After a brief introduction into the history of Database Management Systems different types of NoSQL data stores are characterized. Theoretical background information about sharding mechanisms, horizontal scaling and the CAP theorem are getting explained.
After a comparison of different NoSQL stores you will get to know the pros and cons of the different approaches and you will learn how to take the decision for the best fitting database in your project.
Palestra realizada sobre Data Binding no Android apresentada no Androidos Day (www.androidosday.com) e no LifeRay Meetup (http://goo.gl/16gseo) nos dias 02 e 07 de Julho de 2016.
An introduction to modern web technologies HTML5, including Offline, Storage, and Canvas Embedded JavaScript RESTful WebServices using MVC 3, jQuery, and JSON Going mobile with PhoneGap and HTML and CSS
Palestra realizada sobre Data Binding no Android apresentada no Androidos Day (www.androidosday.com) e no LifeRay Meetup (http://goo.gl/16gseo) nos dias 02 e 07 de Julho de 2016.
An introduction to modern web technologies HTML5, including Offline, Storage, and Canvas Embedded JavaScript RESTful WebServices using MVC 3, jQuery, and JSON Going mobile with PhoneGap and HTML and CSS
Streaming Data Pipelines with MongoDB and Kafka at ao.comMongoDB
At AO.com, our team have been focussing on how we can develop new capabilities delivering business value whilst decoupling from legacy monolithic architectures. The problem is, our existing applications contain a lot of data that we care about.
MongoDB and Content Delivery at Aviary by Nir Zicherman and Jack SissonHakka Labs
Aviary's customizable SDK powers cross-platform photo editing for over 4,500 partners and over 50 million monthly active users across the globe. Some of our notable partners include Walgreens, Squarespace, Yahoo Mail, Flickr, Photobucket, and Wix. Aviary's network has grown to thousands of partners and over 50 million active users since the fall of 2011. To optimize the photo editing experience, we recently built a content delivery system that targets users with customized effects, stickers, frames, etc. Today, we can distribute targeted content based on a seamlessly extendable set of parameters, including a user's location, language, app, and device.
Modern architectures are moving away from a "one size fits all" approach. We are well aware that we need to use the best tools for the job. Given the large selection of options available today, chances are that you will end up managing data in MongoDB for your operational workload and with Spark for your high speed data processing needs.
Description: When we model documents or data structures there are some key aspects that need to be examined not only for functional and architectural purposes but also to take into consideration the distribution of data nodes, streaming capabilities, aggregation and queryability options and how we can integrate the different data processing software, like Spark, that can benefit from subtle but substantial model changes. A clear example is when embedding or referencing documents and their implications on high speed processing.
Over the course of this talk we will detail the benefits of a good document model for the operational workload. As well as what type of transformations we should incorporate in our document model to adjust for the high speed processing capabilities of Spark.
We will look into the different options that we have to connect these two different systems, how to model according to different workloads, what kind of operators we need to be aware of for top performance and what kind of design and architectures we should put in place to make sure that all of these systems work well together.
Over the course of the talk we will showcase different libraries that enable the integration between spark and MongoDB, such as MongoDB Hadoop Connector, Stratio Connector and MongoDB Spark Native Connector.
By the end of the talk I expect the attendees to have an understanding of:
How they connect their MongoDB clusters with Spark
Which use cases show a net benefit for connecting these two systems
What kind of architecture design should be considered for making the most of Spark + MongoDB
How documents can be modeled for better performance and operational process, while processing these data sets stored in MongoDB.
The talk is suitable for:
Developers that want to understand how to leverage Spark
Architects that want to integrate their existing MongoDB cluster and have real time high speed processing needs
Data scientists that know about Spark, are playing with Spark and want to integrate with MongoDB for their persistency layer
Windows 8 Pure Imagination - 2012-11-24 - Getting your HTML5 game Windows 8 r...Frédéric Harper
You already created an HTML5 game, and you want to make it a Windows 8 game to get all the benefits of this new platform? The recipe is simple: take your HTML5 games, add some WinJS dressing, use Visual Studio to make these stick together, and get in the Windows Store oven to get a perfectly cooked app!
Montreal Sql saturday: moving data from no sql db to azure data lakeDiponkar Paul
NoSQL database have grown popularity in recent years due to the flexibility of data modeling and scaling up capabilities. NoSQL database also have been using in big data landscape. The demo rich session will elaborate difference between SQL and NoSQL. And end to end solution for data moving capabilities from NoSQL database MongoDB by using Azure data factory.
