NuoDB is an elastic SQL database that uses an emergent architecture where everything is represented as autonomous atoms. Atoms can replicate themselves across nodes to provide scalability without compromising on ACID transactions or requiring additional administration. Unlike traditional SQL databases, NuoDB's distributed model allows it to scale elastically in the cloud while providing the full functionality of SQL and high availability even with node failures.
Big Data, IoT, data lake, unstructured data, Hadoop, cloud, and massively parallel processing (MPP) are all just fancy words unless you can find uses cases for all this technology. Join me as I talk about the many use cases I have seen, from streaming data to advanced analytics, broken down by industry. I’ll show you how all this technology fits together by discussing various architectures and the most common approaches to solving data problems and hopefully set off light bulbs in your head on how big data can help your organization make better business decisions.
The SQL Server Health Check process is divided into phases during which we collect both technical information at the database level and the applications that exploit them, trying to offer a global point of view and focusing on SQL Server.
Big Data, IoT, data lake, unstructured data, Hadoop, cloud, and massively parallel processing (MPP) are all just fancy words unless you can find uses cases for all this technology. Join me as I talk about the many use cases I have seen, from streaming data to advanced analytics, broken down by industry. I’ll show you how all this technology fits together by discussing various architectures and the most common approaches to solving data problems and hopefully set off light bulbs in your head on how big data can help your organization make better business decisions.
The SQL Server Health Check process is divided into phases during which we collect both technical information at the database level and the applications that exploit them, trying to offer a global point of view and focusing on SQL Server.
Using Spark Streaming and NiFi for the next generation of ETL in the enterpriseDataWorks Summit
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story? This session will cover the Royal Bank of Canada’s (RBC) journey of moving away from traditional ETL batch processing with Teradata towards using the Hadoop ecosystem for ingesting data. One of the first systems to leverage this new approach was the Event Standardization Service (ESS). This service provides a centralized “client event” ingestion point for the bank’s internal systems through either a web service or text file daily batch feed. ESS allows down stream reporting applications and end users to query these centralized events.
We discuss the drivers and expected benefits of changing the existing event processing. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
Speakers
Darryl Sutton, T4G, Principal Consultant
Kenneth Poon, RBC, Director, Data Engineering
The biggest headine at the 2009 Oracle OpenWorld was when Larry Ellison announced that Oracle was entering the hardware business with a pre-built database machine, engineered by Oracle. Since then businesses around the world have started to use these engineered systems. This beginner/intermediate-level session will take you through my first 100 days of starting to administer an Exadata machine and all the roadblocks and all the success I had along this new path.
The Most Trusted In-Memory database in the world- AltibaseAltibase
Life is a database. How you manage data defines business. ALTIBASE HDB with its Hybrid architecture combines the extreme speed of an In-Memory Database with the storage capacity of an On-Disk Database’ in a single unified engine.
ALTIBASE® HDB™ is the only Hybrid DBMS in the industry that combines an in-memory DBMS with an on-disk DBMS, with a single uniform interface, enabling real-time access to large volumes of data, while simplifying and revolutionizing data processing. ALTIBASE XDB is the world’s fastest in-memory DBMS, featuring unprecedented high performance, and supports SQL-99 standard for wide applicability.
Altibase is provider of In-Memory data solutions for real-time access, analysis and distribution of high volumes of data in mission-critical environments.
Please visit our website (www.altibase.com) to learn more about our products and read more about our case studies. Or contact us at info@altibase.com. We look forward to helping you!
What Comes After The Star Schema? Dimensional Modeling For Enterprise Data HubsCloudera, Inc.
Dimensional modeling and the star schema are some of the most important ideas in the history of analytics and data management. They provided a common language and set of patterns that allowed a broad class of users to analyze business processes and spawned an entire ecosystem. With the rise of enterprise data hubs that allow us to combine ETL, search, SQL, and machine learning in a single platform, we need to extend the principles of dimensional modeling to support new and diverse analytical workloads and users. We'll illustrate these concepts by walking through the design of a customer-centric data hub that uses all of the components of an EDH to enable everyone to understand the way that customers experience a company.
