(爱大毕业证书)爱丁堡大学毕业证如何办理

Learn SQL from basic queries to Advance queries

Global Situational Awareness of A.I. and where its headed

STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...

一比一原版(UO毕业证)渥太华大学毕业证如何办理

一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理

My burning issue is homelessness K.C.M.O.

Palo Alto Cortex XDR presentation .......

办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样

06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...

一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理

A presentation that explain the Power BI Licensing

Analysis insight about a Flyball dog competition team's performance

一比一原版(Harvard毕业证书)哈佛大学毕业证如何办理

Everything you wanted to know about LIHTC

Experts live - Improving user adoption with AI

一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理

一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理

The Ipsos - AI - Monitor 2024 Report.pdf

Population Growth in Bataan: The effects of population growth around rural pl...

Is there a SQL for NoSQL?

1. Is SPARQL the SQL for NoSQL? Arthur Keen SPARQL City http://www.sparqlcity.com

2. Outline • The Business Case • Why SPARQL? • Developer Perspective • Common Questions and Misconceptions • Questions

3. Why Do We Need a SQL for NoSQL? • How are we going to • Process far more data (than SQL), • With fewer people (than SQL), • With skills fragmented over many databases?

4. Too Much Data > 30X Structured

5. Scarce Resources: SQL > 15 X NoSQL *Source LinkedIn IndividualsReportingskillonLinkedin

6. Fragmented Skills IndividualsReportingskillonLinkedin *Source LinkedIn

7. Millions Know SQL IndividualsReportingskillonLinkedin *Source LinkedIn

8. Key Features for SQL 4 NoSQL • Schema-free • Accommodate different NoSQL Data Models • SQL-like (Syntax and Features) – Easier to transition from SQL – Lower impedance mismatch to existing tools – Joins, Aggregations, Filters, Sub Queries, … • An Accepted Standard • SPARQL fits this bill

9. What is SPARQL? SPARQL is a modern schema free SQL-like query language, originally developed by W3C for the semantic web, that has found a home behind the firewall in the Enterprise. There are > 40 SPARQL implementations Many exist for most Popular NoSQL systems. The same SPARQL queries can run unmodified on different NoSQL systems that support SPARQL. SPARQL’s Logical Model is an RDF Graph.

10. SPARQL In Action a1 a2 a3 a4 b1 b2 b3 b4 c1 c2 c3 c4 a b c Tabular Results Graph Results Algebra Parse Optimize Plan NoSQL APIs SPARQL (Declarative) Execute NoSQLFiles Remote SPARQL Federate WHAT? HOW?

11. NoSQL Data Models As Graphs a1 a2 b1 b2 b3 b4 c1 c2 a1 a2 b1 b2 b3 b4 c1 c2 b5 a2 b6 b5 a2 b6 Tables A, B, and C Graph of Table Structure A B C CA B

12. NoSQL Data Models As Graphs v1 d2/k2 d1 k1 v4 v5 d2 k3 v2 d1/k1 k2 c1 d1 k1 v1 d2 k3 v4 v5 k2 v5 Documents d1and d2 in collection c1 Graph of Table Cells

13. Developer Perspective: Why use SPARQL? • Ease of Use – Define What, Let Optimizer determine How – Libraries for most programming languages • Performance and Scalability – Let Optimizer/Planner distribute Query/Data • Interoperability – Query Portability – Access to Common Tools • Query Generation – Modern Clean Query Syntax

14. Where Data Navigation Machine Learning Graph Algorithms Imperative Languages Purpose Built Analytics Complex Query Ad-hoc analytics SPARQL

15. Crossing the Chasm

16. Common Questions • Is SPARQL slow? • Is SPARQL scalable? • Isn’t SPARQL just for the Semantics? • Isn’t SPARQL just for Graphs? • Can SPARQL query document databases? • Can SPARQL query property graphs? • Does SPARQL support analytics? • Can SPARQL iterate?

17. For More Information • W3C http://www.w3.org/TR/sparql11-query/

18. Questions

19. Backup Slides

20. NoSQL Query Languages • UnQL (Couchbase, SQLite) • Cypher (neo4j) • Schemaless SQL (Hadapt/Teradata) • Hive/Pig (Hbase) • CQL (Cassandra) • … • W3C SPARQL (Over 40 implementations) – Oracle, SQL Server, MySQL, DB2, … – MongoDB, Redis, SOLR, MemcacheD, Cassandra, Hbase – Neo4j,…

21. SPARQL Features • INSERT/ASK/DESCRIBE/SELECT/CONSTRUCT • UNION, MINUS • Aggregations • Filters • Sub queries • Optionals • Built in functions, • Expressions • Casting

22. Beyond the SPARQL 1.1 Standard • Text Search • Streaming • Geospatial • Temporal • Inference/Rules • Constraint Checking • Probabilistic • User Defined Extensions • Result Formatting • Workflow/Dataflow • Stored Procedures • Windowed Aggregates • Recursion/iteration • Graph Algorithms • Social Network Analysis • Machine Learning

23. The NoSQL Landscape SQL NSQL NoSQL Row K-V D-K-V Column Graph RDF

Editor's Notes

Do document ID’s need to be RDF IRI’s? Do keys need to be RDF Predicates?

Is there a SQL for NoSQL?

Recommended

Recommended

More Related Content

What's hot

What's hot (19)

Similar to Is there a SQL for NoSQL?

Similar to Is there a SQL for NoSQL? (20)

Recently uploaded

Recently uploaded (20)

Is there a SQL for NoSQL?

Editor's Notes