MongoDB Schema Design: Practical Applications and ImplicationsMongoDB
Presented by Austin Zellner, Solutions Architect, MongoDB
Schema design is as much art as it is science, but it is central to understanding how to get the most out of MongoDB. Attendees will walk away with an understanding of how to approach schema design, what influences it, and the science behind the art. After this session, attendees will be ready to design new schemas, as well as re-evaluate existing schemas with a new mental model.
Building LinkedIn's Learning Platform with MongoDBJake Dejno
Slides from LinkedIn's presentation at MongoDB World 2014, description below:
LearnIn is LinkedIn’s internal learning platform packed with a huge variety of resources that will help our employees learn, develop, and grow professionally. In this talk, we will discuss how a small team of web developers built this platform’s API using MongoDB and a full JavaScript stack including Node.js. In particular, we will look at schema design and document validation using Mongoose ODM for Node.js, as well as quick document search utilizing MongoDB full-text search and our move to ElasticSearch using the MongoDB oplog.
Streaming Data Pipelines with MongoDB and Kafka at ao.comMongoDB
At AO.com, our team have been focussing on how we can develop new capabilities delivering business value whilst decoupling from legacy monolithic architectures. The problem is, our existing applications contain a lot of data that we care about.
MongoDB and Content Delivery at Aviary by Nir Zicherman and Jack SissonHakka Labs
Aviary's customizable SDK powers cross-platform photo editing for over 4,500 partners and over 50 million monthly active users across the globe. Some of our notable partners include Walgreens, Squarespace, Yahoo Mail, Flickr, Photobucket, and Wix. Aviary's network has grown to thousands of partners and over 50 million active users since the fall of 2011. To optimize the photo editing experience, we recently built a content delivery system that targets users with customized effects, stickers, frames, etc. Today, we can distribute targeted content based on a seamlessly extendable set of parameters, including a user's location, language, app, and device.
Modern architectures are moving away from a "one size fits all" approach. We are well aware that we need to use the best tools for the job. Given the large selection of options available today, chances are that you will end up managing data in MongoDB for your operational workload and with Spark for your high speed data processing needs.
Description: When we model documents or data structures there are some key aspects that need to be examined not only for functional and architectural purposes but also to take into consideration the distribution of data nodes, streaming capabilities, aggregation and queryability options and how we can integrate the different data processing software, like Spark, that can benefit from subtle but substantial model changes. A clear example is when embedding or referencing documents and their implications on high speed processing.
Over the course of this talk we will detail the benefits of a good document model for the operational workload. As well as what type of transformations we should incorporate in our document model to adjust for the high speed processing capabilities of Spark.
We will look into the different options that we have to connect these two different systems, how to model according to different workloads, what kind of operators we need to be aware of for top performance and what kind of design and architectures we should put in place to make sure that all of these systems work well together.
Over the course of the talk we will showcase different libraries that enable the integration between spark and MongoDB, such as MongoDB Hadoop Connector, Stratio Connector and MongoDB Spark Native Connector.
By the end of the talk I expect the attendees to have an understanding of:
How they connect their MongoDB clusters with Spark
Which use cases show a net benefit for connecting these two systems
What kind of architecture design should be considered for making the most of Spark + MongoDB
How documents can be modeled for better performance and operational process, while processing these data sets stored in MongoDB.
The talk is suitable for:
Developers that want to understand how to leverage Spark
Architects that want to integrate their existing MongoDB cluster and have real time high speed processing needs
Data scientists that know about Spark, are playing with Spark and want to integrate with MongoDB for their persistency layer
Windows 8 Pure Imagination - 2012-11-24 - Getting your HTML5 game Windows 8 r...Frédéric Harper
You already created an HTML5 game, and you want to make it a Windows 8 game to get all the benefits of this new platform? The recipe is simple: take your HTML5 games, add some WinJS dressing, use Visual Studio to make these stick together, and get in the Windows Store oven to get a perfectly cooked app!
Montreal Sql saturday: moving data from no sql db to azure data lakeDiponkar Paul
NoSQL database have grown popularity in recent years due to the flexibility of data modeling and scaling up capabilities. NoSQL database also have been using in big data landscape. The demo rich session will elaborate difference between SQL and NoSQL. And end to end solution for data moving capabilities from NoSQL database MongoDB by using Azure data factory.