Presenter:
Josh Wills, Senior Director Data Science
Updated: October 6, 2014
Uwe Ricken at SQL in the City 2016.
Waits, as they’re known in the SQL Server world, indicate that a worker thread inside SQL Server is waiting for a resource to become available before it can proceed with executing. They’re often a major source of performance issues.
In this session, we’ll walk through an optimal performance troubleshooting process for a variety of scenarios, and illustrate both the strengths and weaknesses of using a waits-only approach to troubleshooting.
Data Warehouse Design and Best PracticesIvo Andreev
A data warehouse is a database designed for query and analysis rather than for transaction processing. An appropriate design leads to scalable, balanced and flexible architecture that is capable to meet both present and long-term future needs. This session covers a comparison of the main data warehouse architectures together with best practices for the logical and physical design that support staging, load and querying.
Data Con LA 2020
Description
In this session, I introduce the Amazon Redshift lake house architecture which enables you to query data across your data warehouse, data lake, and operational databases to gain faster and deeper insights. With a lake house architecture, you can store data in open file formats in your Amazon S3 data lake.
Speaker
Antje Barth, Amazon Web Services, Sr. Developer Advocate, AI and Machine Learning
The new Microsoft Azure SQL Data Warehouse (SQL DW) is an elastic data warehouse-as-a-service and is a Massively Parallel Processing (MPP) solution for "big data" with true enterprise class features. The SQL DW service is built for data warehouse workloads from a few hundred gigabytes to petabytes of data with truly unique features like disaggregated compute and storage allowing for customers to be able to utilize the service to match their needs. In this presentation, we take an in-depth look at implementing a SQL DW, elastic scale (grow, shrink, and pause), and hybrid data clouds with Hadoop integration via Polybase allowing for a true SQL experience across structured and unstructured data.
Ceph Object Storage Reference Architecture Performance and Sizing GuideKaran Singh
Together with my colleagues at Red Hat Storage Team, i am very proud to have worked on this reference architecture for Ceph Object Storage.
If you are building Ceph object storage at scale, this document is for you.
Netflix’s Big Data Platform team manages data warehouse in Amazon S3 with over 60 petabytes of data and writes hundreds of terabytes of data every day. With a data warehouse at this scale, it is a constant challenge to keep improving performance. This talk will focus on Iceberg, a new table metadata format that is designed for managing huge tables backed by S3 storage. Iceberg decreases job planning time from minutes to under a second, while also isolating reads from writes to guarantee jobs always use consistent table snapshots.
In this session, you'll learn:
• Some background about big data at Netflix
• Why Iceberg is needed and the drawbacks of the current tables used by Spark and Hive
• How Iceberg maintains table metadata to make queries fast and reliable
• The benefits of Iceberg's design and how it is changing the way Netflix manages its data warehouse
• How you can get started using Iceberg
Speaker
Ryan Blue, Software Engineer, Netflix
Delta Lake OSS: Create reliable and performant Data Lake by Quentin AmbardParis Data Engineers !
Delta Lake is an open source framework living on top of parquet in your data lake to provide Reliability and performances. It has been open-sourced by Databricks this year and is gaining traction to become the defacto delta lake format.
We’ll see all the goods Delta Lake can do to your data with ACID transactions, DDL operations, Schema enforcement, batch and stream support etc !
Three Best Practices for Optimizing your IT Infrastructure
In a survey by the Uptime Institute, 42% of enterprise data center managers reported that they would run out of power capacity within 24 months. This statistic isn't surprising when you consider that today's IT hardware requires more power-distribution air conditioning and UPS capacity than in the past.
What steps is your data center taking to mitigate the detrimental disruptions of availability, reliability and uptime caused by a loss of capacity?
