The document discusses LDAP Synchronization Connector (LSC), an open source tool for automatically synchronizing identity data across different directory and database systems. It provides an overview of LSC's goals, architecture, features and capabilities for synchronizing user accounts and attributes between heterogeneous systems in a standardized way. Examples are given of configuring LSC to synchronize a MySQL user table to an OpenLDAP directory.
Sysadmins are often responsible for various identity stores in a company: directories, applications with built-in account databases, etc...
Ldap Synchronization Connector offers a solution to link these repositories and ensure nobody\’s going to get fired because you forgot to disable an account.
LSC is an open source project under the BSD license - http://lsc-project.org/
Your LDAP Directory, such as Active Directory, already knows lots of things about your users, computers, groups, and more. By leveraging that information, we can learn how to automate and integrate your KACE Appliances using your existing infrastructure. Learn more: http://dell.to/1GDYpr8
LDAP stands for Lightweight Directory Access Protocol. It is an application protocol used over an IP network to manage and access the distributed directory information service. This video gives you a high level overview of LDAP and some examples of software that utilize LDAP, such as Active Directory.
JDBC : Java Database Connectivity
JDBC is used to connect java application with database.
JDBC is an API used to communicate Java application to database in database independent and platform independent manner.
It provides classes and interfaces to connect or communicate Java application with database.
Sysadmins are often responsible for various identity stores in a company: directories, applications with built-in account databases, etc...
Ldap Synchronization Connector offers a solution to link these repositories and ensure nobody\’s going to get fired because you forgot to disable an account.
LSC is an open source project under the BSD license - http://lsc-project.org/
Your LDAP Directory, such as Active Directory, already knows lots of things about your users, computers, groups, and more. By leveraging that information, we can learn how to automate and integrate your KACE Appliances using your existing infrastructure. Learn more: http://dell.to/1GDYpr8
LDAP stands for Lightweight Directory Access Protocol. It is an application protocol used over an IP network to manage and access the distributed directory information service. This video gives you a high level overview of LDAP and some examples of software that utilize LDAP, such as Active Directory.
JDBC : Java Database Connectivity
JDBC is used to connect java application with database.
JDBC is an API used to communicate Java application to database in database independent and platform independent manner.
It provides classes and interfaces to connect or communicate Java application with database.
Active Directory is a common interface for organizing and maintaining information related to resources connected to a variety of network directories.
Lightweight Directory Access Protocol (LDAP) is an Internet protocol used to access information directories.
A directory service is a distributed database application designed to manage the entries and attributes in a directory.
Active Directory & LDAP Authentication Without TriggersPerforce
See how to build Active Directory and LDAP authentication into the Perforce Server, streamlining the process of linking your Perforce environment with your enterprise authentication system—no triggers required!
Spark SQL Tutorial | Spark SQL Using Scala | Apache Spark Tutorial For Beginn...Simplilearn
This presentation about Spark SQL will help you understand what is Spark SQL, Spark SQL features, architecture, data frame API, data source API, catalyst optimizer, running SQL queries and a demo on Spark SQL. Spark SQL is an Apache Spark's module for working with structured and semi-structured data. It is originated to overcome the limitations of Apache Hive. Now, let us get started and understand Spark SQL in detail.
Below topics are explained in this Spark SQL presentation:
1. What is Spark SQL?
2. Spark SQL features
3. Spark SQL architecture
4. Spark SQL - Dataframe API
5. Spark SQL - Data source API
6. Spark SQL - Catalyst optimizer
7. Running SQL queries
8. Spark SQL demo
This Apache Spark and Scala certification training is designed to advance your expertise working with the Big Data Hadoop Ecosystem. You will master essential skills of the Apache Spark open source framework and the Scala programming language, including Spark Streaming, Spark SQL, machine learning programming, GraphX programming, and Shell Scripting Spark. This Scala Certification course will give you vital skillsets and a competitive advantage for an exciting career as a Hadoop Developer.
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
Simplilearn’s Apache Spark and Scala certification training are designed to:
1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
Spark, the ultra-fast, general purpose big data computing platform provides some very flexible options for processing and accessing data. In a previous meetup we covered PySpark and the Schema RDD. In this session we reviewed and expanded on this, with an in-depth exploration of Spark SQL.
- Overview of Spark in the Hadoop ecosystem
- Deep dive into Spark SQL with step by steps on how to implement and use it
If you have questions about the presentation or want to learn more about our services, please visit our website: http://casertaconcepts.com/
User administration without you - integrating LDAPMongoDB
*Configure MongoDB and MongoDB Atlas with LDAP authorization
*Test your user's access with mongoldap and other native tools
*Craft LDAP queries to optimize your LDAP accesses
*Adjust query templates and user-to-distinguished-name mappings to account for disparate LDAP trees
*Avoid common configuration mistakes
Active Directory is a common interface for organizing and maintaining information related to resources connected to a variety of network directories.
Lightweight Directory Access Protocol (LDAP) is an Internet protocol used to access information directories.
