Data Warehouse

The data warehouse is devised to perform the reporting and analysis functions. The warehouse gathers data from varied databases of an organization to carry out data analysis. It is a database where data is gathered, but, is additionally optimized to handle the analytics. The reports drawn from this analysis through a data warehouse helps to land on business decisions.

Data warehouse is an integrated view of all kinds of data drawn from a range of other databases to be scrutinized and examined. It helps to establish the relation between different data that is stored in an organization to further build new business strategies. Analysis or data processing in a warehouse is done by intricate interrogation and questions. It is an Online analytical processing (OLAP) that takes use of standard languages to handle relational data where the data is stored in a tabular form only including rows and columns, indexes, etc. The data stored in a warehouse is applicable to many functions and databases.

The data warehouse is well developed and optimized for amassing and collecting large quantities of data for analyzing it. Data in a warehouse is standardized for boosting the response time for analytical queries and making the data normalized to be used by businessmen. Data analysis and business reporting in a warehouse can be done in many different ways like diagnostic, predictive, descriptive or prescriptive. Since warehouse includes related data all in one place, it uses lesser disk space than databases for those related data. A data warehouse can also store historical data while also real time or current data for handing over most recent information.

Database

Database includes information or data in a tabular form arranged in rows and columns or chronologically indexed data to make access easy. All, whether small or large enterprises require databases to store their information and a database management system that handles and manages the large sets of data stored. For instance, customer information database or product information or inventory database are all different databases for storing information about the customers and products respectively.

The data in a database is stored only for access, storage and data retrieving purposes. There are different kinds of databases available like CSV files, XML files, Excel Spread sheets, etc. Databases are often used for online transaction processing which allows adding, updating or deleting the data from a database by the users. Database makes the task of accessing a specific data very easy and hassle free to carry out other tasks properly. They are like day to day transaction system of data for any organization.

Such transactional databases are not responsible for carrying out analytics or reporting tasks, but, are only optimized for transactional purposes. Database only have a single application of carrying one kind of data in an organized tabular format. Real-time transactions are also applicable in a database which is developed for speedy recording of a new data, e.g. name of a new product category in the product inventory database. Only read and write operation can be carried out in a database and response time is optimized for a few seconds. No analytical task can be initiated in a database as it blocks all other users out of it and slows down the entire performance of a database.

Related – Data Warehousing and Data Mining

Comparison Table: Database vs Data Warehouse

Below table summarizes the differences between Database and Data Warehouse:

BASIS	DATA WAREHOUSE	DATABASE
Definition	A kind of database optimized for gathering information from different sources for analysis and business reporting.	Data storage or collection in an organized manner for storage, updating, accessing and recovering a data.
Data Structure	Denormalized data structure is used for enhanced analytical response time.	Normalized data structure is there in a database in separate tables.
Data timeline	Historical data is stored for analytics while current data can also be used for real-time analysis.	Day to day processing and transaction of data is done in a database.
Optimization	Warehouse is optimized to perform analytical processing on large data through complex queries.	Optimized for speedy updating of data to maximize enhanced data access.
Analysis	Dynamic and quick analysis of data is done.	Transactional function is carried out, though analytic is possible but are difficult to perform due to complexity of normalized data.

Download the difference table: Database vs Datawarehouse

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Business Intelligence vs Data Warehouse

Top 10 Data Mining Tools

In today’s digital world, Data is wealth, Data is Power and Data is everything. Thus a business should give large importance to Users and their data. 

The Database monitoring tools can help us to a wide number of variables and keep track of the performance metrics of our database or server. Today in this article you will get to know about the top 10 database monitoring tools that every business should have. 

Okay without further ado let’s get started. 

List of Top Database Monitoring Tools

1.SolarWinds Database Performance Analyzer 

It is a database monitor that identifies the problems pinpoint in real-time. 

They offer 14 days free trial and after that, it is available at the price of $1,995. It is suitable for Windows, Linux, Unix, etc… 

PROS:

• Dashboards are highly customizable 
• This database management system is tailored for medium and large-size databases.
• Graphs and alerts in a different color for critical warnings. 

2.DataDog Database Monitoring

It is a SaaS monitoring solution that monitors your cloud infrastructure, applications, and  serverless features. The major advantage of this platform is that it gives a full observability solution with metrics, logs, security, real user, etc…

It gives annual billing and demand billing options. You can also use it free for the first 14 days for an unlimited number of servers. It supports more than 400 Integrations. 

3.OpsView

These database monitoring tools are designed to provide a unified view, which includes both cloud and on premise systems. It operates famous databases like Oracle, IBM DB2, Amazon RDS, etc… 

It offers two types of plans as OpsView Cloud and Enterprise. The former one starts with 150 hosts to 50,000+ hosts and the latter starts with 300 hosts to 50,000+ hosts. 

4.Paessler PRTG Network Monitor

It is a network monitor tool that is compatible with many different databases and can monitor your complete IT Infrastructure. And the interface and dashboards are flexible and customizable. 

It tracks Applications, Cloud Services, Web Services, and other network metrics. You can build your custom configuration or else use the PRTG default ones. 

5.Site 24 x 7

Site 24×7 is a SaaS-based unified cloud monitoring for DevOps and IT operation in both small and large organizations. Site 24×7 is an all-in-one solution that works on Desktop, Linux, Windows and mobile devices.

It is not a specialized tool like the previous one but it is a cloud-based monitoring service, which can help database monitoring. It offers a 30 days free trial and there is also a free version that limits only to five servers. 

6.AppOptics APM

It is also a cloud-based service from SolarWinds, however, there is a lower edition called AppOptics Infrastructure which focuses on Performance and monitoring of databases. 

It has a specialized screen for different application databases and is easily scalable to build as a cloud service. It offers a 14-day free trial. 

