Overview By digitally signing an email message, you apply your unique digital mark to the message. The digital signature includes your certificate and public key, which originate from your digital ID. A digitally signed message proves to the recipient that you, not an imposter, signed the contents of the message, and that the contents haven’t been altered in transit. For additional privacy, you can also encrypt email messages. A digital ID is issued by an independent certification authority. Your organization may have policies that require a different procedure. See the network administrator for more information. You can also look here to find other sources of digital certificates. Caution: While obtaining the personal Digital ID, you must ensure that the email address used in the certificate registration matches the email id used by outlook. Steps: Obtain a free email certificate from any certificate authority Download the certificate and install in your PC local certificate store Confirm installation of the certificate in your machine’s local certificate store: Export the certificate from your certificate store into a FPX file Import the certificate in outlook Activate encryption for mails in Outlook Send Email from Outlook and choose Encrypt or Put a Digital Signature 1. Obtain a free email certificate from comodo For Demonstration, I have chosen Coomodo for my CA. Visit this page Fill out your personal details for certificate issuance Accept Subscriber Agreement 2. Download the certificate and install in your PC local certificate store Confirmation about the certificate registration will be send to the email id provided Check for confirmation mail to download the certificate in the mail id provided by you. Login to their site and provide the information sent in email Successful login will automatically install the certificate in your local certificate store You can view the certificate by clicking on View button. 3. Confirm installation of the certificate in your machine’s local certificate store: Open MMC by typic MMC in search window of Windows Start Menu Add certificate snapin from MMC File Menu Choose Certificate Snapin from the list and click Add Select my user account and then confirm by clicking Finish, and then OK. View the certificate from the store reflecting your personal emal ID. 4. Export the certificate from your certificate store into a FPX file From the above store, select the certificate, right click and choose ALL Task -> Export Follow the onscreen Certificate Export Wizard and go to next window Export Private Key Select the default FPX option Select a password for your private key Select a folder location to store your certificate and provide a certificate name Select Finish from export wizard confirmation window 5. Import the certificate in outlook Open Outlook Click the File tab Click Options Click Trust Center Under Microsoft Outlook Trust Center, click Trust Center Settings On the E-mail Security tab, under Digital ID, select Import/Export Browse to the location where the certificate was exported and select the PFX file Provide the password for your private key as set earlier and click OK Click OK 6. Activate encryption for mails in Outlook Once you are done importing the certificate in outlook, its time to actually activate the additional email security features in Outlook before a mail can be encrypted. On the E-mail Security tab, under Encrypted Mail, select the Add digital signature to outgoing messages check box Select Setting for your Encrypted e-mail You can choose different certificates for Signing certificate and encryption certificate or same certificate. Click on "Choose" button If available, you can select one of the following options: If you want recipients who don’t have S/MIME security to be able to read the message, select the Send clear text signed message when sending signed messages check box. By default, this check box is selected. To verify that your digitally signed message was received unaltered by the intended recipients, select the Request S/MIME receipt for all S/MIME signed messages check box. You can request notification telling you who opened the message and when it was opened, When you send a message that uses an S/MIME return receipt request, this verification information is returned as a message sent to your Inbox. Provide a name to current Security settings and click OK To change additional settings, such as choosing between multiple certificates to use, click Settings. Click OK on each open dialog box. 7. Send Email from Outlook and choose Encrypt or Enforce a Digital Signature Conclusion: By obtaining and using a personal email certificate to digitally sign your messages you can help to stem the tide of spam and malware being distributed in your name. If your friends and family are conditioned to know that messages from you will contain your digital signature, when they receive an unsigned message with your email address spoofed as the source they will realize that its not really from you and delete it. And its free to obtain a personal certificate that you can always use to make sure your confidential communications reach their intended targets and vice-versa.

