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Introduction to Storage Virtualization

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Types of Storage Virtualization

Widely these storage systems provide two kinds of access to our system either block or file-based access.

So we can broadly classify our storage virtualization as:

1. Block Virtualization

In block virtualization, we basically separate our logical storage from that of the physical so that the user/administrator can access without having to access the physical storage, basically doing this way helps the administrator in giving a lot of flexibility in managing different storage.

2. File Virtualization

In File virtualization, it basically removes the dependencies caused in accessing the data at file level to that of the location where they are actually present. This basically helps in overcoming the challenges faced with network-attached storage and they also help in optimizing the storage usage and also help us to do some file migrations in a non-disruptive way.

Methods of Virtualization

Virtualization typically refers to the pooling of different available storage and maintaining them in single storage in virtual environment, recent technologies such as hyper-converged infrastructure makes use of not only virtual storage but also power and network as well.

1. Host-Based Virtualization Approach 2. Array-Based Virtualization Approach 3. Network-Based Virtualization Approach Configurations of Storage Virtualization

Ideally, we have two different ways in configuring our storage virtualization they are:

1. In-Band Approach (Symmetric)

In this method, we store the virtual environment configuration in the data path itself as in the data as well as the control flow. This kind of solution is considered easy/simple to implement as we do not use any kind of software. We do different levels of abstraction inside the data path. These kinds of solutions help us to improve our device’s performance majorly and also prolong the useful life of the devices. One of the examples of an in-band based solution is that IBM’s total storage area network volume controller.

2. Out-Band Approach (Asymmetric)

In this approach, the implementation of the virtual environment is done outside of the data path as in the data flow and the control flow are separated which can be achieved by separating our Metadata from data and putting them in different places. This kind of virtualization involves in transferring all the tables to a Metadata controller which has all the Metadata files. By separating both the flows we achieve the usage of complete bandwidth that is offered by the storage area network.

Benefits of Storage Virtualization

Now that we have seen what is storage virtualization and its types and also how do we implement them, now let us see some of the benefits of going to storage virtualization:

Our data does not get compromised easily even if the host fails as we store our data in a different and convenient place.

It is easy for us to protect, provide and use our data as we implement some level of abstraction in our storage.

Additional functions such as recovery, duplication, replication, etc. can be done with ease.


By now from the article you would have understood quite a lot about storage virtualization and its techniques, pros, and cons. It is necessary for us to go towards these kinds of virtualization approaches as it helps in reducing the complexity of the way data is stored and also helps the storage administrator in performing tasks such as disaster recovery, backup or archival of data easily in less amount of time.

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Learn The Types And Methods Of Mapreduce Api

Introduction to MapReduce API

Hadoop can be developed in programming languages like Python and C++. MapReduce Hadoop is a software framework for ease in writing applications of software processing huge amounts of data. MapReduce is a framework which splits the chunk of data, sorts the map outputs and input to reduce tasks. A File-system stores the output and input of jobs. Re-execution of failed tasks, scheduling them, and monitoring them is the task of the framework. We will learn how to write a code in Hadoop in MapReduce and not involve Jython to translate code into Java. In this topic, we are going to learn about the MapReduce API.

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MapReduce Class

Here are the following MapReduce Class

1. Map Phase

The map phase splits the input data into two parts. They are Keys and Values. Writable and comparable is the key in the processing stage where only in the processing stage, Value is writable. For example, let’s say a client gives input data to a Hadoop system; the task tracker is assigned tasks by the job tracker.

Mini reducer, which is commonly called a combiner, the reducer code places input as the combiner. Network bandwidth is high when a huge amount of data is required. Hash is the default partition used. The partition module plays a key role in Hadoop. More performance is given by reducing the pressure of the petitioner on the reducer.

2. Processing in Intermediate 3. Reducer Phase

The reducer takes in the data input that is sorted and shuffled. All the input data is going to be combined, and similar key-value pairs are to be written to the hdfs system. For searching and mapping purposes, a reducer is not always necessary. Setting some properties for enabling to set the number of reducers for each task. During job processing, speculative execution plays a prominent role.