7.SentryOne SQL Sentry

It is a database monitoring tool that takes a traditional approach, the user interface is not as attractive as the other products in this list however it gets the job done. 

It is dedicated to SQL, thus it will be a good choice if you have any other monitoring tools, and has more than 100 alerts. It is a little expensive and offers only a 14-day free trial. 

8.ManageEngine Applications Manager

It is an application managing system provided by the managing engine however it works well for database monitoring and server monitoring. It is available with 30-day free trial plans. 

It can map out interdependencies between applications and works both on premise and cloud Infrastructure. 

9.Spiceworks 

If you don’t have any advanced or complex use for your database monitoring tool then Spiceworks will do the job. It is a free tool compatible with SQL Server databases. It is customizable and has simple data visualization.

10.dbWatch

It is a simple and easy-to-install tool. It works well at multiple cross platforms and has a good reporting method. It operates in real-time and historical data. There is also a zoom-in option. 

Conclusion

There are many database monitoring tools out there in the market, and you can choose the one that suits the best as per your requirements. Please share your thoughts and doubts in the comment section below. 

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Top 10 Serverless Compute Providers

Top 10 Cloud Monitoring Tools

Data storage has been an integral part of the IT ecosystem since the earliest emergence of computer systems in mid-20 century. Initial days data storage was simpler, a basic file storage system housed inside the physical data centers. As technologies evolved the need for more refined methods of managing and information access grew. As the need for flexibility and scalability grew cloud storage took the precedence to handle structured data for analytical purposes and non-structured data such as NoSQL databases to accommodate flexibility required around images and audio files type of data. 

In today’s article we understand and compare the difference between database and data storage, how databases and data storage work? Where to use a database or data storage and understand their key characteristics. 

What is Database

Database is a structured data repository to provide storage, management and retrieval. Databases support various functions such as querying, indexing and transactions handling and are meant for applications which require organized and structured data which is quickly and easily accessible. 

Some examples of databases are relational databases (MySQL, PostgreSQL) which use structured query language (SQL) for data management. Data is organized into tables and has a schema to ensure data integrity and relationships. 

NoSQL databases (MongoDB, Cassandra) handle unstructured data efficiently with flexibility and scalability. MongoDB uses JSON to store documents and Cassandra uses a wide column store model. 

Graph databases (Neo4J, Dgraph) store data as edges and nodes to represent entities and their relationships. Efficient queries with complex relationships and patterns are supported by them. 

Characteristics of a Database

• Efficient management of storage 
• Data integrity with enforced consistency and eliminating data duplication
• Handling large volumes of data 
• Strong security features to support data integrity and protection  

Related: Database and Data Warehouse

What is Data Storage

Data storage is meant for data retrieval and persistence. It is a repository to store, manage and retrieve data. There could be different types of data stores such as databases, file systems, key value stores and object stores. The choice of data storage type is determined by its performance, scalability and data structure. Data can be in structured format and organized into tables or an unstructured format such as NoSQL to handle large scale applications. 

Characteristics of a Data Storage

• A digital repository to store and manage information.
• Datastore can be a network connected storage, distributed cloud storage, or virtual storage
• Can store both structured and unstructured data types 
• Data distribution efficiently with high availability and fault tolerance 

Comparison: Database vs Data Storage

Below table summarizes the difference between the two:

Download the comparison table: Database vs Data Storage

In today’s topic we will learn and compare Storage area network (SAN) and Hyper Converged Infrastructures (HCI) technologies, their key differences and understand their working. 

What is SAN? 

SAN is designed to connect storage devices within enterprises, data centers, offices or any other environments. In SAN storage, all storage devices, routers, switches and other networking technologies are connected to a single network fabric. Protocols such as Ethernet and Fiber channel are used to enable network nodes or connection points such as switches for communication. 

Host bus adapter (HBA) connects host servers to the SAN to provide interfacing to storage devices or network devices within the network. The purpose of this is to pool storage resources concurrently. Regardless of the physical location one can access servers, flash arrays, and other storage devices in the network. SANs are useful for large organizations which need to connect to storage resources – especially critical applications in big enterprises. They are good candidates for customizing storage network as per organization needs. 

Advantages of SAN

• Provides automatic backup
• It provides high data storage capacities
• Reduction in cost of deploying multiple servers
• Improved performance
• Improved backup and recovery options 

What is HCI?

Hyper Converged infrastructure (HCI) is a combination of products – storage, compute and networking. The HCI platform includes both hardware and software components. These can be commodity hardware or vendor specific hardware. All HCI components are virtual – separated from the underlying physical layer of compute, storage and networking which are managed by a hypervisor or software which runs a virtual machine. Everything on the HCI platform comes from one vendor due to which traditional HCI systems are prone to vendor lock-in. 

Advantages of HCI

• Ease of scaling, in and out based on business needs extra nodes can be added
• Improves business agility in enterprises 
• Improved data protection and disaster recovery with each node in pool contributes to a reliable and redundant shared storage
• Supports hybrid cloud environment 
• Supports faster deployment 
• Reduction in footprint as all components of data center are combined into a single appliance 

SAN vs HCI

Download the comparison table: san vs hci

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Hyper Converged Infrastructure (HCI) vs Converged Infrastructure (CI)

Difference between File Level Storage and Block Level Storage

Today we look more in detail about states of data – data at Rest / still, data in transit / motion and data in Use, techniques used in protection, challenges of protecting data in different states. 

The three States of Data 

Data or information could reside in 3 states. Data at Rest/still, Data in transit/motion and Data in Use. 

Data at Rest – 

Data Rest means data is not in moving state and neither being accessed and it is either stored physically or logically somewhere. Data at Rest could be on disks, tapes, CDs/DVDs, USB drives. Could be stored physically on systems, servers, databases or on cloud such as Microsoft Azure, Google GCP, Amazon AWS cloud providers etc. 