Overview You have deployed SharePoint 2013 with Reporting Services in the SharePoint Integrated mode. From the Central Administration, System Settings>Manage Services on Server, you try to start the SQL Server Reporting Services service.  You receive the following error message: "SQL Server Reporting Services, Registry key access denied " Cause & Solution You are missing the necessary pre-requisites for SQL Server Reporting Services in the SharePoint Integrated mode to be installed and configured. Be aware installation and configuration requirements for hosting the SSRS Service on the Server before you try to start the SSRS Service on a SharePoint server. Looking forward to your comments!

  Professional development is not exclusively for CPAs, attorneys, physicians, engineers and others who must satisfy the requirements for renewing a professional license. It can help businesses be more competitive by fostering an environment in which employees are more productive, loyal and content. Professional development, also called continuing education, was once the domain of those who were required to show proof when renewing a professional license that they had kept their skills current. Today, however, employers should actively support professional development for all of their employees. Some employers claim...

I had a great time attending the WPC 2015 in Orlando last week. The time went by networking, sharing, learning and celebrating with our Partners. Meeting our partners and learning the new development inside Microsoft were some of the most exciting things apart from the celebrations at the Walt Disney World Resort  Here’s some excerpts from my trip: Microsoft’s new mission statement is to "empower every person and every organization on the planet to achieve more". They plan to do this with the focus on 3 tenants of business transformation: Create more personal computing Reinvent productivity and business processes Build the intelligent cloud platform We as a consulting organization in Microsoft technology look forward to our continued dedication and alignment with Microsoft by focusing / contributing in the following ways: Build solutions on Dynamics CRM / Online which will add strong value over other competitive solutions in the market. Deliver cross workload cloud solutions that increase usage of all Office 365 and Dynamics CRM Online workloads Build next generation productivity solutions with using the new capabilities of Delve, Office 365 Organization Analytics and GigJam Increase Azure consumption through both the release of a Time Entry and Reporting SaaS Solution built on Azure using LightSwitch and through Cloud Infrastructure and Development Projects for our clients Pursue involvement in the CityNext Program and expand our value-add and impact in the public sector Continue the evolution of our IP across many industry verticals into products and managed services built on Microsoft’s Cloud Solutions and Platform Look out for more posts on these topics in the near future. Follow more conversations @WPC and #WPC15.

Overview (*This topic was presented at (SharePoint Saturday Silicon Valley, June 22nd, 2013) Over the years working with SharePoint we all have built out various SharePoint related environments for the purpose of Development. In the past (Prior to SharePoint 2013) we could install MOSS 2007 and SP 2010 right in our Lap Top/Desk top over Windows 7. Alternatively we could spin up a VM over VMWare workstation and run right in our lap top. All of the above combination worked very well and we were on our happy path to build, learn and demo. So initially I did the same with the new SharePoint 2013. But like we all know installing on Client OS (Win 7/8) is no more supported. Next step was to start with a local VM in VMWare Workstation. Sure I got all of that. But I had faced a significant processing and memory problems. First for lack of processing and memory capabilities on a Lap Top type of device and second the SharePoint 2013 platform now introduces quite number of services that are processor and memory hungry. Click here to view full post!

Guest post by Kayvan Iradjpanah, an attorney with Littler Employment & Labor Law Solutions Worldwide The traditional boundaries of the workplace are quickly becoming a relic of the past due to... Visit site for full story...

On June 17, @shrmnextchat chatted with Kevin Mottram (@KevyDMottram) and Alex Alonso (@shrmresearchVP) about "Can Holacracy Work at Work." In case you missed this excellet chat on the Holacracy operating model, you can see all the great tweets here and below:    [View the story "#Nextchat RECAP: Can Holacracy Work at Work? " on Storify]  ...