Types of methods in MapReduce API

Let us see all the classes and methods are provided for MapReduce programming.

1. Mapper Class of MapReduce


void map

(VALUEIN value, KEYIN key, Context context)

(Context context)

(Context context)

(Context context)

This method is called for every key-value pair in the input split and only at once.

This method is called at the end of the task and only at once.

This method is called at the beginning of the task and only at once.

This method overrides to control the Mapper execution.

2. Reducer Class of MapReduce


void map (Context context)

(Context context)

(Context context)

This method is called for each key and only once.

This method is called only at the beginning of the task and only at once.

This method is used to control all the Reducer tasks.

3. Job Class of MpReduce


String getJobFile()

(Configuration conf, String JobName)

(Configuration conf)

Job getInstance()

long getFinishTime()

Counters getCounters()

This method is used for setting the Reducer for jobs.

This method is used for setting the number of Reducers for jobs.

This method is used to set the job of the Mapper.

This method is used for mapper output data to set the value class.

The user-specified job name is to be set by using this method.

Providing the class name with the extension .class, this method is used to set the jar.

This method is used to get the job function with a schedule.

The user-specified job name is obtained by using this method.

The path of the submitted configuration of the job is obtained by using this method.

Generates a new Job without a cluster and with provided job name and configuration.

Generates a new Job without a cluster and with provided job name and configuration.

New Job with no cluster.

Finish time of Job.

Counters of the job.

Understanding how to submit a job with an example:


Imagine you have lots of documents, which is huge data. And you need to count the number of occurrences of each word throughout the documents. It might seem like an arbitrary task, but the basic idea is that let’s say you have a lot of web pages, and you want to make them available for search queries.  Aggregation of data is done by the reducer, and it consists of all the keys and combines them all for similar key-value pairs, which is basically the Hadoop shuffling process.

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Different Types Of Frames In Html & Types Of Frame Tags

Introduction to Html Frames

Html frames are useful at dividing the web page or the web browser into multiple sections; they separate sections then load differently.

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A-frame displays content independent of its container. Multiple frames form a collection and are known as a frameset. The arrangement can be considered similar to the orientation of rows and columns in a table. The frame tag has been deprecated in HTML5.

Each frame has a frame tag to indicate. The horizontal frame is defined by the row attribute of the frame tag, and the vertical attribute is defined by the column attribute of the frame tag in the HTML document. The same window in one frame might display a static banner,  a second navigation menu, etc.

Syntax of Frames

Similarly, as we saw above, the attribute rows take the value in pixels, and they help at specifying the number and size of rows in a frameset.

Different Type of Frames in HTML

The different and some commonly used attributes in the frame are attributes such as border, scrolling, src, name, etc.

Types of Frame Tag

Following are the different types of frame tags:

1. Col: The col attribute gives the vertical frames. The width, however, can be specified in 4 ways:

Pixels: absolute values can be mentioned in pixels. If we have to create three vertical frames, we can give the value: cols=”100,50,100”.

Percentage: The percentage of the browser window can be mentioned. If we have to create three vertical frames, we can give the value: cols=”40%,20%,40%.”

We can also make use of the wildcard here (*) and let the wildcard take the remaining of the window, which remains un-mentioned.



2. Rows: The row attribute gives the horizontal frames. It specifies the rows in a frameset. If we have to create three horizontal frames we use:

Eg: Rows=”10%,80%,10%’.

We can also set the height of each row as we had done in the case of columns previously.


3. Border: It specifies the width of the border of each frame in pixels.

E.g., border=”4”. If border=”0”, it means there is no border.

4. Frameborder: If a three-dimensional border needs to be displayed between frames, then we use this attribute. The value which the attribute takes is either 1 or 0 ( Yes or No).

E.g.: frameborder=”0” means no border.

5. Framespacing: This attribute specifies the amount of space between frames in a frameset. Any integer value can be given here for this attribute.

E.g., framespacing=”12” means between the frames, there should be the spacing of 12 pixels.

There are 8 attributes that can be listed here in this section.

1. src: We provide the file name to this attribute that is supposed to be loaded into the frame. The value of this attribute can be any URL.

