Ditching the Middleware

•

0 j'aime•289 vues

This document discusses ditching middleware in favor of a NoSQL approach. It notes that NoSQL solutions are defined by what they don't contain compared to traditional databases. The document also discusses how data model changes can affect all tiers of an application from presentation to data schema. It provides examples of using a controller model to access data without middleware layers.

Technologie

Ditching the Middleware

NoSQL Now! 2011

David Wood
3 Round Stones
david@3roundstones.com

#nosql, like organic food, is deﬁned
by what it doesn't contain.
Comparing noSQL solutions is like
comparing organic apples and
oranges...

Eric van der Vlist
@evlist
25 July 2011

l

l

l

Data Access Business Logic Presentation Logic

Data model changes
effect all tiers:
• your presentation
• your processing model
• your data schema.

(CPU and disk harder to quantify/compare)

Data Access

Controller

Business Logic

View Model

Presentation Logic

Controller
RDF Store
Web server
HTTP GET Class
Resource
request

Viewable

RDF response

SPARQL query XML Template
template apply.xsl
Engine

HTTP
response HTML

HTTP GET
e.g. c
302 (Found)
PURL
l
i
Service
e HTTP GET
n Web
t 200 (OK)
Service

Data Master

Normal Ops

Dynamic
DNS

Failover /
Spillover

Data Slave

PURL Services
by different parties
A.org

Normal Ops
for A.org

Write-Segmented
Dynamic
data source DNS

B.com

e.g. Failover /
Spillover
for A.org

C.net

PURL Services
by different parties
A.org

Normal Ops
for C.net
to any host
B.com
Dynamic
DNS

C.net

This work is Copyright © 2011 3 Round Stones Inc.
It is licensed under the Creative Commons Attribution 3.0 Unported License
Full details at: http://creativecommons.org/licenses/by/3.0/

You are free:

to Share — to copy, distribute and transmit the work

to Remix — to adapt the work

Under the following conditions:
Attribution. You must attribute the work in the manner specified by the author or
licensor (but not in any way that suggests that they endorse you or your use of the
work).

• For any reuse or distribution, you must make clear to others the license terms of this work.
• Any of the above conditions can be waived if you get permission from the copyright holder.
• Nothing in this license impairs or restricts the author's moral rights.
• Some Content in the work may be licensed under different terms, this is noted separately.

Contenu connexe

Tendances

Many Organizations are currently processing various types of data and in different formats. Most often this data will be in free form, As the consumers of this data growing it’s imperative that this free-flowing data needs to adhere to a schema. It will help data consumers to have an expectation of about the type of data they are getting and also they will be able to avoid immediate impact if the upstream source changes its format. Having a uniform schema representation also gives the Data Pipeline a really easy way to integrate and support various systems that use different data formats. SchemaRegistry is a central repository for storing, evolving schemas. It provides an API & tooling to help developers and users to register a schema and consume that schema without having any impact if the schema changed. Users can tag different schemas and versions, register for notifications of schema changes with versions etc. In this talk, we will go through the need for a schema registry and schema evolution and showcase the integration with Apache NiFi, Apache Kafka, Apache Storm.

Schema Registry - Set you Data Free

DataWorks Summit/Hadoop Summit

Apache Calcite overview

Julian Hyde

InfluxDB IOx Tech Talks: Query Engine Design and the Rust-Based DataFusion in...

InfluxData

Stable and robust data pipelines are a critical component of the data infrastructure of enterprises. Most commonly, data pipelines ingest messy data sources with incorrect, incomplete or inconsistent records and produce curated and/or summarized data for consumption by subsequent applications. In this talk we go over new and upcoming features in Spark that enable it to better serve such workloads. Such features include isolation of corrupt input records and files, useful diagnostic feedback to users and improved support for nested type handling which is common in ETL jobs.

Spark Meetup Amsterdam - Dealing with Bad Actors in ETL, Databricks

GoDataDriven

Seattle Scalability Meetup - Ted Dunning - MapR

clive boulton

Data Storage Formats in Hadoop

Botond Balázs

Redis-ML is a Redis module for high performance, real-time serving of Spark-ML models. It allows users to train large complex models in Spark, and then store and query the models directly on Redis clusters. The high throughput and low latency of Redis-ML allows users to perform heavy classification operations in real time while using a minimal number of servers. This unique architecture enables significant savings in resources compared to current commonly used methods, without loss in precision or server performance. This session will demonstrate how to build a production-level recommendation system from the ground up using Spark-ML and Redis-ML. It will also describe performance and accuracy benchmarks, comparing the results with current standard methods.

Building a Large Scale Recommendation Engine with Spark and Redis-ML with Sha...

Databricks

For fast food recommendation use cases, user behavior sequences and context features (such as time, weather, and location) are both important factors to be taken into consideration. At Burger King, we have developed a new state-of-the-art recommendation model called Transformer Cross Transformer (TxT). It applies Transformer encoders to capture both user behavior sequences and complicated context features and combines both transformers through the latent cross for joint context-aware fast food recommendations. Online A/B testings show not only the superiority of TxT comparing to existing methods results but also TxT can be successfully applied to other fast food recommendation use cases outside of Burger King.