To view the recorded webinar event, please visit http://www.42u.com/it-optimization-webinar.htm
Big Data and Data Warehousing Together with Azure Synapse Analytics (SQLBits ...Michael Rys
SQLBits 2020 presentation on how you can build solutions based on the modern data warehouse pattern with Azure Synapse Spark and SQL including demos of Azure Synapse.
Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. Presto was designed and written from the ground up for interactive analytics and approaches the speed of commercial data warehouses while scaling to the size of organizations like Facebook. One key feature in Presto is the ability to query data where it lives via a uniform ANSI SQL interface. Presto’s connector architecture creates an abstraction layer for anything that can be expressed in a row-like format, such as HDFS, Amazon S3, Azure Storage, NoSQL stores, relational databases, Kafka streams and even proprietary data stores. Furthermore, a single Presto query can combine data from multiple sources, allowing for analytics across your entire organization.
This talk will be co-presented by Facebook and Teradata, the two largest contributors to Presto. The talk will focus on Presto’s ability to query virtually any data source via it’s connector interface. Facebook and Teradata will present some of their use cases of Presto querying various data sources, discuss the existing connectors in Presto, and describe the anatomy of a connector.
DAMA, Oregon Chapter, 2012 presentation - an introduction to Data Vault modeling. I will be covering parts of the methodology, comparison and contrast of issues in general for the EDW space. Followed by a brief technical introduction of the Data Vault modeling method.
After the presentation i I will be providing a demonstration of the ETL loading layers, LIVE!
You can find more on-line training at: http://LearnDataVault.com/training
Using Spark Streaming and NiFi for the next generation of ETL in the enterpriseDataWorks Summit
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story? This session will cover the Royal Bank of Canada’s (RBC) journey of moving away from traditional ETL batch processing with Teradata towards using the Hadoop ecosystem for ingesting data. One of the first systems to leverage this new approach was the Event Standardization Service (ESS). This service provides a centralized “client event” ingestion point for the bank’s internal systems through either a web service or text file daily batch feed. ESS allows down stream reporting applications and end users to query these centralized events.
We discuss the drivers and expected benefits of changing the existing event processing. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
Speakers
Darryl Sutton, T4G, Principal Consultant
Kenneth Poon, RBC, Director, Data Engineering
The biggest headine at the 2009 Oracle OpenWorld was when Larry Ellison announced that Oracle was entering the hardware business with a pre-built database machine, engineered by Oracle. Since then businesses around the world have started to use these engineered systems. This beginner/intermediate-level session will take you through my first 100 days of starting to administer an Exadata machine and all the roadblocks and all the success I had along this new path.
The Most Trusted In-Memory database in the world- AltibaseAltibase
Life is a database. How you manage data defines business. ALTIBASE HDB with its Hybrid architecture combines the extreme speed of an In-Memory Database with the storage capacity of an On-Disk Database’ in a single unified engine.
ALTIBASE® HDB™ is the only Hybrid DBMS in the industry that combines an in-memory DBMS with an on-disk DBMS, with a single uniform interface, enabling real-time access to large volumes of data, while simplifying and revolutionizing data processing. ALTIBASE XDB is the world’s fastest in-memory DBMS, featuring unprecedented high performance, and supports SQL-99 standard for wide applicability.
Altibase is provider of In-Memory data solutions for real-time access, analysis and distribution of high volumes of data in mission-critical environments.
Please visit our website (www.altibase.com) to learn more about our products and read more about our case studies. Or contact us at info@altibase.com. We look forward to helping you!
What Comes After The Star Schema? Dimensional Modeling For Enterprise Data HubsCloudera, Inc.