Data in Transit – 

Data in motion or travelling over the network such as email, web, collaborative business applications such as Microsoft Teams, Slack, instant messaging, or any other private or public communication channels. 

Data in Use –

When data is in use it means it is actively used by applications, files, folders and consumed or accessed by end users.

Securing the 3 States of Data

It is vital to protect the three states of data to ensure it does not fall into wrong hands and be misused. The most widely adopted technique to secure data is encryption. The encryption keys are not stored on the same storage where data resides. Strong key mechanisms such as AES 256 are used to secure data and make it difficult to intercept.

Data Protection at REST 

• Full disk encryption – or hard disk encryption ensures that even if a device is lost or compromised by simply mounting the disk on another system, it does not grant access to data. 
• File level encryption – Here the entire disk is not encrypted but individual files and folders are encrypted. Public key or symmetric key allows encryption of files.
• Database encryption – databases such as MS-SQL, Oracle etc. use TDE – Transparent Data Encryption for protection of data stored in tables. TDE allows data and logs encryption and decryption in real time. 
• Data protection with Digital Rights Management system (DRMS) – DRMS technologies such as SealPath, Forcepoint, IBM , Netskope etc. allow documentation encryption with persistent protection. The documentation is only accessible to users having access right to it and it is stored in encrypted form.
• Mobile device management – MDM applications allow blocking of access to device or encrypt data on mobile devices largely used by large corporations.
• Data leakage prevention (DLP) – block data access to certain users in the event of violation of data policy and store data in encrypted form.
• Cloud Access Security broker (CASB) – Applications allow applying security policies in cloud systems such as office 365, box,  etc. it is a cloud based DLP solution as such.

Related: CASB vs SASE: Which One Is Better?

Challenges of data protection at REST

• Data stored on diverse types of media 
• Data stored / scattered across mobile devices
• In cloud encryption keys are owned by provider for storage 
• Need to comply with different data protection regulations 

Data Protection in Transit

There are different technologies to protect data in transit. 

• Email encryption – end to end protection of email messages, attachments. 
• Managed file transfer (MFT) – This is an alternative to FTP where a file is uploaded on a platform and a link gets created to have it downloaded. You can set expiry date for the link , password to access etc.
• Data leakage prevention (DLP) – DLP technologies also provide protection of data in transit and not just data at Rest. USB data copy etc. policies can be implemented.
• Cloud Access Security Broker (CASB) – CASB can detect if a user tries to upload sensitive data and does not comply with security policy related to data security. 
• Digital Rights management (DRM) – In transit protection is provided by DRM such as restricting email forwarding etc.

Challenges of data protection at TRANSIT

• Infinite means of communication
• Infinite cloud applications
• Not possible to maintain control at receiving end

Data Protection in Use

To protect data in use, the majority of controls are put before content access. 

• Identity management tools – checks that the user trying to access data is a legitimate user. 
• Role based Access control (RBAC) – access to data is controlled based on user role, location, IP etc
• Digital Rights Management (DRM) – Set digital rights control to view, download, print data or information. 

Challenges of data protection at USE

• Most of the tools control access to data before allowing access

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4 Types of Data: Nominal, Ordinal, Discrete, Continuous

What Is Data Masking? Types & 8 Techniques

Today we look more in detail about types of data available, their characterizes, purpose it is used for. 

About Data Categories

Businesses run on data and most organizations use data to get insights into creating and launching new campaigns, design strategies, products, and services. According to a recent report every day at least 2.5 quintillion bytes of data is produced. Qualitative and Quantitative are two types of data, which is further classified as depicted in below figure. 

Types of Data

Qualitative/Categorical Data

This kind of data is not measurable or counted in the form of numbers. This data is sorted by category and consists of audio, video, images, symbols, or text. Some examples of qualitative data are gender – male, female, language we speak, favourite holiday destination, opinions, color etc. It depicts people’s perception and helps market researchers to establish consumer behaviour patterns and design their ideas and strategies around that.

Nominal Data

It is used for labelling variables without any specific order such as color of hair is an example of discrete data which is not comparable to another color. With nominal data we can’t do any numerical tasks or give order to any sort of data. This data is not in any meaningful order and their values are distributed discreetly in distinct categories. Some examples of nominal data are color of hair , marital status, nationality, gender, eye color etc.

Ordinal Data 

It has natural ordering having numbers present in some kind of order by position on scale. This kind of data is used for customer observations such as customer satisfaction, happiness etc but no arithmetic function can be performed on that. Some examples of ordinal data are feedback , experience, or satisfaction on a scale of 0 to 10, letter grades in an exam, people ranking in competition, economic status, education level etc.

Quantitative Data

Quantitative data expression is in numbers which makes it accountable and includes statistical data analysis. This kind of data answers questions like how much, how many. For example, price of a phone, height or weight of a person, age etc. this data is useful for statistical manipulation and can be represented in wide ways such as bar charts, pie, histograms etc. Some examples of this kind of data are height and weight of a person, room temperature, marks, time etc.

Discrete Data 

It means distinct or separate. This kind of data contains values which fall under whole numbers or integers. The total students in a class are examples of discrete data which cant be broken down into decimal  or fractional values. It is countable and has finite value. Some examples of discrete data are students in a class, price of mobile phone, employee headcount, players in a game, days in a week.

Continuous Data

Continuous data can be in the form of fractional numbers. It can be a version of an android phone, height or weight of a person, length of an object etc. continuous data can be further divided into small parts. Some examples of continuous data are height and weight of a person, speed of vehicle, time spent on finishing a task, wi-fi frequency, share price etc.