E.g., name=”abc.htm.”

3. frameborder: This attribute is specified to show if the borders are to be shown or not. The value takes up is 1 or 0 (Yes or No).

4. marginwidth: The attribute is helpful at specifying the width of the space between the left and right of the frame’s border and also the content of the frame. The value is given in pixel.

5. marginheight: The attribute is helpful at specifying the height of the space between the top and the bottom of the frame’s border and also the content of the frame. The value is given in pixel.

E.g., marginheight=”10”.

6. noresize: This attribute basically prevents the user from making any changes to the already present frames. In the absence of this attribute, any frame can be resized.

E.g., noresize=”noresize.”

7. scrolling: This attribute takes control of the appearance of the scroll bars, which are present on the frame. The value either a ‘yes, ‘no’, or ‘auto’.

E.g., scrolling=” no” means it should not have scroll bars.

8. longdesc: This attribute lets you provide a link to another page containing a long description of the content of the frame.

E.g., longdesc=”framedesc.htm.”

Frames and Browser Support

It is a nice approach to put up a message in such scenarios for the user who use old browsers. Messages like “ Sorry! Your browser does not support frames.” to notify.


Frame technology is not supported by quite a number of browsers these days. It is often noticed that the contents of the web page are not displayed properly for smaller devices. Anyway, it is good to have knowledge of what existed so that better models could be developed to be compatible with new technologies.

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Techniques And Their Implementation In Tensorflow

Introduction to Keras Regularization

Keras regularization allows us to apply the penalties in the parameters of layer activities at the optimization time. Those penalties were summed into the function of loss, and it will optimize the network. It applies on a per-layer basis. The exact API depends on the layer, but multiple layers contain a unified API. The layer will expose arguments of 3 keywords.

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Key Takeaways

Suppose we need to configure the regularization using multiple arguments, then implement the subclass into the keras regularization.

We can also implement the class method and get the config to support the serialization. We can also use regularization parameters.

What is Keras Regularization?

The keras regularization prevents the over-fitting penalizing model from containing large weights. There are two popular parameters available, i.e., L1 and L2. L1 is nothing but the Lasso, and L2 is called Ridge. Both of these parameters are defined at the time of learning the linear regression. When working with tensorflow, we can implement the regularization using an optimizer. We are adding regularization to our code by adding a parameter name as kernel_regularizer. While adding L2 regularization, we need to pass the keras regularizers.l2 () function.

This function takes one parameter, which contains the strength of regularization. We pass L1 regularizers by replacing the l2 function with the l1 function. Suppose we need to use L2 and l1 regularization this is called the elastic net. The weight regularization provides an approach to reducing the overfitting of neural network models for deep learning. Activity regularization encourages the neural network to learn the sparse features of internal representations for the raw observations. It is common to seek the representation of spark known for autoencoders called sparse encoders.

How to Add Keras Regularization?

It will generally reduce the model overfitting and help the model generalize. The regularization is a penalized model for overfitting, as we know it has two parameters. Below we are using the l1 parameter for adding keras regularization.

Below steps shows how we can add keras regularization as follows:


python -m pip install tensorflow python –m pip install keras


2. After installing the module of keras and tensorflow now we are checking the installation by importing both modules as follows.


import tensorflow as tf from keras.layers import Dense


3. After checking the installation now in this step we are importing the required model which was used in it. Basically, we are importing the dense, sequential, l1, and activation modules. We are importing the dense module from the layers library, a sequential module from the library, an l1 module from the regularizers library, and an activation module from the layers library.


from sklearn.datasets import make_circles ….. from keras.layers import Activation


4. After importing the dataset now in this step we are preparing the dataset for it. We are preparing the dataset by using x and y values. Also, we are defining the value of X_train, y_train, X_test, and y_test as follows.


