Proud to be Polyglot - Riviera Dev 2015

•

2 likes•951 views

The document discusses the benefits of using multiple programming languages and data stores, or a "polyglot" approach, for modern applications. A polyglot approach allows using the right tool for each task, rather than trying to force a single technology to fit all needs. This improves performance, scalability, and the ability to adapt applications to changing requirements compared to traditional monolithic architectures. The document provides examples of when to use different languages and data stores and concludes that a polyglot approach makes applications easier to maintain over time.

Technology

© 2014 MapR Technologies ‹#›© 2014 MapR Technologies
Proud to be Polyglot!
Tugdual Grall
@tgrall
June 11, 2015

© 2015 MapR Technologies ‹#›@tgrall
Modern Applications
• Modern Applications Must
• Manage any type of data
• Scale
• Be fault tolerant
• Adapt to change

© 2015 MapR Technologies ‹#›@tgrall
Current Architecture
• Mono-Technology
• Single “language/architecture”
• Single Database : RDBMS
• One size fits all approach
JSP/JSF
Services
Messaging
JTA
DAO/JPA

© 2015 MapR Technologies ‹#›@tgrall
Is it good for me?
• Many Files
• XML, Sources, Configuration, …
• Does Hibernate/JPA is good for all?
• Impedance mismatch
• Need to scale
• From 0 to 50 millions users in 6 weeks
• 1Tb of data each day
• How easy is to add new features?
• Changing code, updating data structure, …
• And all this for yesterday !

© 2014 MapR Technologies ‹#›@tgrall
One size does NOT fit all!

© 2014 MapR Technologies ‹#›@tgrall
Let’s look at our data…
6

© 2014 MapR Technologies ‹#›
Big Users
http://www.theconnectivist.com/ & Cisco

© 2015 MapR Technologies ‹#›@tgrall
. . .
App Server
RDBMS
Application
Scale out
Add more “Web” servers
RDBMS
Scale Up
Get bigger server
Stay up!…. and Scale

© 2015 MapR Technologies ‹#›@tgrall
Scaling out RDBMS
• Run many RDBMS Servers
• Data could be partitioned
• Done by the application code
• Caching Layer
. . .
App Server
. . .
Memcached
. . .
MySQL

© 2015 MapR Technologies ‹#›@tgrall
NoSQL to Scale Out!
. . .
NoSQL
App Server
. . .
Application
Scale out
Add more “Web” servers
NoSQL
Scale Out
Add more servers

© 2015 MapR Technologies ‹#›@tgrall
Operational vs Analytics Databases
Warehouse,AnalyticsApplications, Interactions

© 2015 MapR Technologies ‹#›@tgrall
Polyglot Persistence
Big Data/Analysis NoSQL RDBMS
• Log Capture
• Recommendations
• Predictions
• Ad Campaign
• Products
• User Profiles
• Game Actions
• Sessions
• Shopping Cart
• Financial Data
• Reporting

© 2015 MapR Technologies ‹#›@tgrall
Data Service Layer
• Wrap data call in Services
– loosely coupled
. . .
App Server

© 2015 MapR Technologies ‹#›@tgrall
Use Case : Personalization
WebSite / Application
Personalization Database
Data Processing
Logs
Application
Data

© 2015 MapR Technologies ‹#›@tgrall
Use Case : Mobile / API
PIM Database
• Legacy Application
• Product Information
NoSQL
• REST API
• Product Data
• Additional Metadata

© 2014 MapR Technologies ‹#›
Now What?
Now What?

© 2015 MapR Technologies ‹#›@tgrall
Why multiple languages?
• Sometimes we have no choice
• HTML/CSS/JavaScript + Server Side
• Mobile Native Applications
• Java/.Net are slow movers
• Java EE 5.0 is still mainstream (2006!!)
• Many languages and frameworks
• Scala, Clojure, Groovy, Dart, Go, Ruby, Python, F#, Erlang,
Node.js, Rails, Play!, Grails, ...

© 2015 MapR Technologies ‹#›@tgrall
Examples
• Realtime interaction with WebSockets :
• Wait for Java EE 7 to supported by your IT?
• Hack your app server and application with Continuation ?
• Use Node.js and Socket.io ?
• Data Collection and Treatment?
• Find a library?
• Create your library?
• Use Java 8 and Lambdas?
• Use Scala?