Continue Reading:

What is a Data Management Platform (DMP)? CDP vs DMP?

What Is Data Masking? Types & 8 Techniques

Continuous data protection with zero downtime, higher availability and speed requirements govern the business requirements in the current arena.

The purpose of replication is protection from disaster which may occur at one location and operations can resume from alternate location to ensure fault tolerance and avoiding a single point of failure.

Today, we will explore about Storage replication!

Storage Replication

Protection of data is an integral part of Storage replication. Replication helps us in Business continuity.

Storage replication technology enables replication of volumes between servers or clusters to enable redundancies built in the event of failure at the primary data storage location. Storage replication is a subset of ‘disaster recovery’ strategy for enterprises.

Organizations opt for storage replication to ensure in case of failure data storage is always available at alternate site.

Two storage devices can be connected physically or via a Storage Area Network (SAN). In the SAN environment software replicates data from primary storage to secondary storage located at alternate / remote site.

Storage Based Replication Techniques

Full Volume replication (Cloning) –

Initial synchronization happens between source and the replica (Clone). When the Sync is happening between the two replica is not available for access. Post synchronization is completed replica is exact copy of source. The size of clone is same as its source. During subsequent synchronizations only changes are replicated.

Pointer based Virtual replication (Snapshot) –

The target (Snapshot) only contains pointer to actual data however it does not contain data at any point of time. It is virtual replica.

Storage Based Remote Replication Techniques

Synchronous Replication – (Also known as “Two stage commit systems)

‘Writes’ are committed to the source and remote target before sending notification of ‘Write complete’ to the production / Primary server. No additional writes will happen until every earlier write has been successfully completed and acknowledged. This is required to ensure that data is always same at source and target. To ensure remote data is consistent writes happen in same sequence as they were received.

Asynchronous Replication –

It supports data replication across sites which could be 1000 miles apart. As soon as ‘Write’ happens to the source from production server acknowledgement comes immediately from the server.  Data can be replicated thousands of kilometers between source and secondary site. Server ‘Write’ requests are accumulated in a buffer at Source site and this delta is transferred at regular interval to remote site. Adequate buffer capacity needs to be planned according to RPO (Recovery Point Objective) and network bandwidth and ‘Writes’ workload needs to be assessed for planning. To conserve network bandwidth Writes are collected and only final data is sent for transmission.

Multi-site Replication –

Source data is replicated to multiple remote sites. Data at source is replicated to two different locations and two different storage. The source to target (Site 1) is synchronous and Source to remote target (Site 2) is Asynchronous (With RPO in minutes).

In normal scenario all three sites are available, but operations continue from primary site. The difference between data is tracked and incremental re-synchronization occurs to ensure data is  identical as source.

Comparison of Storage Replication techniques

Below table summarizes the comparison between different storage replication techniques:

Download the table here.

Advantages and disadvantages of Storage replication

Storage replication has its own pros and cons:

Download the table here.

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File Level Storage and Block Level Storage

What is Database Replication?

Introduction to Data Gravity

Data gravity is a concept that has been gaining traction in the world of digital transformation. It refers to the idea that data is being pulled towards applications and services in a gravitational-like fashion. In other words, data is attracted to applications and services that are most convenient, and this attraction can have a significant impact on digital transformation.

To understand data gravity and its implications, it’s important to first understand what data is and why it’s important. Data is information that can be collected, stored, and analyzed to make decisions and predictions. It is increasingly becoming the foundation of digital transformation and is used to drive decisions and actions. Therefore, understanding data gravity and its implications for digital transformation is essential for all businesses.

What is Data Gravity?

Data gravity is the concept of data being pulled towards applications and services in a gravitational-like fashion. In other words, data is attracted to applications and services that are most convenient, and this attraction can have a significant impact on digital transformation.

Data gravity was first introduced by Dave McCrory, a software engineer and the Chief Technology Officer at Basho Technologies. According to McCrory, data gravity occurs when applications and services become larger and more powerful, they begin to attract more data. This is similar to how gravity works in physics, as larger objects attract more objects.

The concept of data gravity is important to understand because it can have a significant impact on digital transformation. As data is pulled towards applications and services, it can create “data gravity wells” that can be difficult to escape from. This can limit the ability of a business to make the most of digital transformation, as the “data gravity well” can become an obstacle to progress.

Examples of Data Gravity

Data gravity is a concept that has gained traction in the world of digital transformation. To understand this concept better, let’s look at a few examples of data gravity in action.

• Rise of cloud computing: As cloud computing services have become more powerful and popular, they have become a “data gravity well” for many businesses. This is because businesses have been attracted to the low costs and convenience of cloud computing, and thus have been pulled into the “data gravity well” created by these services.
• Rise of analytics services: As analytics services have become more powerful and popular, they have created a “data gravity well” of their own. This is because businesses have been attracted to the insights and value provided by analytics services, and thus have been pulled into the “data gravity well” created by these services.
• Rise of mobile applications: As mobile applications have become more powerful and popular, they have created a “data gravity well” of their own. This is because businesses have been attracted to the convenience and reach of mobile applications, and thus have been pulled into the “data gravity well” created by these services.

Benefits of Data Gravity

Data gravity can have many benefits for businesses that understand how to use it.

• Cost savings: As data is pulled towards applications and services, businesses can save money by leveraging the existing infrastructure and services. This can reduce the costs associated with digital transformation and make it more accessible for businesses.
• Scalability: Businesses can more easily scale their digital transformation initiatives. This is because the existing infrastructure can handle the increased demand, and businesses can focus on leveraging the existing services rather than building new ones.
• Access to insights: Businesses can leverage the insights provided by these services to make better decisions and take more informed actions. This can help businesses stay ahead of the competition and accelerate their digital transformation.