Dimensional modeling and the star schema are some of the most important ideas in the history of analytics and data management. They provided a common language and set of patterns that allowed a broad class of users to analyze business processes and spawned an entire ecosystem. With the rise of enterprise data hubs that allow us to combine ETL, search, SQL, and machine learning in a single platform, we need to extend the principles of dimensional modeling to support new and diverse analytical workloads and users. We'll illustrate these concepts by walking through the design of a customer-centric data hub that uses all of the components of an EDH to enable everyone to understand the way that customers experience a company.
Presenter:
Josh Wills, Senior Director Data Science
Updated: October 6, 2014
Uwe Ricken at SQL in the City 2016.
Waits, as they’re known in the SQL Server world, indicate that a worker thread inside SQL Server is waiting for a resource to become available before it can proceed with executing. They’re often a major source of performance issues.
In this session, we’ll walk through an optimal performance troubleshooting process for a variety of scenarios, and illustrate both the strengths and weaknesses of using a waits-only approach to troubleshooting.
Data Warehouse Design and Best PracticesIvo Andreev
A data warehouse is a database designed for query and analysis rather than for transaction processing. An appropriate design leads to scalable, balanced and flexible architecture that is capable to meet both present and long-term future needs. This session covers a comparison of the main data warehouse architectures together with best practices for the logical and physical design that support staging, load and querying.
Data Con LA 2020
Description
In this session, I introduce the Amazon Redshift lake house architecture which enables you to query data across your data warehouse, data lake, and operational databases to gain faster and deeper insights. With a lake house architecture, you can store data in open file formats in your Amazon S3 data lake.
Speaker
Antje Barth, Amazon Web Services, Sr. Developer Advocate, AI and Machine Learning
The new Microsoft Azure SQL Data Warehouse (SQL DW) is an elastic data warehouse-as-a-service and is a Massively Parallel Processing (MPP) solution for "big data" with true enterprise class features. The SQL DW service is built for data warehouse workloads from a few hundred gigabytes to petabytes of data with truly unique features like disaggregated compute and storage allowing for customers to be able to utilize the service to match their needs. In this presentation, we take an in-depth look at implementing a SQL DW, elastic scale (grow, shrink, and pause), and hybrid data clouds with Hadoop integration via Polybase allowing for a true SQL experience across structured and unstructured data.
Ceph Object Storage Reference Architecture Performance and Sizing GuideKaran Singh
Together with my colleagues at Red Hat Storage Team, i am very proud to have worked on this reference architecture for Ceph Object Storage.
If you are building Ceph object storage at scale, this document is for you.
Netflix’s Big Data Platform team manages data warehouse in Amazon S3 with over 60 petabytes of data and writes hundreds of terabytes of data every day. With a data warehouse at this scale, it is a constant challenge to keep improving performance. This talk will focus on Iceberg, a new table metadata format that is designed for managing huge tables backed by S3 storage. Iceberg decreases job planning time from minutes to under a second, while also isolating reads from writes to guarantee jobs always use consistent table snapshots.
In this session, you'll learn:
• Some background about big data at Netflix
• Why Iceberg is needed and the drawbacks of the current tables used by Spark and Hive
• How Iceberg maintains table metadata to make queries fast and reliable
• The benefits of Iceberg's design and how it is changing the way Netflix manages its data warehouse
• How you can get started using Iceberg
Speaker
Ryan Blue, Software Engineer, Netflix
Delta Lake OSS: Create reliable and performant Data Lake by Quentin AmbardParis Data Engineers !
Delta Lake is an open source framework living on top of parquet in your data lake to provide Reliability and performances. It has been open-sourced by Databricks this year and is gaining traction to become the defacto delta lake format.
We’ll see all the goods Delta Lake can do to your data with ACID transactions, DDL operations, Schema enforcement, batch and stream support etc !
Three Best Practices for Optimizing your IT Infrastructure
In a survey by the Uptime Institute, 42% of enterprise data center managers reported that they would run out of power capacity within 24 months. This statistic isn't surprising when you consider that today's IT hardware requires more power-distribution air conditioning and UPS capacity than in the past.