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Overview In this blog, I will be discussing about NoSQL databases, how different are NoSQL Databases compared with Relational databases, different types of NoSQL Databases. Introduction Contrary to their name, NoSQL databases are not databases without SQL (Structured Query Language) capabilities nor are they a single product or technology. NoSQL databases are a group of data storage and manipulation technologies that do not have relational capabilities. Some of the NoSQL databases, in fact, do permit querying in SQL or SQL-like languages but they do not have fixed schemas. So, a more appropriate name for these set of products could be NoREL (No Relational) or the acronym NoSQL can be thought of as a short hand for ‘Not Only SQL’. Traditionally, Relational Databases (RDBMS) have been used to store data required for processing in applications. However, over the past couple of decades, as data started exceeding the processing capacity of traditional databases, there became a need to have alternative storage and retrieval mechanisms. Coupled with the advent of Big Data, the problem of having to process large amount (Volume) of unstructured data (Variety) in real-time (Velocity) became even more acute. Since Relational Databases could not address this need, it led to the popularity and prevalence of NoSQL databases. NoSQL databases provide us with mechanisms to store and retrieve data for Big Data Analytics along with capabilities for schema-less data structures, horizontal scaling, high availability and alternative query methods. Differences between Relational and Non-Relational Databases (NoSQL Databases) Relational Databases are set theory based systems where data is stored two-dimensional tables whereas NoSQL databases are a set of technologies that were conceived to solve the challenges of distributed and parallel computing in scalable Internet applications. Relational Databases use schemas for storing their data (every row of data in a table has the same set of information) whereas there are no set schemas in NoSQL databases. NoSQL databases provide alternate mechanisms for storing data such as a Key-Value pair or a Graph. (More on that later) Relational Databases guarantee that all transactions will conform to ACID (Atomicity, Consistency, Isolation and Durability) properties whereas NoSQL databases do not provide any such guarantees. In fact, NoSQL databases only guarantee Eventual Consistency, meaning that the data item will eventually be consistent with the latest updated value. Relational Databases are useful where the data is structured and largely uniform whereas NoSQL databases are well suited to process huge volumes of unstructured or complex data that’s required to scale out horizontally. Considerations for Data Storage Eric Brewer from University of California, Berkeley presented a theory known as the CAP Theorem which identifies three important considerations for building applications in a distributed environment - Consistency, Availability and Partition Tolerance (hence the name - CAP Theorem). Further, it states that, in distributed applications, you can only guarantee two of the above three considerations simultaneously.  While typical Relational Databases guarantee Consistency and Availability, the architecture of NoSQL databases are more oriented towards either providing Consistency and Partition Tolerance or Availability and Partition Tolerance. Nathan Hurst has a nice visual representation of where the various available data stores lie on the CAP Theorem considerations. Different types of NoSQL Databases Key-Value Databases This is the simplest form of NoSQL Databases. A Key-Value (KV) store is implemented using a hash table (or a map) where a unique key points to particular value or data. Due to their simplicity, Key-Value databases are very efficient for accessing data. Some of the common examples of Key-Value databases are Redis, Riak and Voldemort Column-Oriented or Wide Column Databases The column-oriented databases are an extension of Key-Value data stores where data from a given column is stored together. The columns are grouped into column families and are stored as a key-value pairs within the respective families. The column families act as a key for the columns it contains and the row key acts as the key for the data store. HBase and Cassandra are two well-known examples of a Column-Oriented Database. Document Databases In document databases, the data is stored as documents represented in JSON or XML format. These documents are a collection of key-value pairs and its possible to have a nested structure of these key-value pairs within a document. Document databases can be indexed on its unique identifier or any other key within the document. These documents are highly flexible and provide means for adhoc querying and replication. Couple of major open source document databases are - MongoDB and CouchDB. Graph Databases Graph databases, as their name suggests, are based on the Graph Theory and provide means of dealing with highly interconnected data. In these databases, data is represented as nodes and then relationships are defined between these nodes. Using these relationships, traversing through the nodes becomes easy and efficient. Neo4J, Polyglot and infiniteGraph are some examples of graph databases. Conclusion Coupled with Relational Databases, NoSQL Databases provide us with another way to store, retrieve and manage data, specifically unstructured data. Its important to realize that one single type of data store (Relational or Non-relational NoSQL Databases) will not be able to address all of your data requirements. There are various flavors of NoSQL databases available and its best to understand your data requirements, the usage patterns, the service level agreements and the available resources before making a decision on the data storage setup. In the coming blog posts, I will delve deeper into each of the categories of NoSQL databases with specific examples using some of the popular products. This should help in understanding the capabilities and the feature sets provided by the various NoSQL databases.  