Context-aware Fast Food Recommendation with Ray on Apache Spark at Burger King

Databricks

ORC files were originally introduced in Hive, but have now migrated to an independent Apache project. This has sped up the development of ORC and simplified integrating ORC into other projects, such as Hadoop, Spark, Presto, and Nifi. There are also many new tools that are built on top of ORC, such as Hive’s ACID transactions and LLAP, which provides incredibly fast reads for your hot data. LLAP also provides strong security guarantees that allow each user to only see the rows and columns that they have permission for. This talk will discuss the details of the ORC and Parquet formats and what the relevant tradeoffs are. In particular, it will discuss how to format your data and the options to use to maximize your read performance. In particular, we’ll discuss when and how to use ORC’s schema evolution, bloom filters, and predicate push down. It will also show you how to use the tools to translate ORC files into human-readable formats, such as JSON, and display the rich metadata from the file including the type in the file and min, max, and count for each column.

ORC File - Optimizing Your Big Data

DataWorks Summit

Introduction to R and R Studio

Rupak Roy

Apache PIG

Prashant Gupta

Scylla Summit 2017: Running a Soft Real-time Service at One Million QPS

ScyllaDB

Cost-based Query Optimization

DataWorks Summit/Hadoop Summit

Transformation Processing Smackdown; Spark vs Hive vs Pig

Lester Martin

Spark sql

Freeman Zhang

Building highly efficient data lakes using Apache Hudi (Incubating) Even with the exponential growth in data volumes, ingesting/storing/managing big data remains unstandardized & in-efficient. Data lakes are a common architectural pattern to organize big data and democratize access to the organization. In this talk, we will discuss different aspects of building honest data lake architectures, pin pointing technical challenges and areas of inefficiency. We will then re-architect the data lake using Apache Hudi (Incubating), which provides streaming primitives right on top of big data. We will show how upserts & incremental change streams provided by Hudi help optimize data ingestion and ETL processing. Further, Apache Hudi manages growth, sizes files of the resulting data lake using purely open-source file formats, also providing for optimized query performance & file system listing. We will also provide hands-on tools and guides for trying this out on your own data lake. Speaker: Vinoth Chandar (Uber) Vinoth is Technical Lead at Uber Data Infrastructure Team

SF Big Analytics 20190612: Building highly efficient data lakes using Apache ...

Chester Chen

Introduction to Spark with Python

Gokhan Atil

* Title * Spark After Dark 1.5: Deep Dive Into Latest Perf and Scale Improvements in Spark Ecosystem * Abstract * Combining the most popular and technically-deep material from his wildly popular Advanced Apache Spark Meetup, Chris Fregly will provide code-level deep dives into the latest performance and scalability advancements within the Apache Spark Ecosystem by exploring the following: 1) Building a Scalable and Performant Spark SQL/DataFrames Data Source Connector such as Spark-CSV, Spark-Cassandra, Spark-ElasticSearch, and Spark-Redshift 2) Speeding Up Spark SQL Queries using Partition Pruning and Predicate Pushdowns with CSV, JSON, Parquet, Avro, and ORC 3) Tuning Spark Streaming Performance and Fault Tolerance with KafkaRDD and KinesisRDD 4) Maintaining Stability during High Scale Streaming Ingestion using Approximations and Probabilistic Data Structures from Spark, Redis, and Twitter's Algebird 5) Building Effective Machine Learning Models using Feature Engineering, Dimension Reduction, and Natural Language Processing with MLlib/GraphX, ML Pipelines, DIMSUM, Locality Sensitive Hashing, and Stanford's CoreNLP 6) Tuning Core Spark Performance by Acknowledging Mechanical Sympathy for the Physical Limitations of OS and Hardware Resources such as CPU, Memory, Network, and Disk with Project Tungsten, Asynchronous Netty, and Linux epoll * Demos * This talk features many interesting and audience-interactive demos - as well as code-level deep dives into many of the projects listed above. All demo code is available on Github at the following link: https://github.com/fluxcapacitor/pipeline/wiki In addition, the entire demo environment has been Dockerized and made available for download on Docker Hub at the following link: https://hub.docker.com/r/fluxcapacitor/pipeline/ * Speaker Bio * Chris Fregly is a Principal Data Solutions Engineer for the newly-formed IBM Spark Technology Center, an Apache Spark Contributor, a Netflix Open Source Committer, as well as the Organizer of the global Advanced Apache Spark Meetup and Author of the Upcoming Book, Advanced Spark. Previously, Chris was a Data Solutions Engineer at Databricks and a Streaming Data Engineer at Netflix. When Chris isn’t contributing to Spark and other open source projects, he’s creating book chapters, slides, and demos to share knowledge with his peers at meetups and conferences throughout the world.

Paris Spark Meetup Oct 26, 2015 - Spark After Dark v1.5 - Best of Advanced Ap...

Chris Fregly

Sqoop2 refactoring for generic data transfer - NYC Sqoop Meetup

gethue

Oracle & sql server comparison 2

Mohsen B

Tendances (20)

Schema Registry - Set you Data Free

Apache Calcite overview

InfluxDB IOx Tech Talks: Query Engine Design and the Rust-Based DataFusion in...

Spark Meetup Amsterdam - Dealing with Bad Actors in ETL, Databricks

Seattle Scalability Meetup - Ted Dunning - MapR

Data Storage Formats in Hadoop

Building a Large Scale Recommendation Engine with Spark and Redis-ML with Sha...

Context-aware Fast Food Recommendation with Ray on Apache Spark at Burger King

ORC File - Optimizing Your Big Data

Introduction to R and R Studio

Apache PIG

Scylla Summit 2017: Running a Soft Real-time Service at One Million QPS

Cost-based Query Optimization

Transformation Processing Smackdown; Spark vs Hive vs Pig

Spark sql

SF Big Analytics 20190612: Building highly efficient data lakes using Apache ...

Introduction to Spark with Python

Paris Spark Meetup Oct 26, 2015 - Spark After Dark v1.5 - Best of Advanced Ap...