X, y = make_circles() train = 25 X_train, X_test = X[] y_train, y_test = y[]


5. After creating the dataset in this step we are creating the neural network model and adding the regularizer into the input layer as follows. We are adding a sequential model and defining the dense layer as follows.


mod = Sequential() mod.add() mod.add(Activation('relu')) mod.add(Dense(2, activation = 'relu')) mod.summary()


Keras Regularization Layer

The weight regularization layer of keras is applying penalties to the parameters of layers. The weight regularization layer will expose three keyword arguments as follows:

Kernel Regularizer

Bias Regularizer

Activity Regularizer

The below example shows keras weight regularization layer as follows. This layer is dividing the input batch size.


from tensorflow.keras import layers from tensorflow.keras import regularizers we_lay = layers.Dense( units = 44, kernel_regularizer = regularizers.L1L2(), … activity_regularizer = regularizers.L2 (1e-5) ) ten = chúng tôi (shape = (7, 7)) * 3.0 out = we_lay(ten) print(tf.math.reduce_sum (we_lay.losses))


The L1 and L2 regularizers are available as part of a module of regularizers. The below example shows the L1 class regularizers module.


from tensorflow.keras import layers from tensorflow.keras import regularizers we_lay = layers.Dense ( units = 44, kernel_regularizer = regularizers.L1L2(), … activity_regularizer = regularizers.L2(1e-5) ) ten = tf.keras.regularizers.L1(l1=0.01 * 3.0) print (tf.math.reduce_sum (we_lay.losses))


The below example shows the L1 class regularizers module as follows. We are importing the layers and regularizers model.


from tensorflow.keras import layers from tensorflow.keras import regularizers we_lay = layers.Dense( units = 44, kernel_regularizer = regularizers.L1L2(), … activity_regularizer = regularizers.L2 (1e-5) ) ten = tf.keras.regularizers.L2 (l2 = 0.01 * 3.0) print(tf.math.reduce_sum(we_lay.losses))


Examples of Keras Regularization Example #1

In the below example we are using L2 arguments.


from sklearn.datasets import make_circles ….. from keras.layers import Activation X, y = make_circles() train = 25 X_train, X_test = X [] y_train, y_test = y [] mod = Sequential() mod.add() mod.add(Activation ('relu')) mod.add(Dense(2, activation = 'relu')) mod.summary()


Example #2

In the below example, we are using L1 arguments.


from sklearn.datasets import make_circles ….. from keras.layers import Activation X, y = make_circles() train = 35 X_train, X_test = X[] y_train, y_test = y[] mod = Sequential() mod.add() mod.add(Activation('relu')) mod.add(Dense(2, activation = 'relu')) mod.summary()



Given below are the FAQs mentioned:

Q1. What is the use of keras regularization?

Answer: It is the technique for preventing the model from large weights. The regularization category is applied to the per-layer basis.

Q2. How many types of weight regularization are in keras?

Answer: Basically there are multiple types of weight regularization like vector norms, L1 and L2. It will require the hyper parameter which is configured.

Q3. Which modules do we need to import at the time of using keras regularization?

Answer: We need to import the keras and tensorflow module at the time of using it. Also, we need to import is a dense layer.


There are two popular keras regularization parameters available i.e. L1 and L2. In that L1 is nothing but the Lasso and L2 is called Ridge. It allows us to apply the penalties to the parameters of layer activities at the time of optimization.

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Types Of Graphs With Examples

A graph is a non-linear data structure that consists of vertices and edges. The vertices contain the information or data, and the edges work as a link between pair of vertices.

Graphs can be of multiple types, depending on the position of the nodes and edges. Here’re some important types of Graphs:

Directed Graph

The edges of the Directed Graph contain arrows that mean the direction. The arrow determines where the edge is pointed to or ends.

Here’s an example of the Directed Graph.

Directed Graph

We can go from Node A to D.

However, we can’t go from node D to node A as the edge points from A to D.

As the Graph does not have weights, traveling from vertex A to D will cost the same as traveling from D to F.

Undirected Graph

Undirected Graph contains edges without pointers. It means we can travel vice versa between two vertices.

Here’s a simple example of the undirected Graph.

Undirected Graph

In the above Graph,

We can move from A to B

We can also move from B to A

Edges contain no directions.

It’s an example of an undirected graph having a finite number of vertices and edges with no weights.