What steps is your data center taking to mitigate the detrimental disruptions of availability, reliability and uptime caused by a loss of capacity?
To view the recorded webinar event, please visit http://www.42u.com/it-optimization-webinar.htm
Big Data and Data Warehousing Together with Azure Synapse Analytics (SQLBits ...Michael Rys
SQLBits 2020 presentation on how you can build solutions based on the modern data warehouse pattern with Azure Synapse Spark and SQL including demos of Azure Synapse.
Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes. Presto was designed and written from the ground up for interactive analytics and approaches the speed of commercial data warehouses while scaling to the size of organizations like Facebook. One key feature in Presto is the ability to query data where it lives via a uniform ANSI SQL interface. Presto’s connector architecture creates an abstraction layer for anything that can be expressed in a row-like format, such as HDFS, Amazon S3, Azure Storage, NoSQL stores, relational databases, Kafka streams and even proprietary data stores. Furthermore, a single Presto query can combine data from multiple sources, allowing for analytics across your entire organization.
This talk will be co-presented by Facebook and Teradata, the two largest contributors to Presto. The talk will focus on Presto’s ability to query virtually any data source via it’s connector interface. Facebook and Teradata will present some of their use cases of Presto querying various data sources, discuss the existing connectors in Presto, and describe the anatomy of a connector.
DAMA, Oregon Chapter, 2012 presentation - an introduction to Data Vault modeling. I will be covering parts of the methodology, comparison and contrast of issues in general for the EDW space. Followed by a brief technical introduction of the Data Vault modeling method.
After the presentation i I will be providing a demonstration of the ETL loading layers, LIVE!
You can find more on-line training at: http://LearnDataVault.com/training
Here is my seminar presentation on No-SQL Databases. it includes all the types of nosql databases, merits & demerits of nosql databases, examples of nosql databases etc.
For seminar report of NoSQL Databases please contact me: ndc@live.in
Quantitative Performance Evaluation of Cloud-Based MySQL (Relational) Vs. Mon...Darshan Gorasiya
To compare the performance of MySQL (Consistency & Availability - CA) with MongoDB (consistency & partition - CP). Yahoo! Cloud Serving Benchmark (YCSB) automated workloads used for quantitative comparison with large and small data volume.
A fotopedia presentation made at the MongoDay 2012 in Paris at Xebia Office.
Talk by Pierre Baillet and Mathieu Poumeyrol.
French Article about the presentation:
http://www.touilleur-express.fr/2012/02/06/mongodb-retour-sur-experience-chez-fotopedia/
Video to come.
Approaches to make your software project upgrade friendly and easy for everyday development. How can we save time and efforts at technology upgrading phases.
Microsoft Blazor which allows developers to leverage the existing skills and makes .NET syntaxes render within the browser with the blend of Razor and the taste of Angular. It supports latest Single Page Application demanding technologies such as Routing, Layouting and Dependency Injection.
The basics of Angular form validation techniques in Angular 2. This will help you to chose the better approach in your software development project in form validation for user inputs.
2. What is NuoDB?
NuoDB is the first and only emergent database that is;
100% SQL compliant
100% ACID transactions
Elastically scalable
Highly resilient and requires minimal database administration
Your SQL database wasn’t designed to scale elastically in the
cloud. However as your traffic grows, the database simply must
scale. But it’s hard.
SQL databases - every single one - were designed to run on the
traditional client server systems that were architected back in the
last century. They are all monolithic, synchronous, and centralized,
which means that performing actions like doing a CRUD operation
for a single record in the database, requires locking the database
on the master node.
3. Key features
100% SQL - NewSQL looks and behaves just like a traditional
SQL database but scales and provides the same flexibility of
NoSQL. Developers can focus on building scalable applications
using the same SQL tools they know and have come to trust like
DbVisualizer, Hibernate and Active Record.
100% ACID - Guaranteed atomicity, consistency, isolation, and
durability (ACID). NuoDB fully supports ACID transactions so no
compromises are required when it comes to the accuracy of
your data.