Sqoop2 refactoring for generic data transfer - NYC Sqoop Meetup

Oracle & sql server comparison 2

En vedette

Fighting Phantom Firms in the UK: From Opening Up Datasets to Reshaping Data ...

Jonathan Gray

Portfolio

3port Marketing Digital

CDOs are a hot topic of discussion, but does the reality support the hype? We spent the last few weeks talking to CDOs, and based on our research, the answer is absolutely “yes.” And then sometimes it’s also “no.” Though there is lots of prognostication about what CDOs “should” be doing, we found a lot of misinformation and misunderstanding about how the role operates in practice. The fact is that CDOs are a multi-faceted bunch. Most of them work on enterprise data strategy, but the rest of their time is highly customized to the needs of their particular organization, including analytics, data operations, and even application development. The real world of today’s CDO is varied, exciting, sometimes frustrating, and often creative! Please join John Ladley and Tony Shaw for their launch of the DATAVERSITY “Status of the CDO” Report, a thorough survey of what CDOs are doing today. We will cover topics including: What motivates an organization to appoint a Chief Data Officer? Who does the CDO report to? What experience does it take to become a CDO? Do CDOs have their own staff and budget? How do functional responsibilities vary from industry to industry? What are the differences between the CIO and CDO roles? What are some of the pitfalls and unrealistic expectations for CDOs? What issues are on the horizon for Chief Data Officers?

The Chief Data Officer's Agenda: The Status of the Chief Data Officer

DATAVERSITY

Campanha de marketing desenvolvida para Academia República em Jundiaí

Douglas Benjamim

I gave this talk Feb 18th, 2013 at Dev Nexus as an introduction to Backbone.js http://www.devnexus.com/s/presentations#id-1481 Moving logic to the client unlocks unlimited potential, but is a challenging endeavor. In this talk we’ll be covering the use cases for Backbone, and show real world examples. We’ll be covering the tenets of client side MVC development, and what tools and resources you need on the server to make this possible, especially when deploying to production. We’ll also get to see how development with a framework like Backbone changes your applications’ workflow, and we’ll be diving into some of those approaches typical in day to day development. You should walk away knowing: Backbone’s role, when to use it, and how it works Typical challenges you’ll need to overcome What server side tools are a necessity Where to go from here Skill Level: Beginner For videos and screencasts visit: http://www.backbonerails.com

Backbone JS - Journey to the Front End [Dev Nexus Conference]

Brian Mann

So many of us have learned data modeling and database design approaches from working with one database or data technology. We may have used only one data modeling tool. That means our vocabularies around identifiers and keys tend to be product specfic. Do you know the difference between a unique index and a unique key? What about the difference between RI, FK and AK? In this webinar we'll look at the generic and proprietary terms for these concepts, as well as where they fit in the data modeling and database design process. We'll also look at implementation options across a few commercial DBMSs and datastores. Bring your developers and DBAs, too. These concepts span data activities and it's important that your team understand each other and where they, their tools and approaches need to support these features, too.

Modeling Webinar: The Key to Keys

DATAVERSITY

Lúcio Neves

kathy franco

NEWSROOM KPI SYSTEM : METRO TV

Arief Adi Wibowo

Case Eli Corrêa Filho

Provnet Agência Web

Thanks to wiki leaks, the NSA and intense regulatory environments, metadata is now a common term. What is the role of the Top Data Job and what are the options to deploy an effective metadata environment. John will explore some new avenues and lessons learned for metadata. In this webinar, we will discuss: Metadata Architectures Metadata Types and Categories Trends in Metadata Technology The CDO’s role in the Metadata Function

CDO Webinar: Metadata and the CDO

DATAVERSITY

Campanha Digital Barbados

Provnet Agência Web

Case Final Barbados

Provnet Agência Web

Gerenciamento De Campanha Digital

Agência Stock interativo

Legal hacks para startups

Catalina Quintana G.

LUCRO REAL. CUSTO. ESTOQUE. PERDA. MEDICAMENTOS E INSUMOS CONTROLADOS. LAUDO ...

Tania Gurgel

In this webinar we will blend two hot topics into one conversation: Data Strategy The Role of The Chief Data Officer Our guest Jim Tyo is ideally placed to provide perspective in both areas, because as Chief Data Officer he heads up the Enterprise Data Office (EDO) of BB&T, where his main focus is to leverage data to drive business insights into revenue opportunities, ensure regulatory compliance and identify/manage risk. John Ladley will discuss with Jim how he manages the EDO to accomplish these objectives, and will delve into the following topics along the way: What was the background to BB&T’s decision to appoint a Chief Data Officer, and what qualifications did Jim bring to the job? What’s the organizational and reporting structure around the role? What authority does the CDO have to get things done? What are the five functional tenets of CDO responsibility at BB&T? How does the CDO provide leadership across various areas including Data Governance, Quality, Analytics and Architecture. What style of leadership has proven most effective? How does the CDO differ from the role of the CIO? Have there been notable successes of the CDO and/or EDO? Have there been any mistakes along the way…lessons learned that might be handled differently next time? What issues are on the horizon for the next year? What’s next up for Jim as the CDO?

The Chief Data Officer's Agenda: Leading the Enterprise Data Strategy and Org...

DATAVERSITY

You learned to data model and you are eager to get started. You have your Normal Forms poster on your cube wall and your data modeling tool templates ready to go. You are ready...but...are you sure you aren't introducing issues that will later cost you a lot of pain for the business? Or for your technical teammates? In this session, we look at the workarounds, technical debt and clever designs that can cause you grief when you least expect it. We will also include perfectly fine features that can haunt you later. We will discuss how these things happen and give you tools to identify the ones that cause the most pain and lead to a lot of extra work. Learn how to keep your job, have happier users and get more rest.