Weighted Graph

Graph that contains weights or costs on the edges is called a weighted Graph. The numerical value generally represents the moving cost from one vertex to another vertex. Both Directed and Undirected Graph can have weights on their edges.

Here’s an example of a weighted graph (Directed).

Directed Graph with weight

A to B, there’s an edge, and the weight is 5, which means moving from A to B will cost us 5.

A point to B, but in this Graph, B has no direct edge over A. So, we can’t travel from B to A.

However, If we want to move from A to F, there are multiple paths. The paths are ADF, ABF. ADF will cost (10+11) or 21.

Here, the path ABF will cost (5+15) or 20. Here we’re adding the weight of each edge in the path.

Here’s an example of an Undirected Graph with weights:

Undirected Graph with weight

Here, the edge has weight but no direction. So, it means traveling from vertex A to D will cost 10 and vice versa.

Bi-Directional Graph

Bi-directional and undirected graphs have a common property. That is

Generally, the undirected Graph can have one edge between two vertexes.

For example:

Here, moving from A to D or D to A will cost 10.

In a Bi-Directional Graph, we can have two edges between two vertices.

Here’s an example:

Bi-Directional Graph

Traveling from A to D will cost us 17, but traveling from D to A will cost us 12. So, we can’t assign two different weights if it is an undirected graph.

Infinite Graph

The Graph will contain an infinite number of edges and nodes. If a graph is Infinite and it’s also a connected graph, then it will contain an infinite number of edges as well. Here, the extended edges mean that more edges might be connected to these nodes via edges.

Here’s an example of the infinite Graph:

Infinite Graph

Null Graph

Null Graph contains only nodes or vertices but with no edges. If given a Graph G = (V, E), where V is vertices and E is edges, it will be null if the number of edges E is zero.

Here’s an example of a Null Graph:

Null Graph

Trivial Graph

A graph data structure is considered trivial if only one vertex or node is present with no edges.

Here’s an example of a Trivial Graph:

Multi Graph

A graph is called a multigraph when multiple edges are present between two vertices, or the vertex has a loop. The term “Loop” in Graph Data Structure means an edge pointing to the same node or vertex. Multigraph can be directed or undirected.

Here’s an example of a Multi Graph:

There’re two edges from B to A. Moreover, vertex E has a self-loop. The above Graph is a directed graph with no weights on edges.

Complete Graph

A graph is complete if each vertex has directed or undirected edges with all other vertices.

Suppose there’s a total V number of vertices and each vertex has exactly V-1 edges. Then, this Graph will be called a Complete Graph. In this type of Graph, each vertex is connected to all other vertices via edges.

Here’s an example of a Complete Graph with five vertices:

You can see in the image the total number of nodes is five, and all the nodes have exactly four edges.

Connected Graph

A Graph is called a Connected graph if we start from a node or vertex and travel all the nodes from the starting node. For this, there should be at least one edge between each pair of nodes or vertices.

Here’s an example of a Connected Graph:

Here’s some explanation of the above Graph:

Assuming there’s no edge between C and F, we can’t travel from A to G. However, the edge C to F enables us to travel to any node from a given node.

A complete Graph is a Connected Graph because we can move from a node to any other node in the given Graph.

Cyclic Graph

A graph is said to be cyclic if there are one or more cycles present in the Graph.

Here’s an example of a Cyclic Graph:

Here, vertex A, B, and C form a cycle.

A graph can have multiple cycles inside it.

Directed Acyclic Graph (DAG)

A Graph is Called Directed Acyclic Graph or DAG if there’re no cycles inside a graph. DAG is important while doing the Topological Sort or finding the execution order. DAG is also important for creating scheduling systems or scanning dependency of resources etc. However, the above Graph above doesn’t contain any cycle inside.

Here’s a simple example of a Directed Acyclic Graph (DAG):

Cycle Graph

Cycle Graph is not the same as the cyclic Graph. In Cycle Graph, each node will have exactly two edges connected, meaning each node will have exactly two degrees.

Here’s an example of a Cycle Graph:

Bipartite Graph

These kinds of Graphs are special kinds of Graph where vertices are assigned to two sets.

Bipartite Graph must follow the rule:

Two sets of vertices should be distinct, which means all the vertices must be divided into two groups or sets.