© 2015 MapR Technologies ‹#›@tgrall
Examples
• Simple CRUD Application
• Use the JavaEE stack with JPA and JSF?
• Use Spring ?
• Use tools like Play!, Grails, Rails, MEAN ?

© 2015 MapR Technologies ‹#›@tgrall
How to chose?
• Maturity/Supportability
• Features Set
• Learning Curve
• Productivity

© 2015 MapR Technologies ‹#›@tgrall
Use Case : Second Screen Experience
. . .
Node.js + Socket IO
Play! Application
Python Scripts

© 2015 MapR Technologies ‹#›@tgrall
Use Case : Recommendation
WebSite / Application
Machine Learning / Recommendation
Data Processing
UserActivity
Application
Data

© 2014 MapR Technologies ‹#›@tgrall
Demonstration

© 2015 MapR Technologies ‹#›@tgrall
Conclusion
• Use the good tool for the good task
• Do not try to “hack” something when you have a simpler
solution
• Think about your users first... so deliver and get feedback
• Learning is part of our job !
• And this will help you and your project
• What about maintenance?
• Do you think it is easier to maintain complex old code?

What's hot

Stsg17 speaker yousunjeong

Yousun Jeong

Last year at Yahoo, we spent great effort in scaling, stabilizing and making Pig on Tez production ready and by the end of the year retired running Pig jobs on Mapreduce. This talk will detail the performance and resource utilization improvements Yahoo achieved after migrating all Pig jobs to run on Tez. After successful migration and the improved performance we shifted our focus to addressing some of the bottlenecks we identified and new optimization ideas that we came up with to make it go even faster. We will go over the new features and work done in Tez to make that happen like custom YARN ShuffleHandler, reworking DAG scheduling order, serialization changes, etc. We will also cover exciting new features that were added to Pig for performance such as bloom join and byte code generation. A distributed bloom join that can create multiple bloom filters in parallel was straightforward to implement with the flexibility of Tez DAGs. It vastly improved performance and reduced disk and network utilization for our large joins. Byte code generation for projection and filtering of records is another big feature that we are targeting for Pig 0.17 which will speed up processing by reducing the virtual function calls.

Yahoo - Moving beyond running 100% of Apache Pig jobs on Apache Tez

DataWorks Summit

From the Hadoop Summit 2015 Session with Tomer Shiran. To deliver real-time impact from big data, organizations must evolve beyond traditional analytic approaches to support a new class of agile, distributed applications. Real-time Hadoop overcomes batch programs reliant on data transformations and schema management. This session highlights how leading organizations are leveraging Hadoop and NoSQL to merge analytics and production data to make adjustments while business is happening to optimize revenue, mitigate risk and reduce operational costs. Details include how companies have achieved real-time impact on their business, collapsed data silos, and automated in-line analytics with operational data for immediate impact.

Real Time and Big Data – It’s About Time

MapR Technologies

Lambda architecture

Szilveszter Molnár

Implementing the Lambda Architecture efficiently with Apache Spark

DataWorks Summit

Overview of stinger interactive query for hive

David Kaiser

In this presentation, we are going to talk about the state of the art infrastructure we have established at Walmart Labs for the Search product using Spark Streaming and DataFrames. First, we have been able to successfully use multiple micro batch spark streaming pipelines to update and process information like product availability, pick up today etc. along with updating our product catalog information in our search index to up to 10,000 kafka events per sec in near real-time. Earlier, all the product catalog changes in the index had a 24 hour delay, using Spark Streaming we have made it possible to see these changes in near real-time. This addition has provided a great boost to the business by giving the end-costumers instant access to features likes availability of a product, store pick up, etc. Second, we have built a scalable anomaly detection framework purely using Spark Data Frames that is being used by our data pipelines to detect abnormality in search data. Anomaly detection is an important problem not only in the search domain but also many domains such as performance monitoring, fraud detection, etc. During this, we realized that not only are Spark DataFrames able to process information faster but also are more flexible to work with. One could write hive like queries, pig like code, UDFs, UDAFs, python like code etc. all at the same place very easily and can build DataFrame template which can be used and reused by multiple teams effectively. We believe that if implemented correctly Spark Data Frames can potentially replace hive/pig in big data space and have the potential of becoming unified data language. We conclude that Spark Streaming and Data Frames are the key to processing extremely large streams of data in real-time with ease of use.