A New TriNet Survey Outlines How Your Expense Reporting Process May be Hurting Your Retention If you’re like most businesses on the fast-track to success, you care deeply about your employees and... Visit site for full story...

Do you feel like you need to have all the answers? Most people do.  This need was ingrained in us from a very early age: When we’re in school and the teacher calls on us, we’re supposed to know the answer. The right answer. And we are expected to have ALL the right answers, all the time, for years... All the way through elementary school. Middle school. High school. College.  We’re expected to have the right answers on aptitude tests. Skills tests. Job interviews! But a funny thing happens...

Overview In this blog series, we will discuss the Hadoop ecosystem (with a focus on Hortonworks distribution of Hadoop for Windows Server and HDInsight Service for Azure) - core technologies such as HDFS and MapReduce, Pig (containing a data flow language to support writing queries and dataset transformation on large datasets using richer data structures than MapReduce), Hive (a SQL abstraction on top of MapReduce for more structured data), Sqoop (a tool to transfers bulk data between Hadoop and relational databases), Mahout (an open source machine-learning library facilitating building scalable matching learning software), and Pegasus (a peta-scale graph or data mining system running on Hadoop). Problems with conventional database system In a previous blog article we mentioned that processing big data exceeds the capacity of conventional database systems. While a large number of CPU cores can be placed in a single server, it’s not feasible to deliver input data (especially big data) to these cores fast enough for processing. Using hard drives that can individually sustain read speeds of approx. 100 MB/s, and 4 independent I/O channels, a 4 TB data set would take over 2 days to read. Thus a distributed system with many servers working in problem is necessary in the big data domain. Solution: Apache Hadoop Framework The Apache Hadoop framework supports distributed processing of large data sets using a cluster of commodity hardware that can scale up to thousands of machines. Each node in the cluster offers local computation and storage and is assumed to be prone to failures. It’s designed to detect and handle failures at the application layer, and therefore transparently delivers a highly-available service without the need for expensive hardware or complex programming. Performing distributed computing on large volumes of data has been done before, what sets Hadoop apart is its simplified programming model for client applications and seamless handling of distribution of data and work across the cluster.  Architecture of Hadoop Let’s begin by looking the basic architecture of Hadoop. A typical Hadoop multi-machine cluster consists of one or two "master" nodes (running NameNode and JobTracker processes), and many "slave" or "worker" nodes (running TaskTracker and DataNode processes) spread across many racks.  Two main components of the Hadoop framework are described below - a distributed file system to store large amounts of data, and a computational paradigm called MapReduce. Hadoop Distributed File System (HDFS) Since the complete data set is unlikely to fit on a single computer’s hard drive, a distributed file system which breaks up input data and stores it on different machines in the cluster is needed. Hadoop Distributed File System (HDFS) is a distributed and scalable file system which is included in the Hadoop framework. It is designed to store a very large amount of information (terabytes or petabytes) reliably and is optimized for long sequential streaming reads rather than random access into the files. HDFS also provides data location awareness (such as the name of the rack or the network switch where a node is). Reliability is achieved by replicating the data across multiple nodes in the cluster rather than traditional means such as RAID storage. The default replication value is 3, so data is stored on three nodes - two on the same rack, and one on a different rack. Thus a single machine failure does not result in any data being unavailable. Individual machines in the cluster that store blocks of an individual files are referred to as DataNodes. DataNodes communicate with each other to rebalance data, and re-replicate it in response to system failures. The Hadoop framework schedules processes on the DataNodes that operate on the local subset of data (moving computation to data instead of the other way around), so data is read from local disk into the CPU without network transfers achieving high performance. The metadata for the file system is stored by a single machine called the NameNode. The large block size and low amount of metadata per file allows NameNode to store all of this information in the main memory, allowing fast access to the metadata from clients. To open a file, a client contacts the NameNode, retrieves a list of DataNodes that contain the blocks that comprise the file, and then reads the file data in bulk directly from the DataNode servers in parallel, without directly involving the NameNode. A secondary NameNode is used to avoid a single point of failure, it regularly connects to the primary NameNode and builds snapshots of the directory information. The Windows Azure HDInsight Service supports HDFS for storing data, but also uses an alternative approach called Azure Storage Vault (ASV) which provides a seamless HDFS interface to Azure Blob Storage, a general purpose Windows Azure storage solution that does not co-locate compute with storage, but offers other benefits. In our next blog, we will explore the HDInsight service in more detail. MapReduce Programming Model Hadoop programs must be written to conform to the "MapReduce" programming model which is designed for processing large volumes of data in parallel by dividing the work into a set of independent tasks. The records are initially processed in isolation by tasks called Mappers, and then their output is then brought together into a second set of tasks called Reducer as shown below. MapReduce input comes from files loaded in the processing cluster in HDFS. The client applications submit MapReduce jobs to the JobTracker node which divides and pushes work out to available TaskTracker nodes in the cluster while trying  to keep the work as close to the data as possible. Hadoop internally manages the cluster topology issues as the rack-aware HDFS file system enables the JobTracker to know which nodes contain the data, and which other machines are nearby. If the work cannot be hosted on one of the node where the data resides, priority is given to nodes in the same rack. This reduces the data moved across the network. When the mapping phase completes, the intermediate (key, value) pairs are exchanged between machines to send all values with the same key to a single reducer. The reduce tasks are spread across the same nodes in the cluster as the mappers. This data transfer is taken care of by the Hadoop infrastructure (guided by the different keys and their associated values) without the individual map or reduce tasks communicating or being aware of one another’s existence. A heartbeat is sent from the TaskTracker to the JobTracker frequently to update its status. If any node or TaskTracker in the cluster fails or times out, that part of the job is rescheduled by the underlying Hadoop layer without any explicit action by the workers. The TaskTracker on each node is spawned off in a separate Java Virtual Machine process to prevent the TaskTracker itself from failing if the running job crashes the JVM. User-level tasks do not communicate explicitly with one another and workers continue to operate leaving the challenging aspects of partially restarting the program to the underlying Hadoop layer. Thus Hadoop distributed system is very reliable and fault tolerant. Hadoop also has a very flat scalability curve. A Hadoop program requires no recoding to work on a much larger data set by using a larger cluster of machines.  Hadoop is designed for work that is batch-oriented rather than real-time in nature (due to the overhead involved in starting MapReduce programs), is very data-intensive, and lends itself to processing pieces of data in parallel. This includes use cases such as log or clickstream analysis, sophisticated data mining, web crawling indexing, archiving data for compliance etc. In subsequent posts, we will look at the MapReduce programming model and other aspects of Hadoop in more detail… Coming soon!