Same set Vertices should not form any edges.

Euler Graph

A Graph Data Structure is said to be an Euler Graph if all the vertices have an even-numbered degree. The term degree of vertices means the number of edges pointing to or pointing out from a particular vertex.

Here’s an example of a Euler graph:

All the vertices have even degrees. Vertex A, D, E, and H have two degrees. Here, node C has four degrees, which is even.

Hamilton Graph

Hamilton Graph is a Connect Graph, where you can visit all the vertices from a given vertex without revisiting the same node or using the same edge. This kind of Connected Graph is known as the “Hamilton Graph.” The path you visit to verify if the given Graph is Hamilton Graph or not is known as Hamiltonian Path.

Here’s a simple example of a Hamilton graph:

In this image, we can visit all the vertices from any node in the above Graph. One of the paths can be A-D-C-H-B-E. It’s also possible to find a Hamilton Cycle. Hamilton Cycle starts and ends at the same vertex. So, the Hamilton Cycle will be A-D-C-H-B-E-A.

Types Of Hr Management Software

Gone are the days of manually recording and managing human resources data. HR software solutions can help businesses digitize and streamline their HR processes. In fact, a report by Grand View Research said the growing HR software market was expected to exceed $10 billion by 2023. 

If your business hasn’t yet implemented HR software, read on to learn what HR management software is and which problems it can solve, and get an overview of the top HR management systems available.

What is HR management software?

You may have heard the terms “human resource management system” (HRMS), “human resources information system” (HRIS) and “human capital management” (HCM). While they vary slightly, HCM, HRIS and HRMS are the same general concept: managing human resources via software. 

Instead of keeping manual HR information records, HR software lets you digitally store and manage core HR functions, such as staffing and onboarding, payroll and benefits administration, workforce management, employee training and development, performance management, and legal risk and compliance.

Bottom Line

HR management software, also known simply as HR software, streamlines HR functions like payroll, employee benefits administration, training and development, performance management, and HR compliance challenges.

What are the HR management system types?

There are several different types of HR software. While some HR systems serve one primary function, others are comprehensive and include a combination of services. 

Here are some of the most common HR management system types and the primary services they include.

1. Recruiting, hiring and onboarding

Some HR platforms can help you find, vet and integrate new employees. These are some such HR platforms:

Recruitment software

Applicant tracking software

Background check software and services

Onboarding and offboarding software


The top background check services protect your business by revealing critical information about a candidate’s criminal history, identity, employment and education history, credit score, and more.

2. Employee compensation

HR software can help you track how many hours your employees work and compensate them accordingly. Here are some examples of these HR platforms:

Employee scheduling software

Time and attendance software

Benefits administration software

Payroll software and services

3. Workforce management

Different types of HR software can help you manage your overall workforce. You can record important HR documents, track employee performance and improve employee engagement. These are some HR platforms like this:

Employee monitoring software

Performance management software

Employee engagement software

HR software

Which problems does HR management software solve?

HR management systems solve a plethora of HR issues. However, some are better than others, depending on your organization’s needs. 

Before choosing an HR solution, determine which of the following functions your HR software should accommodate:

Recruiting and onboarding

Workforce management

Employee compensation

Employee development

Performance management

Legal compliance

Recruiting and onboarding

Finding the right employees can be crucial to your success, but finding and attracting top talent can be a struggle for many businesses, especially those that haven’t hired an HR manager trained in recruiting and onboarding. 

HR software can be a game-changer when you’re recruiting new employees, allowing you to cast a wider (or more targeted) net to find qualified candidates. More businesses are using HR software in recruitment: A survey by PwC found that 58% of businesses use HR technology to find, attract and retain talent.

HR software lets you track applicants throughout the hiring process to ensure they move swiftly through the funnel before they get swooped up by the competition.

Some software and services allow you to conduct background screening by running pre-employment background checks on candidates to ensure they’re appropriately vetted for the job. 

Once a candidate is cleared, you can use HR software to onboard them successfully. The best HR software can digitize documents that candidates can e-sign and store for reference and compliance reasons.