Challenges Posed by Data Gravity

While data gravity can have many benefits, it can also pose some challenges for businesses.

• Data security: As data is pulled towards applications and services, businesses can be at risk of data breaches and other security issues. 
• Data privacy: Businesses must ensure that the data is being used in a responsible and compliant manner. 
• Data governance: Businesses must ensure that the data is being managed in a responsible and compliant manner.

How to Measure Data Gravity

To fully understand the impact of data gravity, it’s important to have a way to measure it. There are several ways to measure data gravity, including the following:

• Data Volume: This is the amount of data being pulled towards applications and services. This can be measured by observing the amount of data stored in a particular application or service.
• Data Velocity: This is the speed at which data is being pulled towards applications and services. This can be measured by observing the rate at which data is being transferred to and from a particular application or service.
• Data Variety: This is the type of data being pulled towards applications and services. This can be measured by observing the types of data stored in a particular application or service.

By measuring these aspects of data gravity, businesses can get a better understanding of its impact on digital transformation.

Data Gravity in the Cloud

Data gravity is an increasingly important concept in the world of cloud computing. As cloud services have become more powerful and popular, they have become a “data gravity well” for many businesses. This is because businesses have been attracted to the low costs and convenience of cloud computing, and thus have been pulled into the “data gravity well” created by these services.

Data gravity can have a significant impact on cloud computing. For example, it can influence the cost of cloud services, as businesses are more likely to use services that are more cost-effective. It can also influence the scalability of cloud services, as businesses are more likely to use services that can handle increasing demand. Finally, it can influence the security of cloud services, as businesses are more likely to use services that are more secure.

Conclusion

Data gravity is an increasingly important concept in the age of digital transformation. It refers to the idea that data is being pulled towards applications and services in a gravitational-like fashion, and understanding how it works and its implications for digital transformation is essential for all businesses.

In this article, we’ve unpacked the concept of data gravity and provided a comprehensive guide for understanding its implications. We’ve discussed the benefits and challenges posed by data gravity, as well as how to measure it and its implications for cloud computing. By understanding data gravity and its implications, businesses can make the most of digital transformation and stay ahead of the competition.

Continue Reading:

Top 10 Database Monitoring Tools

Difference between DBMS and RDBMS: Database Management Systems

What is a Data Management Platform (DMP)?

A Data Management Platform (DMP) allows organizations to collect, store, model, and analyze data from different sources, such as web traffic, demographics, customer transactions, and more. The data is then used to create a “single customer view” that provides a comprehensive understanding of the customer. This helps organizations to better serve their customers and make more informed decisions.

Benefits of using a Data Management Platform

Using a DMP has many benefits for organizations. Here are some of the key benefits of using a DMP:

• Comprehensive customer view: A DMP can collect data from all sources, including websites, mobile apps, social media, and more, providing a comprehensive view of the customer.
• Improved decision-making: Having a comprehensive view of the customer allows organizations to make more informed decisions. This helps organizations to optimize their processes and make better use of their resources.
• Increased efficiency: As DMP allows organizations to collect, store, and analyze data from multiple sources, which helps to save time and resources. This enables organizations to be more efficient and get more done in less time.
• Enhanced customer experience: Having a comprehensive view of the customer allows organizations to understand their customers’ needs and preferences. This helps organizations to create a more personalized experience for their customers and improve customer satisfaction.

Key features of a Data Management Platform

A DMP has many features that make it an invaluable tool for organizations. Here are some of the key features of a DMP:

• Data collection: It enables organizations to collect data from multiple sources.
• Data storage: It allows organizations to store data in a secure and organized manner. This helps organizations to easily access and manage their data.
• Data modeling: It enables organizations to model their data, which helps them to better understand their customers and create more effective marketing campaigns and strategies.
• Data analysis: It allows organizations to analyze their data and create reports and insights.

How data management platforms can help improve your marketing

Data Management Platforms (DMPs) are powerful tools that can help organizations improve their marketing efforts. Here are some of the ways a DMP can help improve your marketing:

• Targeting: A DMP enables organizations to better understand their customers and target them more effectively. This helps organizations to create more relevant and engaging campaigns that are more likely to reach their target audience.
• Personalization: A DMP allows organizations to create more personalized experiences for their customers. This helps to increase engagement and improve customer satisfaction.
• Optimization: A DMP enables organizations to optimize their campaigns and strategies. This helps to ensure that organizations are getting the most out of their campaigns and strategies.
• Insights: A DMP allows organizations to gain insights into their customers. This helps organizations to better understand their customers and create more effective campaigns and strategies.

Different types of data management platforms

There are different types of DMPs available to organizations. Here are some of the most common types of DMPs:

• On-premise DMPs: On-premise DMPs are hosted on the organization’s own servers. This type of DMP is typically used by larger organizations that need more control over their data.
• Cloud-based DMPs: Cloud-based DMPs are hosted on the cloud. This type of DMP is typically used by smaller organizations that don’t have the resources to manage their own servers.
• Hybrid DMPs: Hybrid DMPs are a combination of on-premise and cloud-based DMPs. This type of DMP is typically used by organizations that need the flexibility of both on-premise and cloud-based DMPs.

Challenges of using data management platforms

Using a DMP can be challenging for organizations. Here are some of the challenges of using a DMP:

• Cost: DMPs can be expensive, especially for larger organizations. This can be a deterrent for some organizations.
• Complexity: DMPs can be complex for organizations to set up and manage. This can be a challenge for organizations that don’t have the resources or expertise to manage a DMP.
• Security: DMPs collect and store a lot of sensitive data. This can be a challenge for organizations to ensure that their data is secure.
• Integration: DMPs need to be integrated with other systems to be effective. This can be a challenge for organizations to ensure that their systems are properly integrated.