Each year, employers pay thousands of dollars into state unemployment tax accounts for unemployment benefits. While some of these costs cannot be avoided, there are several ways in which employers... Visit site for full story...

In 2008, I flew to Chicago for my first SHRM national conference.  I traveled alone and didn't know anyone attending the conference.  I spent every possible minute in session after session.  I was at all the keynote speeches. At the end of each day, I ran back to my room to type up my handwritten notes, read through the daily SHRM newspaer, and catch up on work I was missing while away.  At the end of the conference, I even bought the recordings of all the sessions. I thought I'd re-listen to the ones I attended and also...

In this article, we will briefly look at the capabilities of HBase, compare it against technologies that we are already familiar with and look at the underlying architecture. In the upcoming parts, we will explore the core data model and features that enable it to store and manage semi-structured data. Introduction HBase is a column-oriented database that’s an open-source implementation of Google’s Big Table storage architecture. It can manage structured and semi-structured data and has some built-in features such as scalability, versioning, compression and garbage collection. Since its uses write-ahead logging and distributed configuration, it can provide fault-tolerance and quick recovery from individual server failures. HBase built on top of Hadoop / HDFS and the data stored in HBase can be manipulated using Hadoop’s MapReduce capabilities. Let’s now take a look at how HBase (a column-oriented database) is different from some other data structures and concepts that we are familiar with Row-Oriented vs. Column-Oriented data stores. As shown below, in a row-oriented data store, a row is a unit of data that is read or written together. In a column-oriented data store, the data in a column is stored together and hence quickly retrieved. Row-oriented data stores - Data is stored and retrieved one row at a time and hence could read unnecessary data if only some of the data in a row is required. Easy to read and write records Well suited for OLTP systems Not efficient in performing operations applicable to the entire dataset and hence aggregation is an expensive operation Typical compression mechanisms provide less effective results than those on column-oriented data stores Column-oriented data stores - Data is stored and retrieved in columns and hence can read only relevant data if only some data is required Read and Write are typically slower operations Well suited for OLAP systems Can efficiently perform operations applicable to the entire dataset and hence enables aggregation over many rows and columns Permits high compression rates due to few distinct values in columns Introduction Relational Databases vs. HBase When talking of data stores, we first think of Relational Databases with structured data storage and a sophisticated query engine. However, a Relational Database incurs a big penalty to improve performance as the data size increases. HBase, on the other hand, is designed from the ground up to provide scalability and partitioning to enable efficient data structure serialization, storage and retrieval. Broadly, the differences between a Relational Database and HBase are: Relational Database - Is Based on a Fixed Schema Is a Row-oriented datastore Is designed to store Normalized Data Contains thin tables Has no built-in support for partitioning. HBase - Is Schema-less Is a Column-oriented datastore Is designed to store Denormalized Data Contains wide and sparsely populated tables Supports Automatic Partitioning HDFS vs. HBase HDFS is a distributed file system that is well suited for storing large files. It’s designed to support batch processing of data but doesn’t provide fast individual record lookups. HBase is built on top of HDFS and is designed to provide access to single rows of data in large tables. Overall, the differences between HDFS and HBase are HDFS - Is suited for High Latency operations batch processing Data is primarily accessed through MapReduce Is designed for batch processing and hence doesn’t have a concept of random reads/writes HBase - Is built for Low Latency operations Provides access to single rows from