Did You Know?

A bad hire can cost your business 30% of the employee’s first-year earnings, according to the U.S. Department of Labor.

Workforce management

If you want your business to be as efficient as possible, you’ll need to manage your workforce strategically. HR software provides data and features to help you with efficient workforce management. 

For example, you can manage employee schedules to ensure you have the correct number of people working at a specific time. Proper staffing can ensure that your employees aren’t overworked and customers receive proper attention. Similarly, HR software can help with resource allocation.

Employee compensation

Employee compensation management is one of HR’s most important responsibilities, and various HR software options can help. For example, you can use time and attendance software to track how many hours each employee works, the locations where they’re clocking in and out, and how much time they spend on specific projects or jobs. You can also track and manage time-off requests. These features can help you comply with critical labor laws.

HR software can also help you process payroll, file payroll taxes, report new hires to the state, track and manage PTO accruals, and compensate your employees. Some HR solutions also offer employee benefits administration that integrates with your payroll so that each employee is compensated appropriately. 

An all-in-one HR solution, or multiple solutions that integrate, is a great way to ensure your employees receive their total compensation on time.


There may be mandatory sick leave laws in your state, and HR software with compensation management features can help you comply with these laws.

Employee development

Employee development has become a key driver for many job candidates when accepting a new position. A defined employee development process helps you attract and retain top talent while improving your bottom line. With a team of highly skilled employees in place, your organization produces better work all around. 

HR software can help you provide professional development opportunities to your workers, often through e-learning platforms. Although some companies offer in-person training, online courses and workshops have become a popular way to upskill and educate employees. Giving your employees a clear path to their future can help increase employee engagement and satisfaction.

Performance management

In addition to offering employee development opportunities, HR software can help you manage employee performance. For example, employee monitoring software allows you to track when and where your employees are working and how productive they are when they’re on the clock. 

The best software compiles a vast amount of employee data that can be used to create a good employee performance process. You can identify your most efficient workers and reward them accordingly. You can also see who might be reaching burnout or who needs a little extra handholding.


If you notice that an employee is struggling to meet their goals, consider putting them on a performance improvement plan.

Legal compliance

Maintaining compliance with every federal, state and industry law and regulation can be challenging, especially for small businesses. However, HR software can help you maintain legal compliance by streamlining several HR processes. For example, it can store mandatory HR documents, maintain accurate time-tracking metrics in line with pay laws and manage payroll in compliance with tax laws.

Employees can often use HR software to access necessary training, including sexual harassment prevention training. Some HR software providers can also offer HR guidance to help you in your compliance with labor and employment laws, like COBRA compliance and OSHA compliance.

What are the best HR management systems?

There are many HR management software solutions, each with its own features, benefits and limitations. Some systems are more general, while others can be customized for specific businesses or industries. 

We researched and analyzed various HR systems to identify the top solutions. Here are some of our favorite options.

CompanyDescriptionWorkableBest recruitment software for small businessesBambooHRBest applicant tracking and performance management software (read our BambooHR review to learn more)GoodHireBest background check platform BerniePortalBest HR software for employee onboardingQuickBooks TimeBest time and attendance software for mobile workforces (read our QuickBooks Time review for more information)When I WorkBest time and attendance software for restaurants and retail (read our When I Work review for more information)GustoBest HR software for payroll (read our review of Gusto to learn more)InterGuardBest employee monitoring software for remote workforces (read our InterGuard review to learn more)ActivTrakBest employee monitoring software for workforce analytics (read our ActivTrak review to learn more)Paychex FlexBest HR software for remote teams (read our review of Paychex Flex to learn more)SAP SuccessFactorsBest HR software for employee experienceRipplingEasiest HR software to implement (read our Rippling review to learn more)WorkdayBest HR software for compensation management

When choosing HR software, pay attention to how it integrates with your other platforms. For example, if you use one platform for time tracking and another for payroll, they’ll need to integrate properly to ensure you’re compensating your employees correctly.

If you’re looking for a more comprehensive HR platform that can take care of some or all of your HR responsibilities, consider using one of the top HR outsourcing services or the best professional employer organizations available.

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