Elastically Scalable - NuoDB allows you to horizontally scale out
or in without bringing down the system, changing a single line
of code, or giving up the power of SQL. Your applications behave
as expected whether you add one node or one hundred nodes.
4. Built-in Redundancy - Disaster recovery at the database level
is now a thing of the past. You no longer need to spend
double on redundant hardware that will sit idle until there is
a disaster. NuoDB’s distributed architecture will continue to
operate as a single cohesive database even if there are
multiple points of failure. Its peer-to-peer architecture
makes it easy to deal with hardware failures and power
outages without any downtime.
Multi-Tenancy - NuoDB allows you to deploy one or more
databases within a single administrative domain via the
NuoConsole. This greatly reduces the administrative burden
of configuring, managing and monitoring the NuoDB
system.
Auto Replication - Low latency environments often require a
separate replicated database to support operational
reporting requirements. With NuoDB you are no longer
required to setup extraneous processes to support
operational intelligence reporting. Simply add another
storage node to the database and let NuoDB handle the
replication for you.
5. Easy Administration - NuoDB is designed for simplicity and
(almost) zero administration. It’s simple to monitor your
database and add or remove nodes to a running
database. No sharding or caching strategies are needed.
Built-in Security - Cloud deployment always begs the
question “how secure is my data?”. NuoDB is not an SQL
database retrofitted for the cloud; it was born? to live in the
cloud. All asynchronous peer-to-peer communication
between NuoDB transaction and storage nodes is secure
and encrypted by default. And NuoDB can be easily
configured to persist data to an encrypted file store. You
are no longer required to make the security vs.
performance trade-offs associated with traditional
databases.
NuoConsole - Centralized monitoring of your decentralized
database is made easy with the NuoDB monitoring web
app. The NuoConsole allows you to start and stop your
database, add additional resources to your database such
as Transaction Engines and Storage Managers, and monitor
your database in real-time.
6. Developer Tools - Integrate NuoDB with the most popular
development frameworks like JAVA EE, Ruby on Rails,
Zend, Coldfusion, .NET, node.js, Python and many others
to build new applications or to scale existing ones. NuoDB
works seamlessly with many ORM tools such as Hibernate,
Active Record, and PHP PDO.
Heterogeneous Deployment - The Emergent Architecture
provides an unprecedented level of deployment flexibility.
NuoDB is the only database system that can be distributed
across a heterogeneous set of operating systems and
infrastructure resources that include the cloud, large data
centres, or local commodity hardware, and still continue to
function as a single instance of a database. NuoDB runs on
Windows, MacOS, Linux, and Solaris platforms today.
7. Emergent architecture
An emergent architecture is characterized by simple,
autonomous actions by individual components
producing complex, coordinated behaviors in the
overall system.
8. Atomicity
NuoDB is an asynchronous, decentralized, peer-to-peer
database and object-oriented. Objects in NuoDB know how
to perform various actions that create specific behaviors in
the overall database. And every object in NuoDB is an
Atom. An Atom in NuoDB is like a single bird in a flock.
Atoms are self-describing objects (data and metadata) that
together comprise the database. Everything in the NuoDB
database is an Atom, including the schema, the indexes,
and even the underlying data. For example, each table is
an Atom that describes the metadata for the table and can
reference other Atoms; such as Atoms that describe ranges
of records in the table and their versions.
9. Atoms are Powerful
Atoms are intelligent, powerful, self-describing objects
that together form the NuoDB database. Atoms know
how to perform many actions, like these:
Atoms know how to make copies of themselves.
Atoms keep all copies of themselves up to date.
Atoms can broadcast messages. Atoms listen for
events and changes from other Atoms.
Atoms can request data from other Atoms.
Atoms can serialize themselves to persistent
storage.
Atoms can retrieve data from storage.