billions of records Data is accessed through shell commands, Client APIs in Java, REST, Avro or Thrift  HBase Architecture The HBase Physical Architecture consists of servers in a Master-Slave relationship as shown below. Typically, the HBase cluster has one Master node, called HMaster and multiple Region Servers called HRegionServer. Each Region Server contains multiple Regions - HRegions. Just like in a Relational Database, data in HBase is stored in Tables and these Tables are stored in Regions. When a Table becomes too big, the Table is partitioned into multiple Regions. These Regions are assigned to Region Servers across the cluster. Each Region Server hosts roughly the same number of Regions. The HMaster in the HBase is responsible for Performing Administration Managing and Monitoring the Cluster Assigning Regions to the Region Servers Controlling the Load Balancing and Failover On the other hand, the HRegionServer perform the following work Hosting and managing Regions Splitting the Regions automatically Handling the read/write requests Communicating with the Clients directly Each Region Server contains a Write-Ahead Log (called HLog) and multiple Regions. Each Region in turn is made up of a MemStore and multiple StoreFiles (HFile). The data lives in these StoreFiles in the form of Column Families (explained below). The MemStore holds in-memory modifications to the Store (data). The mapping of Regions to Region Server is kept in a system table called .META. When trying to read or write data from HBase, the clients read the required Region information from the .META table and directly communicate with the appropriate Region Server. Each Region is identified by the start key (inclusive) and the end key (exclusive)  HBase Data Model The Data Model in HBase is designed to accommodate semi-structured data that could vary in field size, data type and columns. Additionally, the layout of the data model makes it easier to partition the data and distribute it across the cluster. The Data Model in HBase is made of different logical components such as Tables, Rows, Column Families, Columns, Cells and Versions. Tables - The HBase Tables are more like logical collection of rows stored in separate partitions called Regions. As shown above, every Region is then served by exactly one Region Server. The figure above shows a representation of a Table. Rows - A row is one instance of data in a table and is identified by a rowkey. Rowkeys are unique in a Table and are always treated as a byte[]. Column Families - Data in a row are grouped together as Column Families. Each Column Family has one more Columns and these Columns in a family are stored together in a low level storage file known as HFile. Column Families form the basic unit of physical storage to which certain HBase features like compression are applied. Hence it’s important that proper care be taken when designing Column Families in table. The table above shows Customer and Sales Column Families. The Customer Column Family is made up 2 columns - Name and City, whereas the Sales Column Families is made up to 2 columns - Product and Amount. Columns - A Column Family is made of one or more columns. A Column is identified by a Column Qualifier that consists of the Column Family name concatenated with the Column name using a colon - example: columnfamily:columnname. There can be multiple Columns within a Column Family and Rows within a table can have varied number of Columns. Cell - A Cell stores data and is essentially a unique combination of rowkey, Column Family and the Column (Column Qualifier). The data stored in a Cell is called its value and the data type is always treated as byte[]. Version - The data stored in a cell is versioned and versions of data are identified by the timestamp. The number of versions of data retained in a column family is configurable and this value by default is 3. Conclusion In this article we looked at the major differences between HBase and other commonly used relational data stores and concepts. We also reviewed the HBase Physical Architecture and Logical Data Model. In the next article, we will cover the different ways in which clients can communicate with HBase and some of the other features that make HBase unique and well-suited for distributed data processing. Look forward to your questions and comments!