A directory service is a distributed database application designed to manage the entries and attributes in a directory.
Active Directory & LDAP Authentication Without TriggersPerforce
See how to build Active Directory and LDAP authentication into the Perforce Server, streamlining the process of linking your Perforce environment with your enterprise authentication system—no triggers required!
Spark SQL Tutorial | Spark SQL Using Scala | Apache Spark Tutorial For Beginn...Simplilearn
This presentation about Spark SQL will help you understand what is Spark SQL, Spark SQL features, architecture, data frame API, data source API, catalyst optimizer, running SQL queries and a demo on Spark SQL. Spark SQL is an Apache Spark's module for working with structured and semi-structured data. It is originated to overcome the limitations of Apache Hive. Now, let us get started and understand Spark SQL in detail.
Below topics are explained in this Spark SQL presentation:
1. What is Spark SQL?
2. Spark SQL features
3. Spark SQL architecture
4. Spark SQL - Dataframe API
5. Spark SQL - Data source API
6. Spark SQL - Catalyst optimizer
7. Running SQL queries
8. Spark SQL demo
This Apache Spark and Scala certification training is designed to advance your expertise working with the Big Data Hadoop Ecosystem. You will master essential skills of the Apache Spark open source framework and the Scala programming language, including Spark Streaming, Spark SQL, machine learning programming, GraphX programming, and Shell Scripting Spark. This Scala Certification course will give you vital skillsets and a competitive advantage for an exciting career as a Hadoop Developer.
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
Simplilearn’s Apache Spark and Scala certification training are designed to:
1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
Spark, the ultra-fast, general purpose big data computing platform provides some very flexible options for processing and accessing data. In a previous meetup we covered PySpark and the Schema RDD. In this session we reviewed and expanded on this, with an in-depth exploration of Spark SQL.
- Overview of Spark in the Hadoop ecosystem
- Deep dive into Spark SQL with step by steps on how to implement and use it
If you have questions about the presentation or want to learn more about our services, please visit our website: http://casertaconcepts.com/
User administration without you - integrating LDAPMongoDB
*Configure MongoDB and MongoDB Atlas with LDAP authorization
*Test your user's access with mongoldap and other native tools
*Craft LDAP queries to optimize your LDAP accesses
*Adjust query templates and user-to-distinguished-name mappings to account for disparate LDAP trees
*Avoid common configuration mistakes
Rudder 3.0 was released in January 2015. This talk will bring attendees up to date on recent evolutions in Rudder and show off some of the latest and greatest features like the new compliance dashboard and graphs, redesigned web interface, built-in Technique editor (that automatically builds CFEngine code), basic command line interface, ...
We will then discuss ideas for future features. Last but not least, we should have some time to dig deeper into any parts of Rudder attendees want to know more about - examples could include reporting, ncf, OS support, CFEngine integration, ...
ElasticSearch - index server used as a document databaseRobert Lujo
Presentation held on 5.10.2014 on http://2014.webcampzg.org/talks/.
Although ElasticSearch (ES) primary purpose is to be used as index/search server, in its featureset ES overlaps with common NoSql database; better to say, document database.
Why this could be interesting and how this could be used effectively?
Talk overview:
- ES - history, background, philosophy, featureset overview, focus on indexing/search features
- short presentation on how to get started - installation, indexing and search/retrieving
- Database should provide following functions: store, search, retrieve -> differences between relational, document and search databases
- it is not unusual to use ES additionally as an document database (store and retrieve)
- an use-case will be presented where ES can be used as a single database in the system (benefits and drawbacks)
- what if a relational database is introduced in previosly demonstrated system (benefits and drawbacks)
ES is a nice and in reality ready-to-use example that can change perspective of development of some type of software systems.
SSIS: Flow tasks, containers and precedence constraintsKiki Noviandi
SSIS components such as Flow Task, Container and priority constraints become important parts in conducting ETL Processes, Explanation of SSIS architecture, Control Flow and Data Flow are the main topics in this presentation
CIS13: A Breakthrough in Directory Technology: Meet the Elephant in the Room ...CloudIDSummit
Michel Prompt, Chairman & CEO, Radiant Logic
There's a sea of change coming in terms of scaling identity and access management. This session will look at what's next in directory technology, scalability and possibility.
Jump Start on Apache Spark 2.2 with DatabricksAnyscale
Apache Spark 2.0 and subsequent releases of Spark 2.1 and 2.2 have laid the foundation for many new features and functionality. Its main three themes—easier, faster, and smarter—are pervasive in its unified and simplified high-level APIs for Structured data.