How to choose the right data management platform for your business

Choosing the right DMP for your business can be a challenging task. Here are some tips to help you choose the right DMP for your business:

• Understand your needs: Before choosing a DMP, you need to understand your business needs and objectives. This will help you determine the type of DMP that is best suited for your business.
• Look for features: Look for a DMP that has the features that you need. This will ensure that you are getting the most out of your DMP.
• Consider cost: DMPs can be expensive, so you need to consider the cost of the DMP and ensure that it fits within your budget.
• Look for support: Look for a DMP that offers good customer support. This will ensure that you get the help you need when you need it.

CDP vs DMP

There is often confusion between Customer Data Platforms (CDPs) and Data Management Platforms (DMPs). Here is a quick comparison of the two:

• Purpose: The purpose of a CDP is to create a single customer view, while the purpose of a DMP is to manage and analyze data from multiple sources.
• Data sources: CDPs typically collect data from a single source, while DMPs collect data from multiple sources.
• Data storage: CDPs typically store data in a single repository, while DMPs store data in multiple repositories.
• Data analysis: CDPs typically analyze data for insights, while DMPs analyze data for insights and optimization.

Conclusion

Using a DMP can be a powerful way for organizations to manage and analyze their data. It can help organizations to better understand their customers and create more effective marketing campaigns and strategies. If you’re looking to unlock the power of data management platforms, this article has provided you with the information you need to get started.

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Difference between DBMS and RDBMS: Database Management Systems

What is an Enterprise Resource Management System?

Today we look at some replication technologies !

Storage Replication 

Storage replication technology enables replication of volumes between servers or clusters to enable redundancies built in the event of failure at the primary data storage location. Storage replication is a subset of ‘disaster recovery’ strategy for enterprises. 

Organizations opt for storage replication to ensure in case of failure data storage is always available at alternate site. 

Two storage devices can be connected physically or via a Storage Area Network (SAN). In the SAN environment software replicates data from primary storage to secondary storage located at alternate / remote site.

Check the detailed explanation of Storage Replication in our last blog.

Advantages (Storage Replication)	Disadvantages (Storage Replication)
Operates independently	High operational and management costs
Replication across multiple vendor products	Requires setting up SAN
Supports heterogeneous storage and numerous platforms

Database Replication 

Database replication technologies facilitate electronic copying of data from one database to another database hosted on another server’s at predefined intervals.

It creates a distributed database which users can access data quickly and simultaneously to perform tasks seamlessly without interference from other users. 

At  any given point of time Distributed Database Management Systems ‘DDBMS’ ensures that any changes related to data additions , data deletions performed on data at one place is uniformly reflected for data stored at alternate locations. 

Database Replication – Techniques 

Replication techniques will vary depending upon how data is stored and its purpose for replication. 

Timing of data transfer could be  Asynchronous or Synchronous.

Asynchronous Database Replication

Asynchronous Replication involves a model server or principal server from which clients take data. Model /Principal server will inform client that data is received, and data is copied to replicas at unspecified or specified intervals. (Replica creation with time delays)

Asynchronous Replication is less reliable in the sense confirmation comes before all data is replicated and the process happens in the background so in the event data is lost while the replicating client may not be aware of this. 

Asynchronous Replication offers cost effective disaster recovery solutions to businesses that can endure longer RTOs (Recovery time Objective).

Synchronous Database Replication

In Synchronous Replication data is copied from client server to model/principal server and replicated to all replicas post that only the client is informed about data copy. (Replica creation in real time) . Businesses that can’t afford to compromise on RTO’s (Recovery time Objective) rely on synchronous replication. 

Server architecture also defines types of replications such as Single-leader architecture, Multi-leader architecture and No-leader architecture.

• In Single-leader architecture one server is the ‘Master’ to which clients submit write requests, and all replicas are drawn from that. 
• In Multi-leader architecture more than one server receives the write requests and serves as a ‘Model’ server for the clients.
• In No-leader architecture every server receives write requests and serves as ‘Model’ for all replicas. 

Advantages (Database Replication)	Disadvantages (Database Replication)
Reduction in Load	Loss of data during transit
Improve reliability	Data inconsistency issues
High availability	Bandwidth, Maintenance, and energy costs of multiple servers
Improved support for data analytics
Reduction in latency

Comparison Table: Storage Replication vs Database Replication

Below given table summarizes the differences between the two:

PARAMETER	STORAGE REPLICATION	DATABASE REPLICATION
Definition	Mirroring low level of data and ensure storage consistency	Mirroring of database objects and ensuring database consistency
Location	On-site, off-site, cloud based	On-site, off-site, cloud based
Purpose	Scalability , Redundancy and disaster recovery	Maximize data efficiency and reduction in latency, System resiliency and scalability, reduction of access time, Workload balance between replicas
Replication Types	Synchronous and Asynchronous	Synchronous and Asynchronous
Replication softwares	HPE Store US, Veeam	Qlik Replicate, Informatica Data Replication, Talend Open Studio for Data Integration, Quest SharePlex
Replication Tools	SAP HANA, DELL EMC unity	Microsoft’s SQL integration features, Oracle GoldenGate, IBM’S Db2 SQL replication tool

Download the comparison table: Storage Replication vs Database Replication

Future of Replication and way ahead 

Traditional methods of data replication have challenges of time, money, and bandwidth. Advent of cloud technologies eliminates such challenges. 

Cloud technology allows secure data sharing across regions and enables data portability for seamless migration of accounts 

Replication technologies support incremental refresh to allow quick replication , database failover and failback features provide immediate data recovery at secondary databases in the available region. Also, data is encrypted in transit between regions and cloud providers.