We know many of you are concerned, confused and maybe even going crazy trying to stay in compliance with all the government regulations. Don’t worry - TriNet is here to help. EEO-1 Report? Do I... Visit site for full story...

Issue SharePoint 2013 installation went well without any errors, however, I was not able to get to Central Admin. It consistently gave me access denied error message and login prompt. Solution After a great deal of troubleshooting, it turned out that there was a setting in IIS that needed to be corrected. This setting is easy to overlook. There could be other settings that could lead to similar behavior, But this one worked for me. Here is the entry I needed to create. Here is how I created it: Go to IIS Manager (inetmgr.exe)  Select Server name from left pane, go to Authorization Rules Right click anywhere on the body section and add Select  "All Anonymous Users" section and click ok This loaded the Central Admin for me.

This post was originally published on the TriNet Cloud blog. If there’s one thing that many of us in the HR space are getting tired of, it is the painfully egregious articles that insinuate... Visit site for full story...

                   The recent data breach of the U.S. Office of Personnel Management (OPM), which exposed the Social Security numbers, job assignments, performance ratings, and other personal identifying  information of millions of present and former government employees, has major implications for HR departments worldwide. The hack has left many questioning whether or not their own systems are strong enough to prevent a future breach.  In the SHRM Online article "What are the Lessons for HR in Government Hacking?" by Aliah Wright, data security expert Nigel...

In an Office 365 Tenant, you must be careful with External Sharing in a few different scenarios: 1)      If your organization has migrated from an existing on premise Farm in which you used "NT Authority\Authenticated Users" to grant permissions 2)      If your organization is making use of External sharing via "Everyone" (including external users) in Office 365 If your organization’s Office 365 Admin has allowed External Sharing for Authenticated Users: And has also enabled External Sharing on 1 or more External Site Collections: If a Site user shares anything (a document, folder, library, site etc.) with an external user: That user become part your Organization’s Office 365 Tenant Directory. Once part of this Directory, Any Site Collection that is configured for External Sharing and has permissions granted to securables via "NT Authority\Authenticated Users" or "Everyone" will now be available to all External Users (as well as organization users) with whom anyone at your company has shared anything with Be extremely careful to review your permissions before opening up external sharing!

On June 26, 2015, the Supreme Court of the United States (SCOTUS) issued a ruling in the Obergefell v. Hodges case, requiring that all states must recognize marriages between same-sex couples. For... Visit site for full story...