Heart of Data Modeling Webinar: The Ticking Timebombs in Your Data Model

DATAVERSITY

Emenda Constitucional 87/2015 - PERGUNTAS FREQUENTES SEFA SP

Tania Gurgel

Data is the ultimate cross-enterprise asset. Data and information flow throughout an organization and require both business and IT expertise to manage them effectively. The same data is available for multiple users, unlike physical products that once sold to a buyer cannot be resold to the next customer who walks through the door or places an online order. These properties create unique challenges for the CDO chartered to oversee the data management organization. The purpose of any data management work is to ensure high quality, trusted data and information, yet data quality is a profession within itself. Join us to learn what a CDO needs to know about data quality: The relationship between data quality, governance, and other data management functions Options for structuring within your organization The difference between data quality programs and projects What a CDO can do to help both data quality programs and projects succeed

The Chief Data Officer's Agenda: What a CDO Needs to Know about Data Quality

DATAVERSITY

Oficio sobre a revisão das disposições do Convênio 93/2015 ao Confaz

Tania Gurgel

En vedette (20)

Fighting Phantom Firms in the UK: From Opening Up Datasets to Reshaping Data ...

Portfolio

The Chief Data Officer's Agenda: The Status of the Chief Data Officer

Campanha de marketing desenvolvida para Academia República em Jundiaí

Backbone JS - Journey to the Front End [Dev Nexus Conference]

Modeling Webinar: The Key to Keys

Lúcio Neves

NEWSROOM KPI SYSTEM : METRO TV

Case Eli Corrêa Filho

CDO Webinar: Metadata and the CDO

Campanha Digital Barbados

Case Final Barbados

Gerenciamento De Campanha Digital

Legal hacks para startups

LUCRO REAL. CUSTO. ESTOQUE. PERDA. MEDICAMENTOS E INSUMOS CONTROLADOS. LAUDO ...

The Chief Data Officer's Agenda: Leading the Enterprise Data Strategy and Org...

Heart of Data Modeling Webinar: The Ticking Timebombs in Your Data Model

Emenda Constitucional 87/2015 - PERGUNTAS FREQUENTES SEFA SP

The Chief Data Officer's Agenda: What a CDO Needs to Know about Data Quality

Oficio sobre a revisão das disposições do Convênio 93/2015 ao Confaz

Similaire à Ditching the Middleware

Object- Relational Persistence in Smalltalk

ESUG

Solr -

Hao Chen 陈浩

Ruby On Rails

guest4faf46

SQL and NoSQL in SQL Server

Michael Rys

Hadoop and Big Data Overview

Prabhu Thukkaram

All Aboard the Databus

Amy W. Tang

Sybase To Oracle Migration for DBAs

Clearwater Technical Group Inc

Graphity - Generic Linked Data Platform

Julius Šėporaitis

Rest

Arnon Rotem-Gal-Oz

Ruby on rails

chamomilla

A talk given at ACM SIGMOD 2018 in support of the paper <a href="https://arxiv.org/abs/1802.10233"> Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources</a>. Apache Calcite is a foundational software framework that provides query processing, optimization, and query language support to many popular open-source data processing systems such as Apache Hive, Apache Storm, Apache Flink, Druid, and MapD. Calcite's architecture consists of a modular and extensible query optimizer with hundreds of built-in optimization rules, a query processor capable of processing a variety of query languages, an adapter architecture designed for extensibility, and support for heterogeneous data models and stores (relational, semi-structured, streaming, and geospatial). This flexible, embeddable, and extensible architecture is what makes Calcite an attractive choice for adoption in big-data frameworks. It is an active project that continues to introduce support for the new types of data sources, query languages, and approaches to query processing and optimization.

Apache Calcite: A Foundational Framework for Optimized Query Processing Over ...

Julian Hyde

Traditional disaster recovery (DR) has had a spotty record for enterprises. This session compares conventional approaches to DR to those using the AWS cloud and talks about the four ascending levels of AWS DR options and the benefits and tradeoffs among them. The session goes on to discuss backup and restore architectures both using partner products and solutions that assist in backup, recovery, DR, and continuity of operations (COOP).

Disaster Recovery, Continuity of Operations, Backup, and Archive on AWS | AWS...

Amazon Web Services

Slides published for BeDelphi 2014 Event. Create high performance Client Server ORM SOA REST MVC applications using Open Source Synopse mORMot framework and Delphi. Publish any SQL or NoSQL database content over JSON or XML: SQLite3, PostgreSQL, Oracle, MSSQL, FireBird, MongoDB. Define RESTful services using interfaces. Create MVC web applications, using Mustache templates. Running under Windows or Linux, with VCL/FMX clients on Mac OSX, Android or iOS/iPhone/iPad, or AJAX/PhoneGap.

Delphi ORM SOA MVC SQL NoSQL JSON REST mORMot

Arnaud Bouchez

Front Range PHP NoSQL Databases

Jon Meredith

A glimpse of test automation in hadoop ecosystem by Deepika Achary

QA or the Highway

Databus: LinkedIn's Change Data Capture Pipeline SOCC 2012

Shirshanka Das

I/O & virtualization performance with a search engine based on an xml databa...

lucenerevolution

Restful web services rule financial

Rule_Financial

The objective of this session is to enable customers with any level of DR experience to gain actionable guidance to advance their business up the ladder of DR readiness. AWS enables fast disaster recovery of critical on-premises IT systems without incurring the complexity and expense of a second physical site. With 28 availability zones in 11 regions around the world and a broad set of services, AWS can deliver rapid recovery of on-premises IT infrastructure and data. During this session we will walk you through the ascending levels of DR options made possible with AWS and review the technologies and services that help deliver various DR capabilities, starting from cloud backups all the way up to hot site DR. We will also explore various DR architectures and the balance of recovery time and cost.