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Difference between DBMS and RDBMS: Database Management Systems

What is Storage Replication? Detailed Explanation

The designing of a system puts up challenges of its own. There are several key aspects that qualified professional system designers take into consideration while conceiving the architecture of a system. One such vital aspect is the selection of Flash memory, and what sort of Flash architecture is best to use. While opting for a parallel interface or a serial interface, it is crucial to analyse in advance whether it would require an error correction code (ECC) or not.

Introduction 

In case where the controller or processor supports only a single type of interface then the analysis of Flash memory becomes much easier as there are limited options to look for. But it is not as convenient as it seems to be. There are certain Field-programmable gate array (FPGA) that support serial or parallel NOR flash, while others go with NAND Flash memory in order to store the configured data. In such cases, when in order to choose the right memory, we assess Nand vs Nor Flash Memory we often face difficulty as both are used to capture user data.

Here, we are going to make the line of difference between NAND and NOR Flash Memory on the grounds of certain pivotal characteristics. You can come across these characteristics in the below table distinguishing the two major forms of Flash memories on various points-

Nand vs Nor Flash Memory : Comparison table

CHARACTERISTIC	NOR FLASH	NAND FLASH
Memory Architecture	One end of every Flash memory cell is linked to the source line, while the other end to a bit line that is identical to a NOR gate.	A number of memory cells (typically 8 cells) are linked to a series that is identical to a NAND gate.
Memory Range and Cell Size	Renders enough address lines that can sufficiently map the whole memory range. That provides an edge of short read times and random access, ensuring it good for code execution.	Offers a much smaller cell size and comparatively higher write and erase speed rates
Good Bits	Known to have 100% good bits for the entire life of the part.	Typically has 98% good bits at the time of shipping with additional bit failure during the course of the life of the part. That is the reason why it requires ECC or error correcting code functionality inside the device.
Memory Capacity	The memory density ranges between 64 MB to 2 GB. Making its utility within a limited capacity.	The memory capacity comes within a range of 1 GB to 16 GB. That is the reason why it is primarily used for the data storage application.
Erase, Read and Write Operations	This Nand vs Nor Flash Memory characteristic defines all three operational capacities. Here, each byte requires to be written along with ‘0’ prior to erase, resulting in much slower operation. For example, S70GL02GT Cypress NOR Flash needs ~520ms typically to erase down a similar 128KB data slot.   Reading capacity is directly dependent on the size of block of data. The delay in reading is directly proportional to the increase in size.   The data writing can only be done if there is an empty block. The writing operation is typically slower.	The erase operation is quite straightforward in NAND flash. For example, the requirement of S34ML04G2 Cypress NAND Flash typically needs 3.5ms in order to erase a block of 128KB data. Here, the difference is around 150 times.   The reading capacity of NAND Flash can turn out to be faster for the sequential reads.   Here also, the data writing can only be done in case a block is empty. Similar to read, the writing of data is also done over pages (generally 2 KB). For example, a page singly written down alone on S34ML04G2 NAND Flash typically takes up to 300µS.
Power Consumption	During initial power turn on NOR Flash memories need more current.   But, when put on standby mode, the power consumption is lower than NAND Flash.	Require less current than NOR Flash during initial current flow.   However, in standby mode it requires more current.
Reliability	NOR Flash is considered to be more reliable than NAND Flash as they are shipped with zero bad blocks ensuring no bit-flipping.	NAND Flash suffers from bit-flipping where some of the bit can get reversed (bad blocks randomly scattered across) resulting in damage to the saved data.

Download the comparison table: NAND vs NOR

Conclusion

While having a glance over the segregation table above, we can see that NAND vs NOR Flash Memory have their own set of advantages and disadvantages over each other. In terms of memory range, cell size, good bits, power consumption and reliability, NOR Flash memory can turn out to have an edge, while NAND Flash surpasses it in terms of memory capacity as well as erase, read and write operations. So it is recommended that your priority should be in accordance with the characteristic(s) that you give much preference to while utilizing flash memory.

Related – RAM VS ROM

Highly competitive and agile market demands businesses to run 24*7 and ensure data availability and accessibility with improved resilience and reliability.

Data replication technologies help businesses to quickly recover from a disaster , catastrophe, hardware failure, or a system breach in the event the data is compromised.

In this article, we will get deeper understanding about Database replication, why it is required? Its benefits and so on.

Database Replication

Database replication technologies facilitate the process of storage / retrieval of same data at many locations. Maintaining a replica of databases at multiple location also helps to access data faster hence improving user experience , running multiple replicas of same data on multiple servers enhance accessibility and also frees resource intensive Write operations onto replicas hence freeing processing cycles on Primary server.

Database Replication: Types

Transactional Replication –

Initial copies of full database is received by user and post that only updates are sent. The sender is referred as ‘Publisher’ and receiver is referred as ‘Subscriber’. Data copy happens in real time from Publisher to receiver in sequential order. Consistently and accurately all changes are replicated. It is a periodic in nature and more complex as complete transaction history on database replica is monitored. It is majorly used in scenarios where there are frequent data changes at the source.

Snapshot Replication –

Snapshot as name refers is the actual snap of data at a specific time which is captured and send to users.  Usually it is used to perform initial sync between publisher and subscriber. No change tracking happens in Snapshot replication and used where data changes are less frequent such as change in exchange rates or price lists are updated once a day and it gets replicated to branch servers from main server once a day.

Merge Replication –

Multiple databases are combined into one single database. In Merge replication changes are sent from one publisher to multiple subscribers.  It is a bi-directional replication in a server-client environment and connection is not continuous. Whenever the network connectivity is established replication occurs by Merge agents. This is one of the most complex type of replication and a typical scenario where it could be used is a central warehouse connected to stores so informed requires update in central database and store databases on inventory, delivery etc.

Advantages /Disadvantages of Database Replication

Below table describes the advantages and disadvantages of Database replication:

Download the table here.