Learnings Using Spark Streaming and DataFrames for Walmart Search: Spark Summ...

Spark Summit

Deep Learning is now the standard in object detection, but it is not easy to analyze large amounts of images, especially in an interactive fashion. Traditionally, there has been a gap between Deep Learning frameworks, which excel at image processing, and more traditional ETL and data science tools, which are usually not designed to handle huge batches of complex data types such as images. In this talk, we show how manipulating large corpora of images can be accomplished in a few lines of code because of recent developments in Apache Spark. Thanks to Spark’s unique ability to blend different libraries, we show how to start from satellite images and rapidly build complex queries on high level information such as houses or buildings. This is possible thanks to Magellan, a geospatial package, and Deep Learning Pipelines, a library that streamlines the integration of Deep Learning frameworks in Spark. At the end of this session, you will walk away with the confidence that you can solve your own image detection problems at any scale thanks to the power of Spark.

Geospatial Analytics at Scale with Deep Learning and Apache Spark with Tim hu...

Databricks

MapR-DB – The First In-Hadoop Document Database

MapR Technologies

During the Big Data Warehousing Meetup, we discussed options for enabling real-time/interactive queries to support business intelligence type functionality on Hadoop. Also, Hortonworks provided a deep-dive demo of Stinger! You can access that slideshow here: http://www.slideshare.net/CasertaConcepts/stinger-initiative-hortonworks If you would like more information, please don't hesitate to contact us at info@casertaconcepts.com. Or, visit our website at http://casertaconcepts.com/.

Real Time Interactive Queries IN HADOOP: Big Data Warehousing Meetup

Caserta

SDM is a distributed, reliable and highly available data lake ingestion framework that handles data processing, archival and reconciliation capabilities with an effective change based history management capabilities for batch and streaming data. It is meta-driven and provides automated schema evolution. The SDM platform is built completely on open source software/platforms, making it both extensible and robust. The data management, schema evolution and archival is achieved through Apache NiFi’s in-built capabilities and extensions via custom processors and controller services. The end-of-day construct is generated through an Apache Spark job.Types of Data : Types of Data : 1. Batch a. Full dump b. Incremental c. Hybrid (Daily incremental + Weekly/Monthly full dump) 2. Near Real time a. CDC-Kafka b. JMS-Kafka 3. Extractions a. Incremental based on Change Data Capture tool (IBM Infosphere CDC) b. Sqoop c. JDBC/ODBC 4. Manual File Upload a. Excel Types of Process: 1. File validation a. File integrity (header, trailer, data checksum) b. File de-duplication c. New line and non-printable control characters handling 2. Structural validation (Row validation) a. Fixed width b. Delimited c. XML d. JSON e. Excel (Single/Multi tab) f. Datatype validation g. Constraint validation – Null, primary key and full row de-duplication 3. Defaulting a. Condition based b. Special data-type handling (mainframe systems) 4. Operational assurance a. Row count logging b. Reconciliation with source c. File/Record rejections with reasons 5. Lineage tracking a. Row-id for every single record is generated and referenced against the source file until the processed layer. Storage formats : 1. Raw - Archival 2. Avro – Staged 3. ORC – Processed Benefits 1. Metadata driven 2. Extensible 3. Scalable 4. Flexible Plans Current State : 1. Custom built ingestion framework leveraging upon standard open source software from Apache. 2. Data of 100+ source systems are ingested into the Hadoop data lake using the ingestion framework. Plan : 1. Open sourcing the framework for general consumption. 2. Metadata management UI/API which would serve as a glossary of data available in the data lake with search capabilities. 3. Operational and Exception reporting. 4. Centralized data retention within the framework. 5. Health monitoring and alerting. 6. Provenance data maintenance in Atlas. Speaker Arun Manivannan, Senior Data Engineer, Standard Chartered Bank

SDM (Standardized Data Management) - A Dynamic Adaptive Ingestion Frameworks ...