The SHRM Annual Conference and Exposition starts in one week in Las Vegas !! This venue seems to scream "extrovert" with all of it’s bright lights, shows and casinos. With over 15,000 people, expected the Conference can be overwhelming for everyone. We don’t write about extroverts much because we assume they’re going to fill the space with their outward thoughts anyway !! I have to be honest, most of my friends are not extroverts. I assumed because they were active socially, they’d be extroverted. I am, so why wouldn’t they be? (1st fault of the extrovert . . . ) Being...

In this blog article, I will discuss about how we can easily integrate a WordPress blog with your SharePoint site with the help of SharePoint 2013 designer Work Flow. Introduction Using SharePoint 2013 REST API and building SPD based simple Workflow, we will fetch most recent 2 or more  post from the blog site and add those in a SharePoint list. If you are more interested about the advantages and disadvantages of the REST API, and for a comparison with other API’s, please refer the MSDN site here. REST API Reference Firstly, we will try to get the REST API provided by WordPress. Let’s go to WordPress developer’s resources site. http://developer.wordpress.com/docs/api/ Here, you can get the list of REST API, from which you can choose as per your requirement. In order to retrieve most recent 2 blogs we will be specifying the number=2 in the parameter such as below: https://public-api.wordpress.com/rest/v1/sites/yourwordpressblog.com/posts/?number=2 Building SP2013 SPD Workflow Open the SharePoint Designer and click on site work flow. As you can see in the image, we will build a site workflow named "Get WordPress Recent Blogs", which will read the information from WordPress Blog, create list items in a SharePoint list for further use. Once the site workflow is created, you simply add stages, loops and name them properly, and then link them actions. Name Stage 1 "Get Myblog Recent Items" and then add five actions and one Loop block, as shown in Figure 1. Figure 1. Workflow Stage 1 Action 1 is not really required, but it will add one item to the history list, which can be used for debugging. Action 2 is added to Call HTTP Web Service action. The HTTP URL is set to https://public-api.wordpress.com/rest/v1/sites/yourwordpressblog.com/posts/?number=2  and the HTTP method is set to "GET". Action 3 is again logging to history, the response should be "OK". This means the WF is calling API perfectly. Now we are creating a variable "itemcount "as Integer and setting the value to 2, since we require only 2 blogs. Last action is to create another variable "Index" and set the value to 0. In the "Call HTTP Web Service" action statement we do not set RequestContent or RequestHeaders parameter because we do not need to. We are only interested in the output of that web service. By simply setting the response parameter to a variable ResponseContent, the output of this web service call will get stored in the variable ResponseContent, which is a dictionary type variable. The output of the web service looks like the following: {"found":5,"posts":[{"ID":124,"author":{"ID":7899331,"email":false,"name" …………….. To handle each blog item we could use the "responsecontent" variable so that we can put our action statement like "Get ([%Variable: Index%])/Title from Variable: responsecontent" to get Post’s  property. From the web service output we can see the ID, title. These are within "posts{". Just to make  things simple and to show a clear structure of post inside web service output, the  full path to access post’s properties,  the statement like "Get posts[%Variable: Index%])/ID from Variable: ResponseContent" will retrieve ID property of an item. These are the workflow variable details: Figure 2 shows all the different action items. Firstly, pick the ID Next, delete the same ID if the item exits in the SP list Next four action is updating the variables with posts’ value Last item in the loop is to add an item in the SharePoint List Now increase the counter by 1 and the same actions are for the 2nd Item. This is it! You are now you are ready to publish and start this workflow. If you want to run the workflow daily, then add another action item after the loop end such as below: Now your SharePoint List will have the blog content . That’s it Conclusion Lastly, adding a delay for 24 hours every day will enable the list to update with 2 recent blogs. This is a very simple yet very powerful example of REST API and SPD workflow.

                                     I am grateful to have been chosen as a member of the #SHRM15 bloggers for the 3rd consecutive year. Unfortunately, I will not be able to make it due to family and work commitments. Although I cannot attend this year, I have attended several national conferences and each one has been memorable. The national conference provides yet another opportunity for HR professionals to learn new things, meet new people, and connect with friends....