MongoDB Schema Design: Practical Applications and ImplicationsMongoDB
Presented by Austin Zellner, Solutions Architect, MongoDB
Schema design is as much art as it is science, but it is central to understanding how to get the most out of MongoDB. Attendees will walk away with an understanding of how to approach schema design, what influences it, and the science behind the art. After this session, attendees will be ready to design new schemas, as well as re-evaluate existing schemas with a new mental model.
Building LinkedIn's Learning Platform with MongoDBJake Dejno
Slides from LinkedIn's presentation at MongoDB World 2014, description below:
LearnIn is LinkedIn’s internal learning platform packed with a huge variety of resources that will help our employees learn, develop, and grow professionally. In this talk, we will discuss how a small team of web developers built this platform’s API using MongoDB and a full JavaScript stack including Node.js. In particular, we will look at schema design and document validation using Mongoose ODM for Node.js, as well as quick document search utilizing MongoDB full-text search and our move to ElasticSearch using the MongoDB oplog.
Eagle6 is a product that use system artifacts to create a replica model that represents a near real-time view of system architecture. Eagle6 was built to collect system data (log files, application source code, etc.) and to link system behaviors in such a way that the user is able to quickly identify risks associated with unknown or unwanted behavioral events that may result in unknown impacts to seemingly unrelated down-stream systems. This session is designed to present the capabilities of the Eagle6 modeling product and how we are using MongoDB to support near-real-time analysis of large disparate datasets.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
31. KeyValue Store Characteristics
Most simple data model
DB does not care about data types
Similar to persistent hash map
Fast lookups
Easy to distribute
Inspired by Amazon Dynamo paper
Restricted possibilities of querying
31
32. Open Source Advanced KeyValue Store
In-Memory Store with optional durability
Knows types like strings, hashes, lists, sets
BSD License
Implemented in C
Very small footprint (20k LOC for rel. 2.2)
APIs for C/C++, C#, Closure, Lisp, Erlang, Go, Haskell,
Java, JavaScript, Objective-C, Perl, PHP, Python, Ruby, ...
Used at Twitter, Instagram, flickr, stackoverflow, ...
32
33. Open Source KeyValue Store
Highly available and fault-tolerant
Basho Technologies
Apache License
Implemented in Erlang
APIs for Java, Erlang, Ruby, Php, Python, Closure, C#,
C/C++, HTTP, Node.js, Perl, Scala, Smalltalk, ...
Used at Mozilla, Comcast,AOL
33
34. Open Source KeyValue Store
Big, distributed, persistent, fault-tolerant hash table
Developed by LinkedIn
Implemented in Java
Apache 2.0 License
Dynamo Scale Out
Used at LinkedIn
34
37. Document Store Characteristics
You can query into document structure
You can use natural aggregates as documents
You can retrieve portions of a document
You can update portions of a document
You can have links between documents
Compared to key value data model the document is more
transparent
No schema / implicit schema
Some queries are a pain in the neck!
37
38. Open Source Document Store
„Most popular NoSQL database“
Stores JSON like documents
Implemented in C++
GNU AGPL License
APIs for C/C++, C#, Go, Erlang, Java, JavaScript, Node.js,
Perl, PHP, Python, Ruby, Scala, HTTP/REST
Used at Craigslist, eBay, Foursquare, SourceForge,
NYT, ...
38
39. Open Source Document Store
Ease of Use
No update locks
Stores JSON like documents
Implemented in Erlang
Apache License
APIs for JavaScript, MapReduce, HTTP/REST
Used at BBC, Credit Suisse, Meebo, ...
39
40. Open Source Distributed Document Store
Optimized for interactive applications
Merged from Membase and CouchDB
Implemented in C++, Erlang, C
Apache License / Proprietary
APIs for Java, .NET, PHP, Ruby, Python, C
Used at AOL, Cisco, LinkedIn, Salesforce.com, Zynga, ...
40
42. Schemaless
Schemaless is one of the main reasons of interest
in NoSQL databases
Schemaless reduces ceremony
Schemaless increases flexibility
BUT...