DataWorks Summit

We present the Azure Cognitive Services on Spark, a simple and easy to use extension of the SparkML Library to all Azure Cognitive Services. This integration allows Spark Users to embed cloud intelligence directly into their spark computations, enabling a new generation of intelligent applications on Spark. Furthermore, we show that with our new Containerized Cognitive Services, one can embed cloud intelligence directly into the Spark cluster for ultra-low latency, on-prem, and offline applications. We show how using our Integration, one can compose these cognitive services with other services, SQL computations, and Deep Networks to create sophisticated and intelligent heterogenous applications. Moreover, we show how to redeploy these compositions as Restful Services with Spark Serving. We will also explore the architecture of these contributions which leverage HTTP on Spark, a novel integration between Spark with the widely used Hypertext Transfer Protocol (HTTP). This library can integrate any framework into the Spark ecosystem that is capable of communicating through HTTP. Finally, we demonstrate how to use these services to create a large class of intelligent applications such as custom search engines, realtime facial recognition systems, and unsupervised object detectors.

The Azure Cognitive Services on Spark: Clusters with Embedded Intelligent Ser...

Databricks

Spark/Mesos Seattle Meetup group shares the latest presentation from their recent meetup event on showcasing real world implementations of working with Spark within the context of your Big Data Infrastructure. Session are demo heavy and slide light focusing on getting your development environments up and running including getting up and running, configuration issues, SparkSQL vs. Hive, etc. To learn more about the Seattle meetup: http://www.meetup.com/Seattle-Spark-Meetup/members/21698691/

Unlocking Your Hadoop Data with Apache Spark and CDH5

SAP Concur

Running a global, world-class business with data-driven decision making requires ingesting and processing diverse sets of data at tremendous scale. How does a company achieve this while ensuring quality and honoring their commitment as responsible stewards of data? This session will detail how Starbucks has embraced big data, building robust, high-quality pipelines for faster insights to drive world-class customer experiences.

Operationalizing Big Data Pipelines At Scale

Databricks

What’s important about a technology is what you can use it to do. I’ve looked at what a number of groups are doing with Apache Hadoop and NoSQL in production, and I will relay what worked well for them and what did not. Drawing from real world use cases, I show how people who understand these new approaches can employ them well in conjunction with traditional approaches and existing applications. Thread Detection, Datawarehouse optimization, Marketing Efficiency, Biometric Database are some examples exposed during this presentation.

Tugdual Grall - Real World Use Cases: Hadoop and NoSQL in Production

Codemotion

End-to-End Data Pipelines with Apache Spark

Burak Yavuz

Big Telco - Yousun Jeong

Spark Summit

Evolving from RDBMS to NoSQL + SQL

MapR Technologies

Spark Streaming and MLlib - Hyderabad Spark Group

Phaneendra Chiruvella

AWS Customer Presentation - VMIX AWS Experience

Amazon Web Services

What's hot (20)

Stsg17 speaker yousunjeong

Yahoo - Moving beyond running 100% of Apache Pig jobs on Apache Tez

Real Time and Big Data – It’s About Time

Lambda architecture

Implementing the Lambda Architecture efficiently with Apache Spark

Overview of stinger interactive query for hive

Learnings Using Spark Streaming and DataFrames for Walmart Search: Spark Summ...

Geospatial Analytics at Scale with Deep Learning and Apache Spark with Tim hu...

MapR-DB – The First In-Hadoop Document Database

Real Time Interactive Queries IN HADOOP: Big Data Warehousing Meetup

SDM (Standardized Data Management) - A Dynamic Adaptive Ingestion Frameworks ...

The Azure Cognitive Services on Spark: Clusters with Embedded Intelligent Ser...

Unlocking Your Hadoop Data with Apache Spark and CDH5

Operationalizing Big Data Pipelines At Scale

Tugdual Grall - Real World Use Cases: Hadoop and NoSQL in Production

End-to-End Data Pipelines with Apache Spark

Big Telco - Yousun Jeong

Evolving from RDBMS to NoSQL + SQL

Spark Streaming and MLlib - Hyderabad Spark Group

AWS Customer Presentation - VMIX AWS Experience

Similar to Proud to be Polyglot - Riviera Dev 2015

New developers and teams are now polyglot : - they use multiple programming languages (Java, Javascript, Ruby, ...) - they use multiple persistence store (RDBMS, NoSQL, Hadoop) In this talk you will learn about the benefits if being polyglot: use the good language or framework for the good cause, select the good persistence for specific constraints. This presentation will show how developer could mix the Java platform with other technologies such as NodeJS and AngularJS to build application in a more productive way. This is also the opportunity to talk about the new Command Query Responsibility Segregation (CQRS) pattern to allow developers to be more effective and deliver the proper application to the user quicker. This presentation was delivered during Devfest Nantes 2014