Comparison of different types of Replication

Download the table here.

Database Replication on Cloud

Widespread data availability, Analytics , data integration across multiple platforms, are some of the key reasons for choosing a Cloud based replication.

Data residing on a cloud instance(Primary instance) is replicated or copied to another cloud instance (Standby instance) . Data transmission mechanism can be synchronous or asynchronous depending on the Recovery Time Objectives (RTOs) and Recovery Point Objectives (RPO) requirement of the organization.

In the event of disaster , it is vital that the secondary instance is in a different geographical region from Primary instance (Cloud instance connected over WAN link).

Cloud based replication keeps data offsite so in event of major disaster like fire, flood, earthquake etc. when primary instance is unavailable still the secondary instance is operational over cloud and data and applications can be recovered.

Cloud based replication costs are less as compared to in-house data center . Building a secondary site is a costly affair and organizations can save costs incurred on hardware, maintenance, and support.

On demand scalability s another reason organizations prefer to maintain business agility.

Data replication happens on cloud to that providers can assure their users high availability and Disaster recovery.

Continue Reading:

Data Center vs Disaster Recovery Center

Database and Data Warehouse

File Level Storage:

The storage system of Network Attached Storage and hard drives is a file level storage. Here in this storage system, the NFS (Network file system) or Server Block Message (SBM) or Common Internet file system (CIFS) protocol is used for storage and the files are accessed from the bulk. The simplicity of file level storage is unbeatable in a situation where just some space is required to store the files. This system is centralized in control and can be accessed by any user who wants to use it. The maintenance of file storage system is much easier and inexpensive than the block level storage. The storage systems which are attached to any networks surely rely on this kind of storage system.

The costs of reserving the space in file level storage are much less compared to block level. Also, this level of storage also has to look into assigning permissions and access to other users and it can also be integrated into other security authentication systems. File sharing with other users is very easy using this level of storage.

There is Scale out NAS which is a kind of file level storage system which has the ability to scale up to a lot more petabytes. This solution enhances the capacity and also improves the performance.

Basically, the file level storage is used for mass storage of files.

Block Level Storage:

The storage system of Storage Area Networks is a block level storage. Here, in this kind, there are blocks created for storage which are just like a hard drive in the server. These hard drives or the blocks are used and managed by the operating systems based on the servers. Random files or virtual machine file system or databases can be stored in this level. Fibre channel or iSCSI (Internet Small Computer Systems Interface) mechanism of storage networking is used by this level of storage for the servers and data transfers. Block level storage can boot up the systems that are connected to it.

It has a reliable kind of transportation of the stored data which uses these blocks like a separate hard drive and is managed by the operating system based on an external server. Usually a larger enterprise or organization which is a part of such enormous storage area network, utilizes block storage for fulfilling their storage needs.

Block level storage is unbeatable for its versatility and flexibility as the server uses the raw storage blocks like hard drives. For this very reason, block level storage can be used for various applications like database storage, virtual machine file system volume, file storage, etc.

File sharing in this level of storage is a little longer process wherein an operating system is to be installed with which that block should be attached, only then sharing of files is possible with that host operating system.

For the backup of your storage load, third party tools for backup have to be used to keep your files safe. Also, talking in regard to the maintenance and management, the block level storage is more complex than the file level storage.

Comparison between File Level Storage and Block Level Storage

BASIS	FILE LEVEL STORAGE	BLOCK LEVEL STORAGE
Deployed for	Network attached storage devices	Storage Area Networks
Storage Protocols	NFS, SMB or CIFS	Fibre Channel, iSCSI and FCoE (Fibre Channel over Ethernet)
Kind of storage system	Centralized file storage	Blocks are created to store files which are utilized as hard drives by the server operating system
File and Folder Access	Can be accessed and managed by this storage system	Not able to manage or control smaller blocks of storage
Implementation	More simple to implement	Complex in usage
Flexibility	Less flexible	More flexible
File sharing	Access permissions can be given to the users for sharing the files	For sharing files, an operating system is to be separately installed and attached to the block
Cost	Less expensive to manage	More expensive to manage
Application	A suitable option when only an empty space is required to dump the files	Larger enterprises use it to store data in the form of blocks and their flexibility
Uses	Used for mass storage of files and VMware	It has more uses as it can store files, databases, VMware.
Server boot	Cannot be done	Server reboots can be done if right kind of devices are used

Download the difference table here.

The Blu-ray Discs (BD) were introduced in 2006. With their high storage capacity, these discs can hold and play back large quantities of high-definition video and audio, as well as photos, data and other digital content.

DVD vs BLUE-RAY DISC: COMPARISON TABLE

Download the difference table here.

Notable is that it is called Random Access Memory because any of the data in RAM can be accessed just as fast as any of the other data. RAM is of 2 types –
– SRAM (Static Random Access Memory)
– DRAM (Dynamic Random Access Memory)

While SRAM processes reading and writing functionality very fast, DRAM is considerably slower and requires time to process the data. Infact, SRAM costs not too high in electronic market as compared to DRAM. When we compare power consumption of these 2 flavours, SRAM requires more power compared to its close relative DRAM. Some of benefits that SRAM provides are – there is no need for refresh in order to maintain data while DRAM needs to refresh data thousands of time per second. SRAM also benefits from its low density.

As far as DRAM is considered, its internal architecture and structural arrangement is pretty simple and easy to understand while SRAM is pretty complex. However, DRAM has limitation that it cannot store many bits per chip which is possible in case of SRAM. In terms of usage, SRAM is mostly used as cache memory while DRAM is used in main memory of PCs, laptop and related devices.

In order to simplify the comparison between SRAM and DRAM, it would be worth to illustrate the same through a table as shared below –