42
43. Schemaless means
implicit schema
To query specific attributes
you have to know their names
Schema Managment is shifted from db to code
http://martinfowler.com/articles/schemaless/
43
46. more complicated data model
rich structure
single key (row key)
easy/ fast access to columns/column families in a row
rows can contain 100s or 1000s of columns
aggregate oriented
Column Family Characteristics
46
47. Open Source Wide Column Store
Supports multi data center replication
Good for distributed DBs with massive write loads
Implemented in Java
Apache License 2.0
APIs for C#, C++, Clojure, Erlang, Go, Haskell, Java,
JavaScript, Perl, PHP, Python, Ruby, Scala
Used at CERN, Facebook, Netflix, Rackspace,
SoundCloud,Twitter ...
47
48. Open Source Column Oriented Database
Part of Hadoop, Inspired by Googles BigTable
Implemented in Java
Apache License 2.0
APIs for Restful HTTP,Thrift, C/C++, C#, Groovy, Java,
PHP, Python, Scala
Used at Amazon,Adobe,AOL, Cloudspace, eBay,
Facebook, IBM, Last.fm, LinkedIn, Spotify,Yahoo!, ...
48
52. Graph DBs disassemble things in fragments and
relations
You can do very interesting queries on graph
structures - things you can not event think of in SQL
Good for complex graph structured data
Fast lookups, fast traversing
Whiteboard Friendly
Graph DB Characteristics
52
53. Open Source Graph Database
Embedded, disk-based, fully transactional
Implemented in Java
GPLv3 and AGPLv3 / commercial
APIs for .NET, Clojure, Go, Groovy, Java, JavaScript,
Perl, PHP, Pyhton, Ruby, Scala
Used at Adobe, Cisco,Telekom...
53
54. Open Source Document Database
with Graph oriented extensions
Supports SQL (without join) as query language
Supports ACID transactions
Implemented in Java
Apache License 2.0
Commercial support available
APIs for HTTP/REST, Java, JavaScript, Scala, PHP,
Ruby, .NET, Clojure, Node.js, Python, ...
Used at SKY, Spielo, UltraDNS...
54
58. Hashing Problems
common way of choosing a server:
server = hash(key) mod n
Every object
gets hashed to
a new location!
What happens, if a server goes down?
58
59. Consistent Hashing
Use same hash function for both objects and servers
shards:A, B, C
objects: 1, 2, 3, 4
http://www.tom-e-white.com/2007/11/consistent-hashing.html
59
63. RDBMS will not die
Use a relational database
unless you have good reason not to
63
64. RDBMS have their limits
Vertical scaling is expensive and has hard limits
Horizontal scaling is not possible/ limited
Joins on big and distributed tables too
expenisve/ too slow
Rigid Schema inappropriate for semi
structured/dynamic data (sparse tables)
Consistency is higher rated than availability
64
65. NoSQL come to the rescue
Distribution and scalability are fundamental
design goals of NoSQL DBs
Tradeoff between Consistency,Availability and
horizontal scalability (CAP Theorem, BASE)
Small footprint in favor of ease of use
Outstandingly proven in practice (Google,
Amazon, Facebook, LinkedIn,Twitter, ...)
65
66. There are cons too
Broad spectrum of products is difficult to
understand
You have to get used to designing models for
Key/Value or Column Family stores
Mostly no ad hoc queries
No standards - no portability
Sometimes poor documentation
Few commercial support offers
66
67. RDBMS vs. NoSQL
think about data think about queries
redundancy is bad redundancy is ok
indexes managed by DB manage own indexes
query over relations no joins
always exact results results may be out of date
SQL proprietary APIs
67
70. Polyglot Persistence
NoSQL will break the relational dominance unlike the
OODBMSs in the 80ies
RDBMS is not the one and only option any more
Select the storage technology that best fits your
current situation
Enterprises will use different storage technologies for
different kinds of data
DB is no integration point any more
Apps talk via WebServices and encapsulate their
individual data storage technologies
70
71. NewSQL
The answer of traditional RDBMS vendors to the great
success of NoSQL
Improved RDBMS offer more features and better
scalability
Oracle launches Oracle NoSQL, their own NoSQL DB
based upon a revised Berkley DB
Oracle, Microsoft, Sybase, IBM, Greenplum, Pervuasive
already have a tight Hadoop Integration
„Can‘t fight it? Embrace it!“
71