Proud to be polyglot

Tugdual Grall

IoT and Big Data - Iot Asia 2014

John Berns

Modern big data applications such as social, mobile, web and IoT deal with a larger number of users and larger amount of data than the traditional transactional applications. The datasets associated with these applications evolve rapidly, are often self-describing and can include complex types such as JSON and Parquet. In this demo we will show how Apache Drill can be used to provide low latency queries natively on rapidly evolving multi-structured datasets at scale.

Self-Service BI for big data applications using Apache Drill (Big Data Amster...

Dataconomy Media

Self-Service BI for big data applications using Apache Drill (Big Data Amster...

Mats Uddenfeldt

PyData: The Next Generation

Wes McKinney

The recent boom in big data processing and democratization of the big data space has been enabled by the fact that most of the concepts originated in the research labs of companies such as Google, Amazon, Yahoo and Facebook are now available as open source. Technologies such as Hadoop, Cassandra let businesses around the world to become more data driven and tap into their massive data feeds to mine valuable insights. At the same time, we are still at a certain stage of the maturity curve of these new big data technologies and of the entire big data technology stack. Many of the technologies originated from a particular use case and attempts to apply them in a more generic fashion are hitting the limits of their technological foundations. In some areas, there are several competing technologies for the same set of use cases, which increases risks and costs of big data implementations. We will show how GoodData solves the entire big data pipeline today, starting from raw data feeds all the way up to actionable business insights. All this provided as a hosted multi-tenant environment letting its customers to solve their particular analytical use case or many analytical use cases for thousands of their customers all using the same platform and tools while abstracting them away from the technological details of the big data stack.

Big Data Pipeline for Analytics at Scale @ FIT CVUT 2014

Jaroslav Gergic

AGIT 2015 - Hans Viehmann: "Big Data and Smart Cities"

jstrobl

Knowledge Graph for Machine Learning and Data Science

Cambridge Semantics

Cloud expo june 2013: Building a Real Time Analytics Platform on Big Data in ...

Sanjay Sharma

Hadoop and Your Enterprise Data Warehouse

Edgar Alejandro Villegas

This presentation reviews the key methodologies that all the member of the team should consider such as: - How to prioritize the right application or project for your first Oracle - Tips to execute a well-defined, phased migration process to minimize risk and increase time to value - Handling the common concerns and pitfalls related to a migration project - What resources you can leverage before, during and after your migration - Suggestions on how you can achieve independence from an Oracle database – without sacrificing performance. Target audience: This presentation is intended for IT Decision-Makers and Leaders on the team involved in Database decisions and execution. For more information, please email sales@enterprisedb.com

Migrating from Oracle to Postgres

EDB

PyData: The Next Generation | Data Day Texas 2015

Cloudera, Inc.

Market trends in IT - exchange cala - October 2015

Eduardo Pelegri-Llopart

Using Familiar BI Tools and Hadoop to Analyze Enterprise Networks

DataWorks Summit

From the Hadoop Summit 2015 Session with Nick Amato. This session examines practical ways you can begin leveraging network data sources in Hadoop using familiar technologies like SQL and BI tools. Using the diverse sets of sources available, such as traces, routing protocol data, and direct packet captures from critical network locations, we will examine the capabilities of BI tools in the network context and examine cases for extracting value from data collected from the network infrastructure.

Using Familiar BI Tools and Hadoop to Analyze Enterprise Networks

MapR Technologies

Whither the Hadoop Developer Experience, June Hadoop Meetup, Nitin Motgi

Felicia Haggarty

Simply Business' Data Platform

Dani Solà Lagares

Prototypes are typically re-implemented in another language due to compatibility issues with R in the enterprise, but TIBCO Enterprise Runtime for R (TERR) allows the language to be run on several platforms. Enterprise-level scalability has been brought to the R language, enabling rapid iteration without the need to recode, re-implement and test. This presentation will delve further into these topics, highlighting specific use cases and the true value that can be gained from utilizing R. The session will be followed by a lively, open Q&A discussion.