In this introductory part lecture and part hands-on workshop, you’ll learn how to apply some of these new APIs using Databricks Community Edition. In particular, we will cover the following areas:
Agenda:
• Overview of Spark Fundamentals & Architecture
• What’s new in Spark 2.x
• Unified APIs: SparkSessions, SQL, DataFrames, Datasets
• Introduction to DataFrames, Datasets and Spark SQL
• Introduction to Structured Streaming Concepts
• Four Hands-On Labs
This presentation was shown at Spring Framework Meeting 2009 in Rome (Lazio - Italy) - 31th October 2009.
http://www.open4dev.com/journal/2009/10/26/spring-framework-meeting-2009-rome.html
Abstract:
Spring LDAP basics: how to start to use the LdapTemplate in your custom J2EE application. This how-to will show you how to bind, unbind, search and authenticate users in your LDAP using the LdapTemplate provided by Spring.
Composable Parallel Processing in Apache Spark and WeldDatabricks
The main reason people are productive writing software is composability -- engineers can take libraries and functions written by other developers and easily combine them into a program. However, composability has taken a back seat in early parallel processing APIs. For example, composing MapReduce jobs required writing the output of every job to a file, which is both slow and error-prone. Apache Spark helped simplify cluster programming largely because it enabled efficient composition of parallel functions, leading to a large standard library and high-level APIs in various languages. In this talk, I'll explain how composability has evolved in Spark's newer APIs, and also present a new research project I'm leading at Stanford called Weld to enable much more efficient composition of software on emerging parallel hardware (multicores, GPUs, etc).
Speaker: Matei Zaharia
Interfacing infrastructure-as-code with non-expert usersJonathan Clarke
Implementing a tool to automate IT infrastructure management has many undeniable benefits. But that doesn't mean there aren't some drawbacks too (usually outweighed by the benefits) that we should be considering, and working to reduce or remove.
Implementing a tool like this in a team usually has a pretty significant impact: new processes, new language(s) to learn, new way of doing pretty much everything on your infrastructure. These tools are complex, so a minority of the team tends to become experts in it, and tries to lead the others. Resistance to change is common and understandable, but unfortunately, this can end up with some of the team members being left behind by the sudden and massive changes to their work. This is clearly not a Good Thing, and certainly not showing very good inclusiveness towards everyone. It is also making some configuration management projects fail, which is definitely a Bad Thing for those of us trying to implement them.
How to improve this situation is a topic dear to me. This has always been the focus of the Rudder and ncf open source projects I work on - making our technologies more accessible, easier to adopt and simpler to understand. Some key points we have focused on include: - Avoiding the necessity to write code (user interfaces of course, but that can be combined with other users writing code) - Separating roles so that experts can implement the how and others can focus on the what - Minimising the amount of effort required (sane and non-surprising default values, auto-configuration where possible, ...)
This talk will explain some of these concepts, and show how we have achieved considerable success using the ""ncf builder"" web interface that can be used to write configuration management policy without writing any code (see http://www.ncf.io).
Sharing automation - why we need a language like ncf for this (Ignite @ devop...Jonathan Clarke
Ignite talk that I gave at devopsdays Ghent 2014. devops is about automation and sharing (amongst others) but when we automate IT, often only a minority of the team lead the effort, leaving others behind. Is that good sharing? Could we not do better?
This talk will introduce new CFEngine 3.6 features, we have these bullet points:
User promises
TLS protocol
Math expressions
Dynamic inputs
New language functions
Tags
Data containers
File templating
Presentation by Kristian Amlie of CFEngine
OpenLDAP - Astuces pour en faire l'annuaire d'entreprise idéalJonathan Clarke
OpenLDAP est un annuaire libre puissant, performant et stable qui est déjà bien connu et largement utilisé. Pourtant, il existe de nombreux modules, configurations et bonnes pratiques qui permettent d’en faire l’annuaire d’entreprise idéal qui ne sont pas toujours évidents au premier abord. Cette session présentera quelques unes de ces astuces - intégrité de données, sécurité, architectures pour disponibilité et montée en charge, monitoring - et sera illustrée de nombreux retours d’expériences des annuaires petits et grands sur lesquels l’orateur a eu l’occasion de travailler.
Configuration management: automating and rationalizing server setup with CFEn...Jonathan Clarke
With the advent of virtualization and cloud computing, modern IT management relies more and more on the concept of "create, set up, use and throw away" servers. In this context, the benefits of automating and rationalizing the "set up phase" are obvious. This is where configuration management tools come in to play.
This presentation kicks off with a discussion of some key points of configuration management and their benefits and drawbacks, building on real world examples (well, pseudo examples, mostly too silly to have ever really happened... or maybe not?)
The main contender is then introduced: CFEngine 3. Released in 2009, this is a brand new version of the open source configuration management solution, built on 17+ years of experience from previous versions of the software. We'll introduce the technology's key points, comparing approaches with similar devops-type tools, such as Puppet and Chef (where possible).
last cover the basics of setting up a minimal environment to start automating your configuration with CFEngine 3. We'll cover simple but illustrative examples, and show real-time demos of the technology in action.
15. Employees leaving Jim just got fired. Boss asks you to disable his account. Account S , that is. You do it... All done! But what about the account on the company blog? ARGH! Too late. What now!? FIRE THE SYSADMIN!!!?