Disaster Recovery of on-premises IT infrastructure with AWS

Amazon Web Services

Disaster Recovery of on-premises IT infrastructure with AWS

Amazon Web Services

Similaire à Ditching the Middleware (20)

Object- Relational Persistence in Smalltalk

Solr -

Ruby On Rails

SQL and NoSQL in SQL Server

Hadoop and Big Data Overview

All Aboard the Databus

Sybase To Oracle Migration for DBAs

Graphity - Generic Linked Data Platform

Rest

Ruby on rails

Apache Calcite: A Foundational Framework for Optimized Query Processing Over ...

Disaster Recovery, Continuity of Operations, Backup, and Archive on AWS | AWS...

Delphi ORM SOA MVC SQL NoSQL JSON REST mORMot

Front Range PHP NoSQL Databases

A glimpse of test automation in hadoop ecosystem by Deepika Achary

Databus: LinkedIn's Change Data Capture Pipeline SOCC 2012

I/O & virtualization performance with a search engine based on an xml databa...

Restful web services rule financial

Disaster Recovery of on-premises IT infrastructure with AWS

Plus de DATAVERSITY

Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a comprehensive platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. In this research-based session, I’ll discuss what the components are in multiple modern enterprise analytics stacks (i.e., dedicated compute, storage, data integration, streaming, etc.) and focus on total cost of ownership. A complete machine learning infrastructure cost for the first modern use case at a midsize to large enterprise will be anywhere from $3 million to $22 million. Get this data point as you take the next steps on your journey into the highest spend and return item for most companies in the next several years.

Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...

DATAVERSITY

Do you ever wonder how data-driven organizations fuel analytics, improve customer experience, and accelerate business productivity? They are successful by governing and mastering data effectively so they can get trusted data to those who need it faster. Efficient data discovery, mastering and democratization is critical for swiftly linking accurate data with business consumers. When business teams can quickly and easily locate, interpret, trust, and apply data assets to support sound business judgment, it takes less time to see value. Join data mastering and data governance experts from Informatica—plus a real-world organization empowering trusted data for analytics—for a lively panel discussion. You’ll hear more about how a single cloud-native approach can help global businesses in any economy create more value—faster, more reliably, and with more confidence—by making data management and governance easier to implement.

Data at the Speed of Business with Data Mastering and Governance

DATAVERSITY

What is data literacy? Which organizations, and which workers in those organizations, need to be data-literate? There are seemingly hundreds of definitions of data literacy, along with almost as many opinions about how to achieve it. In a broader perspective, companies must consider whether data literacy is an isolated goal or one component of a broader learning strategy to address skill deficits. How does data literacy compare to other types of skills or “literacy” such as business acumen? This session will position data literacy in the context of other worker skills as a framework for understanding how and where it fits and how to advocate for its importance.

Exploring Levels of Data Literacy

DATAVERSITY

Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.

Building a Data Strategy – Practical Steps for Aligning with Business Goals

DATAVERSITY

Uncover how your business can save money and find new revenue streams. Driving profitability is a top priority for companies globally, especially in uncertain economic times. It's imperative that companies reimagine growth strategies and improve process efficiencies to help cut costs and drive revenue – but how? By leveraging data-driven strategies layered with artificial intelligence, companies can achieve untapped potential and help their businesses save money and drive profitability. In this webinar, you'll learn: - How your company can leverage data and AI to reduce spending and costs - Ways you can monetize data and AI and uncover new growth strategies - How different companies have implemented these strategies to achieve cost optimization benefits

Make Data Work for You

DATAVERSITY

Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data. Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.

Data Catalogs Are the Answer – What is the Question?

DATAVERSITY

Data Catalogs Are the Answer – What Is the Question?

DATAVERSITY

Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data,” “NoSQL,” “Data Scientist,” and so on. Few realize that all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, data modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important the data models driving the engineering and architecture activities of your organization. This webinar illustrates data modeling as a key activity upon which so much technology and business investment depends. Specific learning objectives include: - Understanding what types of challenges require data modeling to be part of the solution - How automation requires standardization on derivable via data modeling techniques - Why only a working partnership between data and the business can produce useful outcomes

Data Modeling Fundamentals

DATAVERSITY

Analytics play a critical role in supporting strategic business initiatives. Despite the obvious value to analytic professionals of providing the analytics for these initiatives, many executives question the economic return of analytics as well as data lakes, machine learning, master data management, and the like. Technology professionals need to calculate and present business value in terms business executives can understand. Unfortunately, most IT professionals lack the knowledge required to develop comprehensive cost-benefit analyses and return on investment (ROI) measurements. This session provides a framework to help technology professionals research, measure, and present the economic value of a proposed or existing analytics initiative, no matter the form that the business benefit arises. The session will provide practical advice about how to calculate ROI and the formulas, and how to collect the necessary information.

Showing ROI for Your Analytic Project

DATAVERSITY

Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls. This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture. Attend this session to learn about: - The role of a Data Mesh in the modern cloud architecture. - How a semantic layer can serve as the binding agent to support decentralization. - How to drive self service with consistency and control.