Big Data Day LA 2016/ Big Data Track - Apply R in Enterprise Applications, Lo...

Data Con LA

Genome Analysis Pipelines, Big Data Style Allen Day, Chief Scientist, MapR Powerful new tools exist for processing large volumes of data quickly across a cluster of networked computers. Typical bioinformatics workflow requirements are well-matched to these tools' capabilities. However, the tool Spark, for example, is not commonly used because many legacy bioinformatics applications make assumptions about their computing environment. These assumptions present a barrier to integrating the tools into more modern computing environments. Fortunately, these barriers are quickly coming down. In this presentation, we'll examine a few operations common to many bioinformatics pipelines, show how they were usually implemented in the past, and how they're being re-implemented right now to save time, money, and make new types of analysis possible. Some code examples will also be provided. Video Presentation: https://youtu.be/iwgfjHiHr7Q

Genome Analysis Pipelines, Big Data Style

Julius Remigio, CBIP

3 Benefits of Multi-Temperature Data Management for Data Analytics

MapR Technologies

Similar to Proud to be Polyglot - Riviera Dev 2015 (20)

Proud to be polyglot

IoT and Big Data - Iot Asia 2014

Self-Service BI for big data applications using Apache Drill (Big Data Amster...

PyData: The Next Generation

Big Data Pipeline for Analytics at Scale @ FIT CVUT 2014

AGIT 2015 - Hans Viehmann: "Big Data and Smart Cities"

Knowledge Graph for Machine Learning and Data Science

Cloud expo june 2013: Building a Real Time Analytics Platform on Big Data in ...

Hadoop and Your Enterprise Data Warehouse

Migrating from Oracle to Postgres

PyData: The Next Generation | Data Day Texas 2015

Market trends in IT - exchange cala - October 2015

Using Familiar BI Tools and Hadoop to Analyze Enterprise Networks

Whither the Hadoop Developer Experience, June Hadoop Meetup, Nitin Motgi

Simply Business' Data Platform

Big Data Day LA 2016/ Big Data Track - Apply R in Enterprise Applications, Lo...

Genome Analysis Pipelines, Big Data Style

3 Benefits of Multi-Temperature Data Management for Data Analytics

More from Tugdual Grall

Introduction to Streaming with Apache Flink

Tugdual Grall

Introduction to Streaming with Apache Flink

Tugdual Grall

Modern cars produce data. Lots of data. And Formula 1 cars produce more than their share. I will present a working demonstration of how modern data streaming can be applied to the data acquisition and analysis problem posed by modern motorsports. Instead of bringing multiple Formula 1 cars to the talk, I will show how we instrumented a high fidelity physics-based automotive simulator to produce realistic data from simulated cars running on the Spa-Francorchamps track. We move data from the cars, to the pits, to the engineers back at HQ. The result is near real-time visualization and comparison of performance and a great exposition of how to move data using messaging systems like Kafka, and process data in real time with Apache Spark, then analyse data using SQL with Apache Drill. Code available here: https://github.com/mapr-demos/racing-time-series

Fast Cars, Big Data - How Streaming Can Help Formula 1

Tugdual Grall

Introduction to NoSQL with MongoDB - SQLi Workshop

Tugdual Grall

Enabling Telco to Build and Run Modern Applications

Tugdual Grall

MongoDB and Hadoop

Tugdual Grall

MongoDB’s basic unit of storage is a document. Documents can represent rich, schema-free data structures, meaning that we have several viable alternatives to the normalized, relational model. In this talk, we’ll discuss the tradeoff of various data modeling strategies in MongoDB using various example. You will learn how to work with documents, evolve your schema, and common schema design patterns. Delivered at Soft Shake '14 and Jug Summer Camp '14

Drop your table ! MongoDB Schema Design

Tugdual Grall

Devoxx 2014 : Atelier MongoDB - Decouverte de MongoDB 2.6

Tugdual Grall

Some cool features of MongoDB

Tugdual Grall

Building Your First MongoDB Application

Tugdual Grall

Opensourceday 2014-iot

Tugdual Grall

Neotys conference

Tugdual Grall

Softshake 2013: Introduction to NoSQL with Couchbase

Tugdual Grall

Introduction to NoSQL with Couchbase

Tugdual Grall

Why and How to integrate Hadoop and NoSQL?