10. Consistency via Asynchronous
Decentralized Messaging
Atoms communicates P2P asynchronously. This is ideal for the
decentralized nature of an elastic cloud infrastructure.
A key reason to a traditional SQL database is so hard to scale in the cloud
is that it operates synchronously, relying on a single master node to lock
the database and orchestrate transactions. They weren’t designed for the
decentralized, asynchronous nature in the cloud.
In a NuoDB database, using object-oriented Atoms, it is able to perform
all the coordinated actions of a traditional SQL database (CRUD a record)
without locking.
When an Atom changes in NuoDB, it informs all other instances of itself in
all other locations, transactionally replicating the changes via
asynchronous message queues.
It is the right mechanism in the context of the decentralized,
geographically disparate, shared-nothing architecture of the cloud.
11. The Atoms are the
Database
Everything in NuoDB database is an Atom, and the
Atoms are the database. The Atoms work in concert to
form both the Transaction and the Storage tiers.
A NuoDB Transaction Engine is a process that executes
the SQL layer and is comprised completely of Atoms.
The Transaction Engine operates on Atoms, listens for
changes, and communicates changes with other
Transaction Engines in the database.
A NuoDB Storage Manager is simply a special kind of
Transaction Engine that allows Atoms to serialize
themselves to permanent storage (such as a local disk
or Amazon S3).
A NuoDB database can be as simple as a single
Transaction Engine and a single Storage Manager, or can
be as complex as tens of Transaction Engines and
Storage Managers distributed across dozens of
computer hosts.
12. 12 Rules of A Cloud Data
Management System
(CDMS)
1. Modern Superset of an RDBMS
2. Elastic Scale-out for Extreme
Performance
3. Single Logical Database
4. Run Anywhere, Scale Anywhere
5. Nonstop Availability
6. Dynamic Multi-tenancy
7. Active/Active Geo-distribution
8. Embrace Cloud
9. Store Anywhere,
Store Redundantly
10. Workload Mix
11. Tuneable Durability Guarantees
12. Distributed Security
13. Empower Developers &
Administrators
Source:
http://go.nuodb.com/rs/nuodb/images/NuoDB_12_Rules_v
4.pdf
13. Development with .NET
JRE – Java Runtime Environment
.NET Framework
NuoDB driver for ADO.NET
NuoDB Provider for Entity Framework (EF5)
14. Video demos
NuoDB in 90 seconds
https://vimeo.com/52935940
The NuoDB Distributed DBMS Architecture
https://vimeo.com/63356635
NuoDB Continuous Availability Demo
https://vimeo.com/119475588
ACID
Atomicity
Atomicity requires that each transaction is "all or nothing": if one part of the transaction fails, the entire transaction fails, and the database state is left unchanged. An atomic system must guarantee atomicity in each and every situation, including power failures, errors, and crashes. To the outside world, a committed transaction appears (by its effects on the database) to be indivisible ("atomic"), and an aborted transaction does not leave effects on the database at all, as if it never existed.
Consistency
The consistency property ensures that any transaction will bring the database from one valid state to another. Any data written to the database must be valid according to all defined rules, including but not limited to constraints, cascades, triggers, and any combination thereof. This does not guarantee correctness of the transaction in all ways the application programmer might have wanted (that is the responsibility of application-level code) but merely that any programming errors do not violate any defined rules.
Isolation
The isolation property ensures that the concurrent execution of transactions results in a system state that would be obtained if transactions were executed serially, i.e. one after the other. Providing isolation is the main goal of concurrency control. Depending on concurrency control method, the effects of an incomplete transaction might not even be visible to another transaction.
Durability
Durability means that once a transaction has been committed, it will remain so, even in the event of power loss, crashes, or errors. In a relational database, for instance, once a group of SQL statements execute, the results need to be stored permanently (even if the database crashes immediately thereafter). To defend against power loss, transactions (or their effects) must be recorded in a non-volatile memory.