How a Semantic Layer Makes Data Mesh Work at Scale

DATAVERSITY

Enterprise data literacy. A worthy objective? Certainly! A realistic goal? That remains to be seen. As companies consider investing in data literacy education, questions arise about its value and purpose. While the destination – having a data-fluent workforce – is attractive, we wonder how (and if) we can get there. Kicking off this webinar series, we begin with a panel discussion to explore the landscape of literacy, including expert positions and results from focus groups: - why it matters, - what it means, - what gets in the way, - who needs it (and how much they need), - what companies believe it will accomplish. In this engaging discussion about literacy, we will set the stage for future webinars to answer specific questions and feature successful literacy efforts.

Is Enterprise Data Literacy Possible?

DATAVERSITY

Change is hard, especially in response to negative stimuli or what is perceived as negative stimuli. So organizations need to reframe how they think about data privacy, security and governance, treating them as value centers to 1) ensure enterprise data can flow where it needs to, 2) prevent – not just react – to internal and external threats, and 3) comply with data privacy and security regulations. Working together, these roles can accelerate faster access to approved, relevant and higher quality data – and that means more successful use cases, faster speed to insights, and better business outcomes. However, both new information and tools are required to make the shift from defense to offense, reducing data drama while increasing its value. Join us for this panel discussion with experts in these fields as they discuss: - Recent research about where data privacy, security and governance stand - The most valuable enterprise data use cases - The common obstacles to data value creation - New approaches to data privacy, security and governance - Their advice on how to shift from a reactive to resilient mindset/culture/organization You’ll be educated, entertained and inspired by this panel and their expertise in using the data trifecta to innovate more often, operate more efficiently, and differentiate more strategically.

The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...

DATAVERSITY

Emerging Trends in Data Architecture – What’s the Next Big Thing?

DATAVERSITY

As DATAVERSITY’s RWDG series hurdles into our 12th year, this webinar takes a quick look behind us, evaluates the present, and predicts the future of Data Governance. Based on webinar numbers, hot Data Governance topics have evolved over the years from policies and best practices, roles and tools, data catalogs and frameworks, to supporting data mesh and fabric, artificial intelligence, virtualization, literacy, and metadata governance. Join Bob Seiner as he reflects on the past and what has and has not worked, while sharing examples of enterprise successes and struggles. In this webinar, Bob will challenge the audience to stay a step ahead by learning from the past and blazing a new trail into the future of Data Governance. In this webinar, Bob will focus on: - Data Governance’s past, present, and future - How trials and tribulations evolve to success - Leveraging lessons learned to improve productivity - The great Data Governance tool explosion - The future of Data Governance

Data Governance Trends - A Look Backwards and Forwards

DATAVERSITY

Would you share your bank account information on social media? How about shouting your social security number on the New York City subway? We didn’t think so either – that’s why data governance is consistently top of mind. In this webinar, we’ll discuss the common Cloud data governance best practices – and how to apply them today. Join us to uncover Google Cloud’s investment in data governance and learn practical and doable methods around key management and confidential computing. Hear real customer experiences and leave with insights that you can share with your team. Let’s get solving. Topics that you will hear addressed in this webinar: - Understanding the basics of Cloud Incident Response (IR) and anticipated data governance trends - Best practices for key management and apply data governance to your day-to-day - The next wave of Confidential Computing and how to get started, including a demo

Data Governance Trends and Best Practices To Implement Today

DATAVERSITY

It is a fascinating, explosive time for enterprise analytics. It is from the position of analytics leadership that the enterprise mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics. The coming years will be full of big changes in enterprise analytics and data architecture. William will kick off the fifth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.

2023 Trends in Enterprise Analytics

DATAVERSITY

Too often I hear the question “Can you help me with our data strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component: the data strategy itself. A more useful request is: “Can you help me apply data strategically?” Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) data strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” This program refocuses efforts on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. It also contributes to three primary organizational data goals. Learn how to improve the following: - Your organization’s data - The way your people use data - The way your people use data to achieve your organizational strategy This will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (data strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including: - A cohesive argument for why data strategy is necessary for effective data governance - An overview of prerequisites for effective strategic use of data strategy, as well as common pitfalls - A repeatable process for identifying and removing data constraints - The importance of balancing business operation and innovation

Data Strategy Best Practices

DATAVERSITY

The question is asked all the time: “What part of the organization should own your Data Governance program?” The typical answers are “the business” and “IT (information technology).” Another answer to that question is “Yes.” The program must be owned and reside somewhere in the organization. You may ask yourself if there is a correct answer to the question. Join this new RWDG webinar with Bob Seiner where Bob will answer the question that is the title of this webinar. Determining ownership of Data Governance is a vital first step. Figuring out the appropriate part of the organization to manage the program is an important second step. This webinar will help you address these questions and more. In this session Bob will share: - What is meant by “the business” when it comes to owning Data Governance - Why some people say that Data Governance in IT is destined to fail - Examples of IT positioned Data Governance success - Considerations for answering the question in your organization - The final answer to the question of who should own Data Governance

Who Should Own Data Governance – IT or Business?

DATAVERSITY

It is clear that Data Management best practices exist and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes – permitting organizations with the opportunity to benefit from the best of both. It also permits organizations to understand: - Their current Data Management practices - Strengths that should be leveraged - Remediation opportunities

Data Management Best Practices

DATAVERSITY

MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in: Faster time to market of ML-based solutions More rapid rate of experimentation, driving innovation Assurance of quality, trustworthiness, and ethical AI MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.

MLOps – Applying DevOps to Competitive Advantage

DATAVERSITY

Plus de DATAVERSITY (20)

Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...

Data at the Speed of Business with Data Mastering and Governance

Exploring Levels of Data Literacy

Building a Data Strategy – Practical Steps for Aligning with Business Goals

Make Data Work for You

Data Catalogs Are the Answer – What is the Question?

Data Catalogs Are the Answer – What Is the Question?

Data Modeling Fundamentals

Showing ROI for Your Analytic Project

How a Semantic Layer Makes Data Mesh Work at Scale

Is Enterprise Data Literacy Possible?

The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...

Emerging Trends in Data Architecture – What’s the Next Big Thing?

Data Governance Trends - A Look Backwards and Forwards

Data Governance Trends and Best Practices To Implement Today

2023 Trends in Enterprise Analytics

Data Strategy Best Practices

Who Should Own Data Governance – IT or Business?

Data Management Best Practices

MLOps – Applying DevOps to Competitive Advantage

Dernier

Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...

Neo4j

CNv6 Instructor Chapter 6 Quality of Service

giselly40

A Domino Admins Adventures (Engage 2024)

Gabriella Davis

In an era where artificial intelligence (AI) stands at the forefront of business innovation, Information Architecture (IA) is at the core of functionality. See “There’s No AI Without IA” – (from 2016 but even more relevant today) Understanding and leveraging how Information Architecture (IA) supports AI synergies between knowledge engineering and prompt engineering is critical for senior leaders looking to successfully deploy AI for internal and externally facing knowledge processes. This webinar be a high-level overview of the methodologies that can elevate AI-driven knowledge processes supporting both employees and customers. Core Insights Include: Strategic Knowledge Engineering: Delve into how structuring AI's knowledge base is required to prevent hallucinations, enable contextual retrieval of accurate information. This will include discussion of gold standard libraries of use cases support testing various LLMs and structures and configurations of knowledge base. Precision in Prompt Engineering: Learn the art of crafting prompts that direct AI to deliver targeted, relevant responses, thereby optimizing customer experiences and business outcomes. Unified Approach for Enhanced AI Performance: Explore the intersection of knowledge and prompt engineering to develop AI systems that are not only more responsive but also aligned with overarching business strategies. Guiding Principles for Implementation: Equip yourself with best practices, ethical guidelines, and strategic considerations for embedding these technologies into your business ecosystem effectively. This webinar is designed to empower business and technology leaders with the knowledge to harness the full potential of AI, ensuring their organizations not only keep pace with digital transformation but lead the charge. Join us to map a roadmap to fully leverage Information Architecture (IA) and AI chart a course towards a future where AI is a key pillar of strategic innovation and business success.

EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx

Earley Information Science

BooK Now Call us at +918448380779 to hire a gorgeous and seductive call girl for sex. Take a Delhi Escort Service. The help of our escort agency is mostly meant for men who want sexual Indian Escorts In Delhi NCR. It should be noted that any impersonator will get 100 attention from our Young Girls Escorts in Delhi. They will assume the position of reliable allies. VIP Call Girl With Original Photos Book Tonight +918448380779 Our Cheap Price 1 Hour not available 2 Hours 5000 Full Night 8000 TAG: Call Girls in Delhi, Noida, Gurgaon, Ghaziabad, Connaught Place, Greater Kailash Delhi, Lajpat Nagar Delhi, Mayur Vihar Delhi, Chanakyapuri Delhi, New Friends Colony Delhi, Majnu Ka Tilla, Karol Bagh, Malviya Nagar, Saket, Khan Market, Noida Sector 18, Noida Sector 76, Noida Sector 51, Gurgaon Mg Road, Iffco Chowk Gurgaon, Rajiv Chowk Gurgaon All Delhi Ncr Free Home Deliver

08448380779 Call Girls In Greater Kailash - I Women Seeking Men

Delhi Call girls

Data Cloud, More than a CDP by Matt Robison

Anna Loughnan Colquhoun

Imagine a world where information flows as swiftly as thought itself, making decision-making as fluid as the data driving it. Every moment is critical, and the right tools can significantly boost your organization’s performance. The power of real-time data automation through FME can turn this vision into reality. Aimed at professionals eager to leverage real-time data for enhanced decision-making and efficiency, this webinar will cover the essentials of real-time data and its significance. We’ll explore: FME’s role in real-time event processing, from data intake and analysis to transformation and reporting An overview of leveraging streams vs. automations FME’s impact across various industries highlighted by real-life case studies Live demonstrations on setting up FME workflows for real-time data Practical advice on getting started, best practices, and tips for effective implementation Join us to enhance your skills in real-time data automation with FME, and take your operational capabilities to the next level.

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

Safe Software

Scaling API-first – The story of a global engineering organization

Radu Cotescu

[2024]Digital Global Overview Report 2024 Meltwater.pdf

hans926745

GenAI Risks & Security Meetup 01052024.pdf

lior mazor

Heather Hedden, Senior Consultant at Enterprise Knowledge, presented “The Role of Taxonomy and Ontology in Semantic Layers” at a webinar hosted by Progress Semaphore on April 16, 2024. Taxonomies at their core enable effective tagging and retrieval of content, and combined with ontologies they extend to the management and understanding of related data. There are even greater benefits of taxonomies and ontologies to enhance your enterprise information architecture when applying them to a semantic layer. A survey by DBP-Institute found that enterprises using a semantic layer see their business outcomes improve by four times, while reducing their data and analytics costs. Extending taxonomies to a semantic layer can be a game-changing solution, allowing you to connect information silos, alleviate knowledge gaps, and derive new insights. Hedden, who specializes in taxonomy design and implementation, presented how the value of taxonomies shouldn’t reside in silos but be integrated with ontologies into a semantic layer. Learn about: - The essence and purpose of taxonomies and ontologies in information and knowledge management; - Advantages of semantic layers leveraging organizational taxonomies; and - Components and approaches to creating a semantic layer, including the integration of taxonomies and ontologies