Tugdual Grall

NoSQL Matters 2013 - Introduction to Map Reduce with Couchbase 2.0

Tugdual Grall

Big Data Paris : Hadoop and NoSQL

Tugdual Grall

Big Data Israel Meetup : Couchbase and Big Data

Tugdual Grall

FOSDEM 2013 : Getting Started with Couchhbase Server 2.0

Tugdual Grall

Open World Forum 2012 : eXo & the Cloud

Tugdual Grall

More from Tugdual Grall (20)

Introduction to Streaming with Apache Flink

Fast Cars, Big Data - How Streaming Can Help Formula 1

Introduction to NoSQL with MongoDB - SQLi Workshop

Enabling Telco to Build and Run Modern Applications

MongoDB and Hadoop

Drop your table ! MongoDB Schema Design

Devoxx 2014 : Atelier MongoDB - Decouverte de MongoDB 2.6

Some cool features of MongoDB

Building Your First MongoDB Application

Opensourceday 2014-iot

Neotys conference

Softshake 2013: Introduction to NoSQL with Couchbase

Introduction to NoSQL with Couchbase

Why and How to integrate Hadoop and NoSQL?

NoSQL Matters 2013 - Introduction to Map Reduce with Couchbase 2.0

Big Data Paris : Hadoop and NoSQL

Big Data Israel Meetup : Couchbase and Big Data

FOSDEM 2013 : Getting Started with Couchhbase Server 2.0

Open World Forum 2012 : eXo & the Cloud

Recently uploaded

As privacy and data protection regulations evolve rapidly, organizations operating in multiple jurisdictions face mounting challenges to ensure compliance and safeguard customer data. With state-specific privacy laws coming up in multiple states this year, it is essential to understand what their unique data protection regulations will require clearly. How will data privacy evolve in the US in 2024? How to stay compliant? Our panellists will guide you through the intricacies of these states' specific data privacy laws, clarifying complex legal frameworks and compliance requirements. This webinar will review: - The essential aspects of each state's privacy landscape and the latest updates - Common compliance challenges faced by organizations operating in multiple states and best practices to achieve regulatory adherence - Valuable insights into potential changes to existing regulations and prepare your organization for the evolving landscape

TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments

TrustArc

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

🐬 The future of MySQL is Postgres 🐘

RTylerCroy

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

Neo4j

How to Troubleshoot Apps for the Modern Connected Worker

ThousandEyes

Enterprise Knowledge’s Urmi Majumder, Principal Data Architecture Consultant, and Fernando Aguilar Islas, Senior Data Science Consultant, presented "Driving Behavioral Change for Information Management through Data-Driven Green Strategy" on March 27, 2024 at Enterprise Data World (EDW) in Orlando, Florida. In this presentation, Urmi and Fernando discussed a case study describing how the information management division in a large supply chain organization drove user behavior change through awareness of the carbon footprint of their duplicated and near-duplicated content, identified via advanced data analytics. Check out their presentation to gain valuable perspectives on utilizing data-driven strategies to influence positive behavioral shifts and support sustainability initiatives within your organization. In this session, participants gained answers to the following questions: - What is a Green Information Management (IM) Strategy, and why should you have one? - How can Artificial Intelligence (AI) and Machine Learning (ML) support your Green IM Strategy through content deduplication? - How can an organization use insights into their data to influence employee behavior for IM? - How can you reap additional benefits from content reduction that go beyond Green IM?

Driving Behavioral Change for Information Management through Data-Driven Gree...

Enterprise Knowledge

CNv6 Instructor Chapter 6 Quality of Service

giselly40

In this session, we will delve into strategic approaches for optimizing knowledge management within Microsoft 365, amidst the evolving landscape of Copilot. From leveraging automatic metadata classification and permission governance with SharePoint Premium, to unlocking Viva Engage for the cultivation of knowledge and communities, you will gain actionable insights to bolster your organization's knowledge-sharing initiatives. In this session, we will also explore how to facilitate solutions to enable your employees to find answers and expertise within Microsoft 365. You will leave equipped with practical techniques and a deeper understanding of how there is more to effective knowledge management than just enabling Copilot, but building actual solutions to prepare the knowledge that Copilot and your employees can use.

Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...

Drew Madelung

Strategies for Landing an Oracle DBA Job as a Fresher

Remote DBA Services

Axa Assurance Maroc - Insurer Innovation Award 2024

The Digital Insurer

A Domino Admins Adventures (Engage 2024)

Gabriella Davis

Finology Group – Insurtech Innovation Award 2024

The Digital Insurer

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

Product Anonymous

Histor y of HAM Radio presentation slide

vu2urc

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

Evaluating the top large language models.pdf

ChristopherTHyatt

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

Handwritten Text Recognition for manuscripts and early printed texts

Maria Levchenko

Presentation on how to chat with PDF using ChatGPT code interpreter

naman860154

How to convert PDF to text with Nanonets

naman860154

Recently uploaded (20)

TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments

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

🐬 The future of MySQL is Postgres 🐘

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

How to Troubleshoot Apps for the Modern Connected Worker

Driving Behavioral Change for Information Management through Data-Driven Gree...

CNv6 Instructor Chapter 6 Quality of Service

Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...

Strategies for Landing an Oracle DBA Job as a Fresher

Axa Assurance Maroc - Insurer Innovation Award 2024

A Domino Admins Adventures (Engage 2024)

Finology Group – Insurtech Innovation Award 2024

ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke

Histor y of HAM Radio presentation slide

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

Evaluating the top large language models.pdf

08448380779 Call Girls In Greater Kailash - I Women Seeking Men

Handwritten Text Recognition for manuscripts and early printed texts

Presentation on how to chat with PDF using ChatGPT code interpreter

How to convert PDF to text with Nanonets

Proud to be Polyglot - Riviera Dev 2015

3. © 2015 MapR Technologies ‹#›@tgrall Current Architecture • Mono-Technology • Single “language/architecture” • Single Database : RDBMS • One size fits all approach JSP/JSF Services Messaging JTA DAO/JPA

4. © 2015 MapR Technologies ‹#›@tgrall Is it good for me? • Many Files • XML, Sources, Configuration, … • Does Hibernate/JPA is good for all? • Impedance mismatch • Need to scale • From 0 to 50 millions users in 6 weeks • 1Tb of data each day • How easy is to add new features? • Changing code, updating data structure, … • And all this for yesterday !

10. © 2015 MapR Technologies ‹#›@tgrall Scaling out RDBMS • Run many RDBMS Servers • Data could be partitioned • Done by the application code • Caching Layer . . . App Server . . . Memcached . . . MySQL

13. © 2015 MapR Technologies ‹#›@tgrall Polyglot Persistence Big Data/Analysis NoSQL RDBMS • Log Capture • Recommendations • Predictions • Ad Campaign • Products • User Profiles • Game Actions • Sessions • Shopping Cart • Financial Data • Reporting

19. © 2015 MapR Technologies ‹#›@tgrall Why multiple languages? • Sometimes we have no choice • HTML/CSS/JavaScript + Server Side • Mobile Native Applications • Java/.Net are slow movers • Java EE 5.0 is still mainstream (2006!!) • Many languages and frameworks • Scala, Clojure, Groovy, Dart, Go, Ruby, Python, F#, Erlang, Node.js, Rails, Play!, Grails, ...

20. © 2015 MapR Technologies ‹#›@tgrall Examples • Realtime interaction with WebSockets : • Wait for Java EE 7 to supported by your IT? • Hack your app server and application with Continuation ? • Use Node.js and Socket.io ? • Data Collection and Treatment? • Find a library? • Create your library? • Use Java 8 and Lambdas? • Use Scala?

26. © 2015 MapR Technologies ‹#›@tgrall Conclusion • Use the good tool for the good task • Do not try to “hack” something when you have a simpler solution • Think about your users first... so deliver and get feedback • Learning is part of our job ! • And this will help you and your project • What about maintenance? • Do you think it is easier to maintain complex old code?

Proud to be Polyglot - Riviera Dev 2015

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Proud to be Polyglot - Riviera Dev 2015

Similar to Proud to be Polyglot - Riviera Dev 2015 (20)

More from Tugdual Grall

More from Tugdual Grall (20)

Recently uploaded

Recently uploaded (20)

Proud to be Polyglot - Riviera Dev 2015