The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf

Enterprise Knowledge

Boost Fertility New Invention Ups Success Rates.pdf

sudhanshuwaghmare1

Evaluating the top large language models.pdf

ChristopherTHyatt

How to Troubleshoot Apps for the Modern Connected Worker

ThousandEyes

With more memory available, system performance of three Dell devices increased, which can translate to a better user experience Conclusion When your system has plenty of RAM to meet your needs, you can efficiently access the applications and data you need to finish projects and to-do lists without sacrificing time and focus. Our test results show that with more memory available, three Dell PCs delivered better performance and took less time to complete the Procyon Office Productivity benchmark. These advantages translate to users being able to complete workflows more quickly and multitask more easily. Whether you need the mobility of the Latitude 5440, the creative capabilities of the Precision 3470, or the high performance of the OptiPlex Tower Plus 7010, configuring your system with more RAM can help keep processes running smoothly, enabling you to do more without compromising performance.

Boost PC performance: How more available memory can improve productivity

Principled Technologies

How to convert PDF to text with Nanonets

naman860154

presentation ICT roal in 21st century education

jfdjdjcjdnsjd

08448380779 Call Girls In Diplomatic Enclave Women Seeking Men

Delhi Call girls

The Raspberry Pi 5 was announced on October 2023. This new version of the popular embedded device comes with a new iteration of Broadcom’s VideoCore GPU platform, and was released with a fully open source driver stack, developed by Igalia. The presentation will discuss some of the major changes required to support this new Video Core iteration, the challenges we faced in the process and the solutions we provided in order to deliver conformant OpenGL ES and Vulkan drivers. The talk will also cover the next steps for the open source Raspberry Pi 5 graphics stack. (c) Embedded Open Source Summit 2024 April 16-18, 2024 Seattle, Washington (US) https://events.linuxfoundation.org/embedded-open-source-summit/ https://eoss24.sched.com/event/1aBEx

Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...

Igalia

Axa Assurance Maroc - Insurer Innovation Award 2024

The Digital Insurer

Dernier (20)

Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...

CNv6 Instructor Chapter 6 Quality of Service

A Domino Admins Adventures (Engage 2024)

EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx

08448380779 Call Girls In Greater Kailash - I Women Seeking Men

Data Cloud, More than a CDP by Matt Robison

From Event to Action: Accelerate Your Decision Making with Real-Time Automation

Scaling API-first – The story of a global engineering organization

[2024]Digital Global Overview Report 2024 Meltwater.pdf

GenAI Risks & Security Meetup 01052024.pdf

The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf

Boost Fertility New Invention Ups Success Rates.pdf

Evaluating the top large language models.pdf

How to Troubleshoot Apps for the Modern Connected Worker

Boost PC performance: How more available memory can improve productivity

How to convert PDF to text with Nanonets

presentation ICT roal in 21st century education

08448380779 Call Girls In Diplomatic Enclave Women Seeking Men

Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...

Axa Assurance Maroc - Insurer Innovation Award 2024

Ditching the Middleware

1. Ditching the Middleware NoSQL Now! 2011 David Wood 3 Round Stones david@3roundstones.com

2. #nosql, like organic food, is deﬁned by what it doesn't contain. Comparing noSQL solutions is like comparing organic apples and oranges... Eric van der Vlist @evlist 25 July 2011

4. l l l Data Access Business Logic Presentation Logic

5. Data model changes effect all tiers: • your presentation • your processing model • your data schema.

7. V e r t i c Horizontal a l

8. (CPU and disk harder to quantify/compare)

9. vs. Data throughput Optimistic reads?

10. Data Access Controller Business Logic View Model Presentation Logic

11. Controller <> Objects View Model

12. l l Callimachus

13. Controller RDF Store Web server HTTP GET Class Resource request Viewable RDF response SPARQL query XML Template template apply.xsl Engine HTTP response HTML

14. Controller <> View Model

15.

16.

17. RDFa queries Results ﬁll into template

18.

19.

20.

21. HTTP GET e.g. c 302 (Found) PURL l i Service e HTTP GET n Web t 200 (OK) Service

22. Data Master Normal Ops Dynamic DNS Failover / Spillover Data Slave

23. PURL Services by different parties A.org Normal Ops for A.org Write-Segmented Dynamic data source DNS B.com e.g. Failover / Spillover for A.org C.net

24. PURL Services by different parties A.org Normal Ops for C.net to any host B.com Dynamic DNS C.net

25. Persistent URLs Callimachus

26.

27. This work is Copyright © 2011 3 Round Stones Inc. It is licensed under the Creative Commons Attribution 3.0 Unported License Full details at: http://creativecommons.org/licenses/by/3.0/ You are free: to Share — to copy, distribute and transmit the work to Remix — to adapt the work Under the following conditions: Attribution. You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work). • For any reuse or distribution, you must make clear to others the license terms of this work. • Any of the above conditions can be waived if you get permission from the copyright holder. • Nothing in this license impairs or restricts the author's moral rights. • Some Content in the work may be licensed under different terms, this is noted separately.

Ditching the Middleware

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

En vedette

En vedette (20)

Similaire à Ditching the Middleware

Similaire à Ditching the Middleware (20)

Plus de DATAVERSITY

Plus de DATAVERSITY (20)

Dernier

Dernier (20)

